From 4d64e1b60e7be56162eecd361d19cb49d96014c2 Mon Sep 17 00:00:00 2001
From: Philippe Tillet <phil@openai.com>
Date: Fri, 3 Jun 2022 00:42:29 +0000
Subject: [PATCH] [GH-PAGES] Updated website

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diff --git a/master/_downloads/034d953b6214fedce6ea03803c712b89/02-fused-softmax.ipynb b/master/_downloads/034d953b6214fedce6ea03803c712b89/02-fused-softmax.ipynb
deleted file mode 100644
index f16e090cb..000000000
--- a/master/_downloads/034d953b6214fedce6ea03803c712b89/02-fused-softmax.ipynb
+++ /dev/null
@@ -1,161 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Fused Softmax\nIn this tutorial, you will write a fused softmax operation that is significantly faster\nthan PyTorch's native op for a particular class of matrices: those whose rows can fit in\nthe GPU's SRAM.\nYou will learn about:\n\n- The benefits of kernel fusion for bandwidth-bound operations.\n- Reduction operators in Triton.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Motivations\nCustom GPU kernels for elementwise additions are educationally valuable but won't get you very far in practice.\nLet us consider instead the case of a simple (numerically stabilized) softmax operation:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import torch\n\nimport triton\nimport triton.language as tl\n\n\n@torch.jit.script\ndef naive_softmax(x):\n    \"\"\"Compute row-wise softmax of X using native pytorch\n\n    We subtract the maximum element in order to avoid overflows. Softmax is invariant to\n    this shift.\n    \"\"\"\n    # read  MN elements ; write M  elements\n    x_max = x.max(dim=1)[0]\n    # read MN + M elements ; write MN elements\n    z = x - x_max[:, None]\n    # read  MN elements ; write MN elements\n    numerator = torch.exp(z)\n    # read  MN elements ; write M  elements\n    denominator = numerator.sum(dim=1)\n    # read MN + M elements ; write MN elements\n    ret = numerator / denominator[:, None]\n    # in total: read 5MN + 2M elements ; wrote 3MN + 2M elements\n    return ret"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "When implemented naively in PyTorch, computing :code:`y = naive_softmax(x)` for $x \\in R^{M \\times N}$\nrequires reading $5MN + 2M$ elements from DRAM and writing back $3MN + 2M$ elements.\nThis is obviously wasteful; we'd prefer to have a custom \"fused\" kernel that only reads\nX once and does all the necessary computations on-chip.\nDoing so would require reading and writing back only $MN$ bytes, so we could\nexpect a theoretical speed-up of ~4x (i.e., $(8MN + 4M) / 2MN$).\nThe `torch.jit.script` flags aims to perform this kind of \"kernel fusion\" automatically\nbut, as we will see later, it is still far from ideal.\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Compute Kernel\nOur softmax kernel works as follows: each program loads a row of the input matrix X,\nnormalizes it and writes back the result to the output Y.\nNote that one important limitation of Triton is that each block must have a\npower-of-two number of elements, so we need to internally \"pad\" each row and guard the\nmemory operations properly if we want to handle any possible input shapes:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "@triton.jit\ndef softmax_kernel(\n    output_ptr, input_ptr, input_row_stride, output_row_stride, n_cols,\n    BLOCK_SIZE: tl.constexpr\n):\n    # The rows of the softmax are independent, so we parallelize across those\n    row_idx = tl.program_id(0)\n    # The stride represents how much we need to increase the pointer to advance 1 row\n    row_start_ptr = input_ptr + row_idx * input_row_stride\n    # The block size is the next power of two greater than n_cols, so we can fit each\n    # row in a single block\n    col_offsets = tl.arange(0, BLOCK_SIZE)\n    input_ptrs = row_start_ptr + col_offsets\n    # Load the row into SRAM, using a mask since BLOCK_SIZE may be > than n_cols\n    row = tl.load(input_ptrs, mask=col_offsets < n_cols, other=-float('inf'))\n    # Substract maximum for numerical stability\n    row_minus_max = row - tl.max(row, axis=0)\n    # Note that exponentials in Triton are fast but approximate (i.e., think __expf in CUDA)\n    numerator = tl.exp(row_minus_max)\n    denominator = tl.sum(numerator, axis=0)\n    softmax_output = numerator / denominator\n    # Write back output to DRAM\n    output_row_start_ptr = output_ptr + row_idx * output_row_stride\n    output_ptrs = output_row_start_ptr + col_offsets\n    tl.store(output_ptrs, softmax_output, mask=col_offsets < n_cols)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can create a helper function that enqueues the kernel and its (meta-)arguments for any given input tensor.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "def softmax(x):\n    n_rows, n_cols = x.shape\n    # The block size is the smallest power of two greater than the number of columns in `x`\n    BLOCK_SIZE = triton.next_power_of_2(n_cols)\n    # Another trick we can use is to ask the compiler to use more threads per row by\n    # increasing the number of warps (`num_warps`) over which each row is distributed.\n    # You will see in the next tutorial how to auto-tune this value in a more natural\n    # way so you don't have to come up with manual heuristics yourself.\n    num_warps = 4\n    if BLOCK_SIZE >= 2048:\n        num_warps = 8\n    if BLOCK_SIZE >= 4096:\n        num_warps = 16\n    # Allocate output\n    y = torch.empty_like(x)\n    # Enqueue kernel. The 1D launch grid is simple: we have one kernel instance per row o\n    # f the input matrix\n    softmax_kernel[(n_rows,)](\n        y,\n        x,\n        x.stride(0),\n        y.stride(0),\n        n_cols,\n        num_warps=num_warps,\n        BLOCK_SIZE=BLOCK_SIZE,\n    )\n    return y"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Unit Test\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We make sure that we test our kernel on a matrix with an irregular number of rows and columns.\nThis will allow us to verify that our padding mechanism works.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "torch.manual_seed(0)\nx = torch.randn(1823, 781, device='cuda')\ny_triton = softmax(x)\ny_torch = torch.softmax(x, axis=1)\nassert torch.allclose(y_triton, y_torch), (y_triton, y_torch)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As expected, the results are identical.\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Benchmark\nHere we will benchmark our operation as a function of the number of columns in the input matrix -- assuming 4096 rows.\nWe will then compare its performance against (1) :code:`torch.softmax` and (2) the :code:`naive_softmax` defined above.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "@triton.testing.perf_report(\n    triton.testing.Benchmark(\n        x_names=['N'],  # argument names to use as an x-axis for the plot\n        x_vals=[\n            128 * i for i in range(2, 100)\n        ],  # different possible values for `x_name`\n        line_arg='provider',  # argument name whose value corresponds to a different line in the plot\n        line_vals=[\n            'triton',\n            'torch-native',\n            'torch-jit',\n        ],  # possible values for `line_arg``\n        line_names=[\n            \"Triton\",\n            \"Torch (native)\",\n            \"Torch (jit)\",\n        ],  # label name for the lines\n        styles=[('blue', '-'), ('green', '-'), ('green', '--')],  # line styles\n        ylabel=\"GB/s\",  # label name for the y-axis\n        plot_name=\"softmax-performance\",  # name for the plot. Used also as a file name for saving the plot.\n        args={'M': 4096},  # values for function arguments not in `x_names` and `y_name`\n    )\n)\ndef benchmark(M, N, provider):\n    x = torch.randn(M, N, device='cuda', dtype=torch.float32)\n    if provider == 'torch-native':\n        ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.softmax(x, axis=-1))\n    if provider == 'triton':\n        ms, min_ms, max_ms = triton.testing.do_bench(lambda: softmax(x))\n    if provider == 'torch-jit':\n        ms, min_ms, max_ms = triton.testing.do_bench(lambda: naive_softmax(x))\n    gbps = lambda ms: 2 * x.nelement() * x.element_size() * 1e-9 / (ms * 1e-3)\n    return gbps(ms), gbps(max_ms), gbps(min_ms)\n\n\nbenchmark.run(show_plots=True, print_data=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In the above plot, we can see that:\n\n - Triton is 4x faster than the Torch JIT. This confirms our suspicions that the Torch JIT does not do any fusion here.\n - Triton is noticeably faster than :code:`torch.softmax` -- in addition to being **easier to read, understand and maintain**.\n   Note however that the PyTorch `softmax` operation is more general and will works on tensors of any shape.\n\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.10"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/master/_downloads/62d97d49a32414049819dd8bb8378080/01-vector-add.py b/master/_downloads/62d97d49a32414049819dd8bb8378080/01-vector-add.py
deleted file mode 100644
index d684106f1..000000000
--- a/master/_downloads/62d97d49a32414049819dd8bb8378080/01-vector-add.py
+++ /dev/null
@@ -1,131 +0,0 @@
-"""
-Vector Addition
-=================
-In this tutorial, you will write a simple vector addition using Triton and learn about:
-
-- The basic programming model of Triton
-- The `triton.jit` decorator, which is used to define Triton kernels.
-- The best practices for validating and benchmarking your custom ops against native reference implementations
-"""
-
-# %%
-# Compute Kernel
-# --------------------------
-
-import torch
-
-import triton
-import triton.language as tl
-
-
-@triton.jit
-def add_kernel(
-    x_ptr,  # *Pointer* to first input vector
-    y_ptr,  # *Pointer* to second input vector
-    output_ptr,  # *Pointer* to output vector
-    n_elements,  # Size of the vector
-    BLOCK_SIZE: tl.constexpr,  # Number of elements each program should process
-                 # NOTE: `constexpr` so it can be used as a shape value
-):
-    # There are multiple 'program's processing different data. We identify which program
-    # we are here
-    pid = tl.program_id(axis=0)  # We use a 1D launch grid so axis is 0
-    # This program will process inputs that are offset from the initial data.
-    # for instance, if you had a vector of length 256 and block_size of 64, the programs
-    # would each access the elements [0:64, 64:128, 128:192, 192:256].
-    # Note that offsets is a list of pointers
-    block_start = pid * BLOCK_SIZE
-    offsets = block_start + tl.arange(0, BLOCK_SIZE)
-    # Create a mask to guard memory operations against out-of-bounds accesses
-    mask = offsets < n_elements
-    # Load x and y from DRAM, masking out any extra elements in case the input is not a
-    # multiple of the block size
-    x = tl.load(x_ptr + offsets, mask=mask)
-    y = tl.load(y_ptr + offsets, mask=mask)
-    output = x + y
-    # Write x + y back to DRAM
-    tl.store(output_ptr + offsets, output, mask=mask)
-
-
-# %%
-# Let's also declare a helper function to (1) allocate the `z` tensor
-# and (2) enqueue the above kernel with appropriate grid/block sizes.
-
-
-def add(x: torch.Tensor, y: torch.Tensor):
-    # We need to preallocate the output
-    output = torch.empty_like(x)
-    assert x.is_cuda and y.is_cuda and output.is_cuda
-    n_elements = output.numel()
-    # The SPMD launch grid denotes the number of kernel instances that run in parallel.
-    # It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]
-    # In this case, we use a 1D grid where the size is the number of blocks
-    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
-    # NOTE:
-    #  - each torch.tensor object is implicitly converted into a pointer to its first element.
-    #  - `triton.jit`'ed functions can be index with a launch grid to obtain a callable GPU kernel
-    #  - don't forget to pass meta-parameters as keywords arguments
-    add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)
-    # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still
-    # running asynchronously at this point.
-    return output
-
-
-# %%
-# We can now use the above function to compute the element-wise sum of two `torch.tensor` objects and test its correctness:
-
-torch.manual_seed(0)
-size = 98432
-x = torch.rand(size, device='cuda')
-y = torch.rand(size, device='cuda')
-output_torch = x + y
-output_triton = add(x, y)
-print(output_torch)
-print(output_triton)
-print(
-    f'The maximum difference between torch and triton is '
-    f'{torch.max(torch.abs(output_torch - output_triton))}'
-)
-
-# %%
-# Seems like we're good to go!
-
-# %%
-# Benchmark
-# -----------
-# We can now benchmark our custom op on vectors of increasing sizes to get a sense of how it does relative to PyTorch.
-# To make things easier, Triton has a set of built-in utilities that allow us to concisely plot the performance of your custom ops
-# for different problem sizes.
-
-
-@triton.testing.perf_report(
-    triton.testing.Benchmark(
-        x_names=['size'],  # argument names to use as an x-axis for the plot
-        x_vals=[
-            2 ** i for i in range(12, 28, 1)
-        ],  # different possible values for `x_name`
-        x_log=True,  # x axis is logarithmic
-        line_arg='provider',  # argument name whose value corresponds to a different line in the plot
-        line_vals=['triton', 'torch'],  # possible values for `line_arg`
-        line_names=['Triton', 'Torch'],  # label name for the lines
-        styles=[('blue', '-'), ('green', '-')],  # line styles
-        ylabel='GB/s',  # label name for the y-axis
-        plot_name='vector-add-performance',  # name for the plot. Used also as a file name for saving the plot.
-        args={},  # values for function arguments not in `x_names` and `y_name`
-    )
-)
-def benchmark(size, provider):
-    x = torch.rand(size, device='cuda', dtype=torch.float32)
-    y = torch.rand(size, device='cuda', dtype=torch.float32)
-    if provider == 'torch':
-        ms, min_ms, max_ms = triton.testing.do_bench(lambda: x + y)
-    if provider == 'triton':
-        ms, min_ms, max_ms = triton.testing.do_bench(lambda: add(x, y))
-    gbps = lambda ms: 12 * size / ms * 1e-6
-    return gbps(ms), gbps(max_ms), gbps(min_ms)
-
-
-# %%
-# We can now run the decorated function above. Pass `print_data=True` to see the performance number, `show_plots=True` to plot them, and/or
-# `save_path='/path/to/results/' to save them to disk along with raw CSV data
-benchmark.run(print_data=True, show_plots=True)
diff --git a/master/_downloads/662999063954282841dc90b8945f85ce/tutorials_jupyter.zip b/master/_downloads/662999063954282841dc90b8945f85ce/tutorials_jupyter.zip
deleted file mode 100644
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diff --git a/master/_downloads/935c0dd0fbeb4b2e69588471cbb2d4b2/05-layer-norm.py b/master/_downloads/935c0dd0fbeb4b2e69588471cbb2d4b2/05-layer-norm.py
deleted file mode 100644
index 1cefc60b9..000000000
--- a/master/_downloads/935c0dd0fbeb4b2e69588471cbb2d4b2/05-layer-norm.py
+++ /dev/null
@@ -1,261 +0,0 @@
-"""
-Layer Normalization
-====================
-"""
-
-import torch
-
-import triton
-import triton.language as tl
-
-try:
-    # This is https://github.com/NVIDIA/apex, NOT the apex on PyPi, so it
-    # should not be added to extras_require in setup.py.
-    import apex
-    HAS_APEX = True
-except ModuleNotFoundError:
-    HAS_APEX = False
-
-
-# Forward Pass
-@triton.jit
-def _layer_norm_fwd_fused(X, Y, W, B, M, V, stride, N, eps,
-                          BLOCK_SIZE: tl.constexpr):
-    # position of elements processed by this program
-    row = tl.program_id(0)
-    cols = tl.arange(0, BLOCK_SIZE)
-    mask = cols < N
-    # offset data pointers to start at the row of interest
-    X += row * stride
-    Y += row * stride
-    # load data and cast to float32
-    x = tl.load(X + cols, mask=mask, other=0).to(tl.float32)
-    # compute mean
-    mean = tl.sum(x, axis=0) / N
-    # compute std
-    xmean = tl.where(mask, x - mean, 0.)
-    var = tl.sum(xmean * xmean, axis=0) / N
-    rstd = 1 / tl.sqrt(var + eps)
-    xhat = xmean * rstd
-    # write-back mean/rstd
-    tl.store(M + row, mean)
-    tl.store(V + row, rstd)
-    # multiply by weight and add bias
-    w = tl.load(W + cols, mask=mask)
-    b = tl.load(B + cols, mask=mask)
-    y = xhat * w + b
-    # write-back
-    tl.store(Y + cols, y, mask=mask)
-
-
-# Backward pass (DX + partial DW + partial DB)
-@triton.jit
-def _layer_norm_bwd_dx_fused(DX, DY, DW, DB, X, W, B, M, V, Lock, stride, N, eps,
-                             GROUP_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr):
-    # position of elements processed by this program
-    row = tl.program_id(0)
-    cols = tl.arange(0, BLOCK_SIZE_N)
-    mask = cols < N
-    # offset data pointers to start at the row of interest
-    X += row * stride
-    DY += row * stride
-    DX += row * stride
-    # offset locks and weight/bias gradient pointer
-    # each kernel instance accumulates partial sums for
-    # DW and DB into one of GROUP_SIZE_M independent buffers
-    # these buffers stay in the L2, which allow this kernel
-    # to be fast
-    lock_id = row % GROUP_SIZE_M
-    Lock += lock_id
-    Count = Lock + GROUP_SIZE_M
-    DW = DW + lock_id * N + cols
-    DB = DB + lock_id * N + cols
-    # load data to SRAM
-    x = tl.load(X + cols, mask=mask, other=0).to(tl.float32)
-    dy = tl.load(DY + cols, mask=mask, other=0).to(tl.float32)
-    w = tl.load(W + cols, mask=mask).to(tl.float32)
-    mean = tl.load(M + row)
-    rstd = tl.load(V + row)
-    # compute dx
-    xhat = (x - mean) * rstd
-    wdy = w * dy
-    xhat = tl.where(mask, xhat, 0.)
-    wdy = tl.where(mask, wdy, 0.)
-    mean1 = tl.sum(xhat * wdy, axis=0) / N
-    mean2 = tl.sum(wdy, axis=0) / N
-    dx = (wdy - (xhat * mean1 + mean2)) * rstd
-    # write-back dx
-    tl.store(DX + cols, dx, mask=mask)
-    # accumulate partial sums for dw/db
-    partial_dw = (dy * xhat).to(w.dtype)
-    partial_db = (dy).to(w.dtype)
-    while tl.atomic_cas(Lock, 0, 1) == 1:
-        pass
-    count = tl.load(Count)
-    # first store doesn't accumulate
-    if count == 0:
-        tl.atomic_xchg(Count, 1)
-    else:
-        partial_dw += tl.load(DW, mask=mask)
-        partial_db += tl.load(DB, mask=mask)
-    tl.store(DW, partial_dw, mask=mask)
-    tl.store(DB, partial_db, mask=mask)
-    # release lock
-    tl.atomic_xchg(Lock, 0)
-
-# Backward pass (total DW + total DB)
-
-
-@triton.jit
-def _layer_norm_bwd_dwdb(DW, DB, FINAL_DW, FINAL_DB, M, N,
-                         BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr):
-    pid = tl.program_id(0)
-    cols = pid * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
-    dw = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
-    db = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
-    for i in range(0, M, BLOCK_SIZE_M):
-        rows = i + tl.arange(0, BLOCK_SIZE_M)
-        mask = (rows[:, None] < M) & (cols[None, :] < N)
-        offs = rows[:, None] * N + cols[None, :]
-        dw += tl.load(DW + offs, mask=mask, other=0.)
-        db += tl.load(DB + offs, mask=mask, other=0.)
-    sum_dw = tl.sum(dw, axis=0)
-    sum_db = tl.sum(db, axis=0)
-    tl.store(FINAL_DW + cols, sum_dw, mask=cols < N)
-    tl.store(FINAL_DB + cols, sum_db, mask=cols < N)
-
-
-class LayerNorm(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, x, normalized_shape, weight, bias, eps):
-        # allocate output
-        y = torch.empty_like(x)
-        # reshape input data into 2D tensor
-        x_arg = x.reshape(-1, x.shape[-1])
-        M, N = x_arg.shape
-        mean = torch.empty((M, ), dtype=torch.float32, device='cuda')
-        rstd = torch.empty((M, ), dtype=torch.float32, device='cuda')
-        # Less than 64KB per feature: enqueue fused kernel
-        MAX_FUSED_SIZE = 65536 // x.element_size()
-        BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
-        if N > BLOCK_SIZE:
-            raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
-        # heuristics for number of warps
-        num_warps = min(max(BLOCK_SIZE // 256, 1), 8)
-        # enqueue kernel
-        _layer_norm_fwd_fused[(M,)](x_arg, y, weight, bias, mean, rstd,
-                                    x_arg.stride(0), N, eps,
-                                    BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps)
-        ctx.save_for_backward(x, weight, bias, mean, rstd)
-        ctx.BLOCK_SIZE = BLOCK_SIZE
-        ctx.num_warps = num_warps
-        ctx.eps = eps
-        return y
-
-    @staticmethod
-    def backward(ctx, dy):
-        x, w, b, m, v = ctx.saved_tensors
-        # heuristics for amount of parallel reduction stream for DG/DB
-        N = w.shape[0]
-        GROUP_SIZE_M = 64
-        if N <= 8192: GROUP_SIZE_M = 96
-        if N <= 4096: GROUP_SIZE_M = 128
-        if N <= 1024: GROUP_SIZE_M = 256
-        # allocate output
-        locks = torch.zeros(2 * GROUP_SIZE_M, dtype=torch.int32, device='cuda')
-        _dw = torch.empty((GROUP_SIZE_M, w.shape[0]), dtype=x.dtype, device=w.device)
-        _db = torch.empty((GROUP_SIZE_M, w.shape[0]), dtype=x.dtype, device=w.device)
-        dw = torch.empty((w.shape[0],), dtype=w.dtype, device=w.device)
-        db = torch.empty((w.shape[0],), dtype=w.dtype, device=w.device)
-        dx = torch.empty_like(dy)
-        # enqueue kernel using forward pass heuristics
-        # also compute partial sums for DW and DB
-        x_arg = x.reshape(-1, x.shape[-1])
-        M, N = x_arg.shape
-        _layer_norm_bwd_dx_fused[(M,)](dx, dy, _dw, _db, x, w, b, m, v, locks,
-                                       x_arg.stride(0), N, ctx.eps,
-                                       BLOCK_SIZE_N=ctx.BLOCK_SIZE,
-                                       GROUP_SIZE_M=GROUP_SIZE_M,
-                                       num_warps=ctx.num_warps)
-        grid = lambda meta: [triton.cdiv(N, meta['BLOCK_SIZE_N'])]
-        # accumulate partial sums in separate kernel
-        _layer_norm_bwd_dwdb[grid](_dw, _db, dw, db, GROUP_SIZE_M, N,
-                                   BLOCK_SIZE_M=32,
-                                   BLOCK_SIZE_N=128)
-        return dx, None, dw, db, None
-
-
-layer_norm = LayerNorm.apply
-
-
-def test_layer_norm(M, N, dtype, eps=1e-5, device='cuda'):
-    # create data
-    x_shape = (M, N)
-    w_shape = (x_shape[-1], )
-    weight = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
-    bias = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
-    x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device='cuda')
-    dy = .1 * torch.randn_like(x)
-    x.requires_grad_(True)
-    # forward pass
-    y_tri = layer_norm(x, w_shape, weight, bias, eps)
-    y_ref = torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps).to(dtype)
-    # backward pass (triton)
-    y_tri.backward(dy, retain_graph=True)
-    dx_tri, dw_tri, db_tri = [_.grad.clone() for _ in [x, weight, bias]]
-    x.grad, weight.grad, bias.grad = None, None, None
-    # backward pass (torch)
-    y_ref.backward(dy, retain_graph=True)
-    dx_ref, dw_ref, db_ref = [_.grad.clone() for _ in [x, weight, bias]]
-    # compare
-    triton.testing.assert_almost_equal(y_tri, y_ref)
-    triton.testing.assert_almost_equal(dx_tri, dx_ref)
-    triton.testing.assert_almost_equal(db_tri, db_ref, decimal=1)
-    triton.testing.assert_almost_equal(dw_tri, dw_ref, decimal=1)
-
-
-@triton.testing.perf_report(
-    triton.testing.Benchmark(
-        x_names=['N'],
-        x_vals=[512 * i for i in range(2, 32)],
-        line_arg='provider',
-        line_vals=['triton', 'torch'] + (['apex'] if HAS_APEX else []),
-        line_names=['Triton', 'Torch'] + (['Apex'] if HAS_APEX else []),
-        styles=[('blue', '-'), ('green', '-'), ('orange', '-')],
-        ylabel='GB/s',
-        plot_name='layer-norm-backward',
-        args={'M': 4096, 'dtype': torch.float16, 'mode': 'backward'}
-    )
-)
-def bench_layer_norm(M, N, dtype, provider, mode='backward', eps=1e-5, device='cuda'):
-    # create data
-    x_shape = (M, N)
-    w_shape = (x_shape[-1], )
-    weight = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
-    bias = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
-    x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device='cuda')
-    dy = .1 * torch.randn_like(x)
-    x.requires_grad_(True)
-    # utility functions
-    if provider == 'triton':
-        y_fwd = lambda: layer_norm(x, w_shape, weight, bias, eps)
-    if provider == 'torch':
-        y_fwd = lambda: torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps)
-    if provider == 'apex':
-        apex_layer_norm = apex.normalization.FusedLayerNorm(w_shape).to(x.device).to(x.dtype)
-        y_fwd = lambda: apex_layer_norm(x)
-    # forward pass
-    if mode == 'forward':
-        gbps = lambda ms: 2 * x.numel() * x.element_size() / ms * 1e-6
-        ms, min_ms, max_ms = triton.testing.do_bench(y_fwd, rep=500)
-    # backward pass
-    if mode == 'backward':
-        gbps = lambda ms: 3 * x.numel() * x.element_size() / ms * 1e-6
-        y = y_fwd()
-        ms, min_ms, max_ms = triton.testing.do_bench(lambda: y.backward(dy, retain_graph=True),
-                                                     grad_to_none=[x], rep=500)
-    return gbps(ms), gbps(max_ms), gbps(min_ms)
-
-
-bench_layer_norm.run(save_path='.', print_data=True)
diff --git a/master/_downloads/ae7fff29e1b574187bc930ed94bcc353/05-layer-norm.ipynb b/master/_downloads/ae7fff29e1b574187bc930ed94bcc353/05-layer-norm.ipynb
deleted file mode 100644
index 4d381dafc..000000000
--- a/master/_downloads/ae7fff29e1b574187bc930ed94bcc353/05-layer-norm.ipynb
+++ /dev/null
@@ -1,54 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Layer Normalization\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import torch\n\nimport triton\nimport triton.language as tl\n\ntry:\n    # This is https://github.com/NVIDIA/apex, NOT the apex on PyPi, so it\n    # should not be added to extras_require in setup.py.\n    import apex\n    HAS_APEX = True\nexcept ModuleNotFoundError:\n    HAS_APEX = False\n\n\n# Forward Pass\n@triton.jit\ndef _layer_norm_fwd_fused(X, Y, W, B, M, V, stride, N, eps,\n                          BLOCK_SIZE: tl.constexpr):\n    # position of elements processed by this program\n    row = tl.program_id(0)\n    cols = tl.arange(0, BLOCK_SIZE)\n    mask = cols < N\n    # offset data pointers to start at the row of interest\n    X += row * stride\n    Y += row * stride\n    # load data and cast to float32\n    x = tl.load(X + cols, mask=mask, other=0).to(tl.float32)\n    # compute mean\n    mean = tl.sum(x, axis=0) / N\n    # compute std\n    xmean = tl.where(mask, x - mean, 0.)\n    var = tl.sum(xmean * xmean, axis=0) / N\n    rstd = 1 / tl.sqrt(var + eps)\n    xhat = xmean * rstd\n    # write-back mean/rstd\n    tl.store(M + row, mean)\n    tl.store(V + row, rstd)\n    # multiply by weight and add bias\n    w = tl.load(W + cols, mask=mask)\n    b = tl.load(B + cols, mask=mask)\n    y = xhat * w + b\n    # write-back\n    tl.store(Y + cols, y, mask=mask)\n\n\n# Backward pass (DX + partial DW + partial DB)\n@triton.jit\ndef _layer_norm_bwd_dx_fused(DX, DY, DW, DB, X, W, B, M, V, Lock, stride, N, eps,\n                             GROUP_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr):\n    # position of elements processed by this program\n    row = tl.program_id(0)\n    cols = tl.arange(0, BLOCK_SIZE_N)\n    mask = cols < N\n    # offset data pointers to start at the row of interest\n    X += row * stride\n    DY += row * stride\n    DX += row * stride\n    # offset locks and weight/bias gradient pointer\n    # each kernel instance accumulates partial sums for\n    # DW and DB into one of GROUP_SIZE_M independent buffers\n    # these buffers stay in the L2, which allow this kernel\n    # to be fast\n    lock_id = row % GROUP_SIZE_M\n    Lock += lock_id\n    Count = Lock + GROUP_SIZE_M\n    DW = DW + lock_id * N + cols\n    DB = DB + lock_id * N + cols\n    # load data to SRAM\n    x = tl.load(X + cols, mask=mask, other=0).to(tl.float32)\n    dy = tl.load(DY + cols, mask=mask, other=0).to(tl.float32)\n    w = tl.load(W + cols, mask=mask).to(tl.float32)\n    mean = tl.load(M + row)\n    rstd = tl.load(V + row)\n    # compute dx\n    xhat = (x - mean) * rstd\n    wdy = w * dy\n    xhat = tl.where(mask, xhat, 0.)\n    wdy = tl.where(mask, wdy, 0.)\n    mean1 = tl.sum(xhat * wdy, axis=0) / N\n    mean2 = tl.sum(wdy, axis=0) / N\n    dx = (wdy - (xhat * mean1 + mean2)) * rstd\n    # write-back dx\n    tl.store(DX + cols, dx, mask=mask)\n    # accumulate partial sums for dw/db\n    partial_dw = (dy * xhat).to(w.dtype)\n    partial_db = (dy).to(w.dtype)\n    while tl.atomic_cas(Lock, 0, 1) == 1:\n        pass\n    count = tl.load(Count)\n    # first store doesn't accumulate\n    if count == 0:\n        tl.atomic_xchg(Count, 1)\n    else:\n        partial_dw += tl.load(DW, mask=mask)\n        partial_db += tl.load(DB, mask=mask)\n    tl.store(DW, partial_dw, mask=mask)\n    tl.store(DB, partial_db, mask=mask)\n    # release lock\n    tl.atomic_xchg(Lock, 0)\n\n# Backward pass (total DW + total DB)\n\n\n@triton.jit\ndef _layer_norm_bwd_dwdb(DW, DB, FINAL_DW, FINAL_DB, M, N,\n                         BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr):\n    pid = tl.program_id(0)\n    cols = pid * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n    dw = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n    db = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n    for i in range(0, M, BLOCK_SIZE_M):\n        rows = i + tl.arange(0, BLOCK_SIZE_M)\n        mask = (rows[:, None] < M) & (cols[None, :] < N)\n        offs = rows[:, None] * N + cols[None, :]\n        dw += tl.load(DW + offs, mask=mask, other=0.)\n        db += tl.load(DB + offs, mask=mask, other=0.)\n    sum_dw = tl.sum(dw, axis=0)\n    sum_db = tl.sum(db, axis=0)\n    tl.store(FINAL_DW + cols, sum_dw, mask=cols < N)\n    tl.store(FINAL_DB + cols, sum_db, mask=cols < N)\n\n\nclass LayerNorm(torch.autograd.Function):\n\n    @staticmethod\n    def forward(ctx, x, normalized_shape, weight, bias, eps):\n        # allocate output\n        y = torch.empty_like(x)\n        # reshape input data into 2D tensor\n        x_arg = x.reshape(-1, x.shape[-1])\n        M, N = x_arg.shape\n        mean = torch.empty((M, ), dtype=torch.float32, device='cuda')\n        rstd = torch.empty((M, ), dtype=torch.float32, device='cuda')\n        # Less than 64KB per feature: enqueue fused kernel\n        MAX_FUSED_SIZE = 65536 // x.element_size()\n        BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))\n        if N > BLOCK_SIZE:\n            raise RuntimeError(\"This layer norm doesn't support feature dim >= 64KB.\")\n        # heuristics for number of warps\n        num_warps = min(max(BLOCK_SIZE // 256, 1), 8)\n        # enqueue kernel\n        _layer_norm_fwd_fused[(M,)](x_arg, y, weight, bias, mean, rstd,\n                                    x_arg.stride(0), N, eps,\n                                    BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps)\n        ctx.save_for_backward(x, weight, bias, mean, rstd)\n        ctx.BLOCK_SIZE = BLOCK_SIZE\n        ctx.num_warps = num_warps\n        ctx.eps = eps\n        return y\n\n    @staticmethod\n    def backward(ctx, dy):\n        x, w, b, m, v = ctx.saved_tensors\n        # heuristics for amount of parallel reduction stream for DG/DB\n        N = w.shape[0]\n        GROUP_SIZE_M = 64\n        if N <= 8192: GROUP_SIZE_M = 96\n        if N <= 4096: GROUP_SIZE_M = 128\n        if N <= 1024: GROUP_SIZE_M = 256\n        # allocate output\n        locks = torch.zeros(2 * GROUP_SIZE_M, dtype=torch.int32, device='cuda')\n        _dw = torch.empty((GROUP_SIZE_M, w.shape[0]), dtype=x.dtype, device=w.device)\n        _db = torch.empty((GROUP_SIZE_M, w.shape[0]), dtype=x.dtype, device=w.device)\n        dw = torch.empty((w.shape[0],), dtype=w.dtype, device=w.device)\n        db = torch.empty((w.shape[0],), dtype=w.dtype, device=w.device)\n        dx = torch.empty_like(dy)\n        # enqueue kernel using forward pass heuristics\n        # also compute partial sums for DW and DB\n        x_arg = x.reshape(-1, x.shape[-1])\n        M, N = x_arg.shape\n        _layer_norm_bwd_dx_fused[(M,)](dx, dy, _dw, _db, x, w, b, m, v, locks,\n                                       x_arg.stride(0), N, ctx.eps,\n                                       BLOCK_SIZE_N=ctx.BLOCK_SIZE,\n                                       GROUP_SIZE_M=GROUP_SIZE_M,\n                                       num_warps=ctx.num_warps)\n        grid = lambda meta: [triton.cdiv(N, meta['BLOCK_SIZE_N'])]\n        # accumulate partial sums in separate kernel\n        _layer_norm_bwd_dwdb[grid](_dw, _db, dw, db, GROUP_SIZE_M, N,\n                                   BLOCK_SIZE_M=32,\n                                   BLOCK_SIZE_N=128)\n        return dx, None, dw, db, None\n\n\nlayer_norm = LayerNorm.apply\n\n\ndef test_layer_norm(M, N, dtype, eps=1e-5, device='cuda'):\n    # create data\n    x_shape = (M, N)\n    w_shape = (x_shape[-1], )\n    weight = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)\n    bias = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)\n    x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device='cuda')\n    dy = .1 * torch.randn_like(x)\n    x.requires_grad_(True)\n    # forward pass\n    y_tri = layer_norm(x, w_shape, weight, bias, eps)\n    y_ref = torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps).to(dtype)\n    # backward pass (triton)\n    y_tri.backward(dy, retain_graph=True)\n    dx_tri, dw_tri, db_tri = [_.grad.clone() for _ in [x, weight, bias]]\n    x.grad, weight.grad, bias.grad = None, None, None\n    # backward pass (torch)\n    y_ref.backward(dy, retain_graph=True)\n    dx_ref, dw_ref, db_ref = [_.grad.clone() for _ in [x, weight, bias]]\n    # compare\n    triton.testing.assert_almost_equal(y_tri, y_ref)\n    triton.testing.assert_almost_equal(dx_tri, dx_ref)\n    triton.testing.assert_almost_equal(db_tri, db_ref, decimal=1)\n    triton.testing.assert_almost_equal(dw_tri, dw_ref, decimal=1)\n\n\n@triton.testing.perf_report(\n    triton.testing.Benchmark(\n        x_names=['N'],\n        x_vals=[512 * i for i in range(2, 32)],\n        line_arg='provider',\n        line_vals=['triton', 'torch'] + (['apex'] if HAS_APEX else []),\n        line_names=['Triton', 'Torch'] + (['Apex'] if HAS_APEX else []),\n        styles=[('blue', '-'), ('green', '-'), ('orange', '-')],\n        ylabel='GB/s',\n        plot_name='layer-norm-backward',\n        args={'M': 4096, 'dtype': torch.float16, 'mode': 'backward'}\n    )\n)\ndef bench_layer_norm(M, N, dtype, provider, mode='backward', eps=1e-5, device='cuda'):\n    # create data\n    x_shape = (M, N)\n    w_shape = (x_shape[-1], )\n    weight = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)\n    bias = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)\n    x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device='cuda')\n    dy = .1 * torch.randn_like(x)\n    x.requires_grad_(True)\n    # utility functions\n    if provider == 'triton':\n        y_fwd = lambda: layer_norm(x, w_shape, weight, bias, eps)\n    if provider == 'torch':\n        y_fwd = lambda: torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps)\n    if provider == 'apex':\n        apex_layer_norm = apex.normalization.FusedLayerNorm(w_shape).to(x.device).to(x.dtype)\n        y_fwd = lambda: apex_layer_norm(x)\n    # forward pass\n    if mode == 'forward':\n        gbps = lambda ms: 2 * x.numel() * x.element_size() / ms * 1e-6\n        ms, min_ms, max_ms = triton.testing.do_bench(y_fwd, rep=500)\n    # backward pass\n    if mode == 'backward':\n        gbps = lambda ms: 3 * x.numel() * x.element_size() / ms * 1e-6\n        y = y_fwd()\n        ms, min_ms, max_ms = triton.testing.do_bench(lambda: y.backward(dy, retain_graph=True),\n                                                     grad_to_none=[x], rep=500)\n    return gbps(ms), gbps(max_ms), gbps(min_ms)\n\n\nbench_layer_norm.run(save_path='.', print_data=True)"
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diff --git a/master/_downloads/b51b68bc1c6b1a5e509f67800b6235af/03-matrix-multiplication.ipynb b/master/_downloads/b51b68bc1c6b1a5e509f67800b6235af/03-matrix-multiplication.ipynb
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-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Matrix Multiplication\nIn this tutorial, you will write a 25-lines high-performance FP16 matrix multiplication\nkernel that achieves performance on par with cuBLAS.\nYou will specifically learn about:\n\n- Block-level matrix multiplications\n- Multi-dimensional pointer arithmetic\n- Program re-ordering for improved L2 cache hit rate\n- Automatic performance tuning\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Motivations\nMatrix multiplications are a key building block of most modern high-performance computing systems.\nThey are notoriously hard to optimize, hence their implementation is generally done by\nhardware vendors themselves as part of so-called \"kernel libraries\" (e.g., cuBLAS).\nUnfortunately, these libraries are often proprietary and cannot be easily customized\nto accomodate the needs of modern deep learning workloads (e.g., fused activation functions).\nIn this tutorial, you will learn how to implement efficient matrix multiplications by\nyourself with Triton, in a way that is easy to customize and extend.\n\nRoughly speaking, the kernel that we will write will implement the following blocked\nalgorithm to multiply a (M, K) by a (K, N) matrix:\n\n .. code-block:: python\n\n   # do in parallel\n   for m in range(0, M, BLOCK_SIZE_M):\n     # do in parallel\n     for n in range(0, N, BLOCK_SIZE_N):\n       acc = zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=float32)\n       for k in range(0, K, BLOCK_SIZE_K):\n         a = A[m : m+BLOCK_SIZE_M, k : k+BLOCK_SIZE_K]\n         b = B[k : k+BLOCK_SIZE_K, n : n+BLOCK_SIZE_N]\n         acc += dot(a, b)\n       C[m : m+BLOCK_SIZE_M, n : n+BLOCK_SIZE_N] = acc;\n\nwhere each iteration of the doubly-nested for-loop is performed by a dedicated Triton program instance.\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Compute Kernel\n\nThe above algorithm is, actually, fairly straightforward to implement in Triton.\nThe main difficulty comes from the computation of the memory locations at which blocks\nof :code:`A` and :code:`B` must be read in the inner loop. For that, we need\nmulti-dimensional pointer arithmetics.\n\n### Pointer Arithmetics\n\nFor a row-major 2D tensor :code:`X`, the memory location of :code:`X[i, j]` is given b\ny :code:`&X[i, j] = X + i*stride_xi + j*stride_xj`.\nTherefore, blocks of pointers for :code:`A[m : m+BLOCK_SIZE_M, k:k+BLOCK_SIZE_K]` and\n:code:`B[k : k+BLOCK_SIZE_K, n : n+BLOCK_SIZE_N]` can be defined in pseudo-code as:\n\n .. code-block:: python\n\n   &A[m : m+BLOCK_SIZE_M, k:k+BLOCK_SIZE_K] =  a_ptr + (m : m+BLOCK_SIZE_M)[:, None]*A.stride(0) + (k : k+BLOCK_SIZE_K)[None, :]*A.stride(1);\n   &B[k : k+BLOCK_SIZE_K, n:n+BLOCK_SIZE_N] =  b_ptr + (k : k+BLOCK_SIZE_K)[:, None]*B.stride(0) + (n : n+BLOCK_SIZE_N)[None, :]*B.stride(1);\n\nWhich means that pointers for blocks of A and B can be initialized (i.e., :code:`k=0`) in Triton as:\n\n .. code-block:: python\n\n   offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n   offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n   offs_k = tl.arange(0, BLOCK_SIZE_K)\n   a_ptrs = a_ptr + (offs_am[:, None]*stride_am + offs_k [None, :]*stride_ak)\n   b_ptrs = b_ptr + (offs_k [:, None]*stride_bk + offs_bn[None, :]*stride_bn)\n\nAnd then updated in the inner loop as follows:\n\n .. code-block:: python\n\n   pa += BLOCK_SIZE_K * stride_ak;\n   pb += BLOCK_SIZE_K * stride_bk;\n\n\n### L2 Cache Optimizations\n\nAs mentioned above, each program instance computes a :code:`[BLOCK_SIZE_M, BLOCK_SIZE_N]`\nblock of :code:`C`.\nIt is important to remember that the order in which these blocks are computed does\nmatter, since it affects the L2 cache hit rate of our program. and unfortunately, a\na simple row-major ordering\n\n .. code-block:: Python\n\n   pid = triton.program_id(0);\n   grid_m = (M + BLOCK_SIZE_M - 1) // BLOCK_SIZE_M;\n   grid_n = (N + BLOCK_SIZE_N - 1) // BLOCK_SIZE_N;\n   pid_m = pid / grid_n;\n   pid_n = pid % grid_n;\n\nis just not going to cut it.\n\nOne possible solution is to launch blocks in an order that promotes data reuse.\nThis can be done by 'super-grouping' blocks in groups of :code:`GROUP_M` rows before\nswitching to the next column:\n\n .. code-block:: python\n\n   # program ID\n   pid = tl.program_id(axis=0)\n   # number of program ids along the M axis\n   num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)\n   # number of programs ids along the N axis\n   num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)\n   # number of programs in group\n   num_pid_in_group = GROUP_SIZE_M * num_pid_n\n   # id of the group this program is in\n   group_id = pid // num_pid_in_group\n   # row-id of the first program in the group\n   first_pid_m = group_id * GROUP_SIZE_M\n   # if `num_pid_m` isn't divisible by `GROUP_SIZE_M`, the last group is smaller\n   group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)\n   # *within groups*, programs are ordered in a column-major order\n   # row-id of the program in the *launch grid*\n   pid_m = first_pid_m + (pid % group_size_m)\n   # col-id of the program in the *launch grid*\n   pid_n = (pid % num_pid_in_group) // group_size_m\n\nFor example, in the following matmul where each matrix is 9 blocks by 9 blocks,\nwe can see that if we compute the output in row-major ordering, we need to load 90\nblocks into SRAM to compute the first 9 output blocks, but if we do it in grouped\nordering, we only need to load 54 blocks.\n  .. image:: grouped_vs_row_major_ordering.png\n\nIn practice, this can improve the performance of our matrix multiplication kernel by\nmore than 10\\% on some hardware architecture (e.g., 220 to 245 TFLOPS on A100).\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Final Result\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import torch\n\nimport triton\nimport triton.language as tl\n\n# %\n# :code:`triton.jit`'ed functions can be auto-tuned by using the `triton.autotune`\n# decorator, which consumes:\n#   - A list of :code:`triton.Config` objects that define different configurations of\n#       meta-parameters (e.g., BLOCK_SIZE_M) and compilation options (e.g., num_warps) to try\n#   - An autotuning *key* whose change in values will trigger evaluation of all the\n#       provided configs\n\n\n@triton.autotune(\n    configs=[\n        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),\n        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),\n        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),\n        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),\n        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),\n    ],\n    key=['M', 'N', 'K'],\n)\n@triton.jit\ndef matmul_kernel(\n    # Pointers to matrices\n    a_ptr, b_ptr, c_ptr,\n    # Matrix dimensions\n    M, N, K,\n    # The stride variables represent how much to increase the ptr by when moving by 1\n    # element in a particular dimension. E.g. stride_am is how much to increase a_ptr\n    # by to get the element one row down (A has M rows)\n    stride_am, stride_ak,\n    stride_bk, stride_bn,\n    stride_cm, stride_cn,\n    # Meta-parameters\n    BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,\n    GROUP_SIZE_M: tl.constexpr,\n    ACTIVATION: tl.constexpr,\n):\n    \"\"\"Kernel for computing the matmul C = A x B.\n    A has shape (M, K), B has shape (K, N) and C has shape (M, N)\n    \"\"\"\n    # -----------------------------------------------------------\n    # Map program ids `pid` to the block of C it should compute.\n    # This is done in a grouped ordering to promote L2 data reuse\n    # See above `L2 Cache Optimizations` section for details\n    pid = tl.program_id(axis=0)\n    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)\n    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)\n    num_pid_in_group = GROUP_SIZE_M * num_pid_n\n    group_id = pid // num_pid_in_group\n    first_pid_m = group_id * GROUP_SIZE_M\n    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)\n    pid_m = first_pid_m + (pid % group_size_m)\n    pid_n = (pid % num_pid_in_group) // group_size_m\n\n    # ----------------------------------------------------------\n    # Create pointers for the first blocks of A and B.\n    # We will advance this pointer as we move in the K direction\n    # and accumulate\n    # a_ptrs is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers\n    # b_ptrs is a block of [BLOCK_SIZE_K, BLOCK_SIZE_n] pointers\n    # see above `Pointer Arithmetics` section for details\n    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n    offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n    offs_k = tl.arange(0, BLOCK_SIZE_K)\n    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)\n    b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)\n\n    # -----------------------------------------------------------\n    # Iterate to compute a block of the C matrix\n    # We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block\n    # of fp32 values for higher accuracy.\n    # `accumulator` will be converted back to fp16 after the loop\n    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)\n    for k in range(0, K, BLOCK_SIZE_K):\n        # Note that for simplicity, we don't apply a mask here.\n        # This means that if K is not a multiple of BLOCK_SIZE_K,\n        # this will access out-of-bounds memory and produce an\n        # error or (worse!) incorrect results.\n        a = tl.load(a_ptrs)\n        b = tl.load(b_ptrs)\n        # We accumulate along the K dimension\n        accumulator += tl.dot(a, b)\n        # Advance the ptrs to the next K block\n        a_ptrs += BLOCK_SIZE_K * stride_ak\n        b_ptrs += BLOCK_SIZE_K * stride_bk\n    # you can fuse arbitrary activation functions here\n    # while the accumulator is still in FP32!\n    if ACTIVATION:\n        accumulator = ACTIVATION(accumulator)\n    c = accumulator.to(tl.float16)\n\n    # -----------------------------------------------------------\n    # Write back the block of the output matrix C\n    offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)\n    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)\n    c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]\n    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)\n    tl.store(c_ptrs, c, mask=c_mask)\n\n\n# we can fuse `leaky_relu` by providing it as an `ACTIVATION` meta-parameter in `_matmul`\n@triton.jit\ndef leaky_relu(x):\n    return tl.where(x >= 0, x, 0.01 * x)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can now create a convenience wrapper function that only takes two input tensors\nand (1) checks any shape constraint; (2) allocates the output; (3) launches the above kernel\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "def matmul(a, b, activation=None):\n    # checks constraints\n    assert a.shape[1] == b.shape[0], \"incompatible dimensions\"\n    assert a.is_contiguous(), \"matrix A must be contiguous\"\n    assert b.is_contiguous(), \"matrix B must be contiguous\"\n    M, K = a.shape\n    K, N = b.shape\n    assert (\n        K % 32 == 0\n    ), \"We don't check memory-out-of-bounds with K so K must be divisible by BLOCK_SIZE_K\"\n    # allocates output\n    c = torch.empty((M, N), device=a.device, dtype=a.dtype)\n    # 1D launch kernel where each block gets its own program.\n    grid = lambda META: (\n        triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),\n    )\n    matmul_kernel[grid](\n        a, b, c,\n        M, N, K,\n        a.stride(0), a.stride(1),\n        b.stride(0), b.stride(1),\n        c.stride(0), c.stride(1),\n        ACTIVATION=activation,\n    )\n    return c"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Unit Test\n\nWe can test our custom matrix multiplication operation against a native torch implementation (i.e., cuBLAS)\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "torch.manual_seed(0)\na = torch.randn((512, 512), device='cuda', dtype=torch.float16)\nb = torch.randn((512, 512), device='cuda', dtype=torch.float16)\ntriton_output = matmul(a, b, activation=None)\ntorch_output = torch.matmul(a, b)\nprint(f\"triton_output={triton_output}\")\nprint(f\"torch_output={torch_output}\")\nif triton.testing.allclose(triton_output, torch_output):\n    print(\"\u2705 Triton and Torch match\")\nelse:\n    print(\"\u274c Triton and Torch differ\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Benchmark\n\n### Square Matrix Performance\nWe can now compare the performance of our kernel against that of cuBLAS. Here we focus on square matrices, but feel free to arrange this script as you wish to benchmark any other matrix shape.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "@triton.testing.perf_report(\n    triton.testing.Benchmark(\n        x_names=['M', 'N', 'K'],  # argument names to use as an x-axis for the plot\n        x_vals=[\n            128 * i for i in range(2, 33)\n        ],  # different possible values for `x_name`\n        line_arg='provider',  # argument name whose value corresponds to a different line in the plot\n        # possible values for `line_arg``\n        line_vals=['cublas', 'cublas + relu', 'triton', 'triton + relu'],\n        # label name for the lines\n        line_names=[\"cuBLAS\", \"cuBLAS (+ torch.nn.LeakyReLU)\", \"Triton\", \"Triton (+ LeakyReLU)\"],\n        # line styles\n        styles=[('green', '-'), ('green', '--'), ('blue', '-'), ('blue', '--')],\n        ylabel=\"TFLOPS\",  # label name for the y-axis\n        plot_name=\"matmul-performance\",  # name for the plot. Used also as a file name for saving the plot.\n        args={},\n    )\n)\ndef benchmark(M, N, K, provider):\n    a = torch.randn((M, K), device='cuda', dtype=torch.float16)\n    b = torch.randn((K, N), device='cuda', dtype=torch.float16)\n    if provider == 'cublas':\n        ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a, b))\n    if provider == 'triton':\n        ms, min_ms, max_ms = triton.testing.do_bench(lambda: matmul(a, b))\n    if provider == 'cublas + relu':\n        torch_relu = torch.nn.ReLU(inplace=True)\n        ms, min_ms, max_ms = triton.testing.do_bench(\n            lambda: torch_relu(torch.matmul(a, b))\n        )\n    if provider == 'triton + relu':\n        ms, min_ms, max_ms = triton.testing.do_bench(\n            lambda: matmul(a, b, activation=leaky_relu)\n        )\n    perf = lambda ms: 2 * M * N * K * 1e-12 / (ms * 1e-3)\n    return perf(ms), perf(max_ms), perf(min_ms)\n\n\nbenchmark.run(show_plots=True, print_data=True)"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.10"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/master/_downloads/bc847dec325798bdc436c4ef5ac8b78a/04-low-memory-dropout.ipynb b/master/_downloads/bc847dec325798bdc436c4ef5ac8b78a/04-low-memory-dropout.ipynb
deleted file mode 100644
index 5a6b1d8fa..000000000
--- a/master/_downloads/bc847dec325798bdc436c4ef5ac8b78a/04-low-memory-dropout.ipynb
+++ /dev/null
@@ -1,100 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Low-Memory Dropout\n\nIn this tutorial, you will write a memory-efficient implementation of dropout whose state\nwill be composed of a single int32 seed. This differs from more traditional implementations of dropout,\nwhose state is generally composed of a bit mask tensor of the same shape as the input. You will learn about:\n\n- The limitations of naive implementations of Dropout with PyTorch\n- Parallel pseudo-random number generation in Triton\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Baseline\nThe *dropout* operator was first introduced in [SRIVASTAVA2014]_ as a way to improve the performance\nof deep neural networks in low-data regime (i.e. regularization).\n\nIt takes a vector as input and produces a vector of the same shape as output. Each scalar in the\noutput has a probability $p$ of being changed to zero and otherwise it is copied from the input.\nThis forces the network to perform well even when only $1 - p$ scalars from the input are available.\n\nAt evaluation time we want to use the full power of the network so we set $p=0$. Naively this would\nincrease the norm of the output (which can be a bad thing, e.g. it can lead to artificial decrease\nin the output softmax temperature). To prevent this we multiply the output by $\\frac{1}{1 - p}$, which\nkeeps the norm consistent regardless of the dropout probability.\n\nLet's first take a look at the baseline implementation.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import tabulate\nimport torch\n\nimport triton\nimport triton.language as tl\n\n\n@triton.jit\ndef _dropout(\n        x_ptr,  # pointer to the input\n        x_keep_ptr,  # pointer to a mask of 0s and 1s\n        output_ptr,  # pointer to the output\n        n_elements,  # number of elements in the `x` tensor\n        p,  # probability that an element of `x` is changed to zero\n        BLOCK_SIZE: tl.constexpr,\n):\n    pid = tl.program_id(axis=0)\n    block_start = pid * BLOCK_SIZE\n    offsets = block_start + tl.arange(0, BLOCK_SIZE)\n    mask = offsets < n_elements\n    # Load data\n    x = tl.load(x_ptr + offsets, mask=mask)\n    x_keep = tl.load(x_keep_ptr + offsets, mask=mask)\n    # The line below is the crucial part, described in the paragraph above!\n    output = tl.where(x_keep, x / (1 - p), 0.0)\n    # Write-back output\n    tl.store(output_ptr + offsets, output, mask=mask)\n\n\ndef dropout(x, x_keep, p):\n    output = torch.empty_like(x)\n    assert x.is_contiguous()\n    n_elements = x.numel()\n    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n    _dropout[grid](x, x_keep, output, n_elements, p, BLOCK_SIZE=1024)\n    return output\n\n\n# Input tensor\nx = torch.randn(size=(10,)).cuda()\n# Dropout mask\np = 0.5\nx_keep = (torch.rand(size=(10,)) > p).to(torch.int32).cuda()\n#\noutput = dropout(x, x_keep=x_keep, p=p)\nprint(tabulate.tabulate([\n    [\"input\"] + x.tolist(),\n    [\"keep mask\"] + x_keep.tolist(),\n    [\"output\"] + output.tolist()\n]))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Seeded dropout\nAbove implementation of dropout works fine, but it can be a bit awkward to deal with. Firstly\nwe need to store the dropout mask for backpropagation. Secondly, dropout state management can get\nvery tricky when using recompute/checkpointing (e.g. see all the notes about `preserve_rng_state` in\nhttps://pytorch.org/docs/1.9.0/checkpoint.html). In this tutorial we'll describe an alternative implementation\nthat (1) has a smaller memory footprint; (2) requires less data movement; and (3) simplifies the management\nof persisting randomness across multiple invocations of the kernel.\n\nPseudorandom number generation in Triton is simple! In this tutorial we will use the\n:code:`triton.language.rand` function which generates a block of uniformly distributed :code:`float32`\nvalues in [0, 1), given a seed and a block of :code:`int32` offsets. But if you need it, Triton also provides\nother `random number generation strategies <Random Number Generation>`.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Triton's implementation of PRNG is based on the Philox algorithm (described on [SALMON2011]_).</p></div>\n\nLet's put it all together.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "@triton.jit\ndef _seeded_dropout(\n        x_ptr,\n        output_ptr,\n        n_elements,\n        p,\n        seed,\n        BLOCK_SIZE: tl.constexpr,\n):\n    # compute memory offsets of elements handled by this instance\n    pid = tl.program_id(axis=0)\n    block_start = pid * BLOCK_SIZE\n    offsets = block_start + tl.arange(0, BLOCK_SIZE)\n    # load data from x\n    mask = offsets < n_elements\n    x = tl.load(x_ptr + offsets, mask=mask)\n    # randomly prune it\n    random = tl.rand(seed, offsets)\n    x_keep = random > p\n    # write-back\n    output = tl.where(x_keep, x / (1 - p), 0.0)\n    tl.store(output_ptr + offsets, output, mask=mask)\n\n\ndef seeded_dropout(x, p, seed):\n    output = torch.empty_like(x)\n    assert x.is_contiguous()\n    n_elements = x.numel()\n    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n    _seeded_dropout[grid](x, output, n_elements, p, seed, BLOCK_SIZE=1024)\n    return output\n\n\nx = torch.randn(size=(10,)).cuda()\n# Compare this to the baseline - dropout mask is never instantiated!\noutput = seeded_dropout(x, p=0.5, seed=123)\noutput2 = seeded_dropout(x, p=0.5, seed=123)\noutput3 = seeded_dropout(x, p=0.5, seed=512)\n\nprint(tabulate.tabulate([\n    [\"input\"] + x.tolist(),\n    [\"output (seed = 123)\"] + output.tolist(),\n    [\"output (seed = 123)\"] + output2.tolist(),\n    [\"output (seed = 512)\"] + output3.tolist()\n]))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Et Voil\u00e0! We have a triton kernel that applies the same dropout mask provided the seed is the same!\nIf you'd like explore further applications of pseudorandomness in GPU programming, we encourage you\nto explore the `triton/language/random` folder!\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Exercises\n1. Extend the kernel to operate over a matrix and use a vector of seeds - one per row.\n2. Add support for striding.\n3. (challenge) Implement a kernel for sparse Johnson-Lindenstrauss transform which generates the projection matrix one the fly each time using a seed.\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## References\n\n.. [SALMON2011] John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw, \"Parallel Random Numbers: As Easy as 1, 2, 3\", 2011\n.. [SRIVASTAVA2014] Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, \"Dropout: A Simple Way to Prevent Neural Networks from Overfitting\", JMLR 2014\n\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.10"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/master/_downloads/c9aed78977a4c05741d675a38dde3d7d/04-low-memory-dropout.py b/master/_downloads/c9aed78977a4c05741d675a38dde3d7d/04-low-memory-dropout.py
deleted file mode 100644
index cf172537a..000000000
--- a/master/_downloads/c9aed78977a4c05741d675a38dde3d7d/04-low-memory-dropout.py
+++ /dev/null
@@ -1,166 +0,0 @@
-"""
-Low-Memory Dropout
-=================
-
-In this tutorial, you will write a memory-efficient implementation of dropout whose state
-will be composed of a single int32 seed. This differs from more traditional implementations of dropout,
-whose state is generally composed of a bit mask tensor of the same shape as the input. You will learn about:
-
-- The limitations of naive implementations of Dropout with PyTorch
-- Parallel pseudo-random number generation in Triton
-"""
-
-# %%
-# Baseline
-# -------------
-# The *dropout* operator was first introduced in [SRIVASTAVA2014]_ as a way to improve the performance
-# of deep neural networks in low-data regime (i.e. regularization).
-#
-# It takes a vector as input and produces a vector of the same shape as output. Each scalar in the
-# output has a probability :math:`p` of being changed to zero and otherwise it is copied from the input.
-# This forces the network to perform well even when only :math:`1 - p` scalars from the input are available.
-#
-# At evaluation time we want to use the full power of the network so we set :math:`p=0`. Naively this would
-# increase the norm of the output (which can be a bad thing, e.g. it can lead to artificial decrease
-# in the output softmax temperature). To prevent this we multiply the output by :math:`\frac{1}{1 - p}`, which
-# keeps the norm consistent regardless of the dropout probability.
-#
-# Let's first take a look at the baseline implementation.
-
-
-import tabulate
-import torch
-
-import triton
-import triton.language as tl
-
-
-@triton.jit
-def _dropout(
-        x_ptr,  # pointer to the input
-        x_keep_ptr,  # pointer to a mask of 0s and 1s
-        output_ptr,  # pointer to the output
-        n_elements,  # number of elements in the `x` tensor
-        p,  # probability that an element of `x` is changed to zero
-        BLOCK_SIZE: tl.constexpr,
-):
-    pid = tl.program_id(axis=0)
-    block_start = pid * BLOCK_SIZE
-    offsets = block_start + tl.arange(0, BLOCK_SIZE)
-    mask = offsets < n_elements
-    # Load data
-    x = tl.load(x_ptr + offsets, mask=mask)
-    x_keep = tl.load(x_keep_ptr + offsets, mask=mask)
-    # The line below is the crucial part, described in the paragraph above!
-    output = tl.where(x_keep, x / (1 - p), 0.0)
-    # Write-back output
-    tl.store(output_ptr + offsets, output, mask=mask)
-
-
-def dropout(x, x_keep, p):
-    output = torch.empty_like(x)
-    assert x.is_contiguous()
-    n_elements = x.numel()
-    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
-    _dropout[grid](x, x_keep, output, n_elements, p, BLOCK_SIZE=1024)
-    return output
-
-
-# Input tensor
-x = torch.randn(size=(10,)).cuda()
-# Dropout mask
-p = 0.5
-x_keep = (torch.rand(size=(10,)) > p).to(torch.int32).cuda()
-#
-output = dropout(x, x_keep=x_keep, p=p)
-print(tabulate.tabulate([
-    ["input"] + x.tolist(),
-    ["keep mask"] + x_keep.tolist(),
-    ["output"] + output.tolist()
-]))
-
-# %%
-# Seeded dropout
-# -------------
-# Above implementation of dropout works fine, but it can be a bit awkward to deal with. Firstly
-# we need to store the dropout mask for backpropagation. Secondly, dropout state management can get
-# very tricky when using recompute/checkpointing (e.g. see all the notes about `preserve_rng_state` in
-# https://pytorch.org/docs/1.9.0/checkpoint.html). In this tutorial we'll describe an alternative implementation
-# that (1) has a smaller memory footprint; (2) requires less data movement; and (3) simplifies the management
-# of persisting randomness across multiple invocations of the kernel.
-#
-# Pseudorandom number generation in Triton is simple! In this tutorial we will use the
-# :code:`triton.language.rand` function which generates a block of uniformly distributed :code:`float32`
-# values in [0, 1), given a seed and a block of :code:`int32` offsets. But if you need it, Triton also provides
-# other :ref:`random number generation strategies <Random Number Generation>`.
-#
-# .. note::
-#    Triton's implementation of PRNG is based on the Philox algorithm (described on [SALMON2011]_).
-#
-# Let's put it all together.
-
-
-@triton.jit
-def _seeded_dropout(
-        x_ptr,
-        output_ptr,
-        n_elements,
-        p,
-        seed,
-        BLOCK_SIZE: tl.constexpr,
-):
-    # compute memory offsets of elements handled by this instance
-    pid = tl.program_id(axis=0)
-    block_start = pid * BLOCK_SIZE
-    offsets = block_start + tl.arange(0, BLOCK_SIZE)
-    # load data from x
-    mask = offsets < n_elements
-    x = tl.load(x_ptr + offsets, mask=mask)
-    # randomly prune it
-    random = tl.rand(seed, offsets)
-    x_keep = random > p
-    # write-back
-    output = tl.where(x_keep, x / (1 - p), 0.0)
-    tl.store(output_ptr + offsets, output, mask=mask)
-
-
-def seeded_dropout(x, p, seed):
-    output = torch.empty_like(x)
-    assert x.is_contiguous()
-    n_elements = x.numel()
-    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
-    _seeded_dropout[grid](x, output, n_elements, p, seed, BLOCK_SIZE=1024)
-    return output
-
-
-x = torch.randn(size=(10,)).cuda()
-# Compare this to the baseline - dropout mask is never instantiated!
-output = seeded_dropout(x, p=0.5, seed=123)
-output2 = seeded_dropout(x, p=0.5, seed=123)
-output3 = seeded_dropout(x, p=0.5, seed=512)
-
-print(tabulate.tabulate([
-    ["input"] + x.tolist(),
-    ["output (seed = 123)"] + output.tolist(),
-    ["output (seed = 123)"] + output2.tolist(),
-    ["output (seed = 512)"] + output3.tolist()
-]))
-
-# %%
-# Et Voilà! We have a triton kernel that applies the same dropout mask provided the seed is the same!
-# If you'd like explore further applications of pseudorandomness in GPU programming, we encourage you
-# to explore the `triton/language/random` folder!
-
-# %%
-# Exercises
-# -------------
-# 1. Extend the kernel to operate over a matrix and use a vector of seeds - one per row.
-# 2. Add support for striding.
-# 3. (challenge) Implement a kernel for sparse Johnson-Lindenstrauss transform which generates the projection matrix one the fly each time using a seed.
-
-# %%
-# References
-# --------------
-#
-# .. [SALMON2011] John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw, "Parallel Random Numbers: As Easy as 1, 2, 3", 2011
-# .. [SRIVASTAVA2014] Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, "Dropout: A Simple Way to Prevent Neural Networks from Overfitting", JMLR 2014
diff --git a/master/_downloads/d5fee5b55a64e47f1b5724ec39adf171/03-matrix-multiplication.py b/master/_downloads/d5fee5b55a64e47f1b5724ec39adf171/03-matrix-multiplication.py
deleted file mode 100644
index f773a3787..000000000
--- a/master/_downloads/d5fee5b55a64e47f1b5724ec39adf171/03-matrix-multiplication.py
+++ /dev/null
@@ -1,355 +0,0 @@
-"""
-Matrix Multiplication
-======================
-In this tutorial, you will write a 25-lines high-performance FP16 matrix multiplication
-kernel that achieves performance on par with cuBLAS.
-You will specifically learn about:
-
-- Block-level matrix multiplications
-- Multi-dimensional pointer arithmetic
-- Program re-ordering for improved L2 cache hit rate
-- Automatic performance tuning
-"""
-
-# %%
-# Motivations
-# -------------
-# Matrix multiplications are a key building block of most modern high-performance computing systems.
-# They are notoriously hard to optimize, hence their implementation is generally done by
-# hardware vendors themselves as part of so-called "kernel libraries" (e.g., cuBLAS).
-# Unfortunately, these libraries are often proprietary and cannot be easily customized
-# to accomodate the needs of modern deep learning workloads (e.g., fused activation functions).
-# In this tutorial, you will learn how to implement efficient matrix multiplications by
-# yourself with Triton, in a way that is easy to customize and extend.
-#
-# Roughly speaking, the kernel that we will write will implement the following blocked
-# algorithm to multiply a (M, K) by a (K, N) matrix:
-#
-#  .. code-block:: python
-#
-#    # do in parallel
-#    for m in range(0, M, BLOCK_SIZE_M):
-#      # do in parallel
-#      for n in range(0, N, BLOCK_SIZE_N):
-#        acc = zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=float32)
-#        for k in range(0, K, BLOCK_SIZE_K):
-#          a = A[m : m+BLOCK_SIZE_M, k : k+BLOCK_SIZE_K]
-#          b = B[k : k+BLOCK_SIZE_K, n : n+BLOCK_SIZE_N]
-#          acc += dot(a, b)
-#        C[m : m+BLOCK_SIZE_M, n : n+BLOCK_SIZE_N] = acc;
-#
-# where each iteration of the doubly-nested for-loop is performed by a dedicated Triton program instance.
-
-# %%
-# Compute Kernel
-# ----------------
-#
-# The above algorithm is, actually, fairly straightforward to implement in Triton.
-# The main difficulty comes from the computation of the memory locations at which blocks
-# of :code:`A` and :code:`B` must be read in the inner loop. For that, we need
-# multi-dimensional pointer arithmetics.
-#
-# Pointer Arithmetics
-# ~~~~~~~~~~~~~~~~~~~~
-#
-# For a row-major 2D tensor :code:`X`, the memory location of :code:`X[i, j]` is given b
-# y :code:`&X[i, j] = X + i*stride_xi + j*stride_xj`.
-# Therefore, blocks of pointers for :code:`A[m : m+BLOCK_SIZE_M, k:k+BLOCK_SIZE_K]` and
-# :code:`B[k : k+BLOCK_SIZE_K, n : n+BLOCK_SIZE_N]` can be defined in pseudo-code as:
-#
-#  .. code-block:: python
-#
-#    &A[m : m+BLOCK_SIZE_M, k:k+BLOCK_SIZE_K] =  a_ptr + (m : m+BLOCK_SIZE_M)[:, None]*A.stride(0) + (k : k+BLOCK_SIZE_K)[None, :]*A.stride(1);
-#    &B[k : k+BLOCK_SIZE_K, n:n+BLOCK_SIZE_N] =  b_ptr + (k : k+BLOCK_SIZE_K)[:, None]*B.stride(0) + (n : n+BLOCK_SIZE_N)[None, :]*B.stride(1);
-#
-# Which means that pointers for blocks of A and B can be initialized (i.e., :code:`k=0`) in Triton as:
-#
-#  .. code-block:: python
-#
-#    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
-#    offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
-#    offs_k = tl.arange(0, BLOCK_SIZE_K)
-#    a_ptrs = a_ptr + (offs_am[:, None]*stride_am + offs_k [None, :]*stride_ak)
-#    b_ptrs = b_ptr + (offs_k [:, None]*stride_bk + offs_bn[None, :]*stride_bn)
-#
-# And then updated in the inner loop as follows:
-#
-#  .. code-block:: python
-#
-#    pa += BLOCK_SIZE_K * stride_ak;
-#    pb += BLOCK_SIZE_K * stride_bk;
-#
-#
-# L2 Cache Optimizations
-# ~~~~~~~~~~~~~~~~~~~~~~~~
-#
-# As mentioned above, each program instance computes a :code:`[BLOCK_SIZE_M, BLOCK_SIZE_N]`
-# block of :code:`C`.
-# It is important to remember that the order in which these blocks are computed does
-# matter, since it affects the L2 cache hit rate of our program. and unfortunately, a
-# a simple row-major ordering
-#
-#  .. code-block:: Python
-#
-#    pid = triton.program_id(0);
-#    grid_m = (M + BLOCK_SIZE_M - 1) // BLOCK_SIZE_M;
-#    grid_n = (N + BLOCK_SIZE_N - 1) // BLOCK_SIZE_N;
-#    pid_m = pid / grid_n;
-#    pid_n = pid % grid_n;
-#
-# is just not going to cut it.
-#
-# One possible solution is to launch blocks in an order that promotes data reuse.
-# This can be done by 'super-grouping' blocks in groups of :code:`GROUP_M` rows before
-# switching to the next column:
-#
-#  .. code-block:: python
-#
-#    # program ID
-#    pid = tl.program_id(axis=0)
-#    # number of program ids along the M axis
-#    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
-#    # number of programs ids along the N axis
-#    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
-#    # number of programs in group
-#    num_pid_in_group = GROUP_SIZE_M * num_pid_n
-#    # id of the group this program is in
-#    group_id = pid // num_pid_in_group
-#    # row-id of the first program in the group
-#    first_pid_m = group_id * GROUP_SIZE_M
-#    # if `num_pid_m` isn't divisible by `GROUP_SIZE_M`, the last group is smaller
-#    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
-#    # *within groups*, programs are ordered in a column-major order
-#    # row-id of the program in the *launch grid*
-#    pid_m = first_pid_m + (pid % group_size_m)
-#    # col-id of the program in the *launch grid*
-#    pid_n = (pid % num_pid_in_group) // group_size_m
-#
-# For example, in the following matmul where each matrix is 9 blocks by 9 blocks,
-# we can see that if we compute the output in row-major ordering, we need to load 90
-# blocks into SRAM to compute the first 9 output blocks, but if we do it in grouped
-# ordering, we only need to load 54 blocks.
-#   .. image:: grouped_vs_row_major_ordering.png
-#
-# In practice, this can improve the performance of our matrix multiplication kernel by
-# more than 10\% on some hardware architecture (e.g., 220 to 245 TFLOPS on A100).
-#
-
-# %%
-# Final Result
-# -------------
-#
-
-import torch
-
-import triton
-import triton.language as tl
-
-# %
-# :code:`triton.jit`'ed functions can be auto-tuned by using the `triton.autotune`
-# decorator, which consumes:
-#   - A list of :code:`triton.Config` objects that define different configurations of
-#       meta-parameters (e.g., BLOCK_SIZE_M) and compilation options (e.g., num_warps) to try
-#   - An autotuning *key* whose change in values will trigger evaluation of all the
-#       provided configs
-
-
-@triton.autotune(
-    configs=[
-        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
-        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
-        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
-        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
-    ],
-    key=['M', 'N', 'K'],
-)
-@triton.jit
-def matmul_kernel(
-    # Pointers to matrices
-    a_ptr, b_ptr, c_ptr,
-    # Matrix dimensions
-    M, N, K,
-    # The stride variables represent how much to increase the ptr by when moving by 1
-    # element in a particular dimension. E.g. stride_am is how much to increase a_ptr
-    # by to get the element one row down (A has M rows)
-    stride_am, stride_ak,
-    stride_bk, stride_bn,
-    stride_cm, stride_cn,
-    # Meta-parameters
-    BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
-    GROUP_SIZE_M: tl.constexpr,
-    ACTIVATION: tl.constexpr,
-):
-    """Kernel for computing the matmul C = A x B.
-    A has shape (M, K), B has shape (K, N) and C has shape (M, N)
-    """
-    # -----------------------------------------------------------
-    # Map program ids `pid` to the block of C it should compute.
-    # This is done in a grouped ordering to promote L2 data reuse
-    # See above `L2 Cache Optimizations` section for details
-    pid = tl.program_id(axis=0)
-    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
-    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
-    num_pid_in_group = GROUP_SIZE_M * num_pid_n
-    group_id = pid // num_pid_in_group
-    first_pid_m = group_id * GROUP_SIZE_M
-    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
-    pid_m = first_pid_m + (pid % group_size_m)
-    pid_n = (pid % num_pid_in_group) // group_size_m
-
-    # ----------------------------------------------------------
-    # Create pointers for the first blocks of A and B.
-    # We will advance this pointer as we move in the K direction
-    # and accumulate
-    # a_ptrs is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
-    # b_ptrs is a block of [BLOCK_SIZE_K, BLOCK_SIZE_n] pointers
-    # see above `Pointer Arithmetics` section for details
-    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
-    offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
-    offs_k = tl.arange(0, BLOCK_SIZE_K)
-    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
-    b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
-
-    # -----------------------------------------------------------
-    # Iterate to compute a block of the C matrix
-    # We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
-    # of fp32 values for higher accuracy.
-    # `accumulator` will be converted back to fp16 after the loop
-    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
-    for k in range(0, K, BLOCK_SIZE_K):
-        # Note that for simplicity, we don't apply a mask here.
-        # This means that if K is not a multiple of BLOCK_SIZE_K,
-        # this will access out-of-bounds memory and produce an
-        # error or (worse!) incorrect results.
-        a = tl.load(a_ptrs)
-        b = tl.load(b_ptrs)
-        # We accumulate along the K dimension
-        accumulator += tl.dot(a, b)
-        # Advance the ptrs to the next K block
-        a_ptrs += BLOCK_SIZE_K * stride_ak
-        b_ptrs += BLOCK_SIZE_K * stride_bk
-    # you can fuse arbitrary activation functions here
-    # while the accumulator is still in FP32!
-    if ACTIVATION:
-        accumulator = ACTIVATION(accumulator)
-    c = accumulator.to(tl.float16)
-
-    # -----------------------------------------------------------
-    # Write back the block of the output matrix C
-    offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
-    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
-    c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
-    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
-    tl.store(c_ptrs, c, mask=c_mask)
-
-
-# we can fuse `leaky_relu` by providing it as an `ACTIVATION` meta-parameter in `_matmul`
-@triton.jit
-def leaky_relu(x):
-    return tl.where(x >= 0, x, 0.01 * x)
-
-
-# %%
-# We can now create a convenience wrapper function that only takes two input tensors
-# and (1) checks any shape constraint; (2) allocates the output; (3) launches the above kernel
-
-
-def matmul(a, b, activation=None):
-    # checks constraints
-    assert a.shape[1] == b.shape[0], "incompatible dimensions"
-    assert a.is_contiguous(), "matrix A must be contiguous"
-    assert b.is_contiguous(), "matrix B must be contiguous"
-    M, K = a.shape
-    K, N = b.shape
-    assert (
-        K % 32 == 0
-    ), "We don't check memory-out-of-bounds with K so K must be divisible by BLOCK_SIZE_K"
-    # allocates output
-    c = torch.empty((M, N), device=a.device, dtype=a.dtype)
-    # 1D launch kernel where each block gets its own program.
-    grid = lambda META: (
-        triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),
-    )
-    matmul_kernel[grid](
-        a, b, c,
-        M, N, K,
-        a.stride(0), a.stride(1),
-        b.stride(0), b.stride(1),
-        c.stride(0), c.stride(1),
-        ACTIVATION=activation,
-    )
-    return c
-
-
-# %%
-# Unit Test
-# -----------
-#
-# We can test our custom matrix multiplication operation against a native torch implementation (i.e., cuBLAS)
-
-torch.manual_seed(0)
-a = torch.randn((512, 512), device='cuda', dtype=torch.float16)
-b = torch.randn((512, 512), device='cuda', dtype=torch.float16)
-triton_output = matmul(a, b, activation=None)
-torch_output = torch.matmul(a, b)
-print(f"triton_output={triton_output}")
-print(f"torch_output={torch_output}")
-if triton.testing.allclose(triton_output, torch_output):
-    print("✅ Triton and Torch match")
-else:
-    print("❌ Triton and Torch differ")
-
-# %%
-# Benchmark
-# --------------
-#
-# Square Matrix Performance
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~
-# We can now compare the performance of our kernel against that of cuBLAS. Here we focus on square matrices, but feel free to arrange this script as you wish to benchmark any other matrix shape.
-
-
-@triton.testing.perf_report(
-    triton.testing.Benchmark(
-        x_names=['M', 'N', 'K'],  # argument names to use as an x-axis for the plot
-        x_vals=[
-            128 * i for i in range(2, 33)
-        ],  # different possible values for `x_name`
-        line_arg='provider',  # argument name whose value corresponds to a different line in the plot
-        # possible values for `line_arg``
-        line_vals=['cublas', 'cublas + relu', 'triton', 'triton + relu'],
-        # label name for the lines
-        line_names=["cuBLAS", "cuBLAS (+ torch.nn.LeakyReLU)", "Triton", "Triton (+ LeakyReLU)"],
-        # line styles
-        styles=[('green', '-'), ('green', '--'), ('blue', '-'), ('blue', '--')],
-        ylabel="TFLOPS",  # label name for the y-axis
-        plot_name="matmul-performance",  # name for the plot. Used also as a file name for saving the plot.
-        args={},
-    )
-)
-def benchmark(M, N, K, provider):
-    a = torch.randn((M, K), device='cuda', dtype=torch.float16)
-    b = torch.randn((K, N), device='cuda', dtype=torch.float16)
-    if provider == 'cublas':
-        ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a, b))
-    if provider == 'triton':
-        ms, min_ms, max_ms = triton.testing.do_bench(lambda: matmul(a, b))
-    if provider == 'cublas + relu':
-        torch_relu = torch.nn.ReLU(inplace=True)
-        ms, min_ms, max_ms = triton.testing.do_bench(
-            lambda: torch_relu(torch.matmul(a, b))
-        )
-    if provider == 'triton + relu':
-        ms, min_ms, max_ms = triton.testing.do_bench(
-            lambda: matmul(a, b, activation=leaky_relu)
-        )
-    perf = lambda ms: 2 * M * N * K * 1e-12 / (ms * 1e-3)
-    return perf(ms), perf(max_ms), perf(min_ms)
-
-
-benchmark.run(show_plots=True, print_data=True)
diff --git a/master/_downloads/d91442ac2982c4e0cc3ab0f43534afbc/02-fused-softmax.py b/master/_downloads/d91442ac2982c4e0cc3ab0f43534afbc/02-fused-softmax.py
deleted file mode 100644
index 7af24e18d..000000000
--- a/master/_downloads/d91442ac2982c4e0cc3ab0f43534afbc/02-fused-softmax.py
+++ /dev/null
@@ -1,191 +0,0 @@
-"""
-Fused Softmax
-=================
-In this tutorial, you will write a fused softmax operation that is significantly faster
-than PyTorch's native op for a particular class of matrices: those whose rows can fit in
-the GPU's SRAM.
-You will learn about:
-
-- The benefits of kernel fusion for bandwidth-bound operations.
-- Reduction operators in Triton.
-"""
-
-# %%
-# Motivations
-# ------------
-# Custom GPU kernels for elementwise additions are educationally valuable but won't get you very far in practice.
-# Let us consider instead the case of a simple (numerically stabilized) softmax operation:
-
-import torch
-
-import triton
-import triton.language as tl
-
-
-@torch.jit.script
-def naive_softmax(x):
-    """Compute row-wise softmax of X using native pytorch
-
-    We subtract the maximum element in order to avoid overflows. Softmax is invariant to
-    this shift.
-    """
-    # read  MN elements ; write M  elements
-    x_max = x.max(dim=1)[0]
-    # read MN + M elements ; write MN elements
-    z = x - x_max[:, None]
-    # read  MN elements ; write MN elements
-    numerator = torch.exp(z)
-    # read  MN elements ; write M  elements
-    denominator = numerator.sum(dim=1)
-    # read MN + M elements ; write MN elements
-    ret = numerator / denominator[:, None]
-    # in total: read 5MN + 2M elements ; wrote 3MN + 2M elements
-    return ret
-
-
-# %%
-# When implemented naively in PyTorch, computing :code:`y = naive_softmax(x)` for :math:`x \in R^{M \times N}`
-# requires reading :math:`5MN + 2M` elements from DRAM and writing back :math:`3MN + 2M` elements.
-# This is obviously wasteful; we'd prefer to have a custom "fused" kernel that only reads
-# X once and does all the necessary computations on-chip.
-# Doing so would require reading and writing back only :math:`MN` bytes, so we could
-# expect a theoretical speed-up of ~4x (i.e., :math:`(8MN + 4M) / 2MN`).
-# The `torch.jit.script` flags aims to perform this kind of "kernel fusion" automatically
-# but, as we will see later, it is still far from ideal.
-
-# %%
-# Compute Kernel
-# ----------------
-# Our softmax kernel works as follows: each program loads a row of the input matrix X,
-# normalizes it and writes back the result to the output Y.
-# Note that one important limitation of Triton is that each block must have a
-# power-of-two number of elements, so we need to internally "pad" each row and guard the
-# memory operations properly if we want to handle any possible input shapes:
-
-
-@triton.jit
-def softmax_kernel(
-    output_ptr, input_ptr, input_row_stride, output_row_stride, n_cols,
-    BLOCK_SIZE: tl.constexpr
-):
-    # The rows of the softmax are independent, so we parallelize across those
-    row_idx = tl.program_id(0)
-    # The stride represents how much we need to increase the pointer to advance 1 row
-    row_start_ptr = input_ptr + row_idx * input_row_stride
-    # The block size is the next power of two greater than n_cols, so we can fit each
-    # row in a single block
-    col_offsets = tl.arange(0, BLOCK_SIZE)
-    input_ptrs = row_start_ptr + col_offsets
-    # Load the row into SRAM, using a mask since BLOCK_SIZE may be > than n_cols
-    row = tl.load(input_ptrs, mask=col_offsets < n_cols, other=-float('inf'))
-    # Substract maximum for numerical stability
-    row_minus_max = row - tl.max(row, axis=0)
-    # Note that exponentials in Triton are fast but approximate (i.e., think __expf in CUDA)
-    numerator = tl.exp(row_minus_max)
-    denominator = tl.sum(numerator, axis=0)
-    softmax_output = numerator / denominator
-    # Write back output to DRAM
-    output_row_start_ptr = output_ptr + row_idx * output_row_stride
-    output_ptrs = output_row_start_ptr + col_offsets
-    tl.store(output_ptrs, softmax_output, mask=col_offsets < n_cols)
-
-
-# %%
-# We can create a helper function that enqueues the kernel and its (meta-)arguments for any given input tensor.
-
-def softmax(x):
-    n_rows, n_cols = x.shape
-    # The block size is the smallest power of two greater than the number of columns in `x`
-    BLOCK_SIZE = triton.next_power_of_2(n_cols)
-    # Another trick we can use is to ask the compiler to use more threads per row by
-    # increasing the number of warps (`num_warps`) over which each row is distributed.
-    # You will see in the next tutorial how to auto-tune this value in a more natural
-    # way so you don't have to come up with manual heuristics yourself.
-    num_warps = 4
-    if BLOCK_SIZE >= 2048:
-        num_warps = 8
-    if BLOCK_SIZE >= 4096:
-        num_warps = 16
-    # Allocate output
-    y = torch.empty_like(x)
-    # Enqueue kernel. The 1D launch grid is simple: we have one kernel instance per row o
-    # f the input matrix
-    softmax_kernel[(n_rows,)](
-        y,
-        x,
-        x.stride(0),
-        y.stride(0),
-        n_cols,
-        num_warps=num_warps,
-        BLOCK_SIZE=BLOCK_SIZE,
-    )
-    return y
-
-
-# %%
-# Unit Test
-# ----------
-
-# %%
-# We make sure that we test our kernel on a matrix with an irregular number of rows and columns.
-# This will allow us to verify that our padding mechanism works.
-
-torch.manual_seed(0)
-x = torch.randn(1823, 781, device='cuda')
-y_triton = softmax(x)
-y_torch = torch.softmax(x, axis=1)
-assert torch.allclose(y_triton, y_torch), (y_triton, y_torch)
-
-# %%
-# As expected, the results are identical.
-
-# %%
-# Benchmark
-# -------------
-# Here we will benchmark our operation as a function of the number of columns in the input matrix -- assuming 4096 rows.
-# We will then compare its performance against (1) :code:`torch.softmax` and (2) the :code:`naive_softmax` defined above.
-
-
-@triton.testing.perf_report(
-    triton.testing.Benchmark(
-        x_names=['N'],  # argument names to use as an x-axis for the plot
-        x_vals=[
-            128 * i for i in range(2, 100)
-        ],  # different possible values for `x_name`
-        line_arg='provider',  # argument name whose value corresponds to a different line in the plot
-        line_vals=[
-            'triton',
-            'torch-native',
-            'torch-jit',
-        ],  # possible values for `line_arg``
-        line_names=[
-            "Triton",
-            "Torch (native)",
-            "Torch (jit)",
-        ],  # label name for the lines
-        styles=[('blue', '-'), ('green', '-'), ('green', '--')],  # line styles
-        ylabel="GB/s",  # label name for the y-axis
-        plot_name="softmax-performance",  # name for the plot. Used also as a file name for saving the plot.
-        args={'M': 4096},  # values for function arguments not in `x_names` and `y_name`
-    )
-)
-def benchmark(M, N, provider):
-    x = torch.randn(M, N, device='cuda', dtype=torch.float32)
-    if provider == 'torch-native':
-        ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.softmax(x, axis=-1))
-    if provider == 'triton':
-        ms, min_ms, max_ms = triton.testing.do_bench(lambda: softmax(x))
-    if provider == 'torch-jit':
-        ms, min_ms, max_ms = triton.testing.do_bench(lambda: naive_softmax(x))
-    gbps = lambda ms: 2 * x.nelement() * x.element_size() * 1e-9 / (ms * 1e-3)
-    return gbps(ms), gbps(max_ms), gbps(min_ms)
-
-
-benchmark.run(show_plots=True, print_data=True)
-
-# %%
-# In the above plot, we can see that:
-#
-#  - Triton is 4x faster than the Torch JIT. This confirms our suspicions that the Torch JIT does not do any fusion here.
-#  - Triton is noticeably faster than :code:`torch.softmax` -- in addition to being **easier to read, understand and maintain**.
-#    Note however that the PyTorch `softmax` operation is more general and will works on tensors of any shape.
diff --git a/master/_downloads/f191ee1e78dc52eb5f7cba88f71cef2f/01-vector-add.ipynb b/master/_downloads/f191ee1e78dc52eb5f7cba88f71cef2f/01-vector-add.ipynb
deleted file mode 100644
index a88ec6569..000000000
--- a/master/_downloads/f191ee1e78dc52eb5f7cba88f71cef2f/01-vector-add.ipynb
+++ /dev/null
@@ -1,140 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Vector Addition\nIn this tutorial, you will write a simple vector addition using Triton and learn about:\n\n- The basic programming model of Triton\n- The `triton.jit` decorator, which is used to define Triton kernels.\n- The best practices for validating and benchmarking your custom ops against native reference implementations\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Compute Kernel\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import torch\n\nimport triton\nimport triton.language as tl\n\n\n@triton.jit\ndef add_kernel(\n    x_ptr,  # *Pointer* to first input vector\n    y_ptr,  # *Pointer* to second input vector\n    output_ptr,  # *Pointer* to output vector\n    n_elements,  # Size of the vector\n    BLOCK_SIZE: tl.constexpr,  # Number of elements each program should process\n                 # NOTE: `constexpr` so it can be used as a shape value\n):\n    # There are multiple 'program's processing different data. We identify which program\n    # we are here\n    pid = tl.program_id(axis=0)  # We use a 1D launch grid so axis is 0\n    # This program will process inputs that are offset from the initial data.\n    # for instance, if you had a vector of length 256 and block_size of 64, the programs\n    # would each access the elements [0:64, 64:128, 128:192, 192:256].\n    # Note that offsets is a list of pointers\n    block_start = pid * BLOCK_SIZE\n    offsets = block_start + tl.arange(0, BLOCK_SIZE)\n    # Create a mask to guard memory operations against out-of-bounds accesses\n    mask = offsets < n_elements\n    # Load x and y from DRAM, masking out any extra elements in case the input is not a\n    # multiple of the block size\n    x = tl.load(x_ptr + offsets, mask=mask)\n    y = tl.load(y_ptr + offsets, mask=mask)\n    output = x + y\n    # Write x + y back to DRAM\n    tl.store(output_ptr + offsets, output, mask=mask)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's also declare a helper function to (1) allocate the `z` tensor\nand (2) enqueue the above kernel with appropriate grid/block sizes.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "def add(x: torch.Tensor, y: torch.Tensor):\n    # We need to preallocate the output\n    output = torch.empty_like(x)\n    assert x.is_cuda and y.is_cuda and output.is_cuda\n    n_elements = output.numel()\n    # The SPMD launch grid denotes the number of kernel instances that run in parallel.\n    # It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]\n    # In this case, we use a 1D grid where the size is the number of blocks\n    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n    # NOTE:\n    #  - each torch.tensor object is implicitly converted into a pointer to its first element.\n    #  - `triton.jit`'ed functions can be index with a launch grid to obtain a callable GPU kernel\n    #  - don't forget to pass meta-parameters as keywords arguments\n    add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)\n    # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still\n    # running asynchronously at this point.\n    return output"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can now use the above function to compute the element-wise sum of two `torch.tensor` objects and test its correctness:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "torch.manual_seed(0)\nsize = 98432\nx = torch.rand(size, device='cuda')\ny = torch.rand(size, device='cuda')\noutput_torch = x + y\noutput_triton = add(x, y)\nprint(output_torch)\nprint(output_triton)\nprint(\n    f'The maximum difference between torch and triton is '\n    f'{torch.max(torch.abs(output_torch - output_triton))}'\n)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Seems like we're good to go!\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Benchmark\nWe can now benchmark our custom op on vectors of increasing sizes to get a sense of how it does relative to PyTorch.\nTo make things easier, Triton has a set of built-in utilities that allow us to concisely plot the performance of your custom ops\nfor different problem sizes.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "@triton.testing.perf_report(\n    triton.testing.Benchmark(\n        x_names=['size'],  # argument names to use as an x-axis for the plot\n        x_vals=[\n            2 ** i for i in range(12, 28, 1)\n        ],  # different possible values for `x_name`\n        x_log=True,  # x axis is logarithmic\n        line_arg='provider',  # argument name whose value corresponds to a different line in the plot\n        line_vals=['triton', 'torch'],  # possible values for `line_arg`\n        line_names=['Triton', 'Torch'],  # label name for the lines\n        styles=[('blue', '-'), ('green', '-')],  # line styles\n        ylabel='GB/s',  # label name for the y-axis\n        plot_name='vector-add-performance',  # name for the plot. Used also as a file name for saving the plot.\n        args={},  # values for function arguments not in `x_names` and `y_name`\n    )\n)\ndef benchmark(size, provider):\n    x = torch.rand(size, device='cuda', dtype=torch.float32)\n    y = torch.rand(size, device='cuda', dtype=torch.float32)\n    if provider == 'torch':\n        ms, min_ms, max_ms = triton.testing.do_bench(lambda: x + y)\n    if provider == 'triton':\n        ms, min_ms, max_ms = triton.testing.do_bench(lambda: add(x, y))\n    gbps = lambda ms: 12 * size / ms * 1e-6\n    return gbps(ms), gbps(max_ms), gbps(min_ms)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can now run the decorated function above. Pass `print_data=True` to see the performance number, `show_plots=True` to plot them, and/or\n`save_path='/path/to/results/' to save them to disk along with raw CSV data\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "benchmark.run(print_data=True, show_plots=True)"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.10"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/master/_images/cuda-parallel-matmul.png b/master/_images/cuda-parallel-matmul.png
deleted file mode 100644
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diff --git a/master/_sources/getting-started/installation.rst.txt b/master/_sources/getting-started/installation.rst.txt
deleted file mode 100644
index 20c4628bc..000000000
--- a/master/_sources/getting-started/installation.rst.txt
+++ /dev/null
@@ -1,55 +0,0 @@
-==============
-Installation
-==============
-
----------------------
-Binary Distributions
----------------------
-
-You can install the latest stable release of Triton from pip:
-
-.. code-block:: bash
-
-      pip install triton
-
-Binary wheels are available for CPython 3.6-3.9 and PyPy 3.6-3.7.
-
-And the latest nightly release:
-
-.. code-block:: bash
-  
-      pip install -U --pre triton
-
-
---------------
-From Source
---------------
-
-+++++++++++++++
-Python Package
-+++++++++++++++
-
-You can install the Python package from source by running the following commands:
-
-.. code-block:: bash
-
-      git clone https://github.com/openai/triton.git;
-      cd triton/python;
-      pip install cmake; # build time dependency
-      pip install -e .
-
-Note that, if llvm-11 is not present on your system, the setup.py script will download the official LLVM11 static libraries link against that.
-
-You can then test your installation by running the unit tests:
-
-.. code-block:: bash
-
-      pip install -e '.[tests]'
-      pytest -vs test/unit/
-
-and the benchmarks
-
-.. code-block:: bash
-      
-      cd bench/
-      python -m run --with-plots --result-dir /tmp/triton-bench
diff --git a/master/_sources/getting-started/tutorials/01-vector-add.rst.txt b/master/_sources/getting-started/tutorials/01-vector-add.rst.txt
deleted file mode 100644
index cb7937efe..000000000
--- a/master/_sources/getting-started/tutorials/01-vector-add.rst.txt
+++ /dev/null
@@ -1,286 +0,0 @@
-
-.. DO NOT EDIT.
-.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
-.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
-.. "getting-started/tutorials/01-vector-add.py"
-.. LINE NUMBERS ARE GIVEN BELOW.
-
-.. only:: html
-
-    .. note::
-        :class: sphx-glr-download-link-note
-
-        Click :ref:`here <sphx_glr_download_getting-started_tutorials_01-vector-add.py>`
-        to download the full example code
-
-.. rst-class:: sphx-glr-example-title
-
-.. _sphx_glr_getting-started_tutorials_01-vector-add.py:
-
-
-Vector Addition
-=================
-In this tutorial, you will write a simple vector addition using Triton and learn about:
-
-- The basic programming model of Triton
-- The `triton.jit` decorator, which is used to define Triton kernels.
-- The best practices for validating and benchmarking your custom ops against native reference implementations
-
-.. GENERATED FROM PYTHON SOURCE LINES 12-14
-
-Compute Kernel
---------------------------
-
-.. GENERATED FROM PYTHON SOURCE LINES 14-50
-
-.. code-block:: default
-
-
-    import torch
-
-    import triton
-    import triton.language as tl
-
-
-    @triton.jit
-    def add_kernel(
-        x_ptr,  # *Pointer* to first input vector
-        y_ptr,  # *Pointer* to second input vector
-        output_ptr,  # *Pointer* to output vector
-        n_elements,  # Size of the vector
-        BLOCK_SIZE: tl.constexpr,  # Number of elements each program should process
-                     # NOTE: `constexpr` so it can be used as a shape value
-    ):
-        # There are multiple 'program's processing different data. We identify which program
-        # we are here
-        pid = tl.program_id(axis=0)  # We use a 1D launch grid so axis is 0
-        # This program will process inputs that are offset from the initial data.
-        # for instance, if you had a vector of length 256 and block_size of 64, the programs
-        # would each access the elements [0:64, 64:128, 128:192, 192:256].
-        # Note that offsets is a list of pointers
-        block_start = pid * BLOCK_SIZE
-        offsets = block_start + tl.arange(0, BLOCK_SIZE)
-        # Create a mask to guard memory operations against out-of-bounds accesses
-        mask = offsets < n_elements
-        # Load x and y from DRAM, masking out any extra elements in case the input is not a
-        # multiple of the block size
-        x = tl.load(x_ptr + offsets, mask=mask)
-        y = tl.load(y_ptr + offsets, mask=mask)
-        output = x + y
-        # Write x + y back to DRAM
-        tl.store(output_ptr + offsets, output, mask=mask)
-
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 51-53
-
-Let's also declare a helper function to (1) allocate the `z` tensor
-and (2) enqueue the above kernel with appropriate grid/block sizes.
-
-.. GENERATED FROM PYTHON SOURCE LINES 53-74
-
-.. code-block:: default
-
-
-
-    def add(x: torch.Tensor, y: torch.Tensor):
-        # We need to preallocate the output
-        output = torch.empty_like(x)
-        assert x.is_cuda and y.is_cuda and output.is_cuda
-        n_elements = output.numel()
-        # The SPMD launch grid denotes the number of kernel instances that run in parallel.
-        # It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]
-        # In this case, we use a 1D grid where the size is the number of blocks
-        grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
-        # NOTE:
-        #  - each torch.tensor object is implicitly converted into a pointer to its first element.
-        #  - `triton.jit`'ed functions can be index with a launch grid to obtain a callable GPU kernel
-        #  - don't forget to pass meta-parameters as keywords arguments
-        add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)
-        # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still
-        # running asynchronously at this point.
-        return output
-
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 75-76
-
-We can now use the above function to compute the element-wise sum of two `torch.tensor` objects and test its correctness:
-
-.. GENERATED FROM PYTHON SOURCE LINES 76-90
-
-.. code-block:: default
-
-
-    torch.manual_seed(0)
-    size = 98432
-    x = torch.rand(size, device='cuda')
-    y = torch.rand(size, device='cuda')
-    output_torch = x + y
-    output_triton = add(x, y)
-    print(output_torch)
-    print(output_triton)
-    print(
-        f'The maximum difference between torch and triton is '
-        f'{torch.max(torch.abs(output_torch - output_triton))}'
-    )
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    tensor([1.3713, 1.3076, 0.4940,  ..., 0.6724, 1.2141, 0.9733], device='cuda:0')
-    tensor([1.3713, 1.3076, 0.4940,  ..., 0.6724, 1.2141, 0.9733], device='cuda:0')
-    The maximum difference between torch and triton is 0.0
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 91-92
-
-Seems like we're good to go!
-
-.. GENERATED FROM PYTHON SOURCE LINES 94-99
-
-Benchmark
------------
-We can now benchmark our custom op on vectors of increasing sizes to get a sense of how it does relative to PyTorch.
-To make things easier, Triton has a set of built-in utilities that allow us to concisely plot the performance of your custom ops
-for different problem sizes.
-
-.. GENERATED FROM PYTHON SOURCE LINES 99-128
-
-.. code-block:: default
-
-
-
-    @triton.testing.perf_report(
-        triton.testing.Benchmark(
-            x_names=['size'],  # argument names to use as an x-axis for the plot
-            x_vals=[
-                2 ** i for i in range(12, 28, 1)
-            ],  # different possible values for `x_name`
-            x_log=True,  # x axis is logarithmic
-            line_arg='provider',  # argument name whose value corresponds to a different line in the plot
-            line_vals=['triton', 'torch'],  # possible values for `line_arg`
-            line_names=['Triton', 'Torch'],  # label name for the lines
-            styles=[('blue', '-'), ('green', '-')],  # line styles
-            ylabel='GB/s',  # label name for the y-axis
-            plot_name='vector-add-performance',  # name for the plot. Used also as a file name for saving the plot.
-            args={},  # values for function arguments not in `x_names` and `y_name`
-        )
-    )
-    def benchmark(size, provider):
-        x = torch.rand(size, device='cuda', dtype=torch.float32)
-        y = torch.rand(size, device='cuda', dtype=torch.float32)
-        if provider == 'torch':
-            ms, min_ms, max_ms = triton.testing.do_bench(lambda: x + y)
-        if provider == 'triton':
-            ms, min_ms, max_ms = triton.testing.do_bench(lambda: add(x, y))
-        gbps = lambda ms: 12 * size / ms * 1e-6
-        return gbps(ms), gbps(max_ms), gbps(min_ms)
-
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 129-131
-
-We can now run the decorated function above. Pass `print_data=True` to see the performance number, `show_plots=True` to plot them, and/or
-`save_path='/path/to/results/' to save them to disk along with raw CSV data
-
-.. GENERATED FROM PYTHON SOURCE LINES 131-132
-
-.. code-block:: default
-
-    benchmark.run(print_data=True, show_plots=True)
-
-
-
-.. image:: /getting-started/tutorials/images/sphx_glr_01-vector-add_001.png
-    :alt: 01 vector add
-    :class: sphx-glr-single-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    vector-add-performance:
-               size      Triton       Torch
-    0        4096.0    9.600000    9.600000
-    1        8192.0   19.200000   19.200000
-    2       16384.0   38.400001   38.400001
-    3       32768.0   76.800002   76.800002
-    4       65536.0  127.999995  127.999995
-    5      131072.0  219.428568  219.428568
-    6      262144.0  341.333321  384.000001
-    7      524288.0  472.615390  472.615390
-    8     1048576.0  614.400016  614.400016
-    9     2097152.0  722.823517  722.823517
-    10    4194304.0  780.190482  780.190482
-    11    8388608.0  812.429770  812.429770
-    12   16777216.0  833.084721  833.084721
-    13   33554432.0  842.004273  842.004273
-    14   67108864.0  847.448255  848.362445
-    15  134217728.0  849.737435  850.656574
-
-
-
-
-
-.. rst-class:: sphx-glr-timing
-
-   **Total running time of the script:** ( 1 minutes  38.189 seconds)
-
-
-.. _sphx_glr_download_getting-started_tutorials_01-vector-add.py:
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-    :class: sphx-glr-footer-example
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-python
-
-     :download:`Download Python source code: 01-vector-add.py <01-vector-add.py>`
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-     :download:`Download Jupyter notebook: 01-vector-add.ipynb <01-vector-add.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/master/_sources/getting-started/tutorials/02-fused-softmax.rst.txt b/master/_sources/getting-started/tutorials/02-fused-softmax.rst.txt
deleted file mode 100644
index 8bcefb043..000000000
--- a/master/_sources/getting-started/tutorials/02-fused-softmax.rst.txt
+++ /dev/null
@@ -1,337 +0,0 @@
-
-.. DO NOT EDIT.
-.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
-.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
-.. "getting-started/tutorials/02-fused-softmax.py"
-.. LINE NUMBERS ARE GIVEN BELOW.
-
-.. only:: html
-
-    .. note::
-        :class: sphx-glr-download-link-note
-
-        Click :ref:`here <sphx_glr_download_getting-started_tutorials_02-fused-softmax.py>`
-        to download the full example code
-
-.. rst-class:: sphx-glr-example-title
-
-.. _sphx_glr_getting-started_tutorials_02-fused-softmax.py:
-
-
-Fused Softmax
-=================
-In this tutorial, you will write a fused softmax operation that is significantly faster
-than PyTorch's native op for a particular class of matrices: those whose rows can fit in
-the GPU's SRAM.
-You will learn about:
-
-- The benefits of kernel fusion for bandwidth-bound operations.
-- Reduction operators in Triton.
-
-.. GENERATED FROM PYTHON SOURCE LINES 14-18
-
-Motivations
-------------
-Custom GPU kernels for elementwise additions are educationally valuable but won't get you very far in practice.
-Let us consider instead the case of a simple (numerically stabilized) softmax operation:
-
-.. GENERATED FROM PYTHON SOURCE LINES 18-46
-
-.. code-block:: default
-
-
-    import torch
-
-    import triton
-    import triton.language as tl
-
-
-    @torch.jit.script
-    def naive_softmax(x):
-        """Compute row-wise softmax of X using native pytorch
-
-        We subtract the maximum element in order to avoid overflows. Softmax is invariant to
-        this shift.
-        """
-        # read  MN elements ; write M  elements
-        x_max = x.max(dim=1)[0]
-        # read MN + M elements ; write MN elements
-        z = x - x_max[:, None]
-        # read  MN elements ; write MN elements
-        numerator = torch.exp(z)
-        # read  MN elements ; write M  elements
-        denominator = numerator.sum(dim=1)
-        # read MN + M elements ; write MN elements
-        ret = numerator / denominator[:, None]
-        # in total: read 5MN + 2M elements ; wrote 3MN + 2M elements
-        return ret
-
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 47-55
-
-When implemented naively in PyTorch, computing :code:`y = naive_softmax(x)` for :math:`x \in R^{M \times N}`
-requires reading :math:`5MN + 2M` elements from DRAM and writing back :math:`3MN + 2M` elements.
-This is obviously wasteful; we'd prefer to have a custom "fused" kernel that only reads
-X once and does all the necessary computations on-chip.
-Doing so would require reading and writing back only :math:`MN` bytes, so we could
-expect a theoretical speed-up of ~4x (i.e., :math:`(8MN + 4M) / 2MN`).
-The `torch.jit.script` flags aims to perform this kind of "kernel fusion" automatically
-but, as we will see later, it is still far from ideal.
-
-.. GENERATED FROM PYTHON SOURCE LINES 57-64
-
-Compute Kernel
-----------------
-Our softmax kernel works as follows: each program loads a row of the input matrix X,
-normalizes it and writes back the result to the output Y.
-Note that one important limitation of Triton is that each block must have a
-power-of-two number of elements, so we need to internally "pad" each row and guard the
-memory operations properly if we want to handle any possible input shapes:
-
-.. GENERATED FROM PYTHON SOURCE LINES 64-93
-
-.. code-block:: default
-
-
-
-    @triton.jit
-    def softmax_kernel(
-        output_ptr, input_ptr, input_row_stride, output_row_stride, n_cols,
-        BLOCK_SIZE: tl.constexpr
-    ):
-        # The rows of the softmax are independent, so we parallelize across those
-        row_idx = tl.program_id(0)
-        # The stride represents how much we need to increase the pointer to advance 1 row
-        row_start_ptr = input_ptr + row_idx * input_row_stride
-        # The block size is the next power of two greater than n_cols, so we can fit each
-        # row in a single block
-        col_offsets = tl.arange(0, BLOCK_SIZE)
-        input_ptrs = row_start_ptr + col_offsets
-        # Load the row into SRAM, using a mask since BLOCK_SIZE may be > than n_cols
-        row = tl.load(input_ptrs, mask=col_offsets < n_cols, other=-float('inf'))
-        # Substract maximum for numerical stability
-        row_minus_max = row - tl.max(row, axis=0)
-        # Note that exponentials in Triton are fast but approximate (i.e., think __expf in CUDA)
-        numerator = tl.exp(row_minus_max)
-        denominator = tl.sum(numerator, axis=0)
-        softmax_output = numerator / denominator
-        # Write back output to DRAM
-        output_row_start_ptr = output_ptr + row_idx * output_row_stride
-        output_ptrs = output_row_start_ptr + col_offsets
-        tl.store(output_ptrs, softmax_output, mask=col_offsets < n_cols)
-
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 94-95
-
-We can create a helper function that enqueues the kernel and its (meta-)arguments for any given input tensor.
-
-.. GENERATED FROM PYTHON SOURCE LINES 95-125
-
-.. code-block:: default
-
-
-    def softmax(x):
-        n_rows, n_cols = x.shape
-        # The block size is the smallest power of two greater than the number of columns in `x`
-        BLOCK_SIZE = triton.next_power_of_2(n_cols)
-        # Another trick we can use is to ask the compiler to use more threads per row by
-        # increasing the number of warps (`num_warps`) over which each row is distributed.
-        # You will see in the next tutorial how to auto-tune this value in a more natural
-        # way so you don't have to come up with manual heuristics yourself.
-        num_warps = 4
-        if BLOCK_SIZE >= 2048:
-            num_warps = 8
-        if BLOCK_SIZE >= 4096:
-            num_warps = 16
-        # Allocate output
-        y = torch.empty_like(x)
-        # Enqueue kernel. The 1D launch grid is simple: we have one kernel instance per row o
-        # f the input matrix
-        softmax_kernel[(n_rows,)](
-            y,
-            x,
-            x.stride(0),
-            y.stride(0),
-            n_cols,
-            num_warps=num_warps,
-            BLOCK_SIZE=BLOCK_SIZE,
-        )
-        return y
-
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 126-128
-
-Unit Test
-----------
-
-.. GENERATED FROM PYTHON SOURCE LINES 130-132
-
-We make sure that we test our kernel on a matrix with an irregular number of rows and columns.
-This will allow us to verify that our padding mechanism works.
-
-.. GENERATED FROM PYTHON SOURCE LINES 132-139
-
-.. code-block:: default
-
-
-    torch.manual_seed(0)
-    x = torch.randn(1823, 781, device='cuda')
-    y_triton = softmax(x)
-    y_torch = torch.softmax(x, axis=1)
-    assert torch.allclose(y_triton, y_torch), (y_triton, y_torch)
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 140-141
-
-As expected, the results are identical.
-
-.. GENERATED FROM PYTHON SOURCE LINES 143-147
-
-Benchmark
--------------
-Here we will benchmark our operation as a function of the number of columns in the input matrix -- assuming 4096 rows.
-We will then compare its performance against (1) :code:`torch.softmax` and (2) the :code:`naive_softmax` defined above.
-
-.. GENERATED FROM PYTHON SOURCE LINES 147-186
-
-.. code-block:: default
-
-
-
-    @triton.testing.perf_report(
-        triton.testing.Benchmark(
-            x_names=['N'],  # argument names to use as an x-axis for the plot
-            x_vals=[
-                128 * i for i in range(2, 100)
-            ],  # different possible values for `x_name`
-            line_arg='provider',  # argument name whose value corresponds to a different line in the plot
-            line_vals=[
-                'triton',
-                'torch-native',
-                'torch-jit',
-            ],  # possible values for `line_arg``
-            line_names=[
-                "Triton",
-                "Torch (native)",
-                "Torch (jit)",
-            ],  # label name for the lines
-            styles=[('blue', '-'), ('green', '-'), ('green', '--')],  # line styles
-            ylabel="GB/s",  # label name for the y-axis
-            plot_name="softmax-performance",  # name for the plot. Used also as a file name for saving the plot.
-            args={'M': 4096},  # values for function arguments not in `x_names` and `y_name`
-        )
-    )
-    def benchmark(M, N, provider):
-        x = torch.randn(M, N, device='cuda', dtype=torch.float32)
-        if provider == 'torch-native':
-            ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.softmax(x, axis=-1))
-        if provider == 'triton':
-            ms, min_ms, max_ms = triton.testing.do_bench(lambda: softmax(x))
-        if provider == 'torch-jit':
-            ms, min_ms, max_ms = triton.testing.do_bench(lambda: naive_softmax(x))
-        gbps = lambda ms: 2 * x.nelement() * x.element_size() * 1e-9 / (ms * 1e-3)
-        return gbps(ms), gbps(max_ms), gbps(min_ms)
-
-
-    benchmark.run(show_plots=True, print_data=True)
-
-
-
-
-.. image:: /getting-started/tutorials/images/sphx_glr_02-fused-softmax_001.png
-    :alt: 02 fused softmax
-    :class: sphx-glr-single-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    softmax-performance:
-              N      Triton  Torch (native)  Torch (jit)
-    0     256.0  546.133347      546.133347   188.321838
-    1     384.0  585.142862      585.142862   151.703707
-    2     512.0  655.360017      606.814814   154.566038
-    3     640.0  682.666684      640.000002   158.759699
-    4     768.0  722.823517      664.216187   163.839992
-    ..      ...         ...             ...          ...
-    93  12160.0  814.058574      405.755985   198.631953
-    94  12288.0  814.111783      415.661740   198.794749
-    95  12416.0  814.163950      412.149375   198.457532
-    96  12544.0  814.214963      412.971190   198.716830
-    97  12672.0  814.265046      411.679167   198.776477
-
-    [98 rows x 4 columns]
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 187-192
-
-In the above plot, we can see that:
-
- - Triton is 4x faster than the Torch JIT. This confirms our suspicions that the Torch JIT does not do any fusion here.
- - Triton is noticeably faster than :code:`torch.softmax` -- in addition to being **easier to read, understand and maintain**.
-   Note however that the PyTorch `softmax` operation is more general and will works on tensors of any shape.
-
-
-.. rst-class:: sphx-glr-timing
-
-   **Total running time of the script:** ( 3 minutes  22.664 seconds)
-
-
-.. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py:
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-    :class: sphx-glr-footer-example
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-python
-
-     :download:`Download Python source code: 02-fused-softmax.py <02-fused-softmax.py>`
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-     :download:`Download Jupyter notebook: 02-fused-softmax.ipynb <02-fused-softmax.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/master/_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt b/master/_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt
deleted file mode 100644
index 5f6010d0d..000000000
--- a/master/_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt
+++ /dev/null
@@ -1,529 +0,0 @@
-
-.. DO NOT EDIT.
-.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
-.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
-.. "getting-started/tutorials/03-matrix-multiplication.py"
-.. LINE NUMBERS ARE GIVEN BELOW.
-
-.. only:: html
-
-    .. note::
-        :class: sphx-glr-download-link-note
-
-        Click :ref:`here <sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py>`
-        to download the full example code
-
-.. rst-class:: sphx-glr-example-title
-
-.. _sphx_glr_getting-started_tutorials_03-matrix-multiplication.py:
-
-
-Matrix Multiplication
-======================
-In this tutorial, you will write a 25-lines high-performance FP16 matrix multiplication
-kernel that achieves performance on par with cuBLAS.
-You will specifically learn about:
-
-- Block-level matrix multiplications
-- Multi-dimensional pointer arithmetic
-- Program re-ordering for improved L2 cache hit rate
-- Automatic performance tuning
-
-.. GENERATED FROM PYTHON SOURCE LINES 15-42
-
-Motivations
--------------
-Matrix multiplications are a key building block of most modern high-performance computing systems.
-They are notoriously hard to optimize, hence their implementation is generally done by
-hardware vendors themselves as part of so-called "kernel libraries" (e.g., cuBLAS).
-Unfortunately, these libraries are often proprietary and cannot be easily customized
-to accomodate the needs of modern deep learning workloads (e.g., fused activation functions).
-In this tutorial, you will learn how to implement efficient matrix multiplications by
-yourself with Triton, in a way that is easy to customize and extend.
-
-Roughly speaking, the kernel that we will write will implement the following blocked
-algorithm to multiply a (M, K) by a (K, N) matrix:
-
- .. code-block:: python
-
-   # do in parallel
-   for m in range(0, M, BLOCK_SIZE_M):
-     # do in parallel
-     for n in range(0, N, BLOCK_SIZE_N):
-       acc = zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=float32)
-       for k in range(0, K, BLOCK_SIZE_K):
-         a = A[m : m+BLOCK_SIZE_M, k : k+BLOCK_SIZE_K]
-         b = B[k : k+BLOCK_SIZE_K, n : n+BLOCK_SIZE_N]
-         acc += dot(a, b)
-       C[m : m+BLOCK_SIZE_M, n : n+BLOCK_SIZE_N] = acc;
-
-where each iteration of the doubly-nested for-loop is performed by a dedicated Triton program instance.
-
-.. GENERATED FROM PYTHON SOURCE LINES 44-137
-
-Compute Kernel
-----------------
-
-The above algorithm is, actually, fairly straightforward to implement in Triton.
-The main difficulty comes from the computation of the memory locations at which blocks
-of :code:`A` and :code:`B` must be read in the inner loop. For that, we need
-multi-dimensional pointer arithmetics.
-
-Pointer Arithmetics
-~~~~~~~~~~~~~~~~~~~~
-
-For a row-major 2D tensor :code:`X`, the memory location of :code:`X[i, j]` is given b
-y :code:`&X[i, j] = X + i*stride_xi + j*stride_xj`.
-Therefore, blocks of pointers for :code:`A[m : m+BLOCK_SIZE_M, k:k+BLOCK_SIZE_K]` and
-:code:`B[k : k+BLOCK_SIZE_K, n : n+BLOCK_SIZE_N]` can be defined in pseudo-code as:
-
- .. code-block:: python
-
-   &A[m : m+BLOCK_SIZE_M, k:k+BLOCK_SIZE_K] =  a_ptr + (m : m+BLOCK_SIZE_M)[:, None]*A.stride(0) + (k : k+BLOCK_SIZE_K)[None, :]*A.stride(1);
-   &B[k : k+BLOCK_SIZE_K, n:n+BLOCK_SIZE_N] =  b_ptr + (k : k+BLOCK_SIZE_K)[:, None]*B.stride(0) + (n : n+BLOCK_SIZE_N)[None, :]*B.stride(1);
-
-Which means that pointers for blocks of A and B can be initialized (i.e., :code:`k=0`) in Triton as:
-
- .. code-block:: python
-
-   offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
-   offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
-   offs_k = tl.arange(0, BLOCK_SIZE_K)
-   a_ptrs = a_ptr + (offs_am[:, None]*stride_am + offs_k [None, :]*stride_ak)
-   b_ptrs = b_ptr + (offs_k [:, None]*stride_bk + offs_bn[None, :]*stride_bn)
-
-And then updated in the inner loop as follows:
-
- .. code-block:: python
-
-   pa += BLOCK_SIZE_K * stride_ak;
-   pb += BLOCK_SIZE_K * stride_bk;
-
-
-L2 Cache Optimizations
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-As mentioned above, each program instance computes a :code:`[BLOCK_SIZE_M, BLOCK_SIZE_N]`
-block of :code:`C`.
-It is important to remember that the order in which these blocks are computed does
-matter, since it affects the L2 cache hit rate of our program. and unfortunately, a
-a simple row-major ordering
-
- .. code-block:: Python
-
-   pid = triton.program_id(0);
-   grid_m = (M + BLOCK_SIZE_M - 1) // BLOCK_SIZE_M;
-   grid_n = (N + BLOCK_SIZE_N - 1) // BLOCK_SIZE_N;
-   pid_m = pid / grid_n;
-   pid_n = pid % grid_n;
-
-is just not going to cut it.
-
-One possible solution is to launch blocks in an order that promotes data reuse.
-This can be done by 'super-grouping' blocks in groups of :code:`GROUP_M` rows before
-switching to the next column:
-
- .. code-block:: python
-
-   # program ID
-   pid = tl.program_id(axis=0)
-   # number of program ids along the M axis
-   num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
-   # number of programs ids along the N axis
-   num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
-   # number of programs in group
-   num_pid_in_group = GROUP_SIZE_M * num_pid_n
-   # id of the group this program is in
-   group_id = pid // num_pid_in_group
-   # row-id of the first program in the group
-   first_pid_m = group_id * GROUP_SIZE_M
-   # if `num_pid_m` isn't divisible by `GROUP_SIZE_M`, the last group is smaller
-   group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
-   # *within groups*, programs are ordered in a column-major order
-   # row-id of the program in the *launch grid*
-   pid_m = first_pid_m + (pid % group_size_m)
-   # col-id of the program in the *launch grid*
-   pid_n = (pid % num_pid_in_group) // group_size_m
-
-For example, in the following matmul where each matrix is 9 blocks by 9 blocks,
-we can see that if we compute the output in row-major ordering, we need to load 90
-blocks into SRAM to compute the first 9 output blocks, but if we do it in grouped
-ordering, we only need to load 54 blocks.
-  .. image:: grouped_vs_row_major_ordering.png
-
-In practice, this can improve the performance of our matrix multiplication kernel by
-more than 10\% on some hardware architecture (e.g., 220 to 245 TFLOPS on A100).
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 139-142
-
-Final Result
--------------
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 142-258
-
-.. code-block:: default
-
-
-    import torch
-
-    import triton
-    import triton.language as tl
-
-    # %
-    # :code:`triton.jit`'ed functions can be auto-tuned by using the `triton.autotune`
-    # decorator, which consumes:
-    #   - A list of :code:`triton.Config` objects that define different configurations of
-    #       meta-parameters (e.g., BLOCK_SIZE_M) and compilation options (e.g., num_warps) to try
-    #   - An autotuning *key* whose change in values will trigger evaluation of all the
-    #       provided configs
-
-
-    @triton.autotune(
-        configs=[
-            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
-            triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
-            triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-            triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-            triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-            triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
-            triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
-            triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
-        ],
-        key=['M', 'N', 'K'],
-    )
-    @triton.jit
-    def matmul_kernel(
-        # Pointers to matrices
-        a_ptr, b_ptr, c_ptr,
-        # Matrix dimensions
-        M, N, K,
-        # The stride variables represent how much to increase the ptr by when moving by 1
-        # element in a particular dimension. E.g. stride_am is how much to increase a_ptr
-        # by to get the element one row down (A has M rows)
-        stride_am, stride_ak,
-        stride_bk, stride_bn,
-        stride_cm, stride_cn,
-        # Meta-parameters
-        BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
-        GROUP_SIZE_M: tl.constexpr,
-        ACTIVATION: tl.constexpr,
-    ):
-        """Kernel for computing the matmul C = A x B.
-        A has shape (M, K), B has shape (K, N) and C has shape (M, N)
-        """
-        # -----------------------------------------------------------
-        # Map program ids `pid` to the block of C it should compute.
-        # This is done in a grouped ordering to promote L2 data reuse
-        # See above `L2 Cache Optimizations` section for details
-        pid = tl.program_id(axis=0)
-        num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
-        num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
-        num_pid_in_group = GROUP_SIZE_M * num_pid_n
-        group_id = pid // num_pid_in_group
-        first_pid_m = group_id * GROUP_SIZE_M
-        group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
-        pid_m = first_pid_m + (pid % group_size_m)
-        pid_n = (pid % num_pid_in_group) // group_size_m
-
-        # ----------------------------------------------------------
-        # Create pointers for the first blocks of A and B.
-        # We will advance this pointer as we move in the K direction
-        # and accumulate
-        # a_ptrs is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
-        # b_ptrs is a block of [BLOCK_SIZE_K, BLOCK_SIZE_n] pointers
-        # see above `Pointer Arithmetics` section for details
-        offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
-        offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
-        offs_k = tl.arange(0, BLOCK_SIZE_K)
-        a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
-        b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
-
-        # -----------------------------------------------------------
-        # Iterate to compute a block of the C matrix
-        # We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
-        # of fp32 values for higher accuracy.
-        # `accumulator` will be converted back to fp16 after the loop
-        accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
-        for k in range(0, K, BLOCK_SIZE_K):
-            # Note that for simplicity, we don't apply a mask here.
-            # This means that if K is not a multiple of BLOCK_SIZE_K,
-            # this will access out-of-bounds memory and produce an
-            # error or (worse!) incorrect results.
-            a = tl.load(a_ptrs)
-            b = tl.load(b_ptrs)
-            # We accumulate along the K dimension
-            accumulator += tl.dot(a, b)
-            # Advance the ptrs to the next K block
-            a_ptrs += BLOCK_SIZE_K * stride_ak
-            b_ptrs += BLOCK_SIZE_K * stride_bk
-        # you can fuse arbitrary activation functions here
-        # while the accumulator is still in FP32!
-        if ACTIVATION:
-            accumulator = ACTIVATION(accumulator)
-        c = accumulator.to(tl.float16)
-
-        # -----------------------------------------------------------
-        # Write back the block of the output matrix C
-        offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
-        offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
-        c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
-        c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
-        tl.store(c_ptrs, c, mask=c_mask)
-
-
-    # we can fuse `leaky_relu` by providing it as an `ACTIVATION` meta-parameter in `_matmul`
-    @triton.jit
-    def leaky_relu(x):
-        return tl.where(x >= 0, x, 0.01 * x)
-
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 259-261
-
-We can now create a convenience wrapper function that only takes two input tensors
-and (1) checks any shape constraint; (2) allocates the output; (3) launches the above kernel
-
-.. GENERATED FROM PYTHON SOURCE LINES 261-290
-
-.. code-block:: default
-
-
-
-    def matmul(a, b, activation=None):
-        # checks constraints
-        assert a.shape[1] == b.shape[0], "incompatible dimensions"
-        assert a.is_contiguous(), "matrix A must be contiguous"
-        assert b.is_contiguous(), "matrix B must be contiguous"
-        M, K = a.shape
-        K, N = b.shape
-        assert (
-            K % 32 == 0
-        ), "We don't check memory-out-of-bounds with K so K must be divisible by BLOCK_SIZE_K"
-        # allocates output
-        c = torch.empty((M, N), device=a.device, dtype=a.dtype)
-        # 1D launch kernel where each block gets its own program.
-        grid = lambda META: (
-            triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),
-        )
-        matmul_kernel[grid](
-            a, b, c,
-            M, N, K,
-            a.stride(0), a.stride(1),
-            b.stride(0), b.stride(1),
-            c.stride(0), c.stride(1),
-            ACTIVATION=activation,
-        )
-        return c
-
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 291-295
-
-Unit Test
------------
-
-We can test our custom matrix multiplication operation against a native torch implementation (i.e., cuBLAS)
-
-.. GENERATED FROM PYTHON SOURCE LINES 295-308
-
-.. code-block:: default
-
-
-    torch.manual_seed(0)
-    a = torch.randn((512, 512), device='cuda', dtype=torch.float16)
-    b = torch.randn((512, 512), device='cuda', dtype=torch.float16)
-    triton_output = matmul(a, b, activation=None)
-    torch_output = torch.matmul(a, b)
-    print(f"triton_output={triton_output}")
-    print(f"torch_output={torch_output}")
-    if triton.testing.allclose(triton_output, torch_output):
-        print("✅ Triton and Torch match")
-    else:
-        print("❌ Triton and Torch differ")
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    triton_output=tensor([[  1.1045, -36.9688,  31.4688,  ..., -11.3984,  24.4531, -32.3438],
-            [  6.3555, -19.6094,  34.0938,  ...,  -5.8945,   5.2891,   6.8867],
-            [-32.0625,   5.9492,  15.3984,  ..., -21.3906, -23.9844, -10.1328],
-            ...,
-            [ -5.7031,   7.4492,   8.2656,  ..., -10.6953, -40.0000,  17.7500],
-            [ 25.5000,  24.3281,  -8.4688,  ..., -18.9375,  32.5312, -29.9219],
-            [ -5.3477,   4.9844,  11.8906,  ...,   5.5898,   6.4023, -17.3125]],
-           device='cuda:0', dtype=torch.float16)
-    torch_output=tensor([[  1.1045, -36.9688,  31.4688,  ..., -11.3906,  24.4531, -32.3438],
-            [  6.3516, -19.6094,  34.0938,  ...,  -5.8906,   5.2812,   6.8828],
-            [-32.0625,   5.9531,  15.3984,  ..., -21.4062, -23.9844, -10.1328],
-            ...,
-            [ -5.7070,   7.4492,   8.2656,  ..., -10.6953, -40.0000,  17.7500],
-            [ 25.5000,  24.3438,  -8.4609,  ..., -18.9375,  32.5312, -29.9219],
-            [ -5.3477,   4.9805,  11.8828,  ...,   5.5859,   6.4023, -17.3125]],
-           device='cuda:0', dtype=torch.float16)
-    ✅ Triton and Torch match
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 309-315
-
-Benchmark
---------------
-
-Square Matrix Performance
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-We can now compare the performance of our kernel against that of cuBLAS. Here we focus on square matrices, but feel free to arrange this script as you wish to benchmark any other matrix shape.
-
-.. GENERATED FROM PYTHON SOURCE LINES 315-356
-
-.. code-block:: default
-
-
-
-    @triton.testing.perf_report(
-        triton.testing.Benchmark(
-            x_names=['M', 'N', 'K'],  # argument names to use as an x-axis for the plot
-            x_vals=[
-                128 * i for i in range(2, 33)
-            ],  # different possible values for `x_name`
-            line_arg='provider',  # argument name whose value corresponds to a different line in the plot
-            # possible values for `line_arg``
-            line_vals=['cublas', 'cublas + relu', 'triton', 'triton + relu'],
-            # label name for the lines
-            line_names=["cuBLAS", "cuBLAS (+ torch.nn.LeakyReLU)", "Triton", "Triton (+ LeakyReLU)"],
-            # line styles
-            styles=[('green', '-'), ('green', '--'), ('blue', '-'), ('blue', '--')],
-            ylabel="TFLOPS",  # label name for the y-axis
-            plot_name="matmul-performance",  # name for the plot. Used also as a file name for saving the plot.
-            args={},
-        )
-    )
-    def benchmark(M, N, K, provider):
-        a = torch.randn((M, K), device='cuda', dtype=torch.float16)
-        b = torch.randn((K, N), device='cuda', dtype=torch.float16)
-        if provider == 'cublas':
-            ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a, b))
-        if provider == 'triton':
-            ms, min_ms, max_ms = triton.testing.do_bench(lambda: matmul(a, b))
-        if provider == 'cublas + relu':
-            torch_relu = torch.nn.ReLU(inplace=True)
-            ms, min_ms, max_ms = triton.testing.do_bench(
-                lambda: torch_relu(torch.matmul(a, b))
-            )
-        if provider == 'triton + relu':
-            ms, min_ms, max_ms = triton.testing.do_bench(
-                lambda: matmul(a, b, activation=leaky_relu)
-            )
-        perf = lambda ms: 2 * M * N * K * 1e-12 / (ms * 1e-3)
-        return perf(ms), perf(max_ms), perf(min_ms)
-
-
-    benchmark.run(show_plots=True, print_data=True)
-
-
-
-.. image:: /getting-started/tutorials/images/sphx_glr_03-matrix-multiplication_001.png
-    :alt: 03 matrix multiplication
-    :class: sphx-glr-single-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    matmul-performance:
-             M     cuBLAS  ...     Triton  Triton (+ LeakyReLU)
-    0    256.0   2.978909  ...   3.276800              2.978909
-    1    384.0   7.372800  ...   7.899428              8.507077
-    2    512.0  14.563555  ...  16.384000             16.384000
-    3    640.0  22.260869  ...  24.380953             24.380953
-    4    768.0  32.768000  ...  35.389441             34.028308
-    5    896.0  37.971025  ...  40.140799             39.025776
-    6   1024.0  49.932191  ...  53.773130             52.428801
-    7   1152.0  45.242181  ...  48.161033             47.396572
-    8   1280.0  51.200001  ...  57.690139             57.690139
-    9   1408.0  64.138541  ...  68.147202             67.305878
-    10  1536.0  79.526831  ...  79.526831             79.526831
-    11  1664.0  62.929456  ...  63.372618             62.929456
-    12  1792.0  72.512412  ...  63.142831             62.790080
-    13  1920.0  69.120002  ...  71.257735             70.892307
-    14  2048.0  73.262953  ...  78.033565             77.672296
-    15  2176.0  83.155572  ...  87.115360             86.739860
-    16  2304.0  68.251065  ...  77.810656             77.810656
-    17  2432.0  71.305746  ...  75.522751             75.320281
-    18  2560.0  77.833728  ...  82.539044             82.125311
-    19  2688.0  83.737433  ...  91.185232             90.748936
-    20  2816.0  84.523664  ...  81.445766             84.523664
-    21  2944.0  81.298583  ...  83.899046             84.040530
-    22  3072.0  82.540970  ...  90.164177             89.735509
-    23  3200.0  84.656085  ...  96.385543             96.240602
-    24  3328.0  83.226931  ...  85.602017             84.249616
-    25  3456.0  81.849303  ...  85.133652             84.864807
-    26  3584.0  87.466332  ...  99.684470             99.354022
-    27  3712.0  81.548851  ...  84.159518             87.706180
-    28  3840.0  84.874902  ...  86.535214             91.398346
-    29  3968.0  88.040360  ...  92.442373             86.480463
-    30  4096.0  93.760204  ...  91.992956             90.871857
-
-    [31 rows x 5 columns]
-
-
-
-
-
-.. rst-class:: sphx-glr-timing
-
-   **Total running time of the script:** ( 6 minutes  30.096 seconds)
-
-
-.. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-    :class: sphx-glr-footer-example
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-python
-
-     :download:`Download Python source code: 03-matrix-multiplication.py <03-matrix-multiplication.py>`
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-     :download:`Download Jupyter notebook: 03-matrix-multiplication.ipynb <03-matrix-multiplication.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/master/_sources/getting-started/tutorials/04-low-memory-dropout.rst.txt b/master/_sources/getting-started/tutorials/04-low-memory-dropout.rst.txt
deleted file mode 100644
index 5b75c1d5e..000000000
--- a/master/_sources/getting-started/tutorials/04-low-memory-dropout.rst.txt
+++ /dev/null
@@ -1,271 +0,0 @@
-
-.. DO NOT EDIT.
-.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
-.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
-.. "getting-started/tutorials/04-low-memory-dropout.py"
-.. LINE NUMBERS ARE GIVEN BELOW.
-
-.. only:: html
-
-    .. note::
-        :class: sphx-glr-download-link-note
-
-        Click :ref:`here <sphx_glr_download_getting-started_tutorials_04-low-memory-dropout.py>`
-        to download the full example code
-
-.. rst-class:: sphx-glr-example-title
-
-.. _sphx_glr_getting-started_tutorials_04-low-memory-dropout.py:
-
-
-Low-Memory Dropout
-=================
-
-In this tutorial, you will write a memory-efficient implementation of dropout whose state
-will be composed of a single int32 seed. This differs from more traditional implementations of dropout,
-whose state is generally composed of a bit mask tensor of the same shape as the input. You will learn about:
-
-- The limitations of naive implementations of Dropout with PyTorch
-- Parallel pseudo-random number generation in Triton
-
-.. GENERATED FROM PYTHON SOURCE LINES 14-29
-
-Baseline
--------------
-The *dropout* operator was first introduced in [SRIVASTAVA2014]_ as a way to improve the performance
-of deep neural networks in low-data regime (i.e. regularization).
-
-It takes a vector as input and produces a vector of the same shape as output. Each scalar in the
-output has a probability :math:`p` of being changed to zero and otherwise it is copied from the input.
-This forces the network to perform well even when only :math:`1 - p` scalars from the input are available.
-
-At evaluation time we want to use the full power of the network so we set :math:`p=0`. Naively this would
-increase the norm of the output (which can be a bad thing, e.g. it can lead to artificial decrease
-in the output softmax temperature). To prevent this we multiply the output by :math:`\frac{1}{1 - p}`, which
-keeps the norm consistent regardless of the dropout probability.
-
-Let's first take a look at the baseline implementation.
-
-.. GENERATED FROM PYTHON SOURCE LINES 29-82
-
-.. code-block:: default
-
-
-
-    import tabulate
-    import torch
-
-    import triton
-    import triton.language as tl
-
-
-    @triton.jit
-    def _dropout(
-            x_ptr,  # pointer to the input
-            x_keep_ptr,  # pointer to a mask of 0s and 1s
-            output_ptr,  # pointer to the output
-            n_elements,  # number of elements in the `x` tensor
-            p,  # probability that an element of `x` is changed to zero
-            BLOCK_SIZE: tl.constexpr,
-    ):
-        pid = tl.program_id(axis=0)
-        block_start = pid * BLOCK_SIZE
-        offsets = block_start + tl.arange(0, BLOCK_SIZE)
-        mask = offsets < n_elements
-        # Load data
-        x = tl.load(x_ptr + offsets, mask=mask)
-        x_keep = tl.load(x_keep_ptr + offsets, mask=mask)
-        # The line below is the crucial part, described in the paragraph above!
-        output = tl.where(x_keep, x / (1 - p), 0.0)
-        # Write-back output
-        tl.store(output_ptr + offsets, output, mask=mask)
-
-
-    def dropout(x, x_keep, p):
-        output = torch.empty_like(x)
-        assert x.is_contiguous()
-        n_elements = x.numel()
-        grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
-        _dropout[grid](x, x_keep, output, n_elements, p, BLOCK_SIZE=1024)
-        return output
-
-
-    # Input tensor
-    x = torch.randn(size=(10,)).cuda()
-    # Dropout mask
-    p = 0.5
-    x_keep = (torch.rand(size=(10,)) > p).to(torch.int32).cuda()
-    #
-    output = dropout(x, x_keep=x_keep, p=p)
-    print(tabulate.tabulate([
-        ["input"] + x.tolist(),
-        ["keep mask"] + x_keep.tolist(),
-        ["output"] + output.tolist()
-    ]))
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    ---------  -------  ---------  --------  --------  --------  --------  --------  --------  ---------  ---------
-    input      1.541    -0.293429  -2.17879  0.568431  -1.08452  -1.3986   0.403347  0.838026  -0.719258  -0.403344
-    keep mask  1         1          0        1          0         1        1         0          0          0
-    output     3.08199  -0.586858   0        1.13686    0        -2.79719  0.806694  0          0          0
-    ---------  -------  ---------  --------  --------  --------  --------  --------  --------  ---------  ---------
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 83-101
-
-Seeded dropout
--------------
-Above implementation of dropout works fine, but it can be a bit awkward to deal with. Firstly
-we need to store the dropout mask for backpropagation. Secondly, dropout state management can get
-very tricky when using recompute/checkpointing (e.g. see all the notes about `preserve_rng_state` in
-https://pytorch.org/docs/1.9.0/checkpoint.html). In this tutorial we'll describe an alternative implementation
-that (1) has a smaller memory footprint; (2) requires less data movement; and (3) simplifies the management
-of persisting randomness across multiple invocations of the kernel.
-
-Pseudorandom number generation in Triton is simple! In this tutorial we will use the
-:code:`triton.language.rand` function which generates a block of uniformly distributed :code:`float32`
-values in [0, 1), given a seed and a block of :code:`int32` offsets. But if you need it, Triton also provides
-other :ref:`random number generation strategies <Random Number Generation>`.
-
-.. note::
-   Triton's implementation of PRNG is based on the Philox algorithm (described on [SALMON2011]_).
-
-Let's put it all together.
-
-.. GENERATED FROM PYTHON SOURCE LINES 101-149
-
-.. code-block:: default
-
-
-
-    @triton.jit
-    def _seeded_dropout(
-            x_ptr,
-            output_ptr,
-            n_elements,
-            p,
-            seed,
-            BLOCK_SIZE: tl.constexpr,
-    ):
-        # compute memory offsets of elements handled by this instance
-        pid = tl.program_id(axis=0)
-        block_start = pid * BLOCK_SIZE
-        offsets = block_start + tl.arange(0, BLOCK_SIZE)
-        # load data from x
-        mask = offsets < n_elements
-        x = tl.load(x_ptr + offsets, mask=mask)
-        # randomly prune it
-        random = tl.rand(seed, offsets)
-        x_keep = random > p
-        # write-back
-        output = tl.where(x_keep, x / (1 - p), 0.0)
-        tl.store(output_ptr + offsets, output, mask=mask)
-
-
-    def seeded_dropout(x, p, seed):
-        output = torch.empty_like(x)
-        assert x.is_contiguous()
-        n_elements = x.numel()
-        grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
-        _seeded_dropout[grid](x, output, n_elements, p, seed, BLOCK_SIZE=1024)
-        return output
-
-
-    x = torch.randn(size=(10,)).cuda()
-    # Compare this to the baseline - dropout mask is never instantiated!
-    output = seeded_dropout(x, p=0.5, seed=123)
-    output2 = seeded_dropout(x, p=0.5, seed=123)
-    output3 = seeded_dropout(x, p=0.5, seed=512)
-
-    print(tabulate.tabulate([
-        ["input"] + x.tolist(),
-        ["output (seed = 123)"] + output.tolist(),
-        ["output (seed = 123)"] + output2.tolist(),
-        ["output (seed = 512)"] + output3.tolist()
-    ]))
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    -------------------  ---------  --------  --------  -------  --------  --------  ---------  ---------  ---------  ---------
-    input                -0.952835  0.371721  0.408716  1.42142  0.149397  -0.67086  -0.214186  -0.431969  -0.707878  -0.106434
-    output (seed = 123)   0         0.743443  0         0        0         -1.34172   0          0         -1.41576   -0.212868
-    output (seed = 123)   0         0.743443  0         0        0         -1.34172   0          0         -1.41576   -0.212868
-    output (seed = 512)   0         0         0.817432  2.84284  0         -1.34172  -0.428372   0          0          0
-    -------------------  ---------  --------  --------  -------  --------  --------  ---------  ---------  ---------  ---------
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 150-153
-
-Et Voilà! We have a triton kernel that applies the same dropout mask provided the seed is the same!
-If you'd like explore further applications of pseudorandomness in GPU programming, we encourage you
-to explore the `triton/language/random` folder!
-
-.. GENERATED FROM PYTHON SOURCE LINES 155-160
-
-Exercises
--------------
-1. Extend the kernel to operate over a matrix and use a vector of seeds - one per row.
-2. Add support for striding.
-3. (challenge) Implement a kernel for sparse Johnson-Lindenstrauss transform which generates the projection matrix one the fly each time using a seed.
-
-.. GENERATED FROM PYTHON SOURCE LINES 162-167
-
-References
---------------
-
-.. [SALMON2011] John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw, "Parallel Random Numbers: As Easy as 1, 2, 3", 2011
-.. [SRIVASTAVA2014] Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, "Dropout: A Simple Way to Prevent Neural Networks from Overfitting", JMLR 2014
-
-
-.. rst-class:: sphx-glr-timing
-
-   **Total running time of the script:** ( 0 minutes  0.324 seconds)
-
-
-.. _sphx_glr_download_getting-started_tutorials_04-low-memory-dropout.py:
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-    :class: sphx-glr-footer-example
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-python
-
-     :download:`Download Python source code: 04-low-memory-dropout.py <04-low-memory-dropout.py>`
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-     :download:`Download Jupyter notebook: 04-low-memory-dropout.ipynb <04-low-memory-dropout.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/master/_sources/getting-started/tutorials/05-layer-norm.rst.txt b/master/_sources/getting-started/tutorials/05-layer-norm.rst.txt
deleted file mode 100644
index 6aa377425..000000000
--- a/master/_sources/getting-started/tutorials/05-layer-norm.rst.txt
+++ /dev/null
@@ -1,370 +0,0 @@
-
-.. DO NOT EDIT.
-.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
-.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
-.. "getting-started/tutorials/05-layer-norm.py"
-.. LINE NUMBERS ARE GIVEN BELOW.
-
-.. only:: html
-
-    .. note::
-        :class: sphx-glr-download-link-note
-
-        Click :ref:`here <sphx_glr_download_getting-started_tutorials_05-layer-norm.py>`
-        to download the full example code
-
-.. rst-class:: sphx-glr-example-title
-
-.. _sphx_glr_getting-started_tutorials_05-layer-norm.py:
-
-
-Layer Normalization
-====================
-
-.. GENERATED FROM PYTHON SOURCE LINES 5-262
-
-
-
-.. image:: /getting-started/tutorials/images/sphx_glr_05-layer-norm_001.png
-    :alt: 05 layer norm
-    :class: sphx-glr-single-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    layer-norm-backward:
-              N      Triton       Torch        Apex
-    0    1024.0  361.411758   97.912354  303.407414
-    1    1536.0  409.599994  134.540150  341.333333
-    2    2048.0  491.520012  161.154101  323.368435
-    3    2560.0  461.954908  181.238943  326.808501
-    4    3072.0  515.580429  192.501302  320.556515
-    5    3584.0  554.941930  208.271186  311.652167
-    6    4096.0  561.737163  220.907859  294.323343
-    7    4608.0  502.690905  232.825259  291.799469
-    8    5120.0  525.128191  242.366855  289.129408
-    9    5632.0  540.671974  243.107920  288.820505
-    10   6144.0  546.133354  248.661056  286.322318
-    11   6656.0  527.207907  256.000009  285.257135
-    12   7168.0  503.017523  260.063480  285.293536
-    13   7680.0  483.779539  262.938666  275.928134
-    14   8192.0  463.698115  266.767970  284.526763
-    15   8704.0  413.655443  267.472468  284.987724
-    16   9216.0  427.822068  271.724806  288.375482
-    17   9728.0  436.396262  280.615388  289.667485
-    18  10240.0  446.025405  286.433562  290.153487
-    19  10752.0  428.651173  246.935876  290.267711
-    20  11264.0  426.397479  245.536784  286.980888
-    21  11776.0  420.571432  249.667843  288.981596
-    22  12288.0  416.542386  254.673582  294.617366
-    23  12800.0  410.695192  253.884294  288.180121
-    24  13312.0  409.075539  252.959629  290.443638
-    25  13824.0  404.604870  257.390218  292.056329
-    26  14336.0  394.116833  254.862216  286.959121
-    27  14848.0  385.662341  257.852379  289.952797
-    28  15360.0  380.433442  257.970599  286.433562
-    29  15872.0  370.192407  261.626369  290.562936
-
-
-
-
-
-
-|
-
-.. code-block:: default
-
-
-    import torch
-
-    import triton
-    import triton.language as tl
-
-    try:
-        # This is https://github.com/NVIDIA/apex, NOT the apex on PyPi, so it
-        # should not be added to extras_require in setup.py.
-        import apex
-        HAS_APEX = True
-    except ModuleNotFoundError:
-        HAS_APEX = False
-
-
-    # Forward Pass
-    @triton.jit
-    def _layer_norm_fwd_fused(X, Y, W, B, M, V, stride, N, eps,
-                              BLOCK_SIZE: tl.constexpr):
-        # position of elements processed by this program
-        row = tl.program_id(0)
-        cols = tl.arange(0, BLOCK_SIZE)
-        mask = cols < N
-        # offset data pointers to start at the row of interest
-        X += row * stride
-        Y += row * stride
-        # load data and cast to float32
-        x = tl.load(X + cols, mask=mask, other=0).to(tl.float32)
-        # compute mean
-        mean = tl.sum(x, axis=0) / N
-        # compute std
-        xmean = tl.where(mask, x - mean, 0.)
-        var = tl.sum(xmean * xmean, axis=0) / N
-        rstd = 1 / tl.sqrt(var + eps)
-        xhat = xmean * rstd
-        # write-back mean/rstd
-        tl.store(M + row, mean)
-        tl.store(V + row, rstd)
-        # multiply by weight and add bias
-        w = tl.load(W + cols, mask=mask)
-        b = tl.load(B + cols, mask=mask)
-        y = xhat * w + b
-        # write-back
-        tl.store(Y + cols, y, mask=mask)
-
-
-    # Backward pass (DX + partial DW + partial DB)
-    @triton.jit
-    def _layer_norm_bwd_dx_fused(DX, DY, DW, DB, X, W, B, M, V, Lock, stride, N, eps,
-                                 GROUP_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr):
-        # position of elements processed by this program
-        row = tl.program_id(0)
-        cols = tl.arange(0, BLOCK_SIZE_N)
-        mask = cols < N
-        # offset data pointers to start at the row of interest
-        X += row * stride
-        DY += row * stride
-        DX += row * stride
-        # offset locks and weight/bias gradient pointer
-        # each kernel instance accumulates partial sums for
-        # DW and DB into one of GROUP_SIZE_M independent buffers
-        # these buffers stay in the L2, which allow this kernel
-        # to be fast
-        lock_id = row % GROUP_SIZE_M
-        Lock += lock_id
-        Count = Lock + GROUP_SIZE_M
-        DW = DW + lock_id * N + cols
-        DB = DB + lock_id * N + cols
-        # load data to SRAM
-        x = tl.load(X + cols, mask=mask, other=0).to(tl.float32)
-        dy = tl.load(DY + cols, mask=mask, other=0).to(tl.float32)
-        w = tl.load(W + cols, mask=mask).to(tl.float32)
-        mean = tl.load(M + row)
-        rstd = tl.load(V + row)
-        # compute dx
-        xhat = (x - mean) * rstd
-        wdy = w * dy
-        xhat = tl.where(mask, xhat, 0.)
-        wdy = tl.where(mask, wdy, 0.)
-        mean1 = tl.sum(xhat * wdy, axis=0) / N
-        mean2 = tl.sum(wdy, axis=0) / N
-        dx = (wdy - (xhat * mean1 + mean2)) * rstd
-        # write-back dx
-        tl.store(DX + cols, dx, mask=mask)
-        # accumulate partial sums for dw/db
-        partial_dw = (dy * xhat).to(w.dtype)
-        partial_db = (dy).to(w.dtype)
-        while tl.atomic_cas(Lock, 0, 1) == 1:
-            pass
-        count = tl.load(Count)
-        # first store doesn't accumulate
-        if count == 0:
-            tl.atomic_xchg(Count, 1)
-        else:
-            partial_dw += tl.load(DW, mask=mask)
-            partial_db += tl.load(DB, mask=mask)
-        tl.store(DW, partial_dw, mask=mask)
-        tl.store(DB, partial_db, mask=mask)
-        # release lock
-        tl.atomic_xchg(Lock, 0)
-
-    # Backward pass (total DW + total DB)
-
-
-    @triton.jit
-    def _layer_norm_bwd_dwdb(DW, DB, FINAL_DW, FINAL_DB, M, N,
-                             BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr):
-        pid = tl.program_id(0)
-        cols = pid * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
-        dw = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
-        db = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
-        for i in range(0, M, BLOCK_SIZE_M):
-            rows = i + tl.arange(0, BLOCK_SIZE_M)
-            mask = (rows[:, None] < M) & (cols[None, :] < N)
-            offs = rows[:, None] * N + cols[None, :]
-            dw += tl.load(DW + offs, mask=mask, other=0.)
-            db += tl.load(DB + offs, mask=mask, other=0.)
-        sum_dw = tl.sum(dw, axis=0)
-        sum_db = tl.sum(db, axis=0)
-        tl.store(FINAL_DW + cols, sum_dw, mask=cols < N)
-        tl.store(FINAL_DB + cols, sum_db, mask=cols < N)
-
-
-    class LayerNorm(torch.autograd.Function):
-
-        @staticmethod
-        def forward(ctx, x, normalized_shape, weight, bias, eps):
-            # allocate output
-            y = torch.empty_like(x)
-            # reshape input data into 2D tensor
-            x_arg = x.reshape(-1, x.shape[-1])
-            M, N = x_arg.shape
-            mean = torch.empty((M, ), dtype=torch.float32, device='cuda')
-            rstd = torch.empty((M, ), dtype=torch.float32, device='cuda')
-            # Less than 64KB per feature: enqueue fused kernel
-            MAX_FUSED_SIZE = 65536 // x.element_size()
-            BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
-            if N > BLOCK_SIZE:
-                raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
-            # heuristics for number of warps
-            num_warps = min(max(BLOCK_SIZE // 256, 1), 8)
-            # enqueue kernel
-            _layer_norm_fwd_fused[(M,)](x_arg, y, weight, bias, mean, rstd,
-                                        x_arg.stride(0), N, eps,
-                                        BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps)
-            ctx.save_for_backward(x, weight, bias, mean, rstd)
-            ctx.BLOCK_SIZE = BLOCK_SIZE
-            ctx.num_warps = num_warps
-            ctx.eps = eps
-            return y
-
-        @staticmethod
-        def backward(ctx, dy):
-            x, w, b, m, v = ctx.saved_tensors
-            # heuristics for amount of parallel reduction stream for DG/DB
-            N = w.shape[0]
-            GROUP_SIZE_M = 64
-            if N <= 8192: GROUP_SIZE_M = 96
-            if N <= 4096: GROUP_SIZE_M = 128
-            if N <= 1024: GROUP_SIZE_M = 256
-            # allocate output
-            locks = torch.zeros(2 * GROUP_SIZE_M, dtype=torch.int32, device='cuda')
-            _dw = torch.empty((GROUP_SIZE_M, w.shape[0]), dtype=x.dtype, device=w.device)
-            _db = torch.empty((GROUP_SIZE_M, w.shape[0]), dtype=x.dtype, device=w.device)
-            dw = torch.empty((w.shape[0],), dtype=w.dtype, device=w.device)
-            db = torch.empty((w.shape[0],), dtype=w.dtype, device=w.device)
-            dx = torch.empty_like(dy)
-            # enqueue kernel using forward pass heuristics
-            # also compute partial sums for DW and DB
-            x_arg = x.reshape(-1, x.shape[-1])
-            M, N = x_arg.shape
-            _layer_norm_bwd_dx_fused[(M,)](dx, dy, _dw, _db, x, w, b, m, v, locks,
-                                           x_arg.stride(0), N, ctx.eps,
-                                           BLOCK_SIZE_N=ctx.BLOCK_SIZE,
-                                           GROUP_SIZE_M=GROUP_SIZE_M,
-                                           num_warps=ctx.num_warps)
-            grid = lambda meta: [triton.cdiv(N, meta['BLOCK_SIZE_N'])]
-            # accumulate partial sums in separate kernel
-            _layer_norm_bwd_dwdb[grid](_dw, _db, dw, db, GROUP_SIZE_M, N,
-                                       BLOCK_SIZE_M=32,
-                                       BLOCK_SIZE_N=128)
-            return dx, None, dw, db, None
-
-
-    layer_norm = LayerNorm.apply
-
-
-    def test_layer_norm(M, N, dtype, eps=1e-5, device='cuda'):
-        # create data
-        x_shape = (M, N)
-        w_shape = (x_shape[-1], )
-        weight = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
-        bias = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
-        x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device='cuda')
-        dy = .1 * torch.randn_like(x)
-        x.requires_grad_(True)
-        # forward pass
-        y_tri = layer_norm(x, w_shape, weight, bias, eps)
-        y_ref = torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps).to(dtype)
-        # backward pass (triton)
-        y_tri.backward(dy, retain_graph=True)
-        dx_tri, dw_tri, db_tri = [_.grad.clone() for _ in [x, weight, bias]]
-        x.grad, weight.grad, bias.grad = None, None, None
-        # backward pass (torch)
-        y_ref.backward(dy, retain_graph=True)
-        dx_ref, dw_ref, db_ref = [_.grad.clone() for _ in [x, weight, bias]]
-        # compare
-        triton.testing.assert_almost_equal(y_tri, y_ref)
-        triton.testing.assert_almost_equal(dx_tri, dx_ref)
-        triton.testing.assert_almost_equal(db_tri, db_ref, decimal=1)
-        triton.testing.assert_almost_equal(dw_tri, dw_ref, decimal=1)
-
-
-    @triton.testing.perf_report(
-        triton.testing.Benchmark(
-            x_names=['N'],
-            x_vals=[512 * i for i in range(2, 32)],
-            line_arg='provider',
-            line_vals=['triton', 'torch'] + (['apex'] if HAS_APEX else []),
-            line_names=['Triton', 'Torch'] + (['Apex'] if HAS_APEX else []),
-            styles=[('blue', '-'), ('green', '-'), ('orange', '-')],
-            ylabel='GB/s',
-            plot_name='layer-norm-backward',
-            args={'M': 4096, 'dtype': torch.float16, 'mode': 'backward'}
-        )
-    )
-    def bench_layer_norm(M, N, dtype, provider, mode='backward', eps=1e-5, device='cuda'):
-        # create data
-        x_shape = (M, N)
-        w_shape = (x_shape[-1], )
-        weight = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
-        bias = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
-        x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device='cuda')
-        dy = .1 * torch.randn_like(x)
-        x.requires_grad_(True)
-        # utility functions
-        if provider == 'triton':
-            y_fwd = lambda: layer_norm(x, w_shape, weight, bias, eps)
-        if provider == 'torch':
-            y_fwd = lambda: torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps)
-        if provider == 'apex':
-            apex_layer_norm = apex.normalization.FusedLayerNorm(w_shape).to(x.device).to(x.dtype)
-            y_fwd = lambda: apex_layer_norm(x)
-        # forward pass
-        if mode == 'forward':
-            gbps = lambda ms: 2 * x.numel() * x.element_size() / ms * 1e-6
-            ms, min_ms, max_ms = triton.testing.do_bench(y_fwd, rep=500)
-        # backward pass
-        if mode == 'backward':
-            gbps = lambda ms: 3 * x.numel() * x.element_size() / ms * 1e-6
-            y = y_fwd()
-            ms, min_ms, max_ms = triton.testing.do_bench(lambda: y.backward(dy, retain_graph=True),
-                                                         grad_to_none=[x], rep=500)
-        return gbps(ms), gbps(max_ms), gbps(min_ms)
-
-
-    bench_layer_norm.run(save_path='.', print_data=True)
-
-
-.. rst-class:: sphx-glr-timing
-
-   **Total running time of the script:** ( 2 minutes  14.541 seconds)
-
-
-.. _sphx_glr_download_getting-started_tutorials_05-layer-norm.py:
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-    :class: sphx-glr-footer-example
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-python
-
-     :download:`Download Python source code: 05-layer-norm.py <05-layer-norm.py>`
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-     :download:`Download Jupyter notebook: 05-layer-norm.ipynb <05-layer-norm.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/master/_sources/getting-started/tutorials/index.rst.txt b/master/_sources/getting-started/tutorials/index.rst.txt
deleted file mode 100644
index c8a39cdaf..000000000
--- a/master/_sources/getting-started/tutorials/index.rst.txt
+++ /dev/null
@@ -1,152 +0,0 @@
-:orphan:
-
-
-
-.. _sphx_glr_getting-started_tutorials:
-
-Tutorials
-==================
-
-Below is a gallery of tutorials for writing various basic operations with Triton. It is recommended that you read through the tutorials in order, starting with the simplest one.
-
-To install the dependencies for the tutorials:
-
-.. code-block:: bash
-
-    cd triton
-    pip install -e './python[tutorials]'
-
-
-
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="- The basic programming model of Triton - The triton.jit decorator, which is used to define Tri...">
-
-.. only:: html
-
- .. figure:: /getting-started/tutorials/images/thumb/sphx_glr_01-vector-add_thumb.png
-     :alt: Vector Addition
-
-     :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py`
-
-.. raw:: html
-
-    </div>
-
-
-.. toctree::
-   :hidden:
-
-   /getting-started/tutorials/01-vector-add
-
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="- The benefits of kernel fusion for bandwidth-bound operations. - Reduction operators in Triton...">
-
-.. only:: html
-
- .. figure:: /getting-started/tutorials/images/thumb/sphx_glr_02-fused-softmax_thumb.png
-     :alt: Fused Softmax
-
-     :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py`
-
-.. raw:: html
-
-    </div>
-
-
-.. toctree::
-   :hidden:
-
-   /getting-started/tutorials/02-fused-softmax
-
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="- Block-level matrix multiplications - Multi-dimensional pointer arithmetic - Program re-orderi...">
-
-.. only:: html
-
- .. figure:: /getting-started/tutorials/images/thumb/sphx_glr_03-matrix-multiplication_thumb.png
-     :alt: Matrix Multiplication
-
-     :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py`
-
-.. raw:: html
-
-    </div>
-
-
-.. toctree::
-   :hidden:
-
-   /getting-started/tutorials/03-matrix-multiplication
-
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, you will write a memory-efficient implementation of dropout whose state will ...">
-
-.. only:: html
-
- .. figure:: /getting-started/tutorials/images/thumb/sphx_glr_04-low-memory-dropout_thumb.png
-     :alt: Low-Memory Dropout
-
-     :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py`
-
-.. raw:: html
-
-    </div>
-
-
-.. toctree::
-   :hidden:
-
-   /getting-started/tutorials/04-low-memory-dropout
-
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="Layer Normalization">
-
-.. only:: html
-
- .. figure:: /getting-started/tutorials/images/thumb/sphx_glr_05-layer-norm_thumb.png
-     :alt: Layer Normalization
-
-     :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py`
-
-.. raw:: html
-
-    </div>
-
-
-.. toctree::
-   :hidden:
-
-   /getting-started/tutorials/05-layer-norm
-.. raw:: html
-
-    <div class="sphx-glr-clear"></div>
-
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-    :class: sphx-glr-footer-gallery
-
-
-  .. container:: sphx-glr-download sphx-glr-download-python
-
-    :download:`Download all examples in Python source code: tutorials_python.zip </getting-started/tutorials/tutorials_python.zip>`
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-    :download:`Download all examples in Jupyter notebooks: tutorials_jupyter.zip </getting-started/tutorials/tutorials_jupyter.zip>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/master/_sources/getting-started/tutorials/sg_execution_times.rst.txt b/master/_sources/getting-started/tutorials/sg_execution_times.rst.txt
deleted file mode 100644
index 2e1f6608c..000000000
--- a/master/_sources/getting-started/tutorials/sg_execution_times.rst.txt
+++ /dev/null
@@ -1,20 +0,0 @@
-
-:orphan:
-
-.. _sphx_glr_getting-started_tutorials_sg_execution_times:
-
-Computation times
-=================
-**13:45.813** total execution time for **getting-started_tutorials** files:
-
-+---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:30.096 | 0.0 MB |
-+---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:22.664 | 0.0 MB |
-+---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 02:14.541 | 0.0 MB |
-+---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 01:38.189 | 0.0 MB |
-+---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py` (``04-low-memory-dropout.py``)       | 00:00.324 | 0.0 MB |
-+---------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/master/_sources/index.rst.txt b/master/_sources/index.rst.txt
deleted file mode 100644
index 1da612fe8..000000000
--- a/master/_sources/index.rst.txt
+++ /dev/null
@@ -1,52 +0,0 @@
-Welcome to Triton's documentation!
-==================================
-
-Triton is a language and compiler for parallel programming. It aims to provide a Python-based programming environment for productively writing custom DNN compute kernels capable of running at maximal throughput on modern GPU hardware.
-
-Getting Started
----------------
-
-- Follow the :doc:`installation instructions <getting-started/installation>` for your platform of choice.
-- Take a look at the :doc:`tutorials <getting-started/tutorials/index>` to learn how to write your first Triton program.
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Getting Started
-   :hidden:
-
-   getting-started/installation
-   getting-started/tutorials/index
-
-Python API
--------------------
-
-- :doc:`triton <python-api/triton>`
-- :doc:`triton.language <python-api/triton.language>`
-- :doc:`triton.testing <python-api/triton.testing>`
-
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Python API
-   :hidden:
-
-   python-api/triton
-   python-api/triton.language
-   python-api/triton.testing
-
-   
-Going Further
-------------------
-
-Check out the following documents to learn more about Triton and how it compares against other DSLs for DNNs:
-
-- Chapter 1: :doc:`Introduction <programming-guide/chapter-1/introduction>`
-- Chapter 2: :doc:`Related Work <programming-guide/chapter-2/related-work>`
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Programming Guide
-   :hidden:
-
-   programming-guide/chapter-1/introduction
-   programming-guide/chapter-2/related-work
diff --git a/master/_sources/programming-guide/chapter-1/introduction.rst.txt b/master/_sources/programming-guide/chapter-1/introduction.rst.txt
deleted file mode 100644
index 6e4b36a48..000000000
--- a/master/_sources/programming-guide/chapter-1/introduction.rst.txt
+++ /dev/null
@@ -1,69 +0,0 @@
-==============
-Introduction
-==============
-
---------------
-Motivations
---------------
-
-Over the past decade, Deep Neural Networks (DNNs) have emerged as an important class of Machine Learning (ML) models, capable of  achieving state-of-the-art performance across many domains ranging from natural language processing [SUTSKEVER2014]_ to computer vision [REDMON2016]_ to computational neuroscience [LEE2017]_. The strength of these models lies in their hierarchical structure, composed of a sequence of parametric (e.g., convolutional) and non-parametric (e.g., rectified linearity) *layers*. This pattern, though notoriously computationally expensive, also generates a large amount of highly parallelizable work particularly well suited for multi- and many- core processors.
-
-As a consequence, Graphics Processing Units (GPUs) have become a cheap and accessible resource for exploring and/or deploying novel research ideas in the field. This trend has been accelerated by the release of several frameworks for General-Purpose GPU (GPGPU) computing, such as CUDA and OpenCL, which have made the development of high-performance programs easier. Yet, GPUs remain incredibly challenging to optimize for locality and parallelism, especially for computations that cannot be efficiently implemented using a combination of pre-existing optimized primitives. To make matters worse, GPU architectures are also rapidly evolving and specializing, as evidenced by the addition of tensor cores to NVIDIA (and more recently AMD) micro-architectures.
-
-This tension between the computational opportunities offered by DNNs and the practical difficulty of GPU programming has created substantial academic and industrial interest for Domain-Specific Languages (DSLs) and compilers. Regrettably, these systems -- whether they be based on  polyhedral machinery (*e.g.*, Tiramisu [BAGHDADI2021]_, Tensor Comprehensions [VASILACHE2018]_) or scheduling languages (*e.g.*, Halide [JRK2013]_, TVM [CHEN2018]_) -- remain less flexible and (for the same algorithm) markedly slower than the best handwritten compute kernels available in libraries like `cuBLAS <https://docs.nvidia.com/cuda/cublas/index.html>`_, `cuDNN <https://docs.nvidia.com/deeplearning/cudnn/api/index.html>`_ or `TensorRT <https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html>`_.
-
-The main premise of this project is the following: programming paradigms based on blocked algorithms [LAM1991]_ can facilitate the construction of high-performance compute kernels for neural networks.  We specifically revisit traditional "Single Program, Multiple Data" (SPMD [AUGUIN1983]_) execution models for GPUs, and propose a variant in which programs -- rather than threads -- are blocked. For example, in the case of matrix multiplication, CUDA and Triton differ as follows:
-
-.. table::
-    :widths: 50 50
-
-    +-----------------------------------------------------+-----------------------------------------------------+
-    | CUDA Programming Model                              | Triton Programming Model                            |
-    |                                                     |                                                     |
-    | (Scalar Program, Blocked Threads)                   | (Blocked Program, Scalar Threads)                   |
-    +=====================================================+=====================================================+
-    |                                                     |                                                     |
-    |.. code-block:: C                                    |.. code-block:: C                                    |
-    |                                                     |   :force:                                           |
-    |                                                     |                                                     |
-    |   #pragma parallel                                  |   #pragma parallel                                  |
-    |   for(int m = 0; i < M; m++)                        |   for(int m = 0; m < M; m += MB)                    |
-    |   #pragma parallel                                  |   #pragma parallel                                  |
-    |   for(int n = 0; j < N; n++){                       |   for(int n = 0; n < N; n += NB){                   |
-    |     float acc = 0;                                  |     float acc[MB, NB] = 0;                          |
-    |     for(int k = 0; k < K;k ++)                      |     for(int k = 0; k < K; k += KB)                  |
-    |       acc += A[i, k]* B[k, j];                      |       acc +=  A[m:m+MB, k:k+KB]                     |
-    |                                                     |             @ B[k:k+KB, n:n+NB];                    |
-    |     C[i, j] = acc;                                  |     C[m:m+MB, n:n+NB] = acc;                        |
-    |   }                                                 |   }                                                 |
-    |                                                     |                                                     |
-    +-----------------------------------------------------+-----------------------------------------------------+
-    | |pic1|                                              | |pic2|                                              |
-    +-----------------------------------------------------+-----------------------------------------------------+
-
-
-.. |pic1| image:: cuda-parallel-matmul.png
-
-.. |pic2| image:: triton-parallel-matmul.png
-
-A key benefit of this approach is that it leads to block-structured iteration spaces that offer programmers more flexibility than existing DSLs when implementing sparse operations, all while allowing compilers to aggressively optimize programs for data locality and parallelism.
-
---------------
-Challenges
---------------
-
-The main challenge posed by our proposed paradigm is that of work scheduling, i.e., how the work done by each program instance should be partitioned for efficient execution on modern GPUs. To address this issue, the Triton compiler makes heavy use of *block-level data-flow analysis*, a technique for scheduling iteration blocks statically based on the control- and data-flow structure of the target program. The resulting system actually works surprisingly well: our compiler manages to apply a broad range of interesting optimization automatically (e.g., automatic coalescing, thread swizzling, pre-fetching, automatic vectorization, tensor core-aware instruction selection, shared memory allocation/synchronization, asynchronous copy scheduling). Of course doing all this is not trivial; one of the purposes of this guide is to give you a sense of how it works.
-
---------------
-References
---------------
-
-.. [SUTSKEVER2014] I. Sutskever et al., "Sequence to Sequence Learning with Neural Networks", NIPS 2014
-.. [REDMON2016] J. Redmon et al., "You Only Look Once: Unified, Real-Time Object Detection", CVPR 2016
-.. [LEE2017] K. Lee et al., "Superhuman Accuracy on the SNEMI3D Connectomics Challenge", ArXiV 2017
-.. [BAGHDADI2021] R. Baghdadi et al., "Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code", CGO 2021
-.. [VASILACHE2018] N. Vasilache et al., "Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions", ArXiV 2018
-.. [JRK2013] J. Ragan-Kelley et al., "Halide: A Language and Compiler for Optimizing Parallelism, Locality, and Recomputation in Image Processing Pipelines", PLDI 2013
-.. [CHEN2018] T. Chen et al., "TVM: An Automated End-to-End Optimizing Compiler for Deep Learning", OSDI 2018
-.. [LAM1991] M. Lam et al., "The Cache Performance and Optimizations of Blocked Algorithms", ASPLOS 1991
-.. [AUGUIN1983] M. Auguin et al., "Opsila: an advanced SIMD for numerical analysis and signal processing", EUROMICRO 1983
\ No newline at end of file
diff --git a/master/_sources/programming-guide/chapter-2/related-work.rst.txt b/master/_sources/programming-guide/chapter-2/related-work.rst.txt
deleted file mode 100644
index bb83d4851..000000000
--- a/master/_sources/programming-guide/chapter-2/related-work.rst.txt
+++ /dev/null
@@ -1,210 +0,0 @@
-==============
-Related Work
-==============
-
-At first sight, Triton may seem like just yet another DSL for DNNs. The purpose of this section is to contextualize Triton and highlight its differences with the two leading approaches in this domain: polyhedral compilation and scheduling languages.
-
------------------------
-Polyhedral Compilation
------------------------
-
-Traditional compilers typically rely on intermediate representations, such as LLVM-IR [LATTNER2004]_, that encode control flow information using (un)conditional branches. This relatively low-level format makes it difficult to statically analyze the runtime behavior (e.g., cache misses) of input programs, and to  automatically optimize loops accordingly through the use of tiling [WOLFE1989]_, fusion [DARTE1999]_ and interchange [ALLEN1984]_. To solve this issue, polyhedral compilers [ANCOURT1991]_ rely on program representations that have statically predictable control flow, thereby enabling aggressive compile-time program transformations for data locality and parallelism. Though this strategy has been adopted by many languages and compilers for DNNs such as Tiramisu [BAGHDADI2021]_, Tensor Comprehensions [VASILACHE2018]_, Diesel [ELANGO2018]_ and the Affine dialect in MLIR [LATTNER2019]_, it also comes with a number of limitations that will be described later in this section.
-
-+++++++++++++++++++++++
-Program Representation
-+++++++++++++++++++++++
-
-Polyhedral compilation is a vast area of research. In this section we only outline the most basic aspects of this topic, but readers interested in the solid mathematical foundations underneath may refer to the ample litterature on linear and integer programming.
-
-.. table::
-    :widths: 50 50
-
-    +-----------------------------------------------------+-----------------------------------------------------+
-    |                                                     |                                                     |
-    |.. code-block:: C                                    | |pic1|                                              |
-    |                                                     |                                                     |
-    |   for(int i = 0; i < 3; i++)                        |                                                     |
-    |   for(int j = i; j < 5; j++)                        |                                                     |
-    |     A[i][j] = 0;                                    |                                                     |
-    +-----------------------------------------------------+-----------------------------------------------------+
-
-.. |pic1| image:: polyhedral-iteration.png
-    :width: 300
-
-Polyhedral compilers focus on a class of programs commonly known as **Static Control Parts** (SCoP), *i.e.*, maximal sets of consecutive statements in which conditionals and loop bounds are affine functions of surrounding loop indices and global invariant parameters. As shown above, programs in this format always lead to iteration domains that are bounded by affine inequalities, i.e., polyhedral. These polyhedra can also be defined algebraically; for the above example:
-
-.. math::
-
-  \mathcal{P} = \{ i, j \in \mathbb{Z}^2
-  ~|~
-  \begin{pmatrix}
-  1 & 0 \\
-  -1 & 0 \\
-  -1 & 1 \\
-  0 & -1 \\
-  \end{pmatrix}
-  \begin{pmatrix}
-  i \\
-  j
-  \end{pmatrix}
-  +
-  \begin{pmatrix}
-  0 \\
-  2 \\
-  0 \\
-  4
-  \end{pmatrix}
-  \geq
-  0
-  \}
-
-
-Each point :math:`(i, j)` in :math:`\mathcal{P}` represents a *polyhedral statement*, that is a program statement which (1) does not induce control-flow side effects (e.g., :code:`for`, :code:`if`, :code:`break`) and (2) contains only affine functions of loop indices and global parameters in array accesses. To facilitate alias analysis, array accesses are also mathematically abstracted, using so-called *access function*. In other words, :code:`A[i][j]` is simply :code:`A[f(i,j)]` where the access function :math:`f` is defined by:
-
-.. math::
-
-  f(i, j) = \begin{pmatrix}
-  1 & 0\\
-  0 & 1\\
-  \end{pmatrix}
-  \begin{pmatrix}
-  i\\
-  j
-  \end{pmatrix}
-  =
-  (i, j)
-
-
-Note that the iteration domains of an SCoP does not specify the order in which its statements shall execute. In fact, this iteration domain may be traversed in many different possible legal orders, i.e. *schedules*. Formally, a schedule is defined as a p-dimensional affine transformation :math:`\Theta` of loop indices :math:`\mathbf{x}` and global invariant parameters :math:`\mathbf{g}`:
-
-.. math::
-  \Theta_S(\mathbf{x}) = T_S \begin{pmatrix}
-  \vec{x}\\
-  \vec{g}\\
-  1
-  \end{pmatrix}
-  \qquad
-  T_S \in \mathbb{Z} ^{p \times (\text{dim}(\mathbf{x}) + \text{dim}(\mathbf{g}) + 1)}
-
-
-Where :math:`\Theta_S(\mathbf{x})` is a p-dimensional vector representing the slowest to fastest growing indices (from left to right) when traversing the loop nest surrounding :math:`S`. For the code shown above, the original schedule defined by the loop nest in C can be retrieved by using:
-
-.. math::
-  \Theta_S(\mathbf{x}) = \begin{pmatrix}
-  1 & 0 \\
-  0 & 1 \\
-  \end{pmatrix}
-  \begin{pmatrix}
-  i & j
-  \end{pmatrix}^T
-  =
-  \begin{pmatrix}
-  i & j
-  \end{pmatrix}^T
-
-
-where :math:`i` and :math:`j` are respectively the slowest and fastest growing loop indices in the nest. If :math:`T_S` is a vector (resp. tensor), then :math:`\Theta_S` is a said to be one-dimensional (resp. multi-dimensional).
-
-+++++++++++
-Advantages
-+++++++++++
-
-Programs amenable to polyhedral compilation can be aggressively transformed and optimized. Most of these transformations actually boil down to the production of  schedules and iteration domains that enable loop transformations promoting parallelism and spatial/temporal data locality (e.g., fusion, interchange, tiling, parallelization).
-
-Polyhedral compilers can also automatically go through complex verification processes to ensure that the semantics of their input program is preserved throughout this optimization phase. Note that polyhedral optimizers are not incompatible with more standard optimization techniques. In fact, it is not uncommon for these systems to be implemented as a set of LLVM passes that can be run ahead of more traditional compilation techniques [GROSSER2012]_.
-
-All in all, polyhedral machinery is extremely powerful, when applicable. It has been shown to support most common loop transformations, and has indeed achieved performance comparable to state-of-the-art GPU libraries for dense matrix multiplication [ELANGO2018]_. Additionally, it is also fully automatic and doesn't require any hint from programmers apart from source-code in a C-like format. 
-
-++++++++++++
-Limitations
-++++++++++++
-
-Unfortunately, polyhedral compilers suffer from two major limitations that have prevented its adoption as a universal method for code generation in neural networks.
-
-First, the set of possible program transformations :math:`\Omega = \{ \Theta_S ~|~ S \in \text{program} \}` is large, and grows with the number of statements in the program as well as with the size of their iteration domain. Verifying the legality of each transformation can also require the resolution of complex integer linear programs, making polyhedral compilation very computationally expensive. To make matters worse, hardware properties (e.g., cache size, number of SMs) and contextual characteristics (e.g., input tensor shapes) also have to be taken into account by this framework, leading to expensive auto-tuning procedures [SATO2019]_.
-
-Second, the polyhedral framework is not very generally applicable; SCoPs are relatively common [GIRBAL2006]_ but require loop bounds and array subscripts to be affine functions of loop indices, which typically only occurs in regular, dense computations. For this reason, this framework still has to be successfully applied to sparse -- or even structured-sparse -- neural networks, whose importance has been rapidly rising over the past few years.
-
-On the other hand, blocked program representations advocated by this dissertation are less restricted in scope and can achieve close to peak performance using standard dataflow analysis.
-
------------------------
-Scheduling Languages
------------------------
-
-Separation of concerns [DIJKSTRA82]_ is a well-known design principle in computer science: programs should be decomposed into modular layers of abstraction that separate the semantics of their algorithms from the details of their implementation. Systems like Halide and TVM push this philosophy one step further, and enforce this separation at the grammatical level through the use of a  **scheduling language**. The benefits of this methodology are particularly visible in the case of matrix multiplication, where, as one can see below, the definition of the algorithm (Line 1-7) is completely disjoint from its implementation (Line 8-16), meaning that both can be maintained, optimized and distributed independently. 
-
-.. code-block:: python
-  :linenos:
-
-  // algorithm
-  Var x("x"), y("y");
-  Func matmul("matmul"); 
-  RDom k(0, matrix_size); 
-  RVar ki; 
-  matmul(x, y) = 0.0f; 
-  matmul(x, y) += A(k, y) * B(x, k); 
-  // schedule
-  Var xi("xi"), xo("xo"), yo("yo"), yi("yo"), yii("yii"), xii("xii"); 
-  matmul.vectorize(x, 8); 
-  matmul.update(0) 
-      .split(x, x, xi, block_size).split(xi, xi, xii, 8) 
-      .split(y, y, yi, block_size).split(yi, yi, yii, 4) 
-      .split(k, k, ki, block_size) 
-      .reorder(xii, yii, xi, ki, yi, k, x, y) 
-      .parallel(y).vectorize(xii).unroll(xi).unroll(yii);
-
-
-The resulting code may however not be completely portable, as schedules can sometimes rely on execution models (e.g., SPMD) or hardware intrinsics (e.g., matrix-multiply-accumulate) that are not widely available. This issue can be mitigated by auto-scheduling mechanisms [MULLAPUDI2016]_.
-
-+++++++++++
-Advantages
-+++++++++++
-
-The main advantage of this approach is that it allows programmers to write an algorithm *only once*, and focus on performance optimization separately. It makes it possible to manually specify optimizations that a polyhedral compiler wouldn't be able to figure out automatically using static data-flow analysis.
-
-Scheduling languages are, without a doubt, one of the most popular approaches for neural network code generation. The most popular system for this purpose is probably TVM, which provides good performance across a wide range of platforms as well as built-in automatic scheduling mechanisms.
-
-++++++++++++
-Limitations
-++++++++++++
-
-This ease-of-development comes at a cost. First of all, existing systems that follow this paradigm tend to be noticeably slower than Triton on modern hardware when applicable (e.g., V100/A100 tensor cores w/ equal tile sizes). I do believe that this is not a fundamental issue of scheduling languages -- in the sense that it could probably be solved with more efforts -- but it could mean that these systems are harder to engineer. More importantly, existing scheduling languages generate loops whose bounds and increments cannot depend on surrounding loop indice without at least imposing severe constraints on possible schedules -- if not breaking the system entirely. This is problematic for sparse computations, whose iteration spaces may be irregular.
-
-.. table::
-    :widths: 50 50
-
-    +-----------------------------------------------------+-----------------------------------------------------+
-    |                                                     |                                                     |
-    |.. code-block:: C                                    | |pic2|                                              |
-    |                                                     |                                                     |
-    |   for(int i = 0; i < 4; i++)                        |                                                     |
-    |   for(int j = 0; j < 4; j++)                        |                                                     |
-    |     float acc = 0;                                  |                                                     |
-    |     for(int k = 0; k < K[i]; k++)                   |                                                     |
-    |       acc += A[i][col[i,k]]*B[k][j]                 |                                                     |
-    |     C[i][j] = acc;                                  |                                                     |
-    +-----------------------------------------------------+-----------------------------------------------------+
-
-.. |pic2| image:: halide-iteration.png
-    :width: 300
-
-On the other hand, the block-based program representation that we advocate for through this work allows for block-structured iteration spaces and allows programmers to manually handle load-balancing as they wish.
-
---------------
-References
---------------
-
-.. [LATTNER2004] C. Lattner et al., "LLVM: a compilation framework for lifelong program analysis transformation", CGO 2004
-.. [WOLFE1989] M. Wolfe, "More Iteration Space Tiling", SC 1989
-.. [DARTE1999] A. Darte, "On the Complexity of Loop Fusion", PACT 1999
-.. [ALLEN1984] J. Allen et al., "Automatic Loop Interchange", SIGPLAN Notices 1984
-.. [ANCOURT1991] C. Ancourt et al., "Scanning Polyhedra with DO Loops", PPoPP 1991
-.. [BAGHDADI2021] R. Baghdadi et al., "Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code", CGO 2021
-.. [VASILACHE2018] N. Vasilache et al., "Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions", ArXiV 2018
-.. [ELANGO2018] V. Elango et al. "Diesel: DSL for Linear Algebra and Neural Net Computations on GPUs", MAPL 2018
-.. [LATTNER2019] C. Lattner et al., "MLIR Primer: A Compiler Infrastructure for the End of Moore’s Law", Arxiv 2019
-.. [GROSSER2012] T. Grosser et al., "Polly - Performing Polyhedral Optimizations on a Low-Level Intermediate Representation", Parallel Processing Letters 2012
-.. [SATO2019] Y. Sato et al., "An Autotuning Framework for Scalable Execution of Tiled Code via Iterative Polyhedral Compilation", TACO 2019
-.. [GIRBAL2006] S. Girbal et al., "Semi-Automatic Composition of Loop Transformations for Deep Parallelism and Memory Hierarchies", International Journal of Parallel Programming 2006
-.. [DIJKSTRA82] E. W. Dijkstra et al., "On the role of scientific thought", Selected writings on computing: a personal perspective 1982
-.. [MULLAPUDI2016] R. Mullapudi et al., "Automatically scheduling halide image processing pipelines", TOG 2016
diff --git a/master/_sources/python-api/generated/triton.Config.rst.txt b/master/_sources/python-api/generated/triton.Config.rst.txt
deleted file mode 100644
index 6c6edee75..000000000
--- a/master/_sources/python-api/generated/triton.Config.rst.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-triton.Config
-=============
-
-.. currentmodule:: triton
-
-.. autoclass:: Config
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Config.__init__
-   
-   
-
-   
-   
-   
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.autotune.rst.txt b/master/_sources/python-api/generated/triton.autotune.rst.txt
deleted file mode 100644
index fa87cf698..000000000
--- a/master/_sources/python-api/generated/triton.autotune.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.autotune
-===============
-
-.. currentmodule:: triton
-
-.. autofunction:: autotune
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.heuristics.rst.txt b/master/_sources/python-api/generated/triton.heuristics.rst.txt
deleted file mode 100644
index 24325d928..000000000
--- a/master/_sources/python-api/generated/triton.heuristics.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.heuristics
-=================
-
-.. currentmodule:: triton
-
-.. autofunction:: heuristics
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.jit.rst.txt b/master/_sources/python-api/generated/triton.jit.rst.txt
deleted file mode 100644
index d40d3b664..000000000
--- a/master/_sources/python-api/generated/triton.jit.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.jit
-==========
-
-.. currentmodule:: triton
-
-.. autofunction:: jit
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.arange.rst.txt b/master/_sources/python-api/generated/triton.language.arange.rst.txt
deleted file mode 100644
index ab4baea81..000000000
--- a/master/_sources/python-api/generated/triton.language.arange.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.arange
-======================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: arange
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.atomic_add.rst.txt b/master/_sources/python-api/generated/triton.language.atomic_add.rst.txt
deleted file mode 100644
index 4757956dd..000000000
--- a/master/_sources/python-api/generated/triton.language.atomic_add.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.atomic\_add
-===========================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: atomic_add
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.atomic_cas.rst.txt b/master/_sources/python-api/generated/triton.language.atomic_cas.rst.txt
deleted file mode 100644
index 84403aeca..000000000
--- a/master/_sources/python-api/generated/triton.language.atomic_cas.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.atomic\_cas
-===========================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: atomic_cas
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.atomic_max.rst.txt b/master/_sources/python-api/generated/triton.language.atomic_max.rst.txt
deleted file mode 100644
index c3a4ea43d..000000000
--- a/master/_sources/python-api/generated/triton.language.atomic_max.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.atomic\_max
-===========================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: atomic_max
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.atomic_min.rst.txt b/master/_sources/python-api/generated/triton.language.atomic_min.rst.txt
deleted file mode 100644
index d3e959969..000000000
--- a/master/_sources/python-api/generated/triton.language.atomic_min.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.atomic\_min
-===========================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: atomic_min
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.atomic_xchg.rst.txt b/master/_sources/python-api/generated/triton.language.atomic_xchg.rst.txt
deleted file mode 100644
index 07aa59c41..000000000
--- a/master/_sources/python-api/generated/triton.language.atomic_xchg.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.atomic\_xchg
-============================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: atomic_xchg
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.broadcast_to.rst.txt b/master/_sources/python-api/generated/triton.language.broadcast_to.rst.txt
deleted file mode 100644
index de4be3378..000000000
--- a/master/_sources/python-api/generated/triton.language.broadcast_to.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.broadcast\_to
-=============================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: broadcast_to
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.cos.rst.txt b/master/_sources/python-api/generated/triton.language.cos.rst.txt
deleted file mode 100644
index 3a090bea2..000000000
--- a/master/_sources/python-api/generated/triton.language.cos.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.cos
-===================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: cos
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.dot.rst.txt b/master/_sources/python-api/generated/triton.language.dot.rst.txt
deleted file mode 100644
index dac3564ea..000000000
--- a/master/_sources/python-api/generated/triton.language.dot.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.dot
-===================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: dot
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.exp.rst.txt b/master/_sources/python-api/generated/triton.language.exp.rst.txt
deleted file mode 100644
index dea445eef..000000000
--- a/master/_sources/python-api/generated/triton.language.exp.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.exp
-===================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: exp
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.load.rst.txt b/master/_sources/python-api/generated/triton.language.load.rst.txt
deleted file mode 100644
index b0633ceb6..000000000
--- a/master/_sources/python-api/generated/triton.language.load.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.load
-====================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: load
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.log.rst.txt b/master/_sources/python-api/generated/triton.language.log.rst.txt
deleted file mode 100644
index 24eeeef11..000000000
--- a/master/_sources/python-api/generated/triton.language.log.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.log
-===================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: log
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.max.rst.txt b/master/_sources/python-api/generated/triton.language.max.rst.txt
deleted file mode 100644
index 2601701b6..000000000
--- a/master/_sources/python-api/generated/triton.language.max.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.max
-===================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: max
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.maximum.rst.txt b/master/_sources/python-api/generated/triton.language.maximum.rst.txt
deleted file mode 100644
index 67a81f14f..000000000
--- a/master/_sources/python-api/generated/triton.language.maximum.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.maximum
-=======================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: maximum
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.min.rst.txt b/master/_sources/python-api/generated/triton.language.min.rst.txt
deleted file mode 100644
index b2b907292..000000000
--- a/master/_sources/python-api/generated/triton.language.min.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.min
-===================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: min
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.minimum.rst.txt b/master/_sources/python-api/generated/triton.language.minimum.rst.txt
deleted file mode 100644
index 1fc5ae67d..000000000
--- a/master/_sources/python-api/generated/triton.language.minimum.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.minimum
-=======================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: minimum
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.multiple_of.rst.txt b/master/_sources/python-api/generated/triton.language.multiple_of.rst.txt
deleted file mode 100644
index 4a42ad7db..000000000
--- a/master/_sources/python-api/generated/triton.language.multiple_of.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.multiple\_of
-============================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: multiple_of
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.num_programs.rst.txt b/master/_sources/python-api/generated/triton.language.num_programs.rst.txt
deleted file mode 100644
index d7f85dc88..000000000
--- a/master/_sources/python-api/generated/triton.language.num_programs.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.num\_programs
-=============================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: num_programs
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.program_id.rst.txt b/master/_sources/python-api/generated/triton.language.program_id.rst.txt
deleted file mode 100644
index 84d3865ea..000000000
--- a/master/_sources/python-api/generated/triton.language.program_id.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.program\_id
-===========================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: program_id
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.rand.rst.txt b/master/_sources/python-api/generated/triton.language.rand.rst.txt
deleted file mode 100644
index a07674ddc..000000000
--- a/master/_sources/python-api/generated/triton.language.rand.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.rand
-====================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: rand
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.randint.rst.txt b/master/_sources/python-api/generated/triton.language.randint.rst.txt
deleted file mode 100644
index ee6b64394..000000000
--- a/master/_sources/python-api/generated/triton.language.randint.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.randint
-=======================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: randint
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.randint4x.rst.txt b/master/_sources/python-api/generated/triton.language.randint4x.rst.txt
deleted file mode 100644
index 3f1a8a9ff..000000000
--- a/master/_sources/python-api/generated/triton.language.randint4x.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.randint4x
-=========================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: randint4x
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.randn.rst.txt b/master/_sources/python-api/generated/triton.language.randn.rst.txt
deleted file mode 100644
index 2b4fa0a06..000000000
--- a/master/_sources/python-api/generated/triton.language.randn.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.randn
-=====================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: randn
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.ravel.rst.txt b/master/_sources/python-api/generated/triton.language.ravel.rst.txt
deleted file mode 100644
index 8034529d6..000000000
--- a/master/_sources/python-api/generated/triton.language.ravel.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.ravel
-=====================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: ravel
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.reshape.rst.txt b/master/_sources/python-api/generated/triton.language.reshape.rst.txt
deleted file mode 100644
index eec7bfb7e..000000000
--- a/master/_sources/python-api/generated/triton.language.reshape.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.reshape
-=======================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: reshape
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.sigmoid.rst.txt b/master/_sources/python-api/generated/triton.language.sigmoid.rst.txt
deleted file mode 100644
index 35f572da3..000000000
--- a/master/_sources/python-api/generated/triton.language.sigmoid.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.sigmoid
-=======================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: sigmoid
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.sin.rst.txt b/master/_sources/python-api/generated/triton.language.sin.rst.txt
deleted file mode 100644
index 651f465b8..000000000
--- a/master/_sources/python-api/generated/triton.language.sin.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.sin
-===================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: sin
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.softmax.rst.txt b/master/_sources/python-api/generated/triton.language.softmax.rst.txt
deleted file mode 100644
index 86199cb6f..000000000
--- a/master/_sources/python-api/generated/triton.language.softmax.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.softmax
-=======================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: softmax
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.sqrt.rst.txt b/master/_sources/python-api/generated/triton.language.sqrt.rst.txt
deleted file mode 100644
index b74db8a6e..000000000
--- a/master/_sources/python-api/generated/triton.language.sqrt.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.sqrt
-====================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: sqrt
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.store.rst.txt b/master/_sources/python-api/generated/triton.language.store.rst.txt
deleted file mode 100644
index ecb332ee2..000000000
--- a/master/_sources/python-api/generated/triton.language.store.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.store
-=====================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: store
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.sum.rst.txt b/master/_sources/python-api/generated/triton.language.sum.rst.txt
deleted file mode 100644
index dd23f59f3..000000000
--- a/master/_sources/python-api/generated/triton.language.sum.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.sum
-===================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: sum
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.where.rst.txt b/master/_sources/python-api/generated/triton.language.where.rst.txt
deleted file mode 100644
index c32eb76d6..000000000
--- a/master/_sources/python-api/generated/triton.language.where.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.where
-=====================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: where
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.language.zeros.rst.txt b/master/_sources/python-api/generated/triton.language.zeros.rst.txt
deleted file mode 100644
index 0475de85c..000000000
--- a/master/_sources/python-api/generated/triton.language.zeros.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.language.zeros
-=====================
-
-.. currentmodule:: triton.language
-
-.. autofunction:: zeros
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.testing.Benchmark.rst.txt b/master/_sources/python-api/generated/triton.testing.Benchmark.rst.txt
deleted file mode 100644
index af24d812b..000000000
--- a/master/_sources/python-api/generated/triton.testing.Benchmark.rst.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-triton.testing.Benchmark
-========================
-
-.. currentmodule:: triton.testing
-
-.. autoclass:: Benchmark
-
-   
-   .. automethod:: __init__
-
-   
-   .. rubric:: Methods
-
-   .. autosummary::
-   
-      ~Benchmark.__init__
-   
-   
-
-   
-   
-   
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.testing.do_bench.rst.txt b/master/_sources/python-api/generated/triton.testing.do_bench.rst.txt
deleted file mode 100644
index f6803dc25..000000000
--- a/master/_sources/python-api/generated/triton.testing.do_bench.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.testing.do\_bench
-========================
-
-.. currentmodule:: triton.testing
-
-.. autofunction:: do_bench
\ No newline at end of file
diff --git a/master/_sources/python-api/generated/triton.testing.perf_report.rst.txt b/master/_sources/python-api/generated/triton.testing.perf_report.rst.txt
deleted file mode 100644
index e1274b2df..000000000
--- a/master/_sources/python-api/generated/triton.testing.perf_report.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-triton.testing.perf\_report
-===========================
-
-.. currentmodule:: triton.testing
-
-.. autofunction:: perf_report
\ No newline at end of file
diff --git a/master/_sources/python-api/triton.language.rst.txt b/master/_sources/python-api/triton.language.rst.txt
deleted file mode 100644
index 1f05ce8a6..000000000
--- a/master/_sources/python-api/triton.language.rst.txt
+++ /dev/null
@@ -1,145 +0,0 @@
-triton.language
-================
-
-.. currentmodule:: triton.language
-
-
-Programming Model
--------------------
-
-.. autosummary::
-    :toctree: generated
-    :nosignatures:
-
-    program_id
-    num_programs
-
-
-Creation Ops
--------------
-
-.. autosummary::
-    :toctree: generated
-    :nosignatures:
-
-    arange
-    zeros
-
-
-Shape Manipulation Ops
------------------------
-
-.. autosummary::
-    :toctree: generated
-    :nosignatures:
-
-    broadcast_to
-    reshape
-    ravel
-
-
-
-Linear Algebra Ops
--------------------
-
-.. autosummary::
-    :toctree: generated
-    :nosignatures:
-
-    dot
-
-Memory Ops
---------------------
-
-.. autosummary::
-    :toctree: generated
-    :nosignatures:
-
-    load
-    store
-    atomic_cas
-    atomic_xchg
-
-
-Indexing Ops
---------------
-
-.. autosummary::
-    :toctree: generated
-    :nosignatures:
-
-    where
-
-
-Math Ops
-----------
-
-.. autosummary::
-    :toctree: generated
-    :nosignatures:
-
-    exp
-    log
-    cos
-    sin
-    sqrt
-    sigmoid
-    softmax
-
-
-Reduction Ops
----------------
-
-.. autosummary::
-    :toctree: generated
-    :nosignatures:
-
-    max
-    min
-    sum
-
-Atomic Ops
----------------
-
-.. autosummary::
-    :toctree: generated
-    :nosignatures:
-
-    atomic_cas
-    atomic_add
-    atomic_max
-    atomic_min
-
-
-Comparison ops
----------------
-
-.. autosummary::
-    :toctree: generated
-    :nosignatures:
-
-    minimum
-    maximum
-
-.. _Random Number Generation:
-
-Random Number Generation
--------------------------
-
-.. autosummary::
-    :toctree: generated
-    :nosignatures:
-
-    randint4x
-    randint
-    rand
-    randn
-
-Compiler Hint Ops
--------------------
-
-.. autosummary::
-    :toctree: generated
-    :nosignatures:
-
-    multiple_of
\ No newline at end of file
diff --git a/master/_sources/python-api/triton.rst.txt b/master/_sources/python-api/triton.rst.txt
deleted file mode 100644
index 70bda4931..000000000
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+++ /dev/null
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diff --git a/master/_sources/python-api/triton.testing.rst.txt b/master/_sources/python-api/triton.testing.rst.txt
deleted file mode 100644
index c45e9981b..000000000
--- a/master/_sources/python-api/triton.testing.rst.txt
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diff --git a/master/_static/doctools.js b/master/_static/doctools.js
deleted file mode 100644
index 8cbf1b161..000000000
--- a/master/_static/doctools.js
+++ /dev/null
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-/*
- * doctools.js
- * ~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for all documentation.
- *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
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- *
- */
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-    window.console[names[i]] = function() {};
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- */
-
-/**
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-      window.setTimeout(function() {
-        document.location.href += '';
-      }, 10);
-  },
-
-  /**
-   * highlight the search words provided in the url in the text
-   */
-  highlightSearchWords : function() {
-    var params = $.getQueryParameters();
-    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
-    if (terms.length) {
-      var body = $('div.body');
-      if (!body.length) {
-        body = $('body');
-      }
-      window.setTimeout(function() {
-        $.each(terms, function() {
-          body.highlightText(this.toLowerCase(), 'highlighted');
-        });
-      }, 10);
-      $('<p class="highlight-link"><a href="javascript:Documentation.' +
-        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
-          .appendTo($('#searchbox'));
-    }
-  },
-
-  /**
-   * init the domain index toggle buttons
-   */
-  initIndexTable : function() {
-    var togglers = $('img.toggler').click(function() {
-      var src = $(this).attr('src');
-      var idnum = $(this).attr('id').substr(7);
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-      if (src.substr(-9) === 'minus.png')
-        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
-      else
-        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
-    }).css('display', '');
-    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
-        togglers.click();
-    }
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords : function() {
-    $('#searchbox .highlight-link').fadeOut(300);
-    $('span.highlighted').removeClass('highlighted');
-  },
-
-  /**
-   * make the url absolute
-   */
-  makeURL : function(relativeURL) {
-    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
-  },
-
-  /**
-   * get the current relative url
-   */
-  getCurrentURL : function() {
-    var path = document.location.pathname;
-    var parts = path.split(/\//);
-    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
-      if (this === '..')
-        parts.pop();
-    });
-    var url = parts.join('/');
-    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
-  },
-
-  initOnKeyListeners: function() {
-    $(document).keydown(function(event) {
-      var activeElementType = document.activeElement.tagName;
-      // don't navigate when in search box, textarea, dropdown or button
-      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
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-          && !event.shiftKey) {
-        switch (event.keyCode) {
-          case 37: // left
-            var prevHref = $('link[rel="prev"]').prop('href');
-            if (prevHref) {
-              window.location.href = prevHref;
-              return false;
-            }
-            break;
-          case 39: // right
-            var nextHref = $('link[rel="next"]').prop('href');
-            if (nextHref) {
-              window.location.href = nextHref;
-              return false;
-            }
-            break;
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-      }
-    });
-  }
-};
-
-// quick alias for translations
-_ = Documentation.gettext;
-
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-});
diff --git a/master/_static/documentation_options.js b/master/_static/documentation_options.js
deleted file mode 100644
index 2fa8c97fe..000000000
--- a/master/_static/documentation_options.js
+++ /dev/null
@@ -1,12 +0,0 @@
-var DOCUMENTATION_OPTIONS = {
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-    VERSION: '',
-    LANGUAGE: 'None',
-    COLLAPSE_INDEX: false,
-    BUILDER: 'html',
-    FILE_SUFFIX: '.html',
-    LINK_SUFFIX: '.html',
-    HAS_SOURCE: true,
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-};
\ No newline at end of file
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- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2020-05-04T22:49Z
- */
-( function( global, factory ) {
-
-	"use strict";
-
-	if ( typeof module === "object" && typeof module.exports === "object" ) {
-
-		// For CommonJS and CommonJS-like environments where a proper `window`
-		// is present, execute the factory and get jQuery.
-		// For environments that do not have a `window` with a `document`
-		// (such as Node.js), expose a factory as module.exports.
-		// This accentuates the need for the creation of a real `window`.
-		// e.g. var jQuery = require("jquery")(window);
-		// See ticket #14549 for more info.
-		module.exports = global.document ?
-			factory( global, true ) :
-			function( w ) {
-				if ( !w.document ) {
-					throw new Error( "jQuery requires a window with a document" );
-				}
-				return factory( w );
-			};
-	} else {
-		factory( global );
-	}
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
-	return arr.flat.call( array );
-} : function( array ) {
-	return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
-      // Support: Chrome <=57, Firefox <=52
-      // In some browsers, typeof returns "function" for HTML <object> elements
-      // (i.e., `typeof document.createElement( "object" ) === "function"`).
-      // We don't want to classify *any* DOM node as a function.
-      return typeof obj === "function" && typeof obj.nodeType !== "number";
-  };
-
-
-var isWindow = function isWindow( obj ) {
-		return obj != null && obj === obj.window;
-	};
-
-
-var document = window.document;
-
-
-
-	var preservedScriptAttributes = {
-		type: true,
-		src: true,
-		nonce: true,
-		noModule: true
-	};
-
-	function DOMEval( code, node, doc ) {
-		doc = doc || document;
-
-		var i, val,
-			script = doc.createElement( "script" );
-
-		script.text = code;
-		if ( node ) {
-			for ( i in preservedScriptAttributes ) {
-
-				// Support: Firefox 64+, Edge 18+
-				// Some browsers don't support the "nonce" property on scripts.
-				// On the other hand, just using `getAttribute` is not enough as
-				// the `nonce` attribute is reset to an empty string whenever it
-				// becomes browsing-context connected.
-				// See https://github.com/whatwg/html/issues/2369
-				// See https://html.spec.whatwg.org/#nonce-attributes
-				// The `node.getAttribute` check was added for the sake of
-				// `jQuery.globalEval` so that it can fake a nonce-containing node
-				// via an object.
-				val = node[ i ] || node.getAttribute && node.getAttribute( i );
-				if ( val ) {
-					script.setAttribute( i, val );
-				}
-			}
-		}
-		doc.head.appendChild( script ).parentNode.removeChild( script );
-	}
-
-
-function toType( obj ) {
-	if ( obj == null ) {
-		return obj + "";
-	}
-
-	// Support: Android <=2.3 only (functionish RegExp)
-	return typeof obj === "object" || typeof obj === "function" ?
-		class2type[ toString.call( obj ) ] || "object" :
-		typeof obj;
-}
-/* global Symbol */
-// Defining this global in .eslintrc.json would create a danger of using the global
-// unguarded in another place, it seems safer to define global only for this module
-
-
-
-var
-	version = "3.5.1",
-
-	// Define a local copy of jQuery
-	jQuery = function( selector, context ) {
-
-		// The jQuery object is actually just the init constructor 'enhanced'
-		// Need init if jQuery is called (just allow error to be thrown if not included)
-		return new jQuery.fn.init( selector, context );
-	};
-
-jQuery.fn = jQuery.prototype = {
-
-	// The current version of jQuery being used
-	jquery: version,
-
-	constructor: jQuery,
-
-	// The default length of a jQuery object is 0
-	length: 0,
-
-	toArray: function() {
-		return slice.call( this );
-	},
-
-	// Get the Nth element in the matched element set OR
-	// Get the whole matched element set as a clean array
-	get: function( num ) {
-
-		// Return all the elements in a clean array
-		if ( num == null ) {
-			return slice.call( this );
-		}
-
-		// Return just the one element from the set
-		return num < 0 ? this[ num + this.length ] : this[ num ];
-	},
-
-	// Take an array of elements and push it onto the stack
-	// (returning the new matched element set)
-	pushStack: function( elems ) {
-
-		// Build a new jQuery matched element set
-		var ret = jQuery.merge( this.constructor(), elems );
-
-		// Add the old object onto the stack (as a reference)
-		ret.prevObject = this;
-
-		// Return the newly-formed element set
-		return ret;
-	},
-
-	// Execute a callback for every element in the matched set.
-	each: function( callback ) {
-		return jQuery.each( this, callback );
-	},
-
-	map: function( callback ) {
-		return this.pushStack( jQuery.map( this, function( elem, i ) {
-			return callback.call( elem, i, elem );
-		} ) );
-	},
-
-	slice: function() {
-		return this.pushStack( slice.apply( this, arguments ) );
-	},
-
-	first: function() {
-		return this.eq( 0 );
-	},
-
-	last: function() {
-		return this.eq( -1 );
-	},
-
-	even: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return ( i + 1 ) % 2;
-		} ) );
-	},
-
-	odd: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return i % 2;
-		} ) );
-	},
-
-	eq: function( i ) {
-		var len = this.length,
-			j = +i + ( i < 0 ? len : 0 );
-		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
-	},
-
-	end: function() {
-		return this.prevObject || this.constructor();
-	},
-
-	// For internal use only.
-	// Behaves like an Array's method, not like a jQuery method.
-	push: push,
-	sort: arr.sort,
-	splice: arr.splice
-};
-
-jQuery.extend = jQuery.fn.extend = function() {
-	var options, name, src, copy, copyIsArray, clone,
-		target = arguments[ 0 ] || {},
-		i = 1,
-		length = arguments.length,
-		deep = false;
-
-	// Handle a deep copy situation
-	if ( typeof target === "boolean" ) {
-		deep = target;
-
-		// Skip the boolean and the target
-		target = arguments[ i ] || {};
-		i++;
-	}
-
-	// Handle case when target is a string or something (possible in deep copy)
-	if ( typeof target !== "object" && !isFunction( target ) ) {
-		target = {};
-	}
-
-	// Extend jQuery itself if only one argument is passed
-	if ( i === length ) {
-		target = this;
-		i--;
-	}
-
-	for ( ; i < length; i++ ) {
-
-		// Only deal with non-null/undefined values
-		if ( ( options = arguments[ i ] ) != null ) {
-
-			// Extend the base object
-			for ( name in options ) {
-				copy = options[ name ];
-
-				// Prevent Object.prototype pollution
-				// Prevent never-ending loop
-				if ( name === "__proto__" || target === copy ) {
-					continue;
-				}
-
-				// Recurse if we're merging plain objects or arrays
-				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
-					( copyIsArray = Array.isArray( copy ) ) ) ) {
-					src = target[ name ];
-
-					// Ensure proper type for the source value
-					if ( copyIsArray && !Array.isArray( src ) ) {
-						clone = [];
-					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
-						clone = {};
-					} else {
-						clone = src;
-					}
-					copyIsArray = false;
-
-					// Never move original objects, clone them
-					target[ name ] = jQuery.extend( deep, clone, copy );
-
-				// Don't bring in undefined values
-				} else if ( copy !== undefined ) {
-					target[ name ] = copy;
-				}
-			}
-		}
-	}
-
-	// Return the modified object
-	return target;
-};
-
-jQuery.extend( {
-
-	// Unique for each copy of jQuery on the page
-	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
-
-	// Assume jQuery is ready without the ready module
-	isReady: true,
-
-	error: function( msg ) {
-		throw new Error( msg );
-	},
-
-	noop: function() {},
-
-	isPlainObject: function( obj ) {
-		var proto, Ctor;
-
-		// Detect obvious negatives
-		// Use toString instead of jQuery.type to catch host objects
-		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
-			return false;
-		}
-
-		proto = getProto( obj );
-
-		// Objects with no prototype (e.g., `Object.create( null )`) are plain
-		if ( !proto ) {
-			return true;
-		}
-
-		// Objects with prototype are plain iff they were constructed by a global Object function
-		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
-		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
-	},
-
-	isEmptyObject: function( obj ) {
-		var name;
-
-		for ( name in obj ) {
-			return false;
-		}
-		return true;
-	},
-
-	// Evaluates a script in a provided context; falls back to the global one
-	// if not specified.
-	globalEval: function( code, options, doc ) {
-		DOMEval( code, { nonce: options && options.nonce }, doc );
-	},
-
-	each: function( obj, callback ) {
-		var length, i = 0;
-
-		if ( isArrayLike( obj ) ) {
-			length = obj.length;
-			for ( ; i < length; i++ ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		} else {
-			for ( i in obj ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		}
-
-		return obj;
-	},
-
-	// results is for internal usage only
-	makeArray: function( arr, results ) {
-		var ret = results || [];
-
-		if ( arr != null ) {
-			if ( isArrayLike( Object( arr ) ) ) {
-				jQuery.merge( ret,
-					typeof arr === "string" ?
-					[ arr ] : arr
-				);
-			} else {
-				push.call( ret, arr );
-			}
-		}
-
-		return ret;
-	},
-
-	inArray: function( elem, arr, i ) {
-		return arr == null ? -1 : indexOf.call( arr, elem, i );
-	},
-
-	// Support: Android <=4.0 only, PhantomJS 1 only
-	// push.apply(_, arraylike) throws on ancient WebKit
-	merge: function( first, second ) {
-		var len = +second.length,
-			j = 0,
-			i = first.length;
-
-		for ( ; j < len; j++ ) {
-			first[ i++ ] = second[ j ];
-		}
-
-		first.length = i;
-
-		return first;
-	},
-
-	grep: function( elems, callback, invert ) {
-		var callbackInverse,
-			matches = [],
-			i = 0,
-			length = elems.length,
-			callbackExpect = !invert;
-
-		// Go through the array, only saving the items
-		// that pass the validator function
-		for ( ; i < length; i++ ) {
-			callbackInverse = !callback( elems[ i ], i );
-			if ( callbackInverse !== callbackExpect ) {
-				matches.push( elems[ i ] );
-			}
-		}
-
-		return matches;
-	},
-
-	// arg is for internal usage only
-	map: function( elems, callback, arg ) {
-		var length, value,
-			i = 0,
-			ret = [];
-
-		// Go through the array, translating each of the items to their new values
-		if ( isArrayLike( elems ) ) {
-			length = elems.length;
-			for ( ; i < length; i++ ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-
-		// Go through every key on the object,
-		} else {
-			for ( i in elems ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-		}
-
-		// Flatten any nested arrays
-		return flat( ret );
-	},
-
-	// A global GUID counter for objects
-	guid: 1,
-
-	// jQuery.support is not used in Core but other projects attach their
-	// properties to it so it needs to exist.
-	support: support
-} );
-
-if ( typeof Symbol === "function" ) {
-	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
-}
-
-// Populate the class2type map
-jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-function( _i, name ) {
-	class2type[ "[object " + name + "]" ] = name.toLowerCase();
-} );
-
-function isArrayLike( obj ) {
-
-	// Support: real iOS 8.2 only (not reproducible in simulator)
-	// `in` check used to prevent JIT error (gh-2145)
-	// hasOwn isn't used here due to false negatives
-	// regarding Nodelist length in IE
-	var length = !!obj && "length" in obj && obj.length,
-		type = toType( obj );
-
-	if ( isFunction( obj ) || isWindow( obj ) ) {
-		return false;
-	}
-
-	return type === "array" || length === 0 ||
-		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
-}
-var Sizzle =
-/*!
- * Sizzle CSS Selector Engine v2.3.5
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://js.foundation/
- *
- * Date: 2020-03-14
- */
-( function( window ) {
-var i,
-	support,
-	Expr,
-	getText,
-	isXML,
-	tokenize,
-	compile,
-	select,
-	outermostContext,
-	sortInput,
-	hasDuplicate,
-
-	// Local document vars
-	setDocument,
-	document,
-	docElem,
-	documentIsHTML,
-	rbuggyQSA,
-	rbuggyMatches,
-	matches,
-	contains,
-
-	// Instance-specific data
-	expando = "sizzle" + 1 * new Date(),
-	preferredDoc = window.document,
-	dirruns = 0,
-	done = 0,
-	classCache = createCache(),
-	tokenCache = createCache(),
-	compilerCache = createCache(),
-	nonnativeSelectorCache = createCache(),
-	sortOrder = function( a, b ) {
-		if ( a === b ) {
-			hasDuplicate = true;
-		}
-		return 0;
-	},
-
-	// Instance methods
-	hasOwn = ( {} ).hasOwnProperty,
-	arr = [],
-	pop = arr.pop,
-	pushNative = arr.push,
-	push = arr.push,
-	slice = arr.slice,
-
-	// Use a stripped-down indexOf as it's faster than native
-	// https://jsperf.com/thor-indexof-vs-for/5
-	indexOf = function( list, elem ) {
-		var i = 0,
-			len = list.length;
-		for ( ; i < len; i++ ) {
-			if ( list[ i ] === elem ) {
-				return i;
-			}
-		}
-		return -1;
-	},
-
-	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
-		"ismap|loop|multiple|open|readonly|required|scoped",
-
-	// Regular expressions
-
-	// http://www.w3.org/TR/css3-selectors/#whitespace
-	whitespace = "[\\x20\\t\\r\\n\\f]",
-
-	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
-	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
-		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
-
-	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
-	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
-
-		// Operator (capture 2)
-		"*([*^$|!~]?=)" + whitespace +
-
-		// "Attribute values must be CSS identifiers [capture 5]
-		// or strings [capture 3 or capture 4]"
-		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
-		whitespace + "*\\]",
-
-	pseudos = ":(" + identifier + ")(?:\\((" +
-
-		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
-		// 1. quoted (capture 3; capture 4 or capture 5)
-		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
-
-		// 2. simple (capture 6)
-		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
-
-		// 3. anything else (capture 2)
-		".*" +
-		")\\)|)",
-
-	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
-	rwhitespace = new RegExp( whitespace + "+", "g" ),
-	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
-		whitespace + "+$", "g" ),
-
-	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
-	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
-		"*" ),
-	rdescend = new RegExp( whitespace + "|>" ),
-
-	rpseudo = new RegExp( pseudos ),
-	ridentifier = new RegExp( "^" + identifier + "$" ),
-
-	matchExpr = {
-		"ID": new RegExp( "^#(" + identifier + ")" ),
-		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
-		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
-		"ATTR": new RegExp( "^" + attributes ),
-		"PSEUDO": new RegExp( "^" + pseudos ),
-		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
-			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
-			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
-		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
-
-		// For use in libraries implementing .is()
-		// We use this for POS matching in `select`
-		"needsContext": new RegExp( "^" + whitespace +
-			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
-			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
-	},
-
-	rhtml = /HTML$/i,
-	rinputs = /^(?:input|select|textarea|button)$/i,
-	rheader = /^h\d$/i,
-
-	rnative = /^[^{]+\{\s*\[native \w/,
-
-	// Easily-parseable/retrievable ID or TAG or CLASS selectors
-	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
-
-	rsibling = /[+~]/,
-
-	// CSS escapes
-	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
-	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
-	funescape = function( escape, nonHex ) {
-		var high = "0x" + escape.slice( 1 ) - 0x10000;
-
-		return nonHex ?
-
-			// Strip the backslash prefix from a non-hex escape sequence
-			nonHex :
-
-			// Replace a hexadecimal escape sequence with the encoded Unicode code point
-			// Support: IE <=11+
-			// For values outside the Basic Multilingual Plane (BMP), manually construct a
-			// surrogate pair
-			high < 0 ?
-				String.fromCharCode( high + 0x10000 ) :
-				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
-	},
-
-	// CSS string/identifier serialization
-	// https://drafts.csswg.org/cssom/#common-serializing-idioms
-	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
-	fcssescape = function( ch, asCodePoint ) {
-		if ( asCodePoint ) {
-
-			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
-			if ( ch === "\0" ) {
-				return "\uFFFD";
-			}
-
-			// Control characters and (dependent upon position) numbers get escaped as code points
-			return ch.slice( 0, -1 ) + "\\" +
-				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
-		}
-
-		// Other potentially-special ASCII characters get backslash-escaped
-		return "\\" + ch;
-	},
-
-	// Used for iframes
-	// See setDocument()
-	// Removing the function wrapper causes a "Permission Denied"
-	// error in IE
-	unloadHandler = function() {
-		setDocument();
-	},
-
-	inDisabledFieldset = addCombinator(
-		function( elem ) {
-			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
-		},
-		{ dir: "parentNode", next: "legend" }
-	);
-
-// Optimize for push.apply( _, NodeList )
-try {
-	push.apply(
-		( arr = slice.call( preferredDoc.childNodes ) ),
-		preferredDoc.childNodes
-	);
-
-	// Support: Android<4.0
-	// Detect silently failing push.apply
-	// eslint-disable-next-line no-unused-expressions
-	arr[ preferredDoc.childNodes.length ].nodeType;
-} catch ( e ) {
-	push = { apply: arr.length ?
-
-		// Leverage slice if possible
-		function( target, els ) {
-			pushNative.apply( target, slice.call( els ) );
-		} :
-
-		// Support: IE<9
-		// Otherwise append directly
-		function( target, els ) {
-			var j = target.length,
-				i = 0;
-
-			// Can't trust NodeList.length
-			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
-			target.length = j - 1;
-		}
-	};
-}
-
-function Sizzle( selector, context, results, seed ) {
-	var m, i, elem, nid, match, groups, newSelector,
-		newContext = context && context.ownerDocument,
-
-		// nodeType defaults to 9, since context defaults to document
-		nodeType = context ? context.nodeType : 9;
-
-	results = results || [];
-
-	// Return early from calls with invalid selector or context
-	if ( typeof selector !== "string" || !selector ||
-		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
-
-		return results;
-	}
-
-	// Try to shortcut find operations (as opposed to filters) in HTML documents
-	if ( !seed ) {
-		setDocument( context );
-		context = context || document;
-
-		if ( documentIsHTML ) {
-
-			// If the selector is sufficiently simple, try using a "get*By*" DOM method
-			// (excepting DocumentFragment context, where the methods don't exist)
-			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
-
-				// ID selector
-				if ( ( m = match[ 1 ] ) ) {
-
-					// Document context
-					if ( nodeType === 9 ) {
-						if ( ( elem = context.getElementById( m ) ) ) {
-
-							// Support: IE, Opera, Webkit
-							// TODO: identify versions
-							// getElementById can match elements by name instead of ID
-							if ( elem.id === m ) {
-								results.push( elem );
-								return results;
-							}
-						} else {
-							return results;
-						}
-
-					// Element context
-					} else {
-
-						// Support: IE, Opera, Webkit
-						// TODO: identify versions
-						// getElementById can match elements by name instead of ID
-						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
-							contains( context, elem ) &&
-							elem.id === m ) {
-
-							results.push( elem );
-							return results;
-						}
-					}
-
-				// Type selector
-				} else if ( match[ 2 ] ) {
-					push.apply( results, context.getElementsByTagName( selector ) );
-					return results;
-
-				// Class selector
-				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
-					context.getElementsByClassName ) {
-
-					push.apply( results, context.getElementsByClassName( m ) );
-					return results;
-				}
-			}
-
-			// Take advantage of querySelectorAll
-			if ( support.qsa &&
-				!nonnativeSelectorCache[ selector + " " ] &&
-				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
-
-				// Support: IE 8 only
-				// Exclude object elements
-				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
-
-				newSelector = selector;
-				newContext = context;
-
-				// qSA considers elements outside a scoping root when evaluating child or
-				// descendant combinators, which is not what we want.
-				// In such cases, we work around the behavior by prefixing every selector in the
-				// list with an ID selector referencing the scope context.
-				// The technique has to be used as well when a leading combinator is used
-				// as such selectors are not recognized by querySelectorAll.
-				// Thanks to Andrew Dupont for this technique.
-				if ( nodeType === 1 &&
-					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
-
-					// Expand context for sibling selectors
-					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
-						context;
-
-					// We can use :scope instead of the ID hack if the browser
-					// supports it & if we're not changing the context.
-					if ( newContext !== context || !support.scope ) {
-
-						// Capture the context ID, setting it first if necessary
-						if ( ( nid = context.getAttribute( "id" ) ) ) {
-							nid = nid.replace( rcssescape, fcssescape );
-						} else {
-							context.setAttribute( "id", ( nid = expando ) );
-						}
-					}
-
-					// Prefix every selector in the list
-					groups = tokenize( selector );
-					i = groups.length;
-					while ( i-- ) {
-						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
-							toSelector( groups[ i ] );
-					}
-					newSelector = groups.join( "," );
-				}
-
-				try {
-					push.apply( results,
-						newContext.querySelectorAll( newSelector )
-					);
-					return results;
-				} catch ( qsaError ) {
-					nonnativeSelectorCache( selector, true );
-				} finally {
-					if ( nid === expando ) {
-						context.removeAttribute( "id" );
-					}
-				}
-			}
-		}
-	}
-
-	// All others
-	return select( selector.replace( rtrim, "$1" ), context, results, seed );
-}
-
-/**
- * Create key-value caches of limited size
- * @returns {function(string, object)} Returns the Object data after storing it on itself with
- *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
- *	deleting the oldest entry
- */
-function createCache() {
-	var keys = [];
-
-	function cache( key, value ) {
-
-		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
-		if ( keys.push( key + " " ) > Expr.cacheLength ) {
-
-			// Only keep the most recent entries
-			delete cache[ keys.shift() ];
-		}
-		return ( cache[ key + " " ] = value );
-	}
-	return cache;
-}
-
-/**
- * Mark a function for special use by Sizzle
- * @param {Function} fn The function to mark
- */
-function markFunction( fn ) {
-	fn[ expando ] = true;
-	return fn;
-}
-
-/**
- * Support testing using an element
- * @param {Function} fn Passed the created element and returns a boolean result
- */
-function assert( fn ) {
-	var el = document.createElement( "fieldset" );
-
-	try {
-		return !!fn( el );
-	} catch ( e ) {
-		return false;
-	} finally {
-
-		// Remove from its parent by default
-		if ( el.parentNode ) {
-			el.parentNode.removeChild( el );
-		}
-
-		// release memory in IE
-		el = null;
-	}
-}
-
-/**
- * Adds the same handler for all of the specified attrs
- * @param {String} attrs Pipe-separated list of attributes
- * @param {Function} handler The method that will be applied
- */
-function addHandle( attrs, handler ) {
-	var arr = attrs.split( "|" ),
-		i = arr.length;
-
-	while ( i-- ) {
-		Expr.attrHandle[ arr[ i ] ] = handler;
-	}
-}
-
-/**
- * Checks document order of two siblings
- * @param {Element} a
- * @param {Element} b
- * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
- */
-function siblingCheck( a, b ) {
-	var cur = b && a,
-		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
-			a.sourceIndex - b.sourceIndex;
-
-	// Use IE sourceIndex if available on both nodes
-	if ( diff ) {
-		return diff;
-	}
-
-	// Check if b follows a
-	if ( cur ) {
-		while ( ( cur = cur.nextSibling ) ) {
-			if ( cur === b ) {
-				return -1;
-			}
-		}
-	}
-
-	return a ? 1 : -1;
-}
-
-/**
- * Returns a function to use in pseudos for input types
- * @param {String} type
- */
-function createInputPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return name === "input" && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for buttons
- * @param {String} type
- */
-function createButtonPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return ( name === "input" || name === "button" ) && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for :enabled/:disabled
- * @param {Boolean} disabled true for :disabled; false for :enabled
- */
-function createDisabledPseudo( disabled ) {
-
-	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
-	return function( elem ) {
-
-		// Only certain elements can match :enabled or :disabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
-		if ( "form" in elem ) {
-
-			// Check for inherited disabledness on relevant non-disabled elements:
-			// * listed form-associated elements in a disabled fieldset
-			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
-			// * option elements in a disabled optgroup
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
-			// All such elements have a "form" property.
-			if ( elem.parentNode && elem.disabled === false ) {
-
-				// Option elements defer to a parent optgroup if present
-				if ( "label" in elem ) {
-					if ( "label" in elem.parentNode ) {
-						return elem.parentNode.disabled === disabled;
-					} else {
-						return elem.disabled === disabled;
-					}
-				}
-
-				// Support: IE 6 - 11
-				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
-				return elem.isDisabled === disabled ||
-
-					// Where there is no isDisabled, check manually
-					/* jshint -W018 */
-					elem.isDisabled !== !disabled &&
-					inDisabledFieldset( elem ) === disabled;
-			}
-
-			return elem.disabled === disabled;
-
-		// Try to winnow out elements that can't be disabled before trusting the disabled property.
-		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
-		// even exist on them, let alone have a boolean value.
-		} else if ( "label" in elem ) {
-			return elem.disabled === disabled;
-		}
-
-		// Remaining elements are neither :enabled nor :disabled
-		return false;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for positionals
- * @param {Function} fn
- */
-function createPositionalPseudo( fn ) {
-	return markFunction( function( argument ) {
-		argument = +argument;
-		return markFunction( function( seed, matches ) {
-			var j,
-				matchIndexes = fn( [], seed.length, argument ),
-				i = matchIndexes.length;
-
-			// Match elements found at the specified indexes
-			while ( i-- ) {
-				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
-					seed[ j ] = !( matches[ j ] = seed[ j ] );
-				}
-			}
-		} );
-	} );
-}
-
-/**
- * Checks a node for validity as a Sizzle context
- * @param {Element|Object=} context
- * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
- */
-function testContext( context ) {
-	return context && typeof context.getElementsByTagName !== "undefined" && context;
-}
-
-// Expose support vars for convenience
-support = Sizzle.support = {};
-
-/**
- * Detects XML nodes
- * @param {Element|Object} elem An element or a document
- * @returns {Boolean} True iff elem is a non-HTML XML node
- */
-isXML = Sizzle.isXML = function( elem ) {
-	var namespace = elem.namespaceURI,
-		docElem = ( elem.ownerDocument || elem ).documentElement;
-
-	// Support: IE <=8
-	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
-	// https://bugs.jquery.com/ticket/4833
-	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
-};
-
-/**
- * Sets document-related variables once based on the current document
- * @param {Element|Object} [doc] An element or document object to use to set the document
- * @returns {Object} Returns the current document
- */
-setDocument = Sizzle.setDocument = function( node ) {
-	var hasCompare, subWindow,
-		doc = node ? node.ownerDocument || node : preferredDoc;
-
-	// Return early if doc is invalid or already selected
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
-		return document;
-	}
-
-	// Update global variables
-	document = doc;
-	docElem = document.documentElement;
-	documentIsHTML = !isXML( document );
-
-	// Support: IE 9 - 11+, Edge 12 - 18+
-	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( preferredDoc != document &&
-		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
-
-		// Support: IE 11, Edge
-		if ( subWindow.addEventListener ) {
-			subWindow.addEventListener( "unload", unloadHandler, false );
-
-		// Support: IE 9 - 10 only
-		} else if ( subWindow.attachEvent ) {
-			subWindow.attachEvent( "onunload", unloadHandler );
-		}
-	}
-
-	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
-	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
-	// IE/Edge & older browsers don't support the :scope pseudo-class.
-	// Support: Safari 6.0 only
-	// Safari 6.0 supports :scope but it's an alias of :root there.
-	support.scope = assert( function( el ) {
-		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
-		return typeof el.querySelectorAll !== "undefined" &&
-			!el.querySelectorAll( ":scope fieldset div" ).length;
-	} );
-
-	/* Attributes
-	---------------------------------------------------------------------- */
-
-	// Support: IE<8
-	// Verify that getAttribute really returns attributes and not properties
-	// (excepting IE8 booleans)
-	support.attributes = assert( function( el ) {
-		el.className = "i";
-		return !el.getAttribute( "className" );
-	} );
-
-	/* getElement(s)By*
-	---------------------------------------------------------------------- */
-
-	// Check if getElementsByTagName("*") returns only elements
-	support.getElementsByTagName = assert( function( el ) {
-		el.appendChild( document.createComment( "" ) );
-		return !el.getElementsByTagName( "*" ).length;
-	} );
-
-	// Support: IE<9
-	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
-
-	// Support: IE<10
-	// Check if getElementById returns elements by name
-	// The broken getElementById methods don't pick up programmatically-set names,
-	// so use a roundabout getElementsByName test
-	support.getById = assert( function( el ) {
-		docElem.appendChild( el ).id = expando;
-		return !document.getElementsByName || !document.getElementsByName( expando ).length;
-	} );
-
-	// ID filter and find
-	if ( support.getById ) {
-		Expr.filter[ "ID" ] = function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				return elem.getAttribute( "id" ) === attrId;
-			};
-		};
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var elem = context.getElementById( id );
-				return elem ? [ elem ] : [];
-			}
-		};
-	} else {
-		Expr.filter[ "ID" ] =  function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				var node = typeof elem.getAttributeNode !== "undefined" &&
-					elem.getAttributeNode( "id" );
-				return node && node.value === attrId;
-			};
-		};
-
-		// Support: IE 6 - 7 only
-		// getElementById is not reliable as a find shortcut
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var node, i, elems,
-					elem = context.getElementById( id );
-
-				if ( elem ) {
-
-					// Verify the id attribute
-					node = elem.getAttributeNode( "id" );
-					if ( node && node.value === id ) {
-						return [ elem ];
-					}
-
-					// Fall back on getElementsByName
-					elems = context.getElementsByName( id );
-					i = 0;
-					while ( ( elem = elems[ i++ ] ) ) {
-						node = elem.getAttributeNode( "id" );
-						if ( node && node.value === id ) {
-							return [ elem ];
-						}
-					}
-				}
-
-				return [];
-			}
-		};
-	}
-
-	// Tag
-	Expr.find[ "TAG" ] = support.getElementsByTagName ?
-		function( tag, context ) {
-			if ( typeof context.getElementsByTagName !== "undefined" ) {
-				return context.getElementsByTagName( tag );
-
-			// DocumentFragment nodes don't have gEBTN
-			} else if ( support.qsa ) {
-				return context.querySelectorAll( tag );
-			}
-		} :
-
-		function( tag, context ) {
-			var elem,
-				tmp = [],
-				i = 0,
-
-				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
-				results = context.getElementsByTagName( tag );
-
-			// Filter out possible comments
-			if ( tag === "*" ) {
-				while ( ( elem = results[ i++ ] ) ) {
-					if ( elem.nodeType === 1 ) {
-						tmp.push( elem );
-					}
-				}
-
-				return tmp;
-			}
-			return results;
-		};
-
-	// Class
-	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
-		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
-			return context.getElementsByClassName( className );
-		}
-	};
-
-	/* QSA/matchesSelector
-	---------------------------------------------------------------------- */
-
-	// QSA and matchesSelector support
-
-	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
-	rbuggyMatches = [];
-
-	// qSa(:focus) reports false when true (Chrome 21)
-	// We allow this because of a bug in IE8/9 that throws an error
-	// whenever `document.activeElement` is accessed on an iframe
-	// So, we allow :focus to pass through QSA all the time to avoid the IE error
-	// See https://bugs.jquery.com/ticket/13378
-	rbuggyQSA = [];
-
-	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
-
-		// Build QSA regex
-		// Regex strategy adopted from Diego Perini
-		assert( function( el ) {
-
-			var input;
-
-			// Select is set to empty string on purpose
-			// This is to test IE's treatment of not explicitly
-			// setting a boolean content attribute,
-			// since its presence should be enough
-			// https://bugs.jquery.com/ticket/12359
-			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
-				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
-				"<option selected=''></option></select>";
-
-			// Support: IE8, Opera 11-12.16
-			// Nothing should be selected when empty strings follow ^= or $= or *=
-			// The test attribute must be unknown in Opera but "safe" for WinRT
-			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
-			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
-				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
-			}
-
-			// Support: IE8
-			// Boolean attributes and "value" are not treated correctly
-			if ( !el.querySelectorAll( "[selected]" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
-			}
-
-			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
-			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
-				rbuggyQSA.push( "~=" );
-			}
-
-			// Support: IE 11+, Edge 15 - 18+
-			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
-			// Adding a temporary attribute to the document before the selection works
-			// around the issue.
-			// Interestingly, IE 10 & older don't seem to have the issue.
-			input = document.createElement( "input" );
-			input.setAttribute( "name", "" );
-			el.appendChild( input );
-			if ( !el.querySelectorAll( "[name='']" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
-					whitespace + "*(?:''|\"\")" );
-			}
-
-			// Webkit/Opera - :checked should return selected option elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			// IE8 throws error here and will not see later tests
-			if ( !el.querySelectorAll( ":checked" ).length ) {
-				rbuggyQSA.push( ":checked" );
-			}
-
-			// Support: Safari 8+, iOS 8+
-			// https://bugs.webkit.org/show_bug.cgi?id=136851
-			// In-page `selector#id sibling-combinator selector` fails
-			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
-				rbuggyQSA.push( ".#.+[+~]" );
-			}
-
-			// Support: Firefox <=3.6 - 5 only
-			// Old Firefox doesn't throw on a badly-escaped identifier.
-			el.querySelectorAll( "\\\f" );
-			rbuggyQSA.push( "[\\r\\n\\f]" );
-		} );
-
-		assert( function( el ) {
-			el.innerHTML = "<a href='' disabled='disabled'></a>" +
-				"<select disabled='disabled'><option/></select>";
-
-			// Support: Windows 8 Native Apps
-			// The type and name attributes are restricted during .innerHTML assignment
-			var input = document.createElement( "input" );
-			input.setAttribute( "type", "hidden" );
-			el.appendChild( input ).setAttribute( "name", "D" );
-
-			// Support: IE8
-			// Enforce case-sensitivity of name attribute
-			if ( el.querySelectorAll( "[name=d]" ).length ) {
-				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
-			}
-
-			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
-			// IE8 throws error here and will not see later tests
-			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: IE9-11+
-			// IE's :disabled selector does not pick up the children of disabled fieldsets
-			docElem.appendChild( el ).disabled = true;
-			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: Opera 10 - 11 only
-			// Opera 10-11 does not throw on post-comma invalid pseudos
-			el.querySelectorAll( "*,:x" );
-			rbuggyQSA.push( ",.*:" );
-		} );
-	}
-
-	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
-		docElem.webkitMatchesSelector ||
-		docElem.mozMatchesSelector ||
-		docElem.oMatchesSelector ||
-		docElem.msMatchesSelector ) ) ) ) {
-
-		assert( function( el ) {
-
-			// Check to see if it's possible to do matchesSelector
-			// on a disconnected node (IE 9)
-			support.disconnectedMatch = matches.call( el, "*" );
-
-			// This should fail with an exception
-			// Gecko does not error, returns false instead
-			matches.call( el, "[s!='']:x" );
-			rbuggyMatches.push( "!=", pseudos );
-		} );
-	}
-
-	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
-	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
-
-	/* Contains
-	---------------------------------------------------------------------- */
-	hasCompare = rnative.test( docElem.compareDocumentPosition );
-
-	// Element contains another
-	// Purposefully self-exclusive
-	// As in, an element does not contain itself
-	contains = hasCompare || rnative.test( docElem.contains ) ?
-		function( a, b ) {
-			var adown = a.nodeType === 9 ? a.documentElement : a,
-				bup = b && b.parentNode;
-			return a === bup || !!( bup && bup.nodeType === 1 && (
-				adown.contains ?
-					adown.contains( bup ) :
-					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
-			) );
-		} :
-		function( a, b ) {
-			if ( b ) {
-				while ( ( b = b.parentNode ) ) {
-					if ( b === a ) {
-						return true;
-					}
-				}
-			}
-			return false;
-		};
-
-	/* Sorting
-	---------------------------------------------------------------------- */
-
-	// Document order sorting
-	sortOrder = hasCompare ?
-	function( a, b ) {
-
-		// Flag for duplicate removal
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		// Sort on method existence if only one input has compareDocumentPosition
-		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
-		if ( compare ) {
-			return compare;
-		}
-
-		// Calculate position if both inputs belong to the same document
-		// Support: IE 11+, Edge 17 - 18+
-		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-		// two documents; shallow comparisons work.
-		// eslint-disable-next-line eqeqeq
-		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
-			a.compareDocumentPosition( b ) :
-
-			// Otherwise we know they are disconnected
-			1;
-
-		// Disconnected nodes
-		if ( compare & 1 ||
-			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
-
-			// Choose the first element that is related to our preferred document
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( a == document || a.ownerDocument == preferredDoc &&
-				contains( preferredDoc, a ) ) {
-				return -1;
-			}
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( b == document || b.ownerDocument == preferredDoc &&
-				contains( preferredDoc, b ) ) {
-				return 1;
-			}
-
-			// Maintain original order
-			return sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-		}
-
-		return compare & 4 ? -1 : 1;
-	} :
-	function( a, b ) {
-
-		// Exit early if the nodes are identical
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		var cur,
-			i = 0,
-			aup = a.parentNode,
-			bup = b.parentNode,
-			ap = [ a ],
-			bp = [ b ];
-
-		// Parentless nodes are either documents or disconnected
-		if ( !aup || !bup ) {
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			return a == document ? -1 :
-				b == document ? 1 :
-				/* eslint-enable eqeqeq */
-				aup ? -1 :
-				bup ? 1 :
-				sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-
-		// If the nodes are siblings, we can do a quick check
-		} else if ( aup === bup ) {
-			return siblingCheck( a, b );
-		}
-
-		// Otherwise we need full lists of their ancestors for comparison
-		cur = a;
-		while ( ( cur = cur.parentNode ) ) {
-			ap.unshift( cur );
-		}
-		cur = b;
-		while ( ( cur = cur.parentNode ) ) {
-			bp.unshift( cur );
-		}
-
-		// Walk down the tree looking for a discrepancy
-		while ( ap[ i ] === bp[ i ] ) {
-			i++;
-		}
-
-		return i ?
-
-			// Do a sibling check if the nodes have a common ancestor
-			siblingCheck( ap[ i ], bp[ i ] ) :
-
-			// Otherwise nodes in our document sort first
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			ap[ i ] == preferredDoc ? -1 :
-			bp[ i ] == preferredDoc ? 1 :
-			/* eslint-enable eqeqeq */
-			0;
-	};
-
-	return document;
-};
-
-Sizzle.matches = function( expr, elements ) {
-	return Sizzle( expr, null, null, elements );
-};
-
-Sizzle.matchesSelector = function( elem, expr ) {
-	setDocument( elem );
-
-	if ( support.matchesSelector && documentIsHTML &&
-		!nonnativeSelectorCache[ expr + " " ] &&
-		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
-		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
-
-		try {
-			var ret = matches.call( elem, expr );
-
-			// IE 9's matchesSelector returns false on disconnected nodes
-			if ( ret || support.disconnectedMatch ||
-
-				// As well, disconnected nodes are said to be in a document
-				// fragment in IE 9
-				elem.document && elem.document.nodeType !== 11 ) {
-				return ret;
-			}
-		} catch ( e ) {
-			nonnativeSelectorCache( expr, true );
-		}
-	}
-
-	return Sizzle( expr, document, null, [ elem ] ).length > 0;
-};
-
-Sizzle.contains = function( context, elem ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( context.ownerDocument || context ) != document ) {
-		setDocument( context );
-	}
-	return contains( context, elem );
-};
-
-Sizzle.attr = function( elem, name ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( elem.ownerDocument || elem ) != document ) {
-		setDocument( elem );
-	}
-
-	var fn = Expr.attrHandle[ name.toLowerCase() ],
-
-		// Don't get fooled by Object.prototype properties (jQuery #13807)
-		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
-			fn( elem, name, !documentIsHTML ) :
-			undefined;
-
-	return val !== undefined ?
-		val :
-		support.attributes || !documentIsHTML ?
-			elem.getAttribute( name ) :
-			( val = elem.getAttributeNode( name ) ) && val.specified ?
-				val.value :
-				null;
-};
-
-Sizzle.escape = function( sel ) {
-	return ( sel + "" ).replace( rcssescape, fcssescape );
-};
-
-Sizzle.error = function( msg ) {
-	throw new Error( "Syntax error, unrecognized expression: " + msg );
-};
-
-/**
- * Document sorting and removing duplicates
- * @param {ArrayLike} results
- */
-Sizzle.uniqueSort = function( results ) {
-	var elem,
-		duplicates = [],
-		j = 0,
-		i = 0;
-
-	// Unless we *know* we can detect duplicates, assume their presence
-	hasDuplicate = !support.detectDuplicates;
-	sortInput = !support.sortStable && results.slice( 0 );
-	results.sort( sortOrder );
-
-	if ( hasDuplicate ) {
-		while ( ( elem = results[ i++ ] ) ) {
-			if ( elem === results[ i ] ) {
-				j = duplicates.push( i );
-			}
-		}
-		while ( j-- ) {
-			results.splice( duplicates[ j ], 1 );
-		}
-	}
-
-	// Clear input after sorting to release objects
-	// See https://github.com/jquery/sizzle/pull/225
-	sortInput = null;
-
-	return results;
-};
-
-/**
- * Utility function for retrieving the text value of an array of DOM nodes
- * @param {Array|Element} elem
- */
-getText = Sizzle.getText = function( elem ) {
-	var node,
-		ret = "",
-		i = 0,
-		nodeType = elem.nodeType;
-
-	if ( !nodeType ) {
-
-		// If no nodeType, this is expected to be an array
-		while ( ( node = elem[ i++ ] ) ) {
-
-			// Do not traverse comment nodes
-			ret += getText( node );
-		}
-	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
-
-		// Use textContent for elements
-		// innerText usage removed for consistency of new lines (jQuery #11153)
-		if ( typeof elem.textContent === "string" ) {
-			return elem.textContent;
-		} else {
-
-			// Traverse its children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				ret += getText( elem );
-			}
-		}
-	} else if ( nodeType === 3 || nodeType === 4 ) {
-		return elem.nodeValue;
-	}
-
-	// Do not include comment or processing instruction nodes
-
-	return ret;
-};
-
-Expr = Sizzle.selectors = {
-
-	// Can be adjusted by the user
-	cacheLength: 50,
-
-	createPseudo: markFunction,
-
-	match: matchExpr,
-
-	attrHandle: {},
-
-	find: {},
-
-	relative: {
-		">": { dir: "parentNode", first: true },
-		" ": { dir: "parentNode" },
-		"+": { dir: "previousSibling", first: true },
-		"~": { dir: "previousSibling" }
-	},
-
-	preFilter: {
-		"ATTR": function( match ) {
-			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
-
-			// Move the given value to match[3] whether quoted or unquoted
-			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
-				match[ 5 ] || "" ).replace( runescape, funescape );
-
-			if ( match[ 2 ] === "~=" ) {
-				match[ 3 ] = " " + match[ 3 ] + " ";
-			}
-
-			return match.slice( 0, 4 );
-		},
-
-		"CHILD": function( match ) {
-
-			/* matches from matchExpr["CHILD"]
-				1 type (only|nth|...)
-				2 what (child|of-type)
-				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
-				4 xn-component of xn+y argument ([+-]?\d*n|)
-				5 sign of xn-component
-				6 x of xn-component
-				7 sign of y-component
-				8 y of y-component
-			*/
-			match[ 1 ] = match[ 1 ].toLowerCase();
-
-			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
-
-				// nth-* requires argument
-				if ( !match[ 3 ] ) {
-					Sizzle.error( match[ 0 ] );
-				}
-
-				// numeric x and y parameters for Expr.filter.CHILD
-				// remember that false/true cast respectively to 0/1
-				match[ 4 ] = +( match[ 4 ] ?
-					match[ 5 ] + ( match[ 6 ] || 1 ) :
-					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
-				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
-
-				// other types prohibit arguments
-			} else if ( match[ 3 ] ) {
-				Sizzle.error( match[ 0 ] );
-			}
-
-			return match;
-		},
-
-		"PSEUDO": function( match ) {
-			var excess,
-				unquoted = !match[ 6 ] && match[ 2 ];
-
-			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
-				return null;
-			}
-
-			// Accept quoted arguments as-is
-			if ( match[ 3 ] ) {
-				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
-
-			// Strip excess characters from unquoted arguments
-			} else if ( unquoted && rpseudo.test( unquoted ) &&
-
-				// Get excess from tokenize (recursively)
-				( excess = tokenize( unquoted, true ) ) &&
-
-				// advance to the next closing parenthesis
-				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
-
-				// excess is a negative index
-				match[ 0 ] = match[ 0 ].slice( 0, excess );
-				match[ 2 ] = unquoted.slice( 0, excess );
-			}
-
-			// Return only captures needed by the pseudo filter method (type and argument)
-			return match.slice( 0, 3 );
-		}
-	},
-
-	filter: {
-
-		"TAG": function( nodeNameSelector ) {
-			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
-			return nodeNameSelector === "*" ?
-				function() {
-					return true;
-				} :
-				function( elem ) {
-					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
-				};
-		},
-
-		"CLASS": function( className ) {
-			var pattern = classCache[ className + " " ];
-
-			return pattern ||
-				( pattern = new RegExp( "(^|" + whitespace +
-					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
-						className, function( elem ) {
-							return pattern.test(
-								typeof elem.className === "string" && elem.className ||
-								typeof elem.getAttribute !== "undefined" &&
-									elem.getAttribute( "class" ) ||
-								""
-							);
-				} );
-		},
-
-		"ATTR": function( name, operator, check ) {
-			return function( elem ) {
-				var result = Sizzle.attr( elem, name );
-
-				if ( result == null ) {
-					return operator === "!=";
-				}
-				if ( !operator ) {
-					return true;
-				}
-
-				result += "";
-
-				/* eslint-disable max-len */
-
-				return operator === "=" ? result === check :
-					operator === "!=" ? result !== check :
-					operator === "^=" ? check && result.indexOf( check ) === 0 :
-					operator === "*=" ? check && result.indexOf( check ) > -1 :
-					operator === "$=" ? check && result.slice( -check.length ) === check :
-					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
-					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
-					false;
-				/* eslint-enable max-len */
-
-			};
-		},
-
-		"CHILD": function( type, what, _argument, first, last ) {
-			var simple = type.slice( 0, 3 ) !== "nth",
-				forward = type.slice( -4 ) !== "last",
-				ofType = what === "of-type";
-
-			return first === 1 && last === 0 ?
-
-				// Shortcut for :nth-*(n)
-				function( elem ) {
-					return !!elem.parentNode;
-				} :
-
-				function( elem, _context, xml ) {
-					var cache, uniqueCache, outerCache, node, nodeIndex, start,
-						dir = simple !== forward ? "nextSibling" : "previousSibling",
-						parent = elem.parentNode,
-						name = ofType && elem.nodeName.toLowerCase(),
-						useCache = !xml && !ofType,
-						diff = false;
-
-					if ( parent ) {
-
-						// :(first|last|only)-(child|of-type)
-						if ( simple ) {
-							while ( dir ) {
-								node = elem;
-								while ( ( node = node[ dir ] ) ) {
-									if ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) {
-
-										return false;
-									}
-								}
-
-								// Reverse direction for :only-* (if we haven't yet done so)
-								start = dir = type === "only" && !start && "nextSibling";
-							}
-							return true;
-						}
-
-						start = [ forward ? parent.firstChild : parent.lastChild ];
-
-						// non-xml :nth-child(...) stores cache data on `parent`
-						if ( forward && useCache ) {
-
-							// Seek `elem` from a previously-cached index
-
-							// ...in a gzip-friendly way
-							node = parent;
-							outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-							// Support: IE <9 only
-							// Defend against cloned attroperties (jQuery gh-1709)
-							uniqueCache = outerCache[ node.uniqueID ] ||
-								( outerCache[ node.uniqueID ] = {} );
-
-							cache = uniqueCache[ type ] || [];
-							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-							diff = nodeIndex && cache[ 2 ];
-							node = nodeIndex && parent.childNodes[ nodeIndex ];
-
-							while ( ( node = ++nodeIndex && node && node[ dir ] ||
-
-								// Fallback to seeking `elem` from the start
-								( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-								// When found, cache indexes on `parent` and break
-								if ( node.nodeType === 1 && ++diff && node === elem ) {
-									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
-									break;
-								}
-							}
-
-						} else {
-
-							// Use previously-cached element index if available
-							if ( useCache ) {
-
-								// ...in a gzip-friendly way
-								node = elem;
-								outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-								// Support: IE <9 only
-								// Defend against cloned attroperties (jQuery gh-1709)
-								uniqueCache = outerCache[ node.uniqueID ] ||
-									( outerCache[ node.uniqueID ] = {} );
-
-								cache = uniqueCache[ type ] || [];
-								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-								diff = nodeIndex;
-							}
-
-							// xml :nth-child(...)
-							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
-							if ( diff === false ) {
-
-								// Use the same loop as above to seek `elem` from the start
-								while ( ( node = ++nodeIndex && node && node[ dir ] ||
-									( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-									if ( ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) &&
-										++diff ) {
-
-										// Cache the index of each encountered element
-										if ( useCache ) {
-											outerCache = node[ expando ] ||
-												( node[ expando ] = {} );
-
-											// Support: IE <9 only
-											// Defend against cloned attroperties (jQuery gh-1709)
-											uniqueCache = outerCache[ node.uniqueID ] ||
-												( outerCache[ node.uniqueID ] = {} );
-
-											uniqueCache[ type ] = [ dirruns, diff ];
-										}
-
-										if ( node === elem ) {
-											break;
-										}
-									}
-								}
-							}
-						}
-
-						// Incorporate the offset, then check against cycle size
-						diff -= last;
-						return diff === first || ( diff % first === 0 && diff / first >= 0 );
-					}
-				};
-		},
-
-		"PSEUDO": function( pseudo, argument ) {
-
-			// pseudo-class names are case-insensitive
-			// http://www.w3.org/TR/selectors/#pseudo-classes
-			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
-			// Remember that setFilters inherits from pseudos
-			var args,
-				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
-					Sizzle.error( "unsupported pseudo: " + pseudo );
-
-			// The user may use createPseudo to indicate that
-			// arguments are needed to create the filter function
-			// just as Sizzle does
-			if ( fn[ expando ] ) {
-				return fn( argument );
-			}
-
-			// But maintain support for old signatures
-			if ( fn.length > 1 ) {
-				args = [ pseudo, pseudo, "", argument ];
-				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
-					markFunction( function( seed, matches ) {
-						var idx,
-							matched = fn( seed, argument ),
-							i = matched.length;
-						while ( i-- ) {
-							idx = indexOf( seed, matched[ i ] );
-							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
-						}
-					} ) :
-					function( elem ) {
-						return fn( elem, 0, args );
-					};
-			}
-
-			return fn;
-		}
-	},
-
-	pseudos: {
-
-		// Potentially complex pseudos
-		"not": markFunction( function( selector ) {
-
-			// Trim the selector passed to compile
-			// to avoid treating leading and trailing
-			// spaces as combinators
-			var input = [],
-				results = [],
-				matcher = compile( selector.replace( rtrim, "$1" ) );
-
-			return matcher[ expando ] ?
-				markFunction( function( seed, matches, _context, xml ) {
-					var elem,
-						unmatched = matcher( seed, null, xml, [] ),
-						i = seed.length;
-
-					// Match elements unmatched by `matcher`
-					while ( i-- ) {
-						if ( ( elem = unmatched[ i ] ) ) {
-							seed[ i ] = !( matches[ i ] = elem );
-						}
-					}
-				} ) :
-				function( elem, _context, xml ) {
-					input[ 0 ] = elem;
-					matcher( input, null, xml, results );
-
-					// Don't keep the element (issue #299)
-					input[ 0 ] = null;
-					return !results.pop();
-				};
-		} ),
-
-		"has": markFunction( function( selector ) {
-			return function( elem ) {
-				return Sizzle( selector, elem ).length > 0;
-			};
-		} ),
-
-		"contains": markFunction( function( text ) {
-			text = text.replace( runescape, funescape );
-			return function( elem ) {
-				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
-			};
-		} ),
-
-		// "Whether an element is represented by a :lang() selector
-		// is based solely on the element's language value
-		// being equal to the identifier C,
-		// or beginning with the identifier C immediately followed by "-".
-		// The matching of C against the element's language value is performed case-insensitively.
-		// The identifier C does not have to be a valid language name."
-		// http://www.w3.org/TR/selectors/#lang-pseudo
-		"lang": markFunction( function( lang ) {
-
-			// lang value must be a valid identifier
-			if ( !ridentifier.test( lang || "" ) ) {
-				Sizzle.error( "unsupported lang: " + lang );
-			}
-			lang = lang.replace( runescape, funescape ).toLowerCase();
-			return function( elem ) {
-				var elemLang;
-				do {
-					if ( ( elemLang = documentIsHTML ?
-						elem.lang :
-						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
-
-						elemLang = elemLang.toLowerCase();
-						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
-					}
-				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
-				return false;
-			};
-		} ),
-
-		// Miscellaneous
-		"target": function( elem ) {
-			var hash = window.location && window.location.hash;
-			return hash && hash.slice( 1 ) === elem.id;
-		},
-
-		"root": function( elem ) {
-			return elem === docElem;
-		},
-
-		"focus": function( elem ) {
-			return elem === document.activeElement &&
-				( !document.hasFocus || document.hasFocus() ) &&
-				!!( elem.type || elem.href || ~elem.tabIndex );
-		},
-
-		// Boolean properties
-		"enabled": createDisabledPseudo( false ),
-		"disabled": createDisabledPseudo( true ),
-
-		"checked": function( elem ) {
-
-			// In CSS3, :checked should return both checked and selected elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			var nodeName = elem.nodeName.toLowerCase();
-			return ( nodeName === "input" && !!elem.checked ) ||
-				( nodeName === "option" && !!elem.selected );
-		},
-
-		"selected": function( elem ) {
-
-			// Accessing this property makes selected-by-default
-			// options in Safari work properly
-			if ( elem.parentNode ) {
-				// eslint-disable-next-line no-unused-expressions
-				elem.parentNode.selectedIndex;
-			}
-
-			return elem.selected === true;
-		},
-
-		// Contents
-		"empty": function( elem ) {
-
-			// http://www.w3.org/TR/selectors/#empty-pseudo
-			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
-			//   but not by others (comment: 8; processing instruction: 7; etc.)
-			// nodeType < 6 works because attributes (2) do not appear as children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				if ( elem.nodeType < 6 ) {
-					return false;
-				}
-			}
-			return true;
-		},
-
-		"parent": function( elem ) {
-			return !Expr.pseudos[ "empty" ]( elem );
-		},
-
-		// Element/input types
-		"header": function( elem ) {
-			return rheader.test( elem.nodeName );
-		},
-
-		"input": function( elem ) {
-			return rinputs.test( elem.nodeName );
-		},
-
-		"button": function( elem ) {
-			var name = elem.nodeName.toLowerCase();
-			return name === "input" && elem.type === "button" || name === "button";
-		},
-
-		"text": function( elem ) {
-			var attr;
-			return elem.nodeName.toLowerCase() === "input" &&
-				elem.type === "text" &&
-
-				// Support: IE<8
-				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
-				( ( attr = elem.getAttribute( "type" ) ) == null ||
-					attr.toLowerCase() === "text" );
-		},
-
-		// Position-in-collection
-		"first": createPositionalPseudo( function() {
-			return [ 0 ];
-		} ),
-
-		"last": createPositionalPseudo( function( _matchIndexes, length ) {
-			return [ length - 1 ];
-		} ),
-
-		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
-			return [ argument < 0 ? argument + length : argument ];
-		} ),
-
-		"even": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 0;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"odd": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 1;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ?
-				argument + length :
-				argument > length ?
-					length :
-					argument;
-			for ( ; --i >= 0; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ? argument + length : argument;
-			for ( ; ++i < length; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} )
-	}
-};
-
-Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
-
-// Add button/input type pseudos
-for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
-	Expr.pseudos[ i ] = createInputPseudo( i );
-}
-for ( i in { submit: true, reset: true } ) {
-	Expr.pseudos[ i ] = createButtonPseudo( i );
-}
-
-// Easy API for creating new setFilters
-function setFilters() {}
-setFilters.prototype = Expr.filters = Expr.pseudos;
-Expr.setFilters = new setFilters();
-
-tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
-	var matched, match, tokens, type,
-		soFar, groups, preFilters,
-		cached = tokenCache[ selector + " " ];
-
-	if ( cached ) {
-		return parseOnly ? 0 : cached.slice( 0 );
-	}
-
-	soFar = selector;
-	groups = [];
-	preFilters = Expr.preFilter;
-
-	while ( soFar ) {
-
-		// Comma and first run
-		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
-			if ( match ) {
-
-				// Don't consume trailing commas as valid
-				soFar = soFar.slice( match[ 0 ].length ) || soFar;
-			}
-			groups.push( ( tokens = [] ) );
-		}
-
-		matched = false;
-
-		// Combinators
-		if ( ( match = rcombinators.exec( soFar ) ) ) {
-			matched = match.shift();
-			tokens.push( {
-				value: matched,
-
-				// Cast descendant combinators to space
-				type: match[ 0 ].replace( rtrim, " " )
-			} );
-			soFar = soFar.slice( matched.length );
-		}
-
-		// Filters
-		for ( type in Expr.filter ) {
-			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
-				( match = preFilters[ type ]( match ) ) ) ) {
-				matched = match.shift();
-				tokens.push( {
-					value: matched,
-					type: type,
-					matches: match
-				} );
-				soFar = soFar.slice( matched.length );
-			}
-		}
-
-		if ( !matched ) {
-			break;
-		}
-	}
-
-	// Return the length of the invalid excess
-	// if we're just parsing
-	// Otherwise, throw an error or return tokens
-	return parseOnly ?
-		soFar.length :
-		soFar ?
-			Sizzle.error( selector ) :
-
-			// Cache the tokens
-			tokenCache( selector, groups ).slice( 0 );
-};
-
-function toSelector( tokens ) {
-	var i = 0,
-		len = tokens.length,
-		selector = "";
-	for ( ; i < len; i++ ) {
-		selector += tokens[ i ].value;
-	}
-	return selector;
-}
-
-function addCombinator( matcher, combinator, base ) {
-	var dir = combinator.dir,
-		skip = combinator.next,
-		key = skip || dir,
-		checkNonElements = base && key === "parentNode",
-		doneName = done++;
-
-	return combinator.first ?
-
-		// Check against closest ancestor/preceding element
-		function( elem, context, xml ) {
-			while ( ( elem = elem[ dir ] ) ) {
-				if ( elem.nodeType === 1 || checkNonElements ) {
-					return matcher( elem, context, xml );
-				}
-			}
-			return false;
-		} :
-
-		// Check against all ancestor/preceding elements
-		function( elem, context, xml ) {
-			var oldCache, uniqueCache, outerCache,
-				newCache = [ dirruns, doneName ];
-
-			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
-			if ( xml ) {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						if ( matcher( elem, context, xml ) ) {
-							return true;
-						}
-					}
-				}
-			} else {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
-
-						// Support: IE <9 only
-						// Defend against cloned attroperties (jQuery gh-1709)
-						uniqueCache = outerCache[ elem.uniqueID ] ||
-							( outerCache[ elem.uniqueID ] = {} );
-
-						if ( skip && skip === elem.nodeName.toLowerCase() ) {
-							elem = elem[ dir ] || elem;
-						} else if ( ( oldCache = uniqueCache[ key ] ) &&
-							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
-
-							// Assign to newCache so results back-propagate to previous elements
-							return ( newCache[ 2 ] = oldCache[ 2 ] );
-						} else {
-
-							// Reuse newcache so results back-propagate to previous elements
-							uniqueCache[ key ] = newCache;
-
-							// A match means we're done; a fail means we have to keep checking
-							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
-								return true;
-							}
-						}
-					}
-				}
-			}
-			return false;
-		};
-}
-
-function elementMatcher( matchers ) {
-	return matchers.length > 1 ?
-		function( elem, context, xml ) {
-			var i = matchers.length;
-			while ( i-- ) {
-				if ( !matchers[ i ]( elem, context, xml ) ) {
-					return false;
-				}
-			}
-			return true;
-		} :
-		matchers[ 0 ];
-}
-
-function multipleContexts( selector, contexts, results ) {
-	var i = 0,
-		len = contexts.length;
-	for ( ; i < len; i++ ) {
-		Sizzle( selector, contexts[ i ], results );
-	}
-	return results;
-}
-
-function condense( unmatched, map, filter, context, xml ) {
-	var elem,
-		newUnmatched = [],
-		i = 0,
-		len = unmatched.length,
-		mapped = map != null;
-
-	for ( ; i < len; i++ ) {
-		if ( ( elem = unmatched[ i ] ) ) {
-			if ( !filter || filter( elem, context, xml ) ) {
-				newUnmatched.push( elem );
-				if ( mapped ) {
-					map.push( i );
-				}
-			}
-		}
-	}
-
-	return newUnmatched;
-}
-
-function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
-	if ( postFilter && !postFilter[ expando ] ) {
-		postFilter = setMatcher( postFilter );
-	}
-	if ( postFinder && !postFinder[ expando ] ) {
-		postFinder = setMatcher( postFinder, postSelector );
-	}
-	return markFunction( function( seed, results, context, xml ) {
-		var temp, i, elem,
-			preMap = [],
-			postMap = [],
-			preexisting = results.length,
-
-			// Get initial elements from seed or context
-			elems = seed || multipleContexts(
-				selector || "*",
-				context.nodeType ? [ context ] : context,
-				[]
-			),
-
-			// Prefilter to get matcher input, preserving a map for seed-results synchronization
-			matcherIn = preFilter && ( seed || !selector ) ?
-				condense( elems, preMap, preFilter, context, xml ) :
-				elems,
-
-			matcherOut = matcher ?
-
-				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
-				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
-
-					// ...intermediate processing is necessary
-					[] :
-
-					// ...otherwise use results directly
-					results :
-				matcherIn;
-
-		// Find primary matches
-		if ( matcher ) {
-			matcher( matcherIn, matcherOut, context, xml );
-		}
-
-		// Apply postFilter
-		if ( postFilter ) {
-			temp = condense( matcherOut, postMap );
-			postFilter( temp, [], context, xml );
-
-			// Un-match failing elements by moving them back to matcherIn
-			i = temp.length;
-			while ( i-- ) {
-				if ( ( elem = temp[ i ] ) ) {
-					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
-				}
-			}
-		}
-
-		if ( seed ) {
-			if ( postFinder || preFilter ) {
-				if ( postFinder ) {
-
-					// Get the final matcherOut by condensing this intermediate into postFinder contexts
-					temp = [];
-					i = matcherOut.length;
-					while ( i-- ) {
-						if ( ( elem = matcherOut[ i ] ) ) {
-
-							// Restore matcherIn since elem is not yet a final match
-							temp.push( ( matcherIn[ i ] = elem ) );
-						}
-					}
-					postFinder( null, ( matcherOut = [] ), temp, xml );
-				}
-
-				// Move matched elements from seed to results to keep them synchronized
-				i = matcherOut.length;
-				while ( i-- ) {
-					if ( ( elem = matcherOut[ i ] ) &&
-						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
-
-						seed[ temp ] = !( results[ temp ] = elem );
-					}
-				}
-			}
-
-		// Add elements to results, through postFinder if defined
-		} else {
-			matcherOut = condense(
-				matcherOut === results ?
-					matcherOut.splice( preexisting, matcherOut.length ) :
-					matcherOut
-			);
-			if ( postFinder ) {
-				postFinder( null, results, matcherOut, xml );
-			} else {
-				push.apply( results, matcherOut );
-			}
-		}
-	} );
-}
-
-function matcherFromTokens( tokens ) {
-	var checkContext, matcher, j,
-		len = tokens.length,
-		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
-		implicitRelative = leadingRelative || Expr.relative[ " " ],
-		i = leadingRelative ? 1 : 0,
-
-		// The foundational matcher ensures that elements are reachable from top-level context(s)
-		matchContext = addCombinator( function( elem ) {
-			return elem === checkContext;
-		}, implicitRelative, true ),
-		matchAnyContext = addCombinator( function( elem ) {
-			return indexOf( checkContext, elem ) > -1;
-		}, implicitRelative, true ),
-		matchers = [ function( elem, context, xml ) {
-			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
-				( checkContext = context ).nodeType ?
-					matchContext( elem, context, xml ) :
-					matchAnyContext( elem, context, xml ) );
-
-			// Avoid hanging onto element (issue #299)
-			checkContext = null;
-			return ret;
-		} ];
-
-	for ( ; i < len; i++ ) {
-		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
-			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
-		} else {
-			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
-
-			// Return special upon seeing a positional matcher
-			if ( matcher[ expando ] ) {
-
-				// Find the next relative operator (if any) for proper handling
-				j = ++i;
-				for ( ; j < len; j++ ) {
-					if ( Expr.relative[ tokens[ j ].type ] ) {
-						break;
-					}
-				}
-				return setMatcher(
-					i > 1 && elementMatcher( matchers ),
-					i > 1 && toSelector(
-
-					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
-					tokens
-						.slice( 0, i - 1 )
-						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
-					).replace( rtrim, "$1" ),
-					matcher,
-					i < j && matcherFromTokens( tokens.slice( i, j ) ),
-					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
-					j < len && toSelector( tokens )
-				);
-			}
-			matchers.push( matcher );
-		}
-	}
-
-	return elementMatcher( matchers );
-}
-
-function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
-	var bySet = setMatchers.length > 0,
-		byElement = elementMatchers.length > 0,
-		superMatcher = function( seed, context, xml, results, outermost ) {
-			var elem, j, matcher,
-				matchedCount = 0,
-				i = "0",
-				unmatched = seed && [],
-				setMatched = [],
-				contextBackup = outermostContext,
-
-				// We must always have either seed elements or outermost context
-				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
-
-				// Use integer dirruns iff this is the outermost matcher
-				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
-				len = elems.length;
-
-			if ( outermost ) {
-
-				// Support: IE 11+, Edge 17 - 18+
-				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-				// two documents; shallow comparisons work.
-				// eslint-disable-next-line eqeqeq
-				outermostContext = context == document || context || outermost;
-			}
-
-			// Add elements passing elementMatchers directly to results
-			// Support: IE<9, Safari
-			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
-			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
-				if ( byElement && elem ) {
-					j = 0;
-
-					// Support: IE 11+, Edge 17 - 18+
-					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-					// two documents; shallow comparisons work.
-					// eslint-disable-next-line eqeqeq
-					if ( !context && elem.ownerDocument != document ) {
-						setDocument( elem );
-						xml = !documentIsHTML;
-					}
-					while ( ( matcher = elementMatchers[ j++ ] ) ) {
-						if ( matcher( elem, context || document, xml ) ) {
-							results.push( elem );
-							break;
-						}
-					}
-					if ( outermost ) {
-						dirruns = dirrunsUnique;
-					}
-				}
-
-				// Track unmatched elements for set filters
-				if ( bySet ) {
-
-					// They will have gone through all possible matchers
-					if ( ( elem = !matcher && elem ) ) {
-						matchedCount--;
-					}
-
-					// Lengthen the array for every element, matched or not
-					if ( seed ) {
-						unmatched.push( elem );
-					}
-				}
-			}
-
-			// `i` is now the count of elements visited above, and adding it to `matchedCount`
-			// makes the latter nonnegative.
-			matchedCount += i;
-
-			// Apply set filters to unmatched elements
-			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
-			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
-			// no element matchers and no seed.
-			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
-			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
-			// numerically zero.
-			if ( bySet && i !== matchedCount ) {
-				j = 0;
-				while ( ( matcher = setMatchers[ j++ ] ) ) {
-					matcher( unmatched, setMatched, context, xml );
-				}
-
-				if ( seed ) {
-
-					// Reintegrate element matches to eliminate the need for sorting
-					if ( matchedCount > 0 ) {
-						while ( i-- ) {
-							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
-								setMatched[ i ] = pop.call( results );
-							}
-						}
-					}
-
-					// Discard index placeholder values to get only actual matches
-					setMatched = condense( setMatched );
-				}
-
-				// Add matches to results
-				push.apply( results, setMatched );
-
-				// Seedless set matches succeeding multiple successful matchers stipulate sorting
-				if ( outermost && !seed && setMatched.length > 0 &&
-					( matchedCount + setMatchers.length ) > 1 ) {
-
-					Sizzle.uniqueSort( results );
-				}
-			}
-
-			// Override manipulation of globals by nested matchers
-			if ( outermost ) {
-				dirruns = dirrunsUnique;
-				outermostContext = contextBackup;
-			}
-
-			return unmatched;
-		};
-
-	return bySet ?
-		markFunction( superMatcher ) :
-		superMatcher;
-}
-
-compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
-	var i,
-		setMatchers = [],
-		elementMatchers = [],
-		cached = compilerCache[ selector + " " ];
-
-	if ( !cached ) {
-
-		// Generate a function of recursive functions that can be used to check each element
-		if ( !match ) {
-			match = tokenize( selector );
-		}
-		i = match.length;
-		while ( i-- ) {
-			cached = matcherFromTokens( match[ i ] );
-			if ( cached[ expando ] ) {
-				setMatchers.push( cached );
-			} else {
-				elementMatchers.push( cached );
-			}
-		}
-
-		// Cache the compiled function
-		cached = compilerCache(
-			selector,
-			matcherFromGroupMatchers( elementMatchers, setMatchers )
-		);
-
-		// Save selector and tokenization
-		cached.selector = selector;
-	}
-	return cached;
-};
-
-/**
- * A low-level selection function that works with Sizzle's compiled
- *  selector functions
- * @param {String|Function} selector A selector or a pre-compiled
- *  selector function built with Sizzle.compile
- * @param {Element} context
- * @param {Array} [results]
- * @param {Array} [seed] A set of elements to match against
- */
-select = Sizzle.select = function( selector, context, results, seed ) {
-	var i, tokens, token, type, find,
-		compiled = typeof selector === "function" && selector,
-		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
-
-	results = results || [];
-
-	// Try to minimize operations if there is only one selector in the list and no seed
-	// (the latter of which guarantees us context)
-	if ( match.length === 1 ) {
-
-		// Reduce context if the leading compound selector is an ID
-		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
-		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
-			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
-
-			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
-				.replace( runescape, funescape ), context ) || [] )[ 0 ];
-			if ( !context ) {
-				return results;
-
-			// Precompiled matchers will still verify ancestry, so step up a level
-			} else if ( compiled ) {
-				context = context.parentNode;
-			}
-
-			selector = selector.slice( tokens.shift().value.length );
-		}
-
-		// Fetch a seed set for right-to-left matching
-		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
-		while ( i-- ) {
-			token = tokens[ i ];
-
-			// Abort if we hit a combinator
-			if ( Expr.relative[ ( type = token.type ) ] ) {
-				break;
-			}
-			if ( ( find = Expr.find[ type ] ) ) {
-
-				// Search, expanding context for leading sibling combinators
-				if ( ( seed = find(
-					token.matches[ 0 ].replace( runescape, funescape ),
-					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
-						context
-				) ) ) {
-
-					// If seed is empty or no tokens remain, we can return early
-					tokens.splice( i, 1 );
-					selector = seed.length && toSelector( tokens );
-					if ( !selector ) {
-						push.apply( results, seed );
-						return results;
-					}
-
-					break;
-				}
-			}
-		}
-	}
-
-	// Compile and execute a filtering function if one is not provided
-	// Provide `match` to avoid retokenization if we modified the selector above
-	( compiled || compile( selector, match ) )(
-		seed,
-		context,
-		!documentIsHTML,
-		results,
-		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
-	);
-	return results;
-};
-
-// One-time assignments
-
-// Sort stability
-support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
-
-// Support: Chrome 14-35+
-// Always assume duplicates if they aren't passed to the comparison function
-support.detectDuplicates = !!hasDuplicate;
-
-// Initialize against the default document
-setDocument();
-
-// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
-// Detached nodes confoundingly follow *each other*
-support.sortDetached = assert( function( el ) {
-
-	// Should return 1, but returns 4 (following)
-	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
-} );
-
-// Support: IE<8
-// Prevent attribute/property "interpolation"
-// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert( function( el ) {
-	el.innerHTML = "<a href='#'></a>";
-	return el.firstChild.getAttribute( "href" ) === "#";
-} ) ) {
-	addHandle( "type|href|height|width", function( elem, name, isXML ) {
-		if ( !isXML ) {
-			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
-		}
-	} );
-}
-
-// Support: IE<9
-// Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert( function( el ) {
-	el.innerHTML = "<input/>";
-	el.firstChild.setAttribute( "value", "" );
-	return el.firstChild.getAttribute( "value" ) === "";
-} ) ) {
-	addHandle( "value", function( elem, _name, isXML ) {
-		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
-			return elem.defaultValue;
-		}
-	} );
-}
-
-// Support: IE<9
-// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert( function( el ) {
-	return el.getAttribute( "disabled" ) == null;
-} ) ) {
-	addHandle( booleans, function( elem, name, isXML ) {
-		var val;
-		if ( !isXML ) {
-			return elem[ name ] === true ? name.toLowerCase() :
-				( val = elem.getAttributeNode( name ) ) && val.specified ?
-					val.value :
-					null;
-		}
-	} );
-}
-
-return Sizzle;
-
-} )( window );
-
-
-
-jQuery.find = Sizzle;
-jQuery.expr = Sizzle.selectors;
-
-// Deprecated
-jQuery.expr[ ":" ] = jQuery.expr.pseudos;
-jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
-jQuery.text = Sizzle.getText;
-jQuery.isXMLDoc = Sizzle.isXML;
-jQuery.contains = Sizzle.contains;
-jQuery.escapeSelector = Sizzle.escape;
-
-
-
-
-var dir = function( elem, dir, until ) {
-	var matched = [],
-		truncate = until !== undefined;
-
-	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
-		if ( elem.nodeType === 1 ) {
-			if ( truncate && jQuery( elem ).is( until ) ) {
-				break;
-			}
-			matched.push( elem );
-		}
-	}
-	return matched;
-};
-
-
-var siblings = function( n, elem ) {
-	var matched = [];
-
-	for ( ; n; n = n.nextSibling ) {
-		if ( n.nodeType === 1 && n !== elem ) {
-			matched.push( n );
-		}
-	}
-
-	return matched;
-};
-
-
-var rneedsContext = jQuery.expr.match.needsContext;
-
-
-
-function nodeName( elem, name ) {
-
-  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
-
-};
-var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
-
-
-
-// Implement the identical functionality for filter and not
-function winnow( elements, qualifier, not ) {
-	if ( isFunction( qualifier ) ) {
-		return jQuery.grep( elements, function( elem, i ) {
-			return !!qualifier.call( elem, i, elem ) !== not;
-		} );
-	}
-
-	// Single element
-	if ( qualifier.nodeType ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( elem === qualifier ) !== not;
-		} );
-	}
-
-	// Arraylike of elements (jQuery, arguments, Array)
-	if ( typeof qualifier !== "string" ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
-		} );
-	}
-
-	// Filtered directly for both simple and complex selectors
-	return jQuery.filter( qualifier, elements, not );
-}
-
-jQuery.filter = function( expr, elems, not ) {
-	var elem = elems[ 0 ];
-
-	if ( not ) {
-		expr = ":not(" + expr + ")";
-	}
-
-	if ( elems.length === 1 && elem.nodeType === 1 ) {
-		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
-	}
-
-	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
-		return elem.nodeType === 1;
-	} ) );
-};
-
-jQuery.fn.extend( {
-	find: function( selector ) {
-		var i, ret,
-			len = this.length,
-			self = this;
-
-		if ( typeof selector !== "string" ) {
-			return this.pushStack( jQuery( selector ).filter( function() {
-				for ( i = 0; i < len; i++ ) {
-					if ( jQuery.contains( self[ i ], this ) ) {
-						return true;
-					}
-				}
-			} ) );
-		}
-
-		ret = this.pushStack( [] );
-
-		for ( i = 0; i < len; i++ ) {
-			jQuery.find( selector, self[ i ], ret );
-		}
-
-		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
-	},
-	filter: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], false ) );
-	},
-	not: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], true ) );
-	},
-	is: function( selector ) {
-		return !!winnow(
-			this,
-
-			// If this is a positional/relative selector, check membership in the returned set
-			// so $("p:first").is("p:last") won't return true for a doc with two "p".
-			typeof selector === "string" && rneedsContext.test( selector ) ?
-				jQuery( selector ) :
-				selector || [],
-			false
-		).length;
-	}
-} );
-
-
-// Initialize a jQuery object
-
-
-// A central reference to the root jQuery(document)
-var rootjQuery,
-
-	// A simple way to check for HTML strings
-	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
-	// Strict HTML recognition (#11290: must start with <)
-	// Shortcut simple #id case for speed
-	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
-
-	init = jQuery.fn.init = function( selector, context, root ) {
-		var match, elem;
-
-		// HANDLE: $(""), $(null), $(undefined), $(false)
-		if ( !selector ) {
-			return this;
-		}
-
-		// Method init() accepts an alternate rootjQuery
-		// so migrate can support jQuery.sub (gh-2101)
-		root = root || rootjQuery;
-
-		// Handle HTML strings
-		if ( typeof selector === "string" ) {
-			if ( selector[ 0 ] === "<" &&
-				selector[ selector.length - 1 ] === ">" &&
-				selector.length >= 3 ) {
-
-				// Assume that strings that start and end with <> are HTML and skip the regex check
-				match = [ null, selector, null ];
-
-			} else {
-				match = rquickExpr.exec( selector );
-			}
-
-			// Match html or make sure no context is specified for #id
-			if ( match && ( match[ 1 ] || !context ) ) {
-
-				// HANDLE: $(html) -> $(array)
-				if ( match[ 1 ] ) {
-					context = context instanceof jQuery ? context[ 0 ] : context;
-
-					// Option to run scripts is true for back-compat
-					// Intentionally let the error be thrown if parseHTML is not present
-					jQuery.merge( this, jQuery.parseHTML(
-						match[ 1 ],
-						context && context.nodeType ? context.ownerDocument || context : document,
-						true
-					) );
-
-					// HANDLE: $(html, props)
-					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
-						for ( match in context ) {
-
-							// Properties of context are called as methods if possible
-							if ( isFunction( this[ match ] ) ) {
-								this[ match ]( context[ match ] );
-
-							// ...and otherwise set as attributes
-							} else {
-								this.attr( match, context[ match ] );
-							}
-						}
-					}
-
-					return this;
-
-				// HANDLE: $(#id)
-				} else {
-					elem = document.getElementById( match[ 2 ] );
-
-					if ( elem ) {
-
-						// Inject the element directly into the jQuery object
-						this[ 0 ] = elem;
-						this.length = 1;
-					}
-					return this;
-				}
-
-			// HANDLE: $(expr, $(...))
-			} else if ( !context || context.jquery ) {
-				return ( context || root ).find( selector );
-
-			// HANDLE: $(expr, context)
-			// (which is just equivalent to: $(context).find(expr)
-			} else {
-				return this.constructor( context ).find( selector );
-			}
-
-		// HANDLE: $(DOMElement)
-		} else if ( selector.nodeType ) {
-			this[ 0 ] = selector;
-			this.length = 1;
-			return this;
-
-		// HANDLE: $(function)
-		// Shortcut for document ready
-		} else if ( isFunction( selector ) ) {
-			return root.ready !== undefined ?
-				root.ready( selector ) :
-
-				// Execute immediately if ready is not present
-				selector( jQuery );
-		}
-
-		return jQuery.makeArray( selector, this );
-	};
-
-// Give the init function the jQuery prototype for later instantiation
-init.prototype = jQuery.fn;
-
-// Initialize central reference
-rootjQuery = jQuery( document );
-
-
-var rparentsprev = /^(?:parents|prev(?:Until|All))/,
-
-	// Methods guaranteed to produce a unique set when starting from a unique set
-	guaranteedUnique = {
-		children: true,
-		contents: true,
-		next: true,
-		prev: true
-	};
-
-jQuery.fn.extend( {
-	has: function( target ) {
-		var targets = jQuery( target, this ),
-			l = targets.length;
-
-		return this.filter( function() {
-			var i = 0;
-			for ( ; i < l; i++ ) {
-				if ( jQuery.contains( this, targets[ i ] ) ) {
-					return true;
-				}
-			}
-		} );
-	},
-
-	closest: function( selectors, context ) {
-		var cur,
-			i = 0,
-			l = this.length,
-			matched = [],
-			targets = typeof selectors !== "string" && jQuery( selectors );
-
-		// Positional selectors never match, since there's no _selection_ context
-		if ( !rneedsContext.test( selectors ) ) {
-			for ( ; i < l; i++ ) {
-				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
-
-					// Always skip document fragments
-					if ( cur.nodeType < 11 && ( targets ?
-						targets.index( cur ) > -1 :
-
-						// Don't pass non-elements to Sizzle
-						cur.nodeType === 1 &&
-							jQuery.find.matchesSelector( cur, selectors ) ) ) {
-
-						matched.push( cur );
-						break;
-					}
-				}
-			}
-		}
-
-		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
-	},
-
-	// Determine the position of an element within the set
-	index: function( elem ) {
-
-		// No argument, return index in parent
-		if ( !elem ) {
-			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
-		}
-
-		// Index in selector
-		if ( typeof elem === "string" ) {
-			return indexOf.call( jQuery( elem ), this[ 0 ] );
-		}
-
-		// Locate the position of the desired element
-		return indexOf.call( this,
-
-			// If it receives a jQuery object, the first element is used
-			elem.jquery ? elem[ 0 ] : elem
-		);
-	},
-
-	add: function( selector, context ) {
-		return this.pushStack(
-			jQuery.uniqueSort(
-				jQuery.merge( this.get(), jQuery( selector, context ) )
-			)
-		);
-	},
-
-	addBack: function( selector ) {
-		return this.add( selector == null ?
-			this.prevObject : this.prevObject.filter( selector )
-		);
-	}
-} );
-
-function sibling( cur, dir ) {
-	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
-	return cur;
-}
-
-jQuery.each( {
-	parent: function( elem ) {
-		var parent = elem.parentNode;
-		return parent && parent.nodeType !== 11 ? parent : null;
-	},
-	parents: function( elem ) {
-		return dir( elem, "parentNode" );
-	},
-	parentsUntil: function( elem, _i, until ) {
-		return dir( elem, "parentNode", until );
-	},
-	next: function( elem ) {
-		return sibling( elem, "nextSibling" );
-	},
-	prev: function( elem ) {
-		return sibling( elem, "previousSibling" );
-	},
-	nextAll: function( elem ) {
-		return dir( elem, "nextSibling" );
-	},
-	prevAll: function( elem ) {
-		return dir( elem, "previousSibling" );
-	},
-	nextUntil: function( elem, _i, until ) {
-		return dir( elem, "nextSibling", until );
-	},
-	prevUntil: function( elem, _i, until ) {
-		return dir( elem, "previousSibling", until );
-	},
-	siblings: function( elem ) {
-		return siblings( ( elem.parentNode || {} ).firstChild, elem );
-	},
-	children: function( elem ) {
-		return siblings( elem.firstChild );
-	},
-	contents: function( elem ) {
-		if ( elem.contentDocument != null &&
-
-			// Support: IE 11+
-			// <object> elements with no `data` attribute has an object
-			// `contentDocument` with a `null` prototype.
-			getProto( elem.contentDocument ) ) {
-
-			return elem.contentDocument;
-		}
-
-		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
-		// Treat the template element as a regular one in browsers that
-		// don't support it.
-		if ( nodeName( elem, "template" ) ) {
-			elem = elem.content || elem;
-		}
-
-		return jQuery.merge( [], elem.childNodes );
-	}
-}, function( name, fn ) {
-	jQuery.fn[ name ] = function( until, selector ) {
-		var matched = jQuery.map( this, fn, until );
-
-		if ( name.slice( -5 ) !== "Until" ) {
-			selector = until;
-		}
-
-		if ( selector && typeof selector === "string" ) {
-			matched = jQuery.filter( selector, matched );
-		}
-
-		if ( this.length > 1 ) {
-
-			// Remove duplicates
-			if ( !guaranteedUnique[ name ] ) {
-				jQuery.uniqueSort( matched );
-			}
-
-			// Reverse order for parents* and prev-derivatives
-			if ( rparentsprev.test( name ) ) {
-				matched.reverse();
-			}
-		}
-
-		return this.pushStack( matched );
-	};
-} );
-var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
-
-
-
-// Convert String-formatted options into Object-formatted ones
-function createOptions( options ) {
-	var object = {};
-	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
-		object[ flag ] = true;
-	} );
-	return object;
-}
-
-/*
- * Create a callback list using the following parameters:
- *
- *	options: an optional list of space-separated options that will change how
- *			the callback list behaves or a more traditional option object
- *
- * By default a callback list will act like an event callback list and can be
- * "fired" multiple times.
- *
- * Possible options:
- *
- *	once:			will ensure the callback list can only be fired once (like a Deferred)
- *
- *	memory:			will keep track of previous values and will call any callback added
- *					after the list has been fired right away with the latest "memorized"
- *					values (like a Deferred)
- *
- *	unique:			will ensure a callback can only be added once (no duplicate in the list)
- *
- *	stopOnFalse:	interrupt callings when a callback returns false
- *
- */
-jQuery.Callbacks = function( options ) {
-
-	// Convert options from String-formatted to Object-formatted if needed
-	// (we check in cache first)
-	options = typeof options === "string" ?
-		createOptions( options ) :
-		jQuery.extend( {}, options );
-
-	var // Flag to know if list is currently firing
-		firing,
-
-		// Last fire value for non-forgettable lists
-		memory,
-
-		// Flag to know if list was already fired
-		fired,
-
-		// Flag to prevent firing
-		locked,
-
-		// Actual callback list
-		list = [],
-
-		// Queue of execution data for repeatable lists
-		queue = [],
-
-		// Index of currently firing callback (modified by add/remove as needed)
-		firingIndex = -1,
-
-		// Fire callbacks
-		fire = function() {
-
-			// Enforce single-firing
-			locked = locked || options.once;
-
-			// Execute callbacks for all pending executions,
-			// respecting firingIndex overrides and runtime changes
-			fired = firing = true;
-			for ( ; queue.length; firingIndex = -1 ) {
-				memory = queue.shift();
-				while ( ++firingIndex < list.length ) {
-
-					// Run callback and check for early termination
-					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
-						options.stopOnFalse ) {
-
-						// Jump to end and forget the data so .add doesn't re-fire
-						firingIndex = list.length;
-						memory = false;
-					}
-				}
-			}
-
-			// Forget the data if we're done with it
-			if ( !options.memory ) {
-				memory = false;
-			}
-
-			firing = false;
-
-			// Clean up if we're done firing for good
-			if ( locked ) {
-
-				// Keep an empty list if we have data for future add calls
-				if ( memory ) {
-					list = [];
-
-				// Otherwise, this object is spent
-				} else {
-					list = "";
-				}
-			}
-		},
-
-		// Actual Callbacks object
-		self = {
-
-			// Add a callback or a collection of callbacks to the list
-			add: function() {
-				if ( list ) {
-
-					// If we have memory from a past run, we should fire after adding
-					if ( memory && !firing ) {
-						firingIndex = list.length - 1;
-						queue.push( memory );
-					}
-
-					( function add( args ) {
-						jQuery.each( args, function( _, arg ) {
-							if ( isFunction( arg ) ) {
-								if ( !options.unique || !self.has( arg ) ) {
-									list.push( arg );
-								}
-							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
-
-								// Inspect recursively
-								add( arg );
-							}
-						} );
-					} )( arguments );
-
-					if ( memory && !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Remove a callback from the list
-			remove: function() {
-				jQuery.each( arguments, function( _, arg ) {
-					var index;
-					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
-						list.splice( index, 1 );
-
-						// Handle firing indexes
-						if ( index <= firingIndex ) {
-							firingIndex--;
-						}
-					}
-				} );
-				return this;
-			},
-
-			// Check if a given callback is in the list.
-			// If no argument is given, return whether or not list has callbacks attached.
-			has: function( fn ) {
-				return fn ?
-					jQuery.inArray( fn, list ) > -1 :
-					list.length > 0;
-			},
-
-			// Remove all callbacks from the list
-			empty: function() {
-				if ( list ) {
-					list = [];
-				}
-				return this;
-			},
-
-			// Disable .fire and .add
-			// Abort any current/pending executions
-			// Clear all callbacks and values
-			disable: function() {
-				locked = queue = [];
-				list = memory = "";
-				return this;
-			},
-			disabled: function() {
-				return !list;
-			},
-
-			// Disable .fire
-			// Also disable .add unless we have memory (since it would have no effect)
-			// Abort any pending executions
-			lock: function() {
-				locked = queue = [];
-				if ( !memory && !firing ) {
-					list = memory = "";
-				}
-				return this;
-			},
-			locked: function() {
-				return !!locked;
-			},
-
-			// Call all callbacks with the given context and arguments
-			fireWith: function( context, args ) {
-				if ( !locked ) {
-					args = args || [];
-					args = [ context, args.slice ? args.slice() : args ];
-					queue.push( args );
-					if ( !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Call all the callbacks with the given arguments
-			fire: function() {
-				self.fireWith( this, arguments );
-				return this;
-			},
-
-			// To know if the callbacks have already been called at least once
-			fired: function() {
-				return !!fired;
-			}
-		};
-
-	return self;
-};
-
-
-function Identity( v ) {
-	return v;
-}
-function Thrower( ex ) {
-	throw ex;
-}
-
-function adoptValue( value, resolve, reject, noValue ) {
-	var method;
-
-	try {
-
-		// Check for promise aspect first to privilege synchronous behavior
-		if ( value && isFunction( ( method = value.promise ) ) ) {
-			method.call( value ).done( resolve ).fail( reject );
-
-		// Other thenables
-		} else if ( value && isFunction( ( method = value.then ) ) ) {
-			method.call( value, resolve, reject );
-
-		// Other non-thenables
-		} else {
-
-			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
-			// * false: [ value ].slice( 0 ) => resolve( value )
-			// * true: [ value ].slice( 1 ) => resolve()
-			resolve.apply( undefined, [ value ].slice( noValue ) );
-		}
-
-	// For Promises/A+, convert exceptions into rejections
-	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
-	// Deferred#then to conditionally suppress rejection.
-	} catch ( value ) {
-
-		// Support: Android 4.0 only
-		// Strict mode functions invoked without .call/.apply get global-object context
-		reject.apply( undefined, [ value ] );
-	}
-}
-
-jQuery.extend( {
-
-	Deferred: function( func ) {
-		var tuples = [
-
-				// action, add listener, callbacks,
-				// ... .then handlers, argument index, [final state]
-				[ "notify", "progress", jQuery.Callbacks( "memory" ),
-					jQuery.Callbacks( "memory" ), 2 ],
-				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
-				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
-			],
-			state = "pending",
-			promise = {
-				state: function() {
-					return state;
-				},
-				always: function() {
-					deferred.done( arguments ).fail( arguments );
-					return this;
-				},
-				"catch": function( fn ) {
-					return promise.then( null, fn );
-				},
-
-				// Keep pipe for back-compat
-				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
-					var fns = arguments;
-
-					return jQuery.Deferred( function( newDefer ) {
-						jQuery.each( tuples, function( _i, tuple ) {
-
-							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
-							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
-
-							// deferred.progress(function() { bind to newDefer or newDefer.notify })
-							// deferred.done(function() { bind to newDefer or newDefer.resolve })
-							// deferred.fail(function() { bind to newDefer or newDefer.reject })
-							deferred[ tuple[ 1 ] ]( function() {
-								var returned = fn && fn.apply( this, arguments );
-								if ( returned && isFunction( returned.promise ) ) {
-									returned.promise()
-										.progress( newDefer.notify )
-										.done( newDefer.resolve )
-										.fail( newDefer.reject );
-								} else {
-									newDefer[ tuple[ 0 ] + "With" ](
-										this,
-										fn ? [ returned ] : arguments
-									);
-								}
-							} );
-						} );
-						fns = null;
-					} ).promise();
-				},
-				then: function( onFulfilled, onRejected, onProgress ) {
-					var maxDepth = 0;
-					function resolve( depth, deferred, handler, special ) {
-						return function() {
-							var that = this,
-								args = arguments,
-								mightThrow = function() {
-									var returned, then;
-
-									// Support: Promises/A+ section 2.3.3.3.3
-									// https://promisesaplus.com/#point-59
-									// Ignore double-resolution attempts
-									if ( depth < maxDepth ) {
-										return;
-									}
-
-									returned = handler.apply( that, args );
-
-									// Support: Promises/A+ section 2.3.1
-									// https://promisesaplus.com/#point-48
-									if ( returned === deferred.promise() ) {
-										throw new TypeError( "Thenable self-resolution" );
-									}
-
-									// Support: Promises/A+ sections 2.3.3.1, 3.5
-									// https://promisesaplus.com/#point-54
-									// https://promisesaplus.com/#point-75
-									// Retrieve `then` only once
-									then = returned &&
-
-										// Support: Promises/A+ section 2.3.4
-										// https://promisesaplus.com/#point-64
-										// Only check objects and functions for thenability
-										( typeof returned === "object" ||
-											typeof returned === "function" ) &&
-										returned.then;
-
-									// Handle a returned thenable
-									if ( isFunction( then ) ) {
-
-										// Special processors (notify) just wait for resolution
-										if ( special ) {
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special )
-											);
-
-										// Normal processors (resolve) also hook into progress
-										} else {
-
-											// ...and disregard older resolution values
-											maxDepth++;
-
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special ),
-												resolve( maxDepth, deferred, Identity,
-													deferred.notifyWith )
-											);
-										}
-
-									// Handle all other returned values
-									} else {
-
-										// Only substitute handlers pass on context
-										// and multiple values (non-spec behavior)
-										if ( handler !== Identity ) {
-											that = undefined;
-											args = [ returned ];
-										}
-
-										// Process the value(s)
-										// Default process is resolve
-										( special || deferred.resolveWith )( that, args );
-									}
-								},
-
-								// Only normal processors (resolve) catch and reject exceptions
-								process = special ?
-									mightThrow :
-									function() {
-										try {
-											mightThrow();
-										} catch ( e ) {
-
-											if ( jQuery.Deferred.exceptionHook ) {
-												jQuery.Deferred.exceptionHook( e,
-													process.stackTrace );
-											}
-
-											// Support: Promises/A+ section 2.3.3.3.4.1
-											// https://promisesaplus.com/#point-61
-											// Ignore post-resolution exceptions
-											if ( depth + 1 >= maxDepth ) {
-
-												// Only substitute handlers pass on context
-												// and multiple values (non-spec behavior)
-												if ( handler !== Thrower ) {
-													that = undefined;
-													args = [ e ];
-												}
-
-												deferred.rejectWith( that, args );
-											}
-										}
-									};
-
-							// Support: Promises/A+ section 2.3.3.3.1
-							// https://promisesaplus.com/#point-57
-							// Re-resolve promises immediately to dodge false rejection from
-							// subsequent errors
-							if ( depth ) {
-								process();
-							} else {
-
-								// Call an optional hook to record the stack, in case of exception
-								// since it's otherwise lost when execution goes async
-								if ( jQuery.Deferred.getStackHook ) {
-									process.stackTrace = jQuery.Deferred.getStackHook();
-								}
-								window.setTimeout( process );
-							}
-						};
-					}
-
-					return jQuery.Deferred( function( newDefer ) {
-
-						// progress_handlers.add( ... )
-						tuples[ 0 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onProgress ) ?
-									onProgress :
-									Identity,
-								newDefer.notifyWith
-							)
-						);
-
-						// fulfilled_handlers.add( ... )
-						tuples[ 1 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onFulfilled ) ?
-									onFulfilled :
-									Identity
-							)
-						);
-
-						// rejected_handlers.add( ... )
-						tuples[ 2 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onRejected ) ?
-									onRejected :
-									Thrower
-							)
-						);
-					} ).promise();
-				},
-
-				// Get a promise for this deferred
-				// If obj is provided, the promise aspect is added to the object
-				promise: function( obj ) {
-					return obj != null ? jQuery.extend( obj, promise ) : promise;
-				}
-			},
-			deferred = {};
-
-		// Add list-specific methods
-		jQuery.each( tuples, function( i, tuple ) {
-			var list = tuple[ 2 ],
-				stateString = tuple[ 5 ];
-
-			// promise.progress = list.add
-			// promise.done = list.add
-			// promise.fail = list.add
-			promise[ tuple[ 1 ] ] = list.add;
-
-			// Handle state
-			if ( stateString ) {
-				list.add(
-					function() {
-
-						// state = "resolved" (i.e., fulfilled)
-						// state = "rejected"
-						state = stateString;
-					},
-
-					// rejected_callbacks.disable
-					// fulfilled_callbacks.disable
-					tuples[ 3 - i ][ 2 ].disable,
-
-					// rejected_handlers.disable
-					// fulfilled_handlers.disable
-					tuples[ 3 - i ][ 3 ].disable,
-
-					// progress_callbacks.lock
-					tuples[ 0 ][ 2 ].lock,
-
-					// progress_handlers.lock
-					tuples[ 0 ][ 3 ].lock
-				);
-			}
-
-			// progress_handlers.fire
-			// fulfilled_handlers.fire
-			// rejected_handlers.fire
-			list.add( tuple[ 3 ].fire );
-
-			// deferred.notify = function() { deferred.notifyWith(...) }
-			// deferred.resolve = function() { deferred.resolveWith(...) }
-			// deferred.reject = function() { deferred.rejectWith(...) }
-			deferred[ tuple[ 0 ] ] = function() {
-				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
-				return this;
-			};
-
-			// deferred.notifyWith = list.fireWith
-			// deferred.resolveWith = list.fireWith
-			// deferred.rejectWith = list.fireWith
-			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
-		} );
-
-		// Make the deferred a promise
-		promise.promise( deferred );
-
-		// Call given func if any
-		if ( func ) {
-			func.call( deferred, deferred );
-		}
-
-		// All done!
-		return deferred;
-	},
-
-	// Deferred helper
-	when: function( singleValue ) {
-		var
-
-			// count of uncompleted subordinates
-			remaining = arguments.length,
-
-			// count of unprocessed arguments
-			i = remaining,
-
-			// subordinate fulfillment data
-			resolveContexts = Array( i ),
-			resolveValues = slice.call( arguments ),
-
-			// the master Deferred
-			master = jQuery.Deferred(),
-
-			// subordinate callback factory
-			updateFunc = function( i ) {
-				return function( value ) {
-					resolveContexts[ i ] = this;
-					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
-					if ( !( --remaining ) ) {
-						master.resolveWith( resolveContexts, resolveValues );
-					}
-				};
-			};
-
-		// Single- and empty arguments are adopted like Promise.resolve
-		if ( remaining <= 1 ) {
-			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
-				!remaining );
-
-			// Use .then() to unwrap secondary thenables (cf. gh-3000)
-			if ( master.state() === "pending" ||
-				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
-
-				return master.then();
-			}
-		}
-
-		// Multiple arguments are aggregated like Promise.all array elements
-		while ( i-- ) {
-			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
-		}
-
-		return master.promise();
-	}
-} );
-
-
-// These usually indicate a programmer mistake during development,
-// warn about them ASAP rather than swallowing them by default.
-var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
-
-jQuery.Deferred.exceptionHook = function( error, stack ) {
-
-	// Support: IE 8 - 9 only
-	// Console exists when dev tools are open, which can happen at any time
-	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
-		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
-	}
-};
-
-
-
-
-jQuery.readyException = function( error ) {
-	window.setTimeout( function() {
-		throw error;
-	} );
-};
-
-
-
-
-// The deferred used on DOM ready
-var readyList = jQuery.Deferred();
-
-jQuery.fn.ready = function( fn ) {
-
-	readyList
-		.then( fn )
-
-		// Wrap jQuery.readyException in a function so that the lookup
-		// happens at the time of error handling instead of callback
-		// registration.
-		.catch( function( error ) {
-			jQuery.readyException( error );
-		} );
-
-	return this;
-};
-
-jQuery.extend( {
-
-	// Is the DOM ready to be used? Set to true once it occurs.
-	isReady: false,
-
-	// A counter to track how many items to wait for before
-	// the ready event fires. See #6781
-	readyWait: 1,
-
-	// Handle when the DOM is ready
-	ready: function( wait ) {
-
-		// Abort if there are pending holds or we're already ready
-		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
-			return;
-		}
-
-		// Remember that the DOM is ready
-		jQuery.isReady = true;
-
-		// If a normal DOM Ready event fired, decrement, and wait if need be
-		if ( wait !== true && --jQuery.readyWait > 0 ) {
-			return;
-		}
-
-		// If there are functions bound, to execute
-		readyList.resolveWith( document, [ jQuery ] );
-	}
-} );
-
-jQuery.ready.then = readyList.then;
-
-// The ready event handler and self cleanup method
-function completed() {
-	document.removeEventListener( "DOMContentLoaded", completed );
-	window.removeEventListener( "load", completed );
-	jQuery.ready();
-}
-
-// Catch cases where $(document).ready() is called
-// after the browser event has already occurred.
-// Support: IE <=9 - 10 only
-// Older IE sometimes signals "interactive" too soon
-if ( document.readyState === "complete" ||
-	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
-
-	// Handle it asynchronously to allow scripts the opportunity to delay ready
-	window.setTimeout( jQuery.ready );
-
-} else {
-
-	// Use the handy event callback
-	document.addEventListener( "DOMContentLoaded", completed );
-
-	// A fallback to window.onload, that will always work
-	window.addEventListener( "load", completed );
-}
-
-
-
-
-// Multifunctional method to get and set values of a collection
-// The value/s can optionally be executed if it's a function
-var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
-	var i = 0,
-		len = elems.length,
-		bulk = key == null;
-
-	// Sets many values
-	if ( toType( key ) === "object" ) {
-		chainable = true;
-		for ( i in key ) {
-			access( elems, fn, i, key[ i ], true, emptyGet, raw );
-		}
-
-	// Sets one value
-	} else if ( value !== undefined ) {
-		chainable = true;
-
-		if ( !isFunction( value ) ) {
-			raw = true;
-		}
-
-		if ( bulk ) {
-
-			// Bulk operations run against the entire set
-			if ( raw ) {
-				fn.call( elems, value );
-				fn = null;
-
-			// ...except when executing function values
-			} else {
-				bulk = fn;
-				fn = function( elem, _key, value ) {
-					return bulk.call( jQuery( elem ), value );
-				};
-			}
-		}
-
-		if ( fn ) {
-			for ( ; i < len; i++ ) {
-				fn(
-					elems[ i ], key, raw ?
-					value :
-					value.call( elems[ i ], i, fn( elems[ i ], key ) )
-				);
-			}
-		}
-	}
-
-	if ( chainable ) {
-		return elems;
-	}
-
-	// Gets
-	if ( bulk ) {
-		return fn.call( elems );
-	}
-
-	return len ? fn( elems[ 0 ], key ) : emptyGet;
-};
-
-
-// Matches dashed string for camelizing
-var rmsPrefix = /^-ms-/,
-	rdashAlpha = /-([a-z])/g;
-
-// Used by camelCase as callback to replace()
-function fcamelCase( _all, letter ) {
-	return letter.toUpperCase();
-}
-
-// Convert dashed to camelCase; used by the css and data modules
-// Support: IE <=9 - 11, Edge 12 - 15
-// Microsoft forgot to hump their vendor prefix (#9572)
-function camelCase( string ) {
-	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
-}
-var acceptData = function( owner ) {
-
-	// Accepts only:
-	//  - Node
-	//    - Node.ELEMENT_NODE
-	//    - Node.DOCUMENT_NODE
-	//  - Object
-	//    - Any
-	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
-};
-
-
-
-
-function Data() {
-	this.expando = jQuery.expando + Data.uid++;
-}
-
-Data.uid = 1;
-
-Data.prototype = {
-
-	cache: function( owner ) {
-
-		// Check if the owner object already has a cache
-		var value = owner[ this.expando ];
-
-		// If not, create one
-		if ( !value ) {
-			value = {};
-
-			// We can accept data for non-element nodes in modern browsers,
-			// but we should not, see #8335.
-			// Always return an empty object.
-			if ( acceptData( owner ) ) {
-
-				// If it is a node unlikely to be stringify-ed or looped over
-				// use plain assignment
-				if ( owner.nodeType ) {
-					owner[ this.expando ] = value;
-
-				// Otherwise secure it in a non-enumerable property
-				// configurable must be true to allow the property to be
-				// deleted when data is removed
-				} else {
-					Object.defineProperty( owner, this.expando, {
-						value: value,
-						configurable: true
-					} );
-				}
-			}
-		}
-
-		return value;
-	},
-	set: function( owner, data, value ) {
-		var prop,
-			cache = this.cache( owner );
-
-		// Handle: [ owner, key, value ] args
-		// Always use camelCase key (gh-2257)
-		if ( typeof data === "string" ) {
-			cache[ camelCase( data ) ] = value;
-
-		// Handle: [ owner, { properties } ] args
-		} else {
-
-			// Copy the properties one-by-one to the cache object
-			for ( prop in data ) {
-				cache[ camelCase( prop ) ] = data[ prop ];
-			}
-		}
-		return cache;
-	},
-	get: function( owner, key ) {
-		return key === undefined ?
-			this.cache( owner ) :
-
-			// Always use camelCase key (gh-2257)
-			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
-	},
-	access: function( owner, key, value ) {
-
-		// In cases where either:
-		//
-		//   1. No key was specified
-		//   2. A string key was specified, but no value provided
-		//
-		// Take the "read" path and allow the get method to determine
-		// which value to return, respectively either:
-		//
-		//   1. The entire cache object
-		//   2. The data stored at the key
-		//
-		if ( key === undefined ||
-				( ( key && typeof key === "string" ) && value === undefined ) ) {
-
-			return this.get( owner, key );
-		}
-
-		// When the key is not a string, or both a key and value
-		// are specified, set or extend (existing objects) with either:
-		//
-		//   1. An object of properties
-		//   2. A key and value
-		//
-		this.set( owner, key, value );
-
-		// Since the "set" path can have two possible entry points
-		// return the expected data based on which path was taken[*]
-		return value !== undefined ? value : key;
-	},
-	remove: function( owner, key ) {
-		var i,
-			cache = owner[ this.expando ];
-
-		if ( cache === undefined ) {
-			return;
-		}
-
-		if ( key !== undefined ) {
-
-			// Support array or space separated string of keys
-			if ( Array.isArray( key ) ) {
-
-				// If key is an array of keys...
-				// We always set camelCase keys, so remove that.
-				key = key.map( camelCase );
-			} else {
-				key = camelCase( key );
-
-				// If a key with the spaces exists, use it.
-				// Otherwise, create an array by matching non-whitespace
-				key = key in cache ?
-					[ key ] :
-					( key.match( rnothtmlwhite ) || [] );
-			}
-
-			i = key.length;
-
-			while ( i-- ) {
-				delete cache[ key[ i ] ];
-			}
-		}
-
-		// Remove the expando if there's no more data
-		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
-
-			// Support: Chrome <=35 - 45
-			// Webkit & Blink performance suffers when deleting properties
-			// from DOM nodes, so set to undefined instead
-			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
-			if ( owner.nodeType ) {
-				owner[ this.expando ] = undefined;
-			} else {
-				delete owner[ this.expando ];
-			}
-		}
-	},
-	hasData: function( owner ) {
-		var cache = owner[ this.expando ];
-		return cache !== undefined && !jQuery.isEmptyObject( cache );
-	}
-};
-var dataPriv = new Data();
-
-var dataUser = new Data();
-
-
-
-//	Implementation Summary
-//
-//	1. Enforce API surface and semantic compatibility with 1.9.x branch
-//	2. Improve the module's maintainability by reducing the storage
-//		paths to a single mechanism.
-//	3. Use the same single mechanism to support "private" and "user" data.
-//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
-//	5. Avoid exposing implementation details on user objects (eg. expando properties)
-//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
-
-var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
-	rmultiDash = /[A-Z]/g;
-
-function getData( data ) {
-	if ( data === "true" ) {
-		return true;
-	}
-
-	if ( data === "false" ) {
-		return false;
-	}
-
-	if ( data === "null" ) {
-		return null;
-	}
-
-	// Only convert to a number if it doesn't change the string
-	if ( data === +data + "" ) {
-		return +data;
-	}
-
-	if ( rbrace.test( data ) ) {
-		return JSON.parse( data );
-	}
-
-	return data;
-}
-
-function dataAttr( elem, key, data ) {
-	var name;
-
-	// If nothing was found internally, try to fetch any
-	// data from the HTML5 data-* attribute
-	if ( data === undefined && elem.nodeType === 1 ) {
-		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
-		data = elem.getAttribute( name );
-
-		if ( typeof data === "string" ) {
-			try {
-				data = getData( data );
-			} catch ( e ) {}
-
-			// Make sure we set the data so it isn't changed later
-			dataUser.set( elem, key, data );
-		} else {
-			data = undefined;
-		}
-	}
-	return data;
-}
-
-jQuery.extend( {
-	hasData: function( elem ) {
-		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
-	},
-
-	data: function( elem, name, data ) {
-		return dataUser.access( elem, name, data );
-	},
-
-	removeData: function( elem, name ) {
-		dataUser.remove( elem, name );
-	},
-
-	// TODO: Now that all calls to _data and _removeData have been replaced
-	// with direct calls to dataPriv methods, these can be deprecated.
-	_data: function( elem, name, data ) {
-		return dataPriv.access( elem, name, data );
-	},
-
-	_removeData: function( elem, name ) {
-		dataPriv.remove( elem, name );
-	}
-} );
-
-jQuery.fn.extend( {
-	data: function( key, value ) {
-		var i, name, data,
-			elem = this[ 0 ],
-			attrs = elem && elem.attributes;
-
-		// Gets all values
-		if ( key === undefined ) {
-			if ( this.length ) {
-				data = dataUser.get( elem );
-
-				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
-					i = attrs.length;
-					while ( i-- ) {
-
-						// Support: IE 11 only
-						// The attrs elements can be null (#14894)
-						if ( attrs[ i ] ) {
-							name = attrs[ i ].name;
-							if ( name.indexOf( "data-" ) === 0 ) {
-								name = camelCase( name.slice( 5 ) );
-								dataAttr( elem, name, data[ name ] );
-							}
-						}
-					}
-					dataPriv.set( elem, "hasDataAttrs", true );
-				}
-			}
-
-			return data;
-		}
-
-		// Sets multiple values
-		if ( typeof key === "object" ) {
-			return this.each( function() {
-				dataUser.set( this, key );
-			} );
-		}
-
-		return access( this, function( value ) {
-			var data;
-
-			// The calling jQuery object (element matches) is not empty
-			// (and therefore has an element appears at this[ 0 ]) and the
-			// `value` parameter was not undefined. An empty jQuery object
-			// will result in `undefined` for elem = this[ 0 ] which will
-			// throw an exception if an attempt to read a data cache is made.
-			if ( elem && value === undefined ) {
-
-				// Attempt to get data from the cache
-				// The key will always be camelCased in Data
-				data = dataUser.get( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// Attempt to "discover" the data in
-				// HTML5 custom data-* attrs
-				data = dataAttr( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// We tried really hard, but the data doesn't exist.
-				return;
-			}
-
-			// Set the data...
-			this.each( function() {
-
-				// We always store the camelCased key
-				dataUser.set( this, key, value );
-			} );
-		}, null, value, arguments.length > 1, null, true );
-	},
-
-	removeData: function( key ) {
-		return this.each( function() {
-			dataUser.remove( this, key );
-		} );
-	}
-} );
-
-
-jQuery.extend( {
-	queue: function( elem, type, data ) {
-		var queue;
-
-		if ( elem ) {
-			type = ( type || "fx" ) + "queue";
-			queue = dataPriv.get( elem, type );
-
-			// Speed up dequeue by getting out quickly if this is just a lookup
-			if ( data ) {
-				if ( !queue || Array.isArray( data ) ) {
-					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
-				} else {
-					queue.push( data );
-				}
-			}
-			return queue || [];
-		}
-	},
-
-	dequeue: function( elem, type ) {
-		type = type || "fx";
-
-		var queue = jQuery.queue( elem, type ),
-			startLength = queue.length,
-			fn = queue.shift(),
-			hooks = jQuery._queueHooks( elem, type ),
-			next = function() {
-				jQuery.dequeue( elem, type );
-			};
-
-		// If the fx queue is dequeued, always remove the progress sentinel
-		if ( fn === "inprogress" ) {
-			fn = queue.shift();
-			startLength--;
-		}
-
-		if ( fn ) {
-
-			// Add a progress sentinel to prevent the fx queue from being
-			// automatically dequeued
-			if ( type === "fx" ) {
-				queue.unshift( "inprogress" );
-			}
-
-			// Clear up the last queue stop function
-			delete hooks.stop;
-			fn.call( elem, next, hooks );
-		}
-
-		if ( !startLength && hooks ) {
-			hooks.empty.fire();
-		}
-	},
-
-	// Not public - generate a queueHooks object, or return the current one
-	_queueHooks: function( elem, type ) {
-		var key = type + "queueHooks";
-		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
-			empty: jQuery.Callbacks( "once memory" ).add( function() {
-				dataPriv.remove( elem, [ type + "queue", key ] );
-			} )
-		} );
-	}
-} );
-
-jQuery.fn.extend( {
-	queue: function( type, data ) {
-		var setter = 2;
-
-		if ( typeof type !== "string" ) {
-			data = type;
-			type = "fx";
-			setter--;
-		}
-
-		if ( arguments.length < setter ) {
-			return jQuery.queue( this[ 0 ], type );
-		}
-
-		return data === undefined ?
-			this :
-			this.each( function() {
-				var queue = jQuery.queue( this, type, data );
-
-				// Ensure a hooks for this queue
-				jQuery._queueHooks( this, type );
-
-				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
-					jQuery.dequeue( this, type );
-				}
-			} );
-	},
-	dequeue: function( type ) {
-		return this.each( function() {
-			jQuery.dequeue( this, type );
-		} );
-	},
-	clearQueue: function( type ) {
-		return this.queue( type || "fx", [] );
-	},
-
-	// Get a promise resolved when queues of a certain type
-	// are emptied (fx is the type by default)
-	promise: function( type, obj ) {
-		var tmp,
-			count = 1,
-			defer = jQuery.Deferred(),
-			elements = this,
-			i = this.length,
-			resolve = function() {
-				if ( !( --count ) ) {
-					defer.resolveWith( elements, [ elements ] );
-				}
-			};
-
-		if ( typeof type !== "string" ) {
-			obj = type;
-			type = undefined;
-		}
-		type = type || "fx";
-
-		while ( i-- ) {
-			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
-			if ( tmp && tmp.empty ) {
-				count++;
-				tmp.empty.add( resolve );
-			}
-		}
-		resolve();
-		return defer.promise( obj );
-	}
-} );
-var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
-
-var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
-
-
-var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
-
-var documentElement = document.documentElement;
-
-
-
-	var isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem );
-		},
-		composed = { composed: true };
-
-	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
-	// Check attachment across shadow DOM boundaries when possible (gh-3504)
-	// Support: iOS 10.0-10.2 only
-	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
-	// leading to errors. We need to check for `getRootNode`.
-	if ( documentElement.getRootNode ) {
-		isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem ) ||
-				elem.getRootNode( composed ) === elem.ownerDocument;
-		};
-	}
-var isHiddenWithinTree = function( elem, el ) {
-
-		// isHiddenWithinTree might be called from jQuery#filter function;
-		// in that case, element will be second argument
-		elem = el || elem;
-
-		// Inline style trumps all
-		return elem.style.display === "none" ||
-			elem.style.display === "" &&
-
-			// Otherwise, check computed style
-			// Support: Firefox <=43 - 45
-			// Disconnected elements can have computed display: none, so first confirm that elem is
-			// in the document.
-			isAttached( elem ) &&
-
-			jQuery.css( elem, "display" ) === "none";
-	};
-
-
-
-function adjustCSS( elem, prop, valueParts, tween ) {
-	var adjusted, scale,
-		maxIterations = 20,
-		currentValue = tween ?
-			function() {
-				return tween.cur();
-			} :
-			function() {
-				return jQuery.css( elem, prop, "" );
-			},
-		initial = currentValue(),
-		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
-
-		// Starting value computation is required for potential unit mismatches
-		initialInUnit = elem.nodeType &&
-			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
-			rcssNum.exec( jQuery.css( elem, prop ) );
-
-	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
-
-		// Support: Firefox <=54
-		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
-		initial = initial / 2;
-
-		// Trust units reported by jQuery.css
-		unit = unit || initialInUnit[ 3 ];
-
-		// Iteratively approximate from a nonzero starting point
-		initialInUnit = +initial || 1;
-
-		while ( maxIterations-- ) {
-
-			// Evaluate and update our best guess (doubling guesses that zero out).
-			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
-			jQuery.style( elem, prop, initialInUnit + unit );
-			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
-				maxIterations = 0;
-			}
-			initialInUnit = initialInUnit / scale;
-
-		}
-
-		initialInUnit = initialInUnit * 2;
-		jQuery.style( elem, prop, initialInUnit + unit );
-
-		// Make sure we update the tween properties later on
-		valueParts = valueParts || [];
-	}
-
-	if ( valueParts ) {
-		initialInUnit = +initialInUnit || +initial || 0;
-
-		// Apply relative offset (+=/-=) if specified
-		adjusted = valueParts[ 1 ] ?
-			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
-			+valueParts[ 2 ];
-		if ( tween ) {
-			tween.unit = unit;
-			tween.start = initialInUnit;
-			tween.end = adjusted;
-		}
-	}
-	return adjusted;
-}
-
-
-var defaultDisplayMap = {};
-
-function getDefaultDisplay( elem ) {
-	var temp,
-		doc = elem.ownerDocument,
-		nodeName = elem.nodeName,
-		display = defaultDisplayMap[ nodeName ];
-
-	if ( display ) {
-		return display;
-	}
-
-	temp = doc.body.appendChild( doc.createElement( nodeName ) );
-	display = jQuery.css( temp, "display" );
-
-	temp.parentNode.removeChild( temp );
-
-	if ( display === "none" ) {
-		display = "block";
-	}
-	defaultDisplayMap[ nodeName ] = display;
-
-	return display;
-}
-
-function showHide( elements, show ) {
-	var display, elem,
-		values = [],
-		index = 0,
-		length = elements.length;
-
-	// Determine new display value for elements that need to change
-	for ( ; index < length; index++ ) {
-		elem = elements[ index ];
-		if ( !elem.style ) {
-			continue;
-		}
-
-		display = elem.style.display;
-		if ( show ) {
-
-			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
-			// check is required in this first loop unless we have a nonempty display value (either
-			// inline or about-to-be-restored)
-			if ( display === "none" ) {
-				values[ index ] = dataPriv.get( elem, "display" ) || null;
-				if ( !values[ index ] ) {
-					elem.style.display = "";
-				}
-			}
-			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
-				values[ index ] = getDefaultDisplay( elem );
-			}
-		} else {
-			if ( display !== "none" ) {
-				values[ index ] = "none";
-
-				// Remember what we're overwriting
-				dataPriv.set( elem, "display", display );
-			}
-		}
-	}
-
-	// Set the display of the elements in a second loop to avoid constant reflow
-	for ( index = 0; index < length; index++ ) {
-		if ( values[ index ] != null ) {
-			elements[ index ].style.display = values[ index ];
-		}
-	}
-
-	return elements;
-}
-
-jQuery.fn.extend( {
-	show: function() {
-		return showHide( this, true );
-	},
-	hide: function() {
-		return showHide( this );
-	},
-	toggle: function( state ) {
-		if ( typeof state === "boolean" ) {
-			return state ? this.show() : this.hide();
-		}
-
-		return this.each( function() {
-			if ( isHiddenWithinTree( this ) ) {
-				jQuery( this ).show();
-			} else {
-				jQuery( this ).hide();
-			}
-		} );
-	}
-} );
-var rcheckableType = ( /^(?:checkbox|radio)$/i );
-
-var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
-
-var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
-
-
-
-( function() {
-	var fragment = document.createDocumentFragment(),
-		div = fragment.appendChild( document.createElement( "div" ) ),
-		input = document.createElement( "input" );
-
-	// Support: Android 4.0 - 4.3 only
-	// Check state lost if the name is set (#11217)
-	// Support: Windows Web Apps (WWA)
-	// `name` and `type` must use .setAttribute for WWA (#14901)
-	input.setAttribute( "type", "radio" );
-	input.setAttribute( "checked", "checked" );
-	input.setAttribute( "name", "t" );
-
-	div.appendChild( input );
-
-	// Support: Android <=4.1 only
-	// Older WebKit doesn't clone checked state correctly in fragments
-	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
-	// Support: IE <=11 only
-	// Make sure textarea (and checkbox) defaultValue is properly cloned
-	div.innerHTML = "<textarea>x</textarea>";
-	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-
-	// Support: IE <=9 only
-	// IE <=9 replaces <option> tags with their contents when inserted outside of
-	// the select element.
-	div.innerHTML = "<option></option>";
-	support.option = !!div.lastChild;
-} )();
-
-
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
-
-	// XHTML parsers do not magically insert elements in the
-	// same way that tag soup parsers do. So we cannot shorten
-	// this by omitting <tbody> or other required elements.
-	thead: [ 1, "<table>", "</table>" ],
-	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
-	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
-	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
-
-	_default: [ 0, "", "" ]
-};
-
-wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
-wrapMap.th = wrapMap.td;
-
-// Support: IE <=9 only
-if ( !support.option ) {
-	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
-}
-
-
-function getAll( context, tag ) {
-
-	// Support: IE <=9 - 11 only
-	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
-	var ret;
-
-	if ( typeof context.getElementsByTagName !== "undefined" ) {
-		ret = context.getElementsByTagName( tag || "*" );
-
-	} else if ( typeof context.querySelectorAll !== "undefined" ) {
-		ret = context.querySelectorAll( tag || "*" );
-
-	} else {
-		ret = [];
-	}
-
-	if ( tag === undefined || tag && nodeName( context, tag ) ) {
-		return jQuery.merge( [ context ], ret );
-	}
-
-	return ret;
-}
-
-
-// Mark scripts as having already been evaluated
-function setGlobalEval( elems, refElements ) {
-	var i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		dataPriv.set(
-			elems[ i ],
-			"globalEval",
-			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
-		);
-	}
-}
-
-
-var rhtml = /<|&#?\w+;/;
-
-function buildFragment( elems, context, scripts, selection, ignored ) {
-	var elem, tmp, tag, wrap, attached, j,
-		fragment = context.createDocumentFragment(),
-		nodes = [],
-		i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		elem = elems[ i ];
-
-		if ( elem || elem === 0 ) {
-
-			// Add nodes directly
-			if ( toType( elem ) === "object" ) {
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
-
-			// Convert non-html into a text node
-			} else if ( !rhtml.test( elem ) ) {
-				nodes.push( context.createTextNode( elem ) );
-
-			// Convert html into DOM nodes
-			} else {
-				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
-
-				// Deserialize a standard representation
-				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
-				wrap = wrapMap[ tag ] || wrapMap._default;
-				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
-
-				// Descend through wrappers to the right content
-				j = wrap[ 0 ];
-				while ( j-- ) {
-					tmp = tmp.lastChild;
-				}
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, tmp.childNodes );
-
-				// Remember the top-level container
-				tmp = fragment.firstChild;
-
-				// Ensure the created nodes are orphaned (#12392)
-				tmp.textContent = "";
-			}
-		}
-	}
-
-	// Remove wrapper from fragment
-	fragment.textContent = "";
-
-	i = 0;
-	while ( ( elem = nodes[ i++ ] ) ) {
-
-		// Skip elements already in the context collection (trac-4087)
-		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
-			if ( ignored ) {
-				ignored.push( elem );
-			}
-			continue;
-		}
-
-		attached = isAttached( elem );
-
-		// Append to fragment
-		tmp = getAll( fragment.appendChild( elem ), "script" );
-
-		// Preserve script evaluation history
-		if ( attached ) {
-			setGlobalEval( tmp );
-		}
-
-		// Capture executables
-		if ( scripts ) {
-			j = 0;
-			while ( ( elem = tmp[ j++ ] ) ) {
-				if ( rscriptType.test( elem.type || "" ) ) {
-					scripts.push( elem );
-				}
-			}
-		}
-	}
-
-	return fragment;
-}
-
-
-var
-	rkeyEvent = /^key/,
-	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
-	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
-
-function returnTrue() {
-	return true;
-}
-
-function returnFalse() {
-	return false;
-}
-
-// Support: IE <=9 - 11+
-// focus() and blur() are asynchronous, except when they are no-op.
-// So expect focus to be synchronous when the element is already active,
-// and blur to be synchronous when the element is not already active.
-// (focus and blur are always synchronous in other supported browsers,
-// this just defines when we can count on it).
-function expectSync( elem, type ) {
-	return ( elem === safeActiveElement() ) === ( type === "focus" );
-}
-
-// Support: IE <=9 only
-// Accessing document.activeElement can throw unexpectedly
-// https://bugs.jquery.com/ticket/13393
-function safeActiveElement() {
-	try {
-		return document.activeElement;
-	} catch ( err ) { }
-}
-
-function on( elem, types, selector, data, fn, one ) {
-	var origFn, type;
-
-	// Types can be a map of types/handlers
-	if ( typeof types === "object" ) {
-
-		// ( types-Object, selector, data )
-		if ( typeof selector !== "string" ) {
-
-			// ( types-Object, data )
-			data = data || selector;
-			selector = undefined;
-		}
-		for ( type in types ) {
-			on( elem, type, selector, data, types[ type ], one );
-		}
-		return elem;
-	}
-
-	if ( data == null && fn == null ) {
-
-		// ( types, fn )
-		fn = selector;
-		data = selector = undefined;
-	} else if ( fn == null ) {
-		if ( typeof selector === "string" ) {
-
-			// ( types, selector, fn )
-			fn = data;
-			data = undefined;
-		} else {
-
-			// ( types, data, fn )
-			fn = data;
-			data = selector;
-			selector = undefined;
-		}
-	}
-	if ( fn === false ) {
-		fn = returnFalse;
-	} else if ( !fn ) {
-		return elem;
-	}
-
-	if ( one === 1 ) {
-		origFn = fn;
-		fn = function( event ) {
-
-			// Can use an empty set, since event contains the info
-			jQuery().off( event );
-			return origFn.apply( this, arguments );
-		};
-
-		// Use same guid so caller can remove using origFn
-		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
-	}
-	return elem.each( function() {
-		jQuery.event.add( this, types, fn, data, selector );
-	} );
-}
-
-/*
- * Helper functions for managing events -- not part of the public interface.
- * Props to Dean Edwards' addEvent library for many of the ideas.
- */
-jQuery.event = {
-
-	global: {},
-
-	add: function( elem, types, handler, data, selector ) {
-
-		var handleObjIn, eventHandle, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.get( elem );
-
-		// Only attach events to objects that accept data
-		if ( !acceptData( elem ) ) {
-			return;
-		}
-
-		// Caller can pass in an object of custom data in lieu of the handler
-		if ( handler.handler ) {
-			handleObjIn = handler;
-			handler = handleObjIn.handler;
-			selector = handleObjIn.selector;
-		}
-
-		// Ensure that invalid selectors throw exceptions at attach time
-		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
-		if ( selector ) {
-			jQuery.find.matchesSelector( documentElement, selector );
-		}
-
-		// Make sure that the handler has a unique ID, used to find/remove it later
-		if ( !handler.guid ) {
-			handler.guid = jQuery.guid++;
-		}
-
-		// Init the element's event structure and main handler, if this is the first
-		if ( !( events = elemData.events ) ) {
-			events = elemData.events = Object.create( null );
-		}
-		if ( !( eventHandle = elemData.handle ) ) {
-			eventHandle = elemData.handle = function( e ) {
-
-				// Discard the second event of a jQuery.event.trigger() and
-				// when an event is called after a page has unloaded
-				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
-					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
-			};
-		}
-
-		// Handle multiple events separated by a space
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// There *must* be a type, no attaching namespace-only handlers
-			if ( !type ) {
-				continue;
-			}
-
-			// If event changes its type, use the special event handlers for the changed type
-			special = jQuery.event.special[ type ] || {};
-
-			// If selector defined, determine special event api type, otherwise given type
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-
-			// Update special based on newly reset type
-			special = jQuery.event.special[ type ] || {};
-
-			// handleObj is passed to all event handlers
-			handleObj = jQuery.extend( {
-				type: type,
-				origType: origType,
-				data: data,
-				handler: handler,
-				guid: handler.guid,
-				selector: selector,
-				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
-				namespace: namespaces.join( "." )
-			}, handleObjIn );
-
-			// Init the event handler queue if we're the first
-			if ( !( handlers = events[ type ] ) ) {
-				handlers = events[ type ] = [];
-				handlers.delegateCount = 0;
-
-				// Only use addEventListener if the special events handler returns false
-				if ( !special.setup ||
-					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
-
-					if ( elem.addEventListener ) {
-						elem.addEventListener( type, eventHandle );
-					}
-				}
-			}
-
-			if ( special.add ) {
-				special.add.call( elem, handleObj );
-
-				if ( !handleObj.handler.guid ) {
-					handleObj.handler.guid = handler.guid;
-				}
-			}
-
-			// Add to the element's handler list, delegates in front
-			if ( selector ) {
-				handlers.splice( handlers.delegateCount++, 0, handleObj );
-			} else {
-				handlers.push( handleObj );
-			}
-
-			// Keep track of which events have ever been used, for event optimization
-			jQuery.event.global[ type ] = true;
-		}
-
-	},
-
-	// Detach an event or set of events from an element
-	remove: function( elem, types, handler, selector, mappedTypes ) {
-
-		var j, origCount, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
-
-		if ( !elemData || !( events = elemData.events ) ) {
-			return;
-		}
-
-		// Once for each type.namespace in types; type may be omitted
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// Unbind all events (on this namespace, if provided) for the element
-			if ( !type ) {
-				for ( type in events ) {
-					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
-				}
-				continue;
-			}
-
-			special = jQuery.event.special[ type ] || {};
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-			handlers = events[ type ] || [];
-			tmp = tmp[ 2 ] &&
-				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
-
-			// Remove matching events
-			origCount = j = handlers.length;
-			while ( j-- ) {
-				handleObj = handlers[ j ];
-
-				if ( ( mappedTypes || origType === handleObj.origType ) &&
-					( !handler || handler.guid === handleObj.guid ) &&
-					( !tmp || tmp.test( handleObj.namespace ) ) &&
-					( !selector || selector === handleObj.selector ||
-						selector === "**" && handleObj.selector ) ) {
-					handlers.splice( j, 1 );
-
-					if ( handleObj.selector ) {
-						handlers.delegateCount--;
-					}
-					if ( special.remove ) {
-						special.remove.call( elem, handleObj );
-					}
-				}
-			}
-
-			// Remove generic event handler if we removed something and no more handlers exist
-			// (avoids potential for endless recursion during removal of special event handlers)
-			if ( origCount && !handlers.length ) {
-				if ( !special.teardown ||
-					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
-
-					jQuery.removeEvent( elem, type, elemData.handle );
-				}
-
-				delete events[ type ];
-			}
-		}
-
-		// Remove data and the expando if it's no longer used
-		if ( jQuery.isEmptyObject( events ) ) {
-			dataPriv.remove( elem, "handle events" );
-		}
-	},
-
-	dispatch: function( nativeEvent ) {
-
-		var i, j, ret, matched, handleObj, handlerQueue,
-			args = new Array( arguments.length ),
-
-			// Make a writable jQuery.Event from the native event object
-			event = jQuery.event.fix( nativeEvent ),
-
-			handlers = (
-					dataPriv.get( this, "events" ) || Object.create( null )
-				)[ event.type ] || [],
-			special = jQuery.event.special[ event.type ] || {};
-
-		// Use the fix-ed jQuery.Event rather than the (read-only) native event
-		args[ 0 ] = event;
-
-		for ( i = 1; i < arguments.length; i++ ) {
-			args[ i ] = arguments[ i ];
-		}
-
-		event.delegateTarget = this;
-
-		// Call the preDispatch hook for the mapped type, and let it bail if desired
-		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
-			return;
-		}
-
-		// Determine handlers
-		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
-
-		// Run delegates first; they may want to stop propagation beneath us
-		i = 0;
-		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
-			event.currentTarget = matched.elem;
-
-			j = 0;
-			while ( ( handleObj = matched.handlers[ j++ ] ) &&
-				!event.isImmediatePropagationStopped() ) {
-
-				// If the event is namespaced, then each handler is only invoked if it is
-				// specially universal or its namespaces are a superset of the event's.
-				if ( !event.rnamespace || handleObj.namespace === false ||
-					event.rnamespace.test( handleObj.namespace ) ) {
-
-					event.handleObj = handleObj;
-					event.data = handleObj.data;
-
-					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
-						handleObj.handler ).apply( matched.elem, args );
-
-					if ( ret !== undefined ) {
-						if ( ( event.result = ret ) === false ) {
-							event.preventDefault();
-							event.stopPropagation();
-						}
-					}
-				}
-			}
-		}
-
-		// Call the postDispatch hook for the mapped type
-		if ( special.postDispatch ) {
-			special.postDispatch.call( this, event );
-		}
-
-		return event.result;
-	},
-
-	handlers: function( event, handlers ) {
-		var i, handleObj, sel, matchedHandlers, matchedSelectors,
-			handlerQueue = [],
-			delegateCount = handlers.delegateCount,
-			cur = event.target;
-
-		// Find delegate handlers
-		if ( delegateCount &&
-
-			// Support: IE <=9
-			// Black-hole SVG <use> instance trees (trac-13180)
-			cur.nodeType &&
-
-			// Support: Firefox <=42
-			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
-			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
-			// Support: IE 11 only
-			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
-			!( event.type === "click" && event.button >= 1 ) ) {
-
-			for ( ; cur !== this; cur = cur.parentNode || this ) {
-
-				// Don't check non-elements (#13208)
-				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
-				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
-					matchedHandlers = [];
-					matchedSelectors = {};
-					for ( i = 0; i < delegateCount; i++ ) {
-						handleObj = handlers[ i ];
-
-						// Don't conflict with Object.prototype properties (#13203)
-						sel = handleObj.selector + " ";
-
-						if ( matchedSelectors[ sel ] === undefined ) {
-							matchedSelectors[ sel ] = handleObj.needsContext ?
-								jQuery( sel, this ).index( cur ) > -1 :
-								jQuery.find( sel, this, null, [ cur ] ).length;
-						}
-						if ( matchedSelectors[ sel ] ) {
-							matchedHandlers.push( handleObj );
-						}
-					}
-					if ( matchedHandlers.length ) {
-						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
-					}
-				}
-			}
-		}
-
-		// Add the remaining (directly-bound) handlers
-		cur = this;
-		if ( delegateCount < handlers.length ) {
-			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
-		}
-
-		return handlerQueue;
-	},
-
-	addProp: function( name, hook ) {
-		Object.defineProperty( jQuery.Event.prototype, name, {
-			enumerable: true,
-			configurable: true,
-
-			get: isFunction( hook ) ?
-				function() {
-					if ( this.originalEvent ) {
-							return hook( this.originalEvent );
-					}
-				} :
-				function() {
-					if ( this.originalEvent ) {
-							return this.originalEvent[ name ];
-					}
-				},
-
-			set: function( value ) {
-				Object.defineProperty( this, name, {
-					enumerable: true,
-					configurable: true,
-					writable: true,
-					value: value
-				} );
-			}
-		} );
-	},
-
-	fix: function( originalEvent ) {
-		return originalEvent[ jQuery.expando ] ?
-			originalEvent :
-			new jQuery.Event( originalEvent );
-	},
-
-	special: {
-		load: {
-
-			// Prevent triggered image.load events from bubbling to window.load
-			noBubble: true
-		},
-		click: {
-
-			// Utilize native event to ensure correct state for checkable inputs
-			setup: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Claim the first handler
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					// dataPriv.set( el, "click", ... )
-					leverageNative( el, "click", returnTrue );
-				}
-
-				// Return false to allow normal processing in the caller
-				return false;
-			},
-			trigger: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Force setup before triggering a click
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					leverageNative( el, "click" );
-				}
-
-				// Return non-false to allow normal event-path propagation
-				return true;
-			},
-
-			// For cross-browser consistency, suppress native .click() on links
-			// Also prevent it if we're currently inside a leveraged native-event stack
-			_default: function( event ) {
-				var target = event.target;
-				return rcheckableType.test( target.type ) &&
-					target.click && nodeName( target, "input" ) &&
-					dataPriv.get( target, "click" ) ||
-					nodeName( target, "a" );
-			}
-		},
-
-		beforeunload: {
-			postDispatch: function( event ) {
-
-				// Support: Firefox 20+
-				// Firefox doesn't alert if the returnValue field is not set.
-				if ( event.result !== undefined && event.originalEvent ) {
-					event.originalEvent.returnValue = event.result;
-				}
-			}
-		}
-	}
-};
-
-// Ensure the presence of an event listener that handles manually-triggered
-// synthetic events by interrupting progress until reinvoked in response to
-// *native* events that it fires directly, ensuring that state changes have
-// already occurred before other listeners are invoked.
-function leverageNative( el, type, expectSync ) {
-
-	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
-	if ( !expectSync ) {
-		if ( dataPriv.get( el, type ) === undefined ) {
-			jQuery.event.add( el, type, returnTrue );
-		}
-		return;
-	}
-
-	// Register the controller as a special universal handler for all event namespaces
-	dataPriv.set( el, type, false );
-	jQuery.event.add( el, type, {
-		namespace: false,
-		handler: function( event ) {
-			var notAsync, result,
-				saved = dataPriv.get( this, type );
-
-			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
-
-				// Interrupt processing of the outer synthetic .trigger()ed event
-				// Saved data should be false in such cases, but might be a leftover capture object
-				// from an async native handler (gh-4350)
-				if ( !saved.length ) {
-
-					// Store arguments for use when handling the inner native event
-					// There will always be at least one argument (an event object), so this array
-					// will not be confused with a leftover capture object.
-					saved = slice.call( arguments );
-					dataPriv.set( this, type, saved );
-
-					// Trigger the native event and capture its result
-					// Support: IE <=9 - 11+
-					// focus() and blur() are asynchronous
-					notAsync = expectSync( this, type );
-					this[ type ]();
-					result = dataPriv.get( this, type );
-					if ( saved !== result || notAsync ) {
-						dataPriv.set( this, type, false );
-					} else {
-						result = {};
-					}
-					if ( saved !== result ) {
-
-						// Cancel the outer synthetic event
-						event.stopImmediatePropagation();
-						event.preventDefault();
-						return result.value;
-					}
-
-				// If this is an inner synthetic event for an event with a bubbling surrogate
-				// (focus or blur), assume that the surrogate already propagated from triggering the
-				// native event and prevent that from happening again here.
-				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
-				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
-				// less bad than duplication.
-				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
-					event.stopPropagation();
-				}
-
-			// If this is a native event triggered above, everything is now in order
-			// Fire an inner synthetic event with the original arguments
-			} else if ( saved.length ) {
-
-				// ...and capture the result
-				dataPriv.set( this, type, {
-					value: jQuery.event.trigger(
-
-						// Support: IE <=9 - 11+
-						// Extend with the prototype to reset the above stopImmediatePropagation()
-						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
-						saved.slice( 1 ),
-						this
-					)
-				} );
-
-				// Abort handling of the native event
-				event.stopImmediatePropagation();
-			}
-		}
-	} );
-}
-
-jQuery.removeEvent = function( elem, type, handle ) {
-
-	// This "if" is needed for plain objects
-	if ( elem.removeEventListener ) {
-		elem.removeEventListener( type, handle );
-	}
-};
-
-jQuery.Event = function( src, props ) {
-
-	// Allow instantiation without the 'new' keyword
-	if ( !( this instanceof jQuery.Event ) ) {
-		return new jQuery.Event( src, props );
-	}
-
-	// Event object
-	if ( src && src.type ) {
-		this.originalEvent = src;
-		this.type = src.type;
-
-		// Events bubbling up the document may have been marked as prevented
-		// by a handler lower down the tree; reflect the correct value.
-		this.isDefaultPrevented = src.defaultPrevented ||
-				src.defaultPrevented === undefined &&
-
-				// Support: Android <=2.3 only
-				src.returnValue === false ?
-			returnTrue :
-			returnFalse;
-
-		// Create target properties
-		// Support: Safari <=6 - 7 only
-		// Target should not be a text node (#504, #13143)
-		this.target = ( src.target && src.target.nodeType === 3 ) ?
-			src.target.parentNode :
-			src.target;
-
-		this.currentTarget = src.currentTarget;
-		this.relatedTarget = src.relatedTarget;
-
-	// Event type
-	} else {
-		this.type = src;
-	}
-
-	// Put explicitly provided properties onto the event object
-	if ( props ) {
-		jQuery.extend( this, props );
-	}
-
-	// Create a timestamp if incoming event doesn't have one
-	this.timeStamp = src && src.timeStamp || Date.now();
-
-	// Mark it as fixed
-	this[ jQuery.expando ] = true;
-};
-
-// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
-// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
-jQuery.Event.prototype = {
-	constructor: jQuery.Event,
-	isDefaultPrevented: returnFalse,
-	isPropagationStopped: returnFalse,
-	isImmediatePropagationStopped: returnFalse,
-	isSimulated: false,
-
-	preventDefault: function() {
-		var e = this.originalEvent;
-
-		this.isDefaultPrevented = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.preventDefault();
-		}
-	},
-	stopPropagation: function() {
-		var e = this.originalEvent;
-
-		this.isPropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopPropagation();
-		}
-	},
-	stopImmediatePropagation: function() {
-		var e = this.originalEvent;
-
-		this.isImmediatePropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopImmediatePropagation();
-		}
-
-		this.stopPropagation();
-	}
-};
-
-// Includes all common event props including KeyEvent and MouseEvent specific props
-jQuery.each( {
-	altKey: true,
-	bubbles: true,
-	cancelable: true,
-	changedTouches: true,
-	ctrlKey: true,
-	detail: true,
-	eventPhase: true,
-	metaKey: true,
-	pageX: true,
-	pageY: true,
-	shiftKey: true,
-	view: true,
-	"char": true,
-	code: true,
-	charCode: true,
-	key: true,
-	keyCode: true,
-	button: true,
-	buttons: true,
-	clientX: true,
-	clientY: true,
-	offsetX: true,
-	offsetY: true,
-	pointerId: true,
-	pointerType: true,
-	screenX: true,
-	screenY: true,
-	targetTouches: true,
-	toElement: true,
-	touches: true,
-
-	which: function( event ) {
-		var button = event.button;
-
-		// Add which for key events
-		if ( event.which == null && rkeyEvent.test( event.type ) ) {
-			return event.charCode != null ? event.charCode : event.keyCode;
-		}
-
-		// Add which for click: 1 === left; 2 === middle; 3 === right
-		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
-			if ( button & 1 ) {
-				return 1;
-			}
-
-			if ( button & 2 ) {
-				return 3;
-			}
-
-			if ( button & 4 ) {
-				return 2;
-			}
-
-			return 0;
-		}
-
-		return event.which;
-	}
-}, jQuery.event.addProp );
-
-jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
-	jQuery.event.special[ type ] = {
-
-		// Utilize native event if possible so blur/focus sequence is correct
-		setup: function() {
-
-			// Claim the first handler
-			// dataPriv.set( this, "focus", ... )
-			// dataPriv.set( this, "blur", ... )
-			leverageNative( this, type, expectSync );
-
-			// Return false to allow normal processing in the caller
-			return false;
-		},
-		trigger: function() {
-
-			// Force setup before trigger
-			leverageNative( this, type );
-
-			// Return non-false to allow normal event-path propagation
-			return true;
-		},
-
-		delegateType: delegateType
-	};
-} );
-
-// Create mouseenter/leave events using mouseover/out and event-time checks
-// so that event delegation works in jQuery.
-// Do the same for pointerenter/pointerleave and pointerover/pointerout
-//
-// Support: Safari 7 only
-// Safari sends mouseenter too often; see:
-// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
-// for the description of the bug (it existed in older Chrome versions as well).
-jQuery.each( {
-	mouseenter: "mouseover",
-	mouseleave: "mouseout",
-	pointerenter: "pointerover",
-	pointerleave: "pointerout"
-}, function( orig, fix ) {
-	jQuery.event.special[ orig ] = {
-		delegateType: fix,
-		bindType: fix,
-
-		handle: function( event ) {
-			var ret,
-				target = this,
-				related = event.relatedTarget,
-				handleObj = event.handleObj;
-
-			// For mouseenter/leave call the handler if related is outside the target.
-			// NB: No relatedTarget if the mouse left/entered the browser window
-			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
-				event.type = handleObj.origType;
-				ret = handleObj.handler.apply( this, arguments );
-				event.type = fix;
-			}
-			return ret;
-		}
-	};
-} );
-
-jQuery.fn.extend( {
-
-	on: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn );
-	},
-	one: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn, 1 );
-	},
-	off: function( types, selector, fn ) {
-		var handleObj, type;
-		if ( types && types.preventDefault && types.handleObj ) {
-
-			// ( event )  dispatched jQuery.Event
-			handleObj = types.handleObj;
-			jQuery( types.delegateTarget ).off(
-				handleObj.namespace ?
-					handleObj.origType + "." + handleObj.namespace :
-					handleObj.origType,
-				handleObj.selector,
-				handleObj.handler
-			);
-			return this;
-		}
-		if ( typeof types === "object" ) {
-
-			// ( types-object [, selector] )
-			for ( type in types ) {
-				this.off( type, selector, types[ type ] );
-			}
-			return this;
-		}
-		if ( selector === false || typeof selector === "function" ) {
-
-			// ( types [, fn] )
-			fn = selector;
-			selector = undefined;
-		}
-		if ( fn === false ) {
-			fn = returnFalse;
-		}
-		return this.each( function() {
-			jQuery.event.remove( this, types, fn, selector );
-		} );
-	}
-} );
-
-
-var
-
-	// Support: IE <=10 - 11, Edge 12 - 13 only
-	// In IE/Edge using regex groups here causes severe slowdowns.
-	// See https://connect.microsoft.com/IE/feedback/details/1736512/
-	rnoInnerhtml = /<script|<style|<link/i,
-
-	// checked="checked" or checked
-	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
-	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
-
-// Prefer a tbody over its parent table for containing new rows
-function manipulationTarget( elem, content ) {
-	if ( nodeName( elem, "table" ) &&
-		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
-
-		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
-	}
-
-	return elem;
-}
-
-// Replace/restore the type attribute of script elements for safe DOM manipulation
-function disableScript( elem ) {
-	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
-	return elem;
-}
-function restoreScript( elem ) {
-	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
-		elem.type = elem.type.slice( 5 );
-	} else {
-		elem.removeAttribute( "type" );
-	}
-
-	return elem;
-}
-
-function cloneCopyEvent( src, dest ) {
-	var i, l, type, pdataOld, udataOld, udataCur, events;
-
-	if ( dest.nodeType !== 1 ) {
-		return;
-	}
-
-	// 1. Copy private data: events, handlers, etc.
-	if ( dataPriv.hasData( src ) ) {
-		pdataOld = dataPriv.get( src );
-		events = pdataOld.events;
-
-		if ( events ) {
-			dataPriv.remove( dest, "handle events" );
-
-			for ( type in events ) {
-				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
-					jQuery.event.add( dest, type, events[ type ][ i ] );
-				}
-			}
-		}
-	}
-
-	// 2. Copy user data
-	if ( dataUser.hasData( src ) ) {
-		udataOld = dataUser.access( src );
-		udataCur = jQuery.extend( {}, udataOld );
-
-		dataUser.set( dest, udataCur );
-	}
-}
-
-// Fix IE bugs, see support tests
-function fixInput( src, dest ) {
-	var nodeName = dest.nodeName.toLowerCase();
-
-	// Fails to persist the checked state of a cloned checkbox or radio button.
-	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
-		dest.checked = src.checked;
-
-	// Fails to return the selected option to the default selected state when cloning options
-	} else if ( nodeName === "input" || nodeName === "textarea" ) {
-		dest.defaultValue = src.defaultValue;
-	}
-}
-
-function domManip( collection, args, callback, ignored ) {
-
-	// Flatten any nested arrays
-	args = flat( args );
-
-	var fragment, first, scripts, hasScripts, node, doc,
-		i = 0,
-		l = collection.length,
-		iNoClone = l - 1,
-		value = args[ 0 ],
-		valueIsFunction = isFunction( value );
-
-	// We can't cloneNode fragments that contain checked, in WebKit
-	if ( valueIsFunction ||
-			( l > 1 && typeof value === "string" &&
-				!support.checkClone && rchecked.test( value ) ) ) {
-		return collection.each( function( index ) {
-			var self = collection.eq( index );
-			if ( valueIsFunction ) {
-				args[ 0 ] = value.call( this, index, self.html() );
-			}
-			domManip( self, args, callback, ignored );
-		} );
-	}
-
-	if ( l ) {
-		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
-		first = fragment.firstChild;
-
-		if ( fragment.childNodes.length === 1 ) {
-			fragment = first;
-		}
-
-		// Require either new content or an interest in ignored elements to invoke the callback
-		if ( first || ignored ) {
-			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
-			hasScripts = scripts.length;
-
-			// Use the original fragment for the last item
-			// instead of the first because it can end up
-			// being emptied incorrectly in certain situations (#8070).
-			for ( ; i < l; i++ ) {
-				node = fragment;
-
-				if ( i !== iNoClone ) {
-					node = jQuery.clone( node, true, true );
-
-					// Keep references to cloned scripts for later restoration
-					if ( hasScripts ) {
-
-						// Support: Android <=4.0 only, PhantomJS 1 only
-						// push.apply(_, arraylike) throws on ancient WebKit
-						jQuery.merge( scripts, getAll( node, "script" ) );
-					}
-				}
-
-				callback.call( collection[ i ], node, i );
-			}
-
-			if ( hasScripts ) {
-				doc = scripts[ scripts.length - 1 ].ownerDocument;
-
-				// Reenable scripts
-				jQuery.map( scripts, restoreScript );
-
-				// Evaluate executable scripts on first document insertion
-				for ( i = 0; i < hasScripts; i++ ) {
-					node = scripts[ i ];
-					if ( rscriptType.test( node.type || "" ) &&
-						!dataPriv.access( node, "globalEval" ) &&
-						jQuery.contains( doc, node ) ) {
-
-						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
-
-							// Optional AJAX dependency, but won't run scripts if not present
-							if ( jQuery._evalUrl && !node.noModule ) {
-								jQuery._evalUrl( node.src, {
-									nonce: node.nonce || node.getAttribute( "nonce" )
-								}, doc );
-							}
-						} else {
-							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return collection;
-}
-
-function remove( elem, selector, keepData ) {
-	var node,
-		nodes = selector ? jQuery.filter( selector, elem ) : elem,
-		i = 0;
-
-	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
-		if ( !keepData && node.nodeType === 1 ) {
-			jQuery.cleanData( getAll( node ) );
-		}
-
-		if ( node.parentNode ) {
-			if ( keepData && isAttached( node ) ) {
-				setGlobalEval( getAll( node, "script" ) );
-			}
-			node.parentNode.removeChild( node );
-		}
-	}
-
-	return elem;
-}
-
-jQuery.extend( {
-	htmlPrefilter: function( html ) {
-		return html;
-	},
-
-	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
-		var i, l, srcElements, destElements,
-			clone = elem.cloneNode( true ),
-			inPage = isAttached( elem );
-
-		// Fix IE cloning issues
-		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
-				!jQuery.isXMLDoc( elem ) ) {
-
-			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
-			destElements = getAll( clone );
-			srcElements = getAll( elem );
-
-			for ( i = 0, l = srcElements.length; i < l; i++ ) {
-				fixInput( srcElements[ i ], destElements[ i ] );
-			}
-		}
-
-		// Copy the events from the original to the clone
-		if ( dataAndEvents ) {
-			if ( deepDataAndEvents ) {
-				srcElements = srcElements || getAll( elem );
-				destElements = destElements || getAll( clone );
-
-				for ( i = 0, l = srcElements.length; i < l; i++ ) {
-					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
-				}
-			} else {
-				cloneCopyEvent( elem, clone );
-			}
-		}
-
-		// Preserve script evaluation history
-		destElements = getAll( clone, "script" );
-		if ( destElements.length > 0 ) {
-			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
-		}
-
-		// Return the cloned set
-		return clone;
-	},
-
-	cleanData: function( elems ) {
-		var data, elem, type,
-			special = jQuery.event.special,
-			i = 0;
-
-		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
-			if ( acceptData( elem ) ) {
-				if ( ( data = elem[ dataPriv.expando ] ) ) {
-					if ( data.events ) {
-						for ( type in data.events ) {
-							if ( special[ type ] ) {
-								jQuery.event.remove( elem, type );
-
-							// This is a shortcut to avoid jQuery.event.remove's overhead
-							} else {
-								jQuery.removeEvent( elem, type, data.handle );
-							}
-						}
-					}
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataPriv.expando ] = undefined;
-				}
-				if ( elem[ dataUser.expando ] ) {
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataUser.expando ] = undefined;
-				}
-			}
-		}
-	}
-} );
-
-jQuery.fn.extend( {
-	detach: function( selector ) {
-		return remove( this, selector, true );
-	},
-
-	remove: function( selector ) {
-		return remove( this, selector );
-	},
-
-	text: function( value ) {
-		return access( this, function( value ) {
-			return value === undefined ?
-				jQuery.text( this ) :
-				this.empty().each( function() {
-					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-						this.textContent = value;
-					}
-				} );
-		}, null, value, arguments.length );
-	},
-
-	append: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.appendChild( elem );
-			}
-		} );
-	},
-
-	prepend: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.insertBefore( elem, target.firstChild );
-			}
-		} );
-	},
-
-	before: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this );
-			}
-		} );
-	},
-
-	after: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this.nextSibling );
-			}
-		} );
-	},
-
-	empty: function() {
-		var elem,
-			i = 0;
-
-		for ( ; ( elem = this[ i ] ) != null; i++ ) {
-			if ( elem.nodeType === 1 ) {
-
-				// Prevent memory leaks
-				jQuery.cleanData( getAll( elem, false ) );
-
-				// Remove any remaining nodes
-				elem.textContent = "";
-			}
-		}
-
-		return this;
-	},
-
-	clone: function( dataAndEvents, deepDataAndEvents ) {
-		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
-		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
-
-		return this.map( function() {
-			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
-		} );
-	},
-
-	html: function( value ) {
-		return access( this, function( value ) {
-			var elem = this[ 0 ] || {},
-				i = 0,
-				l = this.length;
-
-			if ( value === undefined && elem.nodeType === 1 ) {
-				return elem.innerHTML;
-			}
-
-			// See if we can take a shortcut and just use innerHTML
-			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
-				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
-
-				value = jQuery.htmlPrefilter( value );
-
-				try {
-					for ( ; i < l; i++ ) {
-						elem = this[ i ] || {};
-
-						// Remove element nodes and prevent memory leaks
-						if ( elem.nodeType === 1 ) {
-							jQuery.cleanData( getAll( elem, false ) );
-							elem.innerHTML = value;
-						}
-					}
-
-					elem = 0;
-
-				// If using innerHTML throws an exception, use the fallback method
-				} catch ( e ) {}
-			}
-
-			if ( elem ) {
-				this.empty().append( value );
-			}
-		}, null, value, arguments.length );
-	},
-
-	replaceWith: function() {
-		var ignored = [];
-
-		// Make the changes, replacing each non-ignored context element with the new content
-		return domManip( this, arguments, function( elem ) {
-			var parent = this.parentNode;
-
-			if ( jQuery.inArray( this, ignored ) < 0 ) {
-				jQuery.cleanData( getAll( this ) );
-				if ( parent ) {
-					parent.replaceChild( elem, this );
-				}
-			}
-
-		// Force callback invocation
-		}, ignored );
-	}
-} );
-
-jQuery.each( {
-	appendTo: "append",
-	prependTo: "prepend",
-	insertBefore: "before",
-	insertAfter: "after",
-	replaceAll: "replaceWith"
-}, function( name, original ) {
-	jQuery.fn[ name ] = function( selector ) {
-		var elems,
-			ret = [],
-			insert = jQuery( selector ),
-			last = insert.length - 1,
-			i = 0;
-
-		for ( ; i <= last; i++ ) {
-			elems = i === last ? this : this.clone( true );
-			jQuery( insert[ i ] )[ original ]( elems );
-
-			// Support: Android <=4.0 only, PhantomJS 1 only
-			// .get() because push.apply(_, arraylike) throws on ancient WebKit
-			push.apply( ret, elems.get() );
-		}
-
-		return this.pushStack( ret );
-	};
-} );
-var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
-
-var getStyles = function( elem ) {
-
-		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
-		// IE throws on elements created in popups
-		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
-		var view = elem.ownerDocument.defaultView;
-
-		if ( !view || !view.opener ) {
-			view = window;
-		}
-
-		return view.getComputedStyle( elem );
-	};
-
-var swap = function( elem, options, callback ) {
-	var ret, name,
-		old = {};
-
-	// Remember the old values, and insert the new ones
-	for ( name in options ) {
-		old[ name ] = elem.style[ name ];
-		elem.style[ name ] = options[ name ];
-	}
-
-	ret = callback.call( elem );
-
-	// Revert the old values
-	for ( name in options ) {
-		elem.style[ name ] = old[ name ];
-	}
-
-	return ret;
-};
-
-
-var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
-
-
-
-( function() {
-
-	// Executing both pixelPosition & boxSizingReliable tests require only one layout
-	// so they're executed at the same time to save the second computation.
-	function computeStyleTests() {
-
-		// This is a singleton, we need to execute it only once
-		if ( !div ) {
-			return;
-		}
-
-		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
-			"margin-top:1px;padding:0;border:0";
-		div.style.cssText =
-			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
-			"margin:auto;border:1px;padding:1px;" +
-			"width:60%;top:1%";
-		documentElement.appendChild( container ).appendChild( div );
-
-		var divStyle = window.getComputedStyle( div );
-		pixelPositionVal = divStyle.top !== "1%";
-
-		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
-		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
-
-		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
-		// Some styles come back with percentage values, even though they shouldn't
-		div.style.right = "60%";
-		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
-
-		// Support: IE 9 - 11 only
-		// Detect misreporting of content dimensions for box-sizing:border-box elements
-		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
-
-		// Support: IE 9 only
-		// Detect overflow:scroll screwiness (gh-3699)
-		// Support: Chrome <=64
-		// Don't get tricked when zoom affects offsetWidth (gh-4029)
-		div.style.position = "absolute";
-		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
-
-		documentElement.removeChild( container );
-
-		// Nullify the div so it wouldn't be stored in the memory and
-		// it will also be a sign that checks already performed
-		div = null;
-	}
-
-	function roundPixelMeasures( measure ) {
-		return Math.round( parseFloat( measure ) );
-	}
-
-	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
-		reliableTrDimensionsVal, reliableMarginLeftVal,
-		container = document.createElement( "div" ),
-		div = document.createElement( "div" );
-
-	// Finish early in limited (non-browser) environments
-	if ( !div.style ) {
-		return;
-	}
-
-	// Support: IE <=9 - 11 only
-	// Style of cloned element affects source element cloned (#8908)
-	div.style.backgroundClip = "content-box";
-	div.cloneNode( true ).style.backgroundClip = "";
-	support.clearCloneStyle = div.style.backgroundClip === "content-box";
-
-	jQuery.extend( support, {
-		boxSizingReliable: function() {
-			computeStyleTests();
-			return boxSizingReliableVal;
-		},
-		pixelBoxStyles: function() {
-			computeStyleTests();
-			return pixelBoxStylesVal;
-		},
-		pixelPosition: function() {
-			computeStyleTests();
-			return pixelPositionVal;
-		},
-		reliableMarginLeft: function() {
-			computeStyleTests();
-			return reliableMarginLeftVal;
-		},
-		scrollboxSize: function() {
-			computeStyleTests();
-			return scrollboxSizeVal;
-		},
-
-		// Support: IE 9 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Behavior in IE 9 is more subtle than in newer versions & it passes
-		// some versions of this test; make sure not to make it pass there!
-		reliableTrDimensions: function() {
-			var table, tr, trChild, trStyle;
-			if ( reliableTrDimensionsVal == null ) {
-				table = document.createElement( "table" );
-				tr = document.createElement( "tr" );
-				trChild = document.createElement( "div" );
-
-				table.style.cssText = "position:absolute;left:-11111px";
-				tr.style.height = "1px";
-				trChild.style.height = "9px";
-
-				documentElement
-					.appendChild( table )
-					.appendChild( tr )
-					.appendChild( trChild );
-
-				trStyle = window.getComputedStyle( tr );
-				reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
-
-				documentElement.removeChild( table );
-			}
-			return reliableTrDimensionsVal;
-		}
-	} );
-} )();
-
-
-function curCSS( elem, name, computed ) {
-	var width, minWidth, maxWidth, ret,
-
-		// Support: Firefox 51+
-		// Retrieving style before computed somehow
-		// fixes an issue with getting wrong values
-		// on detached elements
-		style = elem.style;
-
-	computed = computed || getStyles( elem );
-
-	// getPropertyValue is needed for:
-	//   .css('filter') (IE 9 only, #12537)
-	//   .css('--customProperty) (#3144)
-	if ( computed ) {
-		ret = computed.getPropertyValue( name ) || computed[ name ];
-
-		if ( ret === "" && !isAttached( elem ) ) {
-			ret = jQuery.style( elem, name );
-		}
-
-		// A tribute to the "awesome hack by Dean Edwards"
-		// Android Browser returns percentage for some values,
-		// but width seems to be reliably pixels.
-		// This is against the CSSOM draft spec:
-		// https://drafts.csswg.org/cssom/#resolved-values
-		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
-
-			// Remember the original values
-			width = style.width;
-			minWidth = style.minWidth;
-			maxWidth = style.maxWidth;
-
-			// Put in the new values to get a computed value out
-			style.minWidth = style.maxWidth = style.width = ret;
-			ret = computed.width;
-
-			// Revert the changed values
-			style.width = width;
-			style.minWidth = minWidth;
-			style.maxWidth = maxWidth;
-		}
-	}
-
-	return ret !== undefined ?
-
-		// Support: IE <=9 - 11 only
-		// IE returns zIndex value as an integer.
-		ret + "" :
-		ret;
-}
-
-
-function addGetHookIf( conditionFn, hookFn ) {
-
-	// Define the hook, we'll check on the first run if it's really needed.
-	return {
-		get: function() {
-			if ( conditionFn() ) {
-
-				// Hook not needed (or it's not possible to use it due
-				// to missing dependency), remove it.
-				delete this.get;
-				return;
-			}
-
-			// Hook needed; redefine it so that the support test is not executed again.
-			return ( this.get = hookFn ).apply( this, arguments );
-		}
-	};
-}
-
-
-var cssPrefixes = [ "Webkit", "Moz", "ms" ],
-	emptyStyle = document.createElement( "div" ).style,
-	vendorProps = {};
-
-// Return a vendor-prefixed property or undefined
-function vendorPropName( name ) {
-
-	// Check for vendor prefixed names
-	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
-		i = cssPrefixes.length;
-
-	while ( i-- ) {
-		name = cssPrefixes[ i ] + capName;
-		if ( name in emptyStyle ) {
-			return name;
-		}
-	}
-}
-
-// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
-function finalPropName( name ) {
-	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
-
-	if ( final ) {
-		return final;
-	}
-	if ( name in emptyStyle ) {
-		return name;
-	}
-	return vendorProps[ name ] = vendorPropName( name ) || name;
-}
-
-
-var
-
-	// Swappable if display is none or starts with table
-	// except "table", "table-cell", or "table-caption"
-	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
-	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
-	rcustomProp = /^--/,
-	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
-	cssNormalTransform = {
-		letterSpacing: "0",
-		fontWeight: "400"
-	};
-
-function setPositiveNumber( _elem, value, subtract ) {
-
-	// Any relative (+/-) values have already been
-	// normalized at this point
-	var matches = rcssNum.exec( value );
-	return matches ?
-
-		// Guard against undefined "subtract", e.g., when used as in cssHooks
-		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
-		value;
-}
-
-function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
-	var i = dimension === "width" ? 1 : 0,
-		extra = 0,
-		delta = 0;
-
-	// Adjustment may not be necessary
-	if ( box === ( isBorderBox ? "border" : "content" ) ) {
-		return 0;
-	}
-
-	for ( ; i < 4; i += 2 ) {
-
-		// Both box models exclude margin
-		if ( box === "margin" ) {
-			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
-		}
-
-		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
-		if ( !isBorderBox ) {
-
-			// Add padding
-			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-
-			// For "border" or "margin", add border
-			if ( box !== "padding" ) {
-				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-
-			// But still keep track of it otherwise
-			} else {
-				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-
-		// If we get here with a border-box (content + padding + border), we're seeking "content" or
-		// "padding" or "margin"
-		} else {
-
-			// For "content", subtract padding
-			if ( box === "content" ) {
-				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-			}
-
-			// For "content" or "padding", subtract border
-			if ( box !== "margin" ) {
-				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-		}
-	}
-
-	// Account for positive content-box scroll gutter when requested by providing computedVal
-	if ( !isBorderBox && computedVal >= 0 ) {
-
-		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
-		// Assuming integer scroll gutter, subtract the rest and round down
-		delta += Math.max( 0, Math.ceil(
-			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-			computedVal -
-			delta -
-			extra -
-			0.5
-
-		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
-		// Use an explicit zero to avoid NaN (gh-3964)
-		) ) || 0;
-	}
-
-	return delta;
-}
-
-function getWidthOrHeight( elem, dimension, extra ) {
-
-	// Start with computed style
-	var styles = getStyles( elem ),
-
-		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
-		// Fake content-box until we know it's needed to know the true value.
-		boxSizingNeeded = !support.boxSizingReliable() || extra,
-		isBorderBox = boxSizingNeeded &&
-			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-		valueIsBorderBox = isBorderBox,
-
-		val = curCSS( elem, dimension, styles ),
-		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
-
-	// Support: Firefox <=54
-	// Return a confounding non-pixel value or feign ignorance, as appropriate.
-	if ( rnumnonpx.test( val ) ) {
-		if ( !extra ) {
-			return val;
-		}
-		val = "auto";
-	}
-
-
-	// Support: IE 9 - 11 only
-	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
-	// In those cases, the computed value can be trusted to be border-box.
-	if ( ( !support.boxSizingReliable() && isBorderBox ||
-
-		// Support: IE 10 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
-		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
-
-		// Fall back to offsetWidth/offsetHeight when value is "auto"
-		// This happens for inline elements with no explicit setting (gh-3571)
-		val === "auto" ||
-
-		// Support: Android <=4.1 - 4.3 only
-		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
-		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
-
-		// Make sure the element is visible & connected
-		elem.getClientRects().length ) {
-
-		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
-
-		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
-		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
-		// retrieved value as a content box dimension.
-		valueIsBorderBox = offsetProp in elem;
-		if ( valueIsBorderBox ) {
-			val = elem[ offsetProp ];
-		}
-	}
-
-	// Normalize "" and auto
-	val = parseFloat( val ) || 0;
-
-	// Adjust for the element's box model
-	return ( val +
-		boxModelAdjustment(
-			elem,
-			dimension,
-			extra || ( isBorderBox ? "border" : "content" ),
-			valueIsBorderBox,
-			styles,
-
-			// Provide the current computed size to request scroll gutter calculation (gh-3589)
-			val
-		)
-	) + "px";
-}
-
-jQuery.extend( {
-
-	// Add in style property hooks for overriding the default
-	// behavior of getting and setting a style property
-	cssHooks: {
-		opacity: {
-			get: function( elem, computed ) {
-				if ( computed ) {
-
-					// We should always get a number back from opacity
-					var ret = curCSS( elem, "opacity" );
-					return ret === "" ? "1" : ret;
-				}
-			}
-		}
-	},
-
-	// Don't automatically add "px" to these possibly-unitless properties
-	cssNumber: {
-		"animationIterationCount": true,
-		"columnCount": true,
-		"fillOpacity": true,
-		"flexGrow": true,
-		"flexShrink": true,
-		"fontWeight": true,
-		"gridArea": true,
-		"gridColumn": true,
-		"gridColumnEnd": true,
-		"gridColumnStart": true,
-		"gridRow": true,
-		"gridRowEnd": true,
-		"gridRowStart": true,
-		"lineHeight": true,
-		"opacity": true,
-		"order": true,
-		"orphans": true,
-		"widows": true,
-		"zIndex": true,
-		"zoom": true
-	},
-
-	// Add in properties whose names you wish to fix before
-	// setting or getting the value
-	cssProps: {},
-
-	// Get and set the style property on a DOM Node
-	style: function( elem, name, value, extra ) {
-
-		// Don't set styles on text and comment nodes
-		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
-			return;
-		}
-
-		// Make sure that we're working with the right name
-		var ret, type, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name ),
-			style = elem.style;
-
-		// Make sure that we're working with the right name. We don't
-		// want to query the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Gets hook for the prefixed version, then unprefixed version
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// Check if we're setting a value
-		if ( value !== undefined ) {
-			type = typeof value;
-
-			// Convert "+=" or "-=" to relative numbers (#7345)
-			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
-				value = adjustCSS( elem, name, ret );
-
-				// Fixes bug #9237
-				type = "number";
-			}
-
-			// Make sure that null and NaN values aren't set (#7116)
-			if ( value == null || value !== value ) {
-				return;
-			}
-
-			// If a number was passed in, add the unit (except for certain CSS properties)
-			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
-			// "px" to a few hardcoded values.
-			if ( type === "number" && !isCustomProp ) {
-				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
-			}
-
-			// background-* props affect original clone's values
-			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
-				style[ name ] = "inherit";
-			}
-
-			// If a hook was provided, use that value, otherwise just set the specified value
-			if ( !hooks || !( "set" in hooks ) ||
-				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
-
-				if ( isCustomProp ) {
-					style.setProperty( name, value );
-				} else {
-					style[ name ] = value;
-				}
-			}
-
-		} else {
-
-			// If a hook was provided get the non-computed value from there
-			if ( hooks && "get" in hooks &&
-				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
-
-				return ret;
-			}
-
-			// Otherwise just get the value from the style object
-			return style[ name ];
-		}
-	},
-
-	css: function( elem, name, extra, styles ) {
-		var val, num, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name );
-
-		// Make sure that we're working with the right name. We don't
-		// want to modify the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Try prefixed name followed by the unprefixed name
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// If a hook was provided get the computed value from there
-		if ( hooks && "get" in hooks ) {
-			val = hooks.get( elem, true, extra );
-		}
-
-		// Otherwise, if a way to get the computed value exists, use that
-		if ( val === undefined ) {
-			val = curCSS( elem, name, styles );
-		}
-
-		// Convert "normal" to computed value
-		if ( val === "normal" && name in cssNormalTransform ) {
-			val = cssNormalTransform[ name ];
-		}
-
-		// Make numeric if forced or a qualifier was provided and val looks numeric
-		if ( extra === "" || extra ) {
-			num = parseFloat( val );
-			return extra === true || isFinite( num ) ? num || 0 : val;
-		}
-
-		return val;
-	}
-} );
-
-jQuery.each( [ "height", "width" ], function( _i, dimension ) {
-	jQuery.cssHooks[ dimension ] = {
-		get: function( elem, computed, extra ) {
-			if ( computed ) {
-
-				// Certain elements can have dimension info if we invisibly show them
-				// but it must have a current display style that would benefit
-				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
-
-					// Support: Safari 8+
-					// Table columns in Safari have non-zero offsetWidth & zero
-					// getBoundingClientRect().width unless display is changed.
-					// Support: IE <=11 only
-					// Running getBoundingClientRect on a disconnected node
-					// in IE throws an error.
-					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
-						swap( elem, cssShow, function() {
-							return getWidthOrHeight( elem, dimension, extra );
-						} ) :
-						getWidthOrHeight( elem, dimension, extra );
-			}
-		},
-
-		set: function( elem, value, extra ) {
-			var matches,
-				styles = getStyles( elem ),
-
-				// Only read styles.position if the test has a chance to fail
-				// to avoid forcing a reflow.
-				scrollboxSizeBuggy = !support.scrollboxSize() &&
-					styles.position === "absolute",
-
-				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
-				boxSizingNeeded = scrollboxSizeBuggy || extra,
-				isBorderBox = boxSizingNeeded &&
-					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-				subtract = extra ?
-					boxModelAdjustment(
-						elem,
-						dimension,
-						extra,
-						isBorderBox,
-						styles
-					) :
-					0;
-
-			// Account for unreliable border-box dimensions by comparing offset* to computed and
-			// faking a content-box to get border and padding (gh-3699)
-			if ( isBorderBox && scrollboxSizeBuggy ) {
-				subtract -= Math.ceil(
-					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-					parseFloat( styles[ dimension ] ) -
-					boxModelAdjustment( elem, dimension, "border", false, styles ) -
-					0.5
-				);
-			}
-
-			// Convert to pixels if value adjustment is needed
-			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
-				( matches[ 3 ] || "px" ) !== "px" ) {
-
-				elem.style[ dimension ] = value;
-				value = jQuery.css( elem, dimension );
-			}
-
-			return setPositiveNumber( elem, value, subtract );
-		}
-	};
-} );
-
-jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
-	function( elem, computed ) {
-		if ( computed ) {
-			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
-				elem.getBoundingClientRect().left -
-					swap( elem, { marginLeft: 0 }, function() {
-						return elem.getBoundingClientRect().left;
-					} )
-				) + "px";
-		}
-	}
-);
-
-// These hooks are used by animate to expand properties
-jQuery.each( {
-	margin: "",
-	padding: "",
-	border: "Width"
-}, function( prefix, suffix ) {
-	jQuery.cssHooks[ prefix + suffix ] = {
-		expand: function( value ) {
-			var i = 0,
-				expanded = {},
-
-				// Assumes a single number if not a string
-				parts = typeof value === "string" ? value.split( " " ) : [ value ];
-
-			for ( ; i < 4; i++ ) {
-				expanded[ prefix + cssExpand[ i ] + suffix ] =
-					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
-			}
-
-			return expanded;
-		}
-	};
-
-	if ( prefix !== "margin" ) {
-		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
-	}
-} );
-
-jQuery.fn.extend( {
-	css: function( name, value ) {
-		return access( this, function( elem, name, value ) {
-			var styles, len,
-				map = {},
-				i = 0;
-
-			if ( Array.isArray( name ) ) {
-				styles = getStyles( elem );
-				len = name.length;
-
-				for ( ; i < len; i++ ) {
-					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
-				}
-
-				return map;
-			}
-
-			return value !== undefined ?
-				jQuery.style( elem, name, value ) :
-				jQuery.css( elem, name );
-		}, name, value, arguments.length > 1 );
-	}
-} );
-
-
-function Tween( elem, options, prop, end, easing ) {
-	return new Tween.prototype.init( elem, options, prop, end, easing );
-}
-jQuery.Tween = Tween;
-
-Tween.prototype = {
-	constructor: Tween,
-	init: function( elem, options, prop, end, easing, unit ) {
-		this.elem = elem;
-		this.prop = prop;
-		this.easing = easing || jQuery.easing._default;
-		this.options = options;
-		this.start = this.now = this.cur();
-		this.end = end;
-		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
-	},
-	cur: function() {
-		var hooks = Tween.propHooks[ this.prop ];
-
-		return hooks && hooks.get ?
-			hooks.get( this ) :
-			Tween.propHooks._default.get( this );
-	},
-	run: function( percent ) {
-		var eased,
-			hooks = Tween.propHooks[ this.prop ];
-
-		if ( this.options.duration ) {
-			this.pos = eased = jQuery.easing[ this.easing ](
-				percent, this.options.duration * percent, 0, 1, this.options.duration
-			);
-		} else {
-			this.pos = eased = percent;
-		}
-		this.now = ( this.end - this.start ) * eased + this.start;
-
-		if ( this.options.step ) {
-			this.options.step.call( this.elem, this.now, this );
-		}
-
-		if ( hooks && hooks.set ) {
-			hooks.set( this );
-		} else {
-			Tween.propHooks._default.set( this );
-		}
-		return this;
-	}
-};
-
-Tween.prototype.init.prototype = Tween.prototype;
-
-Tween.propHooks = {
-	_default: {
-		get: function( tween ) {
-			var result;
-
-			// Use a property on the element directly when it is not a DOM element,
-			// or when there is no matching style property that exists.
-			if ( tween.elem.nodeType !== 1 ||
-				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
-				return tween.elem[ tween.prop ];
-			}
-
-			// Passing an empty string as a 3rd parameter to .css will automatically
-			// attempt a parseFloat and fallback to a string if the parse fails.
-			// Simple values such as "10px" are parsed to Float;
-			// complex values such as "rotate(1rad)" are returned as-is.
-			result = jQuery.css( tween.elem, tween.prop, "" );
-
-			// Empty strings, null, undefined and "auto" are converted to 0.
-			return !result || result === "auto" ? 0 : result;
-		},
-		set: function( tween ) {
-
-			// Use step hook for back compat.
-			// Use cssHook if its there.
-			// Use .style if available and use plain properties where available.
-			if ( jQuery.fx.step[ tween.prop ] ) {
-				jQuery.fx.step[ tween.prop ]( tween );
-			} else if ( tween.elem.nodeType === 1 && (
-					jQuery.cssHooks[ tween.prop ] ||
-					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
-				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
-			} else {
-				tween.elem[ tween.prop ] = tween.now;
-			}
-		}
-	}
-};
-
-// Support: IE <=9 only
-// Panic based approach to setting things on disconnected nodes
-Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
-	set: function( tween ) {
-		if ( tween.elem.nodeType && tween.elem.parentNode ) {
-			tween.elem[ tween.prop ] = tween.now;
-		}
-	}
-};
-
-jQuery.easing = {
-	linear: function( p ) {
-		return p;
-	},
-	swing: function( p ) {
-		return 0.5 - Math.cos( p * Math.PI ) / 2;
-	},
-	_default: "swing"
-};
-
-jQuery.fx = Tween.prototype.init;
-
-// Back compat <1.8 extension point
-jQuery.fx.step = {};
-
-
-
-
-var
-	fxNow, inProgress,
-	rfxtypes = /^(?:toggle|show|hide)$/,
-	rrun = /queueHooks$/;
-
-function schedule() {
-	if ( inProgress ) {
-		if ( document.hidden === false && window.requestAnimationFrame ) {
-			window.requestAnimationFrame( schedule );
-		} else {
-			window.setTimeout( schedule, jQuery.fx.interval );
-		}
-
-		jQuery.fx.tick();
-	}
-}
-
-// Animations created synchronously will run synchronously
-function createFxNow() {
-	window.setTimeout( function() {
-		fxNow = undefined;
-	} );
-	return ( fxNow = Date.now() );
-}
-
-// Generate parameters to create a standard animation
-function genFx( type, includeWidth ) {
-	var which,
-		i = 0,
-		attrs = { height: type };
-
-	// If we include width, step value is 1 to do all cssExpand values,
-	// otherwise step value is 2 to skip over Left and Right
-	includeWidth = includeWidth ? 1 : 0;
-	for ( ; i < 4; i += 2 - includeWidth ) {
-		which = cssExpand[ i ];
-		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
-	}
-
-	if ( includeWidth ) {
-		attrs.opacity = attrs.width = type;
-	}
-
-	return attrs;
-}
-
-function createTween( value, prop, animation ) {
-	var tween,
-		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
-		index = 0,
-		length = collection.length;
-	for ( ; index < length; index++ ) {
-		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
-
-			// We're done with this property
-			return tween;
-		}
-	}
-}
-
-function defaultPrefilter( elem, props, opts ) {
-	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
-		isBox = "width" in props || "height" in props,
-		anim = this,
-		orig = {},
-		style = elem.style,
-		hidden = elem.nodeType && isHiddenWithinTree( elem ),
-		dataShow = dataPriv.get( elem, "fxshow" );
-
-	// Queue-skipping animations hijack the fx hooks
-	if ( !opts.queue ) {
-		hooks = jQuery._queueHooks( elem, "fx" );
-		if ( hooks.unqueued == null ) {
-			hooks.unqueued = 0;
-			oldfire = hooks.empty.fire;
-			hooks.empty.fire = function() {
-				if ( !hooks.unqueued ) {
-					oldfire();
-				}
-			};
-		}
-		hooks.unqueued++;
-
-		anim.always( function() {
-
-			// Ensure the complete handler is called before this completes
-			anim.always( function() {
-				hooks.unqueued--;
-				if ( !jQuery.queue( elem, "fx" ).length ) {
-					hooks.empty.fire();
-				}
-			} );
-		} );
-	}
-
-	// Detect show/hide animations
-	for ( prop in props ) {
-		value = props[ prop ];
-		if ( rfxtypes.test( value ) ) {
-			delete props[ prop ];
-			toggle = toggle || value === "toggle";
-			if ( value === ( hidden ? "hide" : "show" ) ) {
-
-				// Pretend to be hidden if this is a "show" and
-				// there is still data from a stopped show/hide
-				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
-					hidden = true;
-
-				// Ignore all other no-op show/hide data
-				} else {
-					continue;
-				}
-			}
-			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
-		}
-	}
-
-	// Bail out if this is a no-op like .hide().hide()
-	propTween = !jQuery.isEmptyObject( props );
-	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
-		return;
-	}
-
-	// Restrict "overflow" and "display" styles during box animations
-	if ( isBox && elem.nodeType === 1 ) {
-
-		// Support: IE <=9 - 11, Edge 12 - 15
-		// Record all 3 overflow attributes because IE does not infer the shorthand
-		// from identically-valued overflowX and overflowY and Edge just mirrors
-		// the overflowX value there.
-		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
-
-		// Identify a display type, preferring old show/hide data over the CSS cascade
-		restoreDisplay = dataShow && dataShow.display;
-		if ( restoreDisplay == null ) {
-			restoreDisplay = dataPriv.get( elem, "display" );
-		}
-		display = jQuery.css( elem, "display" );
-		if ( display === "none" ) {
-			if ( restoreDisplay ) {
-				display = restoreDisplay;
-			} else {
-
-				// Get nonempty value(s) by temporarily forcing visibility
-				showHide( [ elem ], true );
-				restoreDisplay = elem.style.display || restoreDisplay;
-				display = jQuery.css( elem, "display" );
-				showHide( [ elem ] );
-			}
-		}
-
-		// Animate inline elements as inline-block
-		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
-			if ( jQuery.css( elem, "float" ) === "none" ) {
-
-				// Restore the original display value at the end of pure show/hide animations
-				if ( !propTween ) {
-					anim.done( function() {
-						style.display = restoreDisplay;
-					} );
-					if ( restoreDisplay == null ) {
-						display = style.display;
-						restoreDisplay = display === "none" ? "" : display;
-					}
-				}
-				style.display = "inline-block";
-			}
-		}
-	}
-
-	if ( opts.overflow ) {
-		style.overflow = "hidden";
-		anim.always( function() {
-			style.overflow = opts.overflow[ 0 ];
-			style.overflowX = opts.overflow[ 1 ];
-			style.overflowY = opts.overflow[ 2 ];
-		} );
-	}
-
-	// Implement show/hide animations
-	propTween = false;
-	for ( prop in orig ) {
-
-		// General show/hide setup for this element animation
-		if ( !propTween ) {
-			if ( dataShow ) {
-				if ( "hidden" in dataShow ) {
-					hidden = dataShow.hidden;
-				}
-			} else {
-				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
-			}
-
-			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
-			if ( toggle ) {
-				dataShow.hidden = !hidden;
-			}
-
-			// Show elements before animating them
-			if ( hidden ) {
-				showHide( [ elem ], true );
-			}
-
-			/* eslint-disable no-loop-func */
-
-			anim.done( function() {
-
-			/* eslint-enable no-loop-func */
-
-				// The final step of a "hide" animation is actually hiding the element
-				if ( !hidden ) {
-					showHide( [ elem ] );
-				}
-				dataPriv.remove( elem, "fxshow" );
-				for ( prop in orig ) {
-					jQuery.style( elem, prop, orig[ prop ] );
-				}
-			} );
-		}
-
-		// Per-property setup
-		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
-		if ( !( prop in dataShow ) ) {
-			dataShow[ prop ] = propTween.start;
-			if ( hidden ) {
-				propTween.end = propTween.start;
-				propTween.start = 0;
-			}
-		}
-	}
-}
-
-function propFilter( props, specialEasing ) {
-	var index, name, easing, value, hooks;
-
-	// camelCase, specialEasing and expand cssHook pass
-	for ( index in props ) {
-		name = camelCase( index );
-		easing = specialEasing[ name ];
-		value = props[ index ];
-		if ( Array.isArray( value ) ) {
-			easing = value[ 1 ];
-			value = props[ index ] = value[ 0 ];
-		}
-
-		if ( index !== name ) {
-			props[ name ] = value;
-			delete props[ index ];
-		}
-
-		hooks = jQuery.cssHooks[ name ];
-		if ( hooks && "expand" in hooks ) {
-			value = hooks.expand( value );
-			delete props[ name ];
-
-			// Not quite $.extend, this won't overwrite existing keys.
-			// Reusing 'index' because we have the correct "name"
-			for ( index in value ) {
-				if ( !( index in props ) ) {
-					props[ index ] = value[ index ];
-					specialEasing[ index ] = easing;
-				}
-			}
-		} else {
-			specialEasing[ name ] = easing;
-		}
-	}
-}
-
-function Animation( elem, properties, options ) {
-	var result,
-		stopped,
-		index = 0,
-		length = Animation.prefilters.length,
-		deferred = jQuery.Deferred().always( function() {
-
-			// Don't match elem in the :animated selector
-			delete tick.elem;
-		} ),
-		tick = function() {
-			if ( stopped ) {
-				return false;
-			}
-			var currentTime = fxNow || createFxNow(),
-				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
-
-				// Support: Android 2.3 only
-				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
-				temp = remaining / animation.duration || 0,
-				percent = 1 - temp,
-				index = 0,
-				length = animation.tweens.length;
-
-			for ( ; index < length; index++ ) {
-				animation.tweens[ index ].run( percent );
-			}
-
-			deferred.notifyWith( elem, [ animation, percent, remaining ] );
-
-			// If there's more to do, yield
-			if ( percent < 1 && length ) {
-				return remaining;
-			}
-
-			// If this was an empty animation, synthesize a final progress notification
-			if ( !length ) {
-				deferred.notifyWith( elem, [ animation, 1, 0 ] );
-			}
-
-			// Resolve the animation and report its conclusion
-			deferred.resolveWith( elem, [ animation ] );
-			return false;
-		},
-		animation = deferred.promise( {
-			elem: elem,
-			props: jQuery.extend( {}, properties ),
-			opts: jQuery.extend( true, {
-				specialEasing: {},
-				easing: jQuery.easing._default
-			}, options ),
-			originalProperties: properties,
-			originalOptions: options,
-			startTime: fxNow || createFxNow(),
-			duration: options.duration,
-			tweens: [],
-			createTween: function( prop, end ) {
-				var tween = jQuery.Tween( elem, animation.opts, prop, end,
-						animation.opts.specialEasing[ prop ] || animation.opts.easing );
-				animation.tweens.push( tween );
-				return tween;
-			},
-			stop: function( gotoEnd ) {
-				var index = 0,
-
-					// If we are going to the end, we want to run all the tweens
-					// otherwise we skip this part
-					length = gotoEnd ? animation.tweens.length : 0;
-				if ( stopped ) {
-					return this;
-				}
-				stopped = true;
-				for ( ; index < length; index++ ) {
-					animation.tweens[ index ].run( 1 );
-				}
-
-				// Resolve when we played the last frame; otherwise, reject
-				if ( gotoEnd ) {
-					deferred.notifyWith( elem, [ animation, 1, 0 ] );
-					deferred.resolveWith( elem, [ animation, gotoEnd ] );
-				} else {
-					deferred.rejectWith( elem, [ animation, gotoEnd ] );
-				}
-				return this;
-			}
-		} ),
-		props = animation.props;
-
-	propFilter( props, animation.opts.specialEasing );
-
-	for ( ; index < length; index++ ) {
-		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
-		if ( result ) {
-			if ( isFunction( result.stop ) ) {
-				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
-					result.stop.bind( result );
-			}
-			return result;
-		}
-	}
-
-	jQuery.map( props, createTween, animation );
-
-	if ( isFunction( animation.opts.start ) ) {
-		animation.opts.start.call( elem, animation );
-	}
-
-	// Attach callbacks from options
-	animation
-		.progress( animation.opts.progress )
-		.done( animation.opts.done, animation.opts.complete )
-		.fail( animation.opts.fail )
-		.always( animation.opts.always );
-
-	jQuery.fx.timer(
-		jQuery.extend( tick, {
-			elem: elem,
-			anim: animation,
-			queue: animation.opts.queue
-		} )
-	);
-
-	return animation;
-}
-
-jQuery.Animation = jQuery.extend( Animation, {
-
-	tweeners: {
-		"*": [ function( prop, value ) {
-			var tween = this.createTween( prop, value );
-			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
-			return tween;
-		} ]
-	},
-
-	tweener: function( props, callback ) {
-		if ( isFunction( props ) ) {
-			callback = props;
-			props = [ "*" ];
-		} else {
-			props = props.match( rnothtmlwhite );
-		}
-
-		var prop,
-			index = 0,
-			length = props.length;
-
-		for ( ; index < length; index++ ) {
-			prop = props[ index ];
-			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
-			Animation.tweeners[ prop ].unshift( callback );
-		}
-	},
-
-	prefilters: [ defaultPrefilter ],
-
-	prefilter: function( callback, prepend ) {
-		if ( prepend ) {
-			Animation.prefilters.unshift( callback );
-		} else {
-			Animation.prefilters.push( callback );
-		}
-	}
-} );
-
-jQuery.speed = function( speed, easing, fn ) {
-	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
-		complete: fn || !fn && easing ||
-			isFunction( speed ) && speed,
-		duration: speed,
-		easing: fn && easing || easing && !isFunction( easing ) && easing
-	};
-
-	// Go to the end state if fx are off
-	if ( jQuery.fx.off ) {
-		opt.duration = 0;
-
-	} else {
-		if ( typeof opt.duration !== "number" ) {
-			if ( opt.duration in jQuery.fx.speeds ) {
-				opt.duration = jQuery.fx.speeds[ opt.duration ];
-
-			} else {
-				opt.duration = jQuery.fx.speeds._default;
-			}
-		}
-	}
-
-	// Normalize opt.queue - true/undefined/null -> "fx"
-	if ( opt.queue == null || opt.queue === true ) {
-		opt.queue = "fx";
-	}
-
-	// Queueing
-	opt.old = opt.complete;
-
-	opt.complete = function() {
-		if ( isFunction( opt.old ) ) {
-			opt.old.call( this );
-		}
-
-		if ( opt.queue ) {
-			jQuery.dequeue( this, opt.queue );
-		}
-	};
-
-	return opt;
-};
-
-jQuery.fn.extend( {
-	fadeTo: function( speed, to, easing, callback ) {
-
-		// Show any hidden elements after setting opacity to 0
-		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
-
-			// Animate to the value specified
-			.end().animate( { opacity: to }, speed, easing, callback );
-	},
-	animate: function( prop, speed, easing, callback ) {
-		var empty = jQuery.isEmptyObject( prop ),
-			optall = jQuery.speed( speed, easing, callback ),
-			doAnimation = function() {
-
-				// Operate on a copy of prop so per-property easing won't be lost
-				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
-
-				// Empty animations, or finishing resolves immediately
-				if ( empty || dataPriv.get( this, "finish" ) ) {
-					anim.stop( true );
-				}
-			};
-			doAnimation.finish = doAnimation;
-
-		return empty || optall.queue === false ?
-			this.each( doAnimation ) :
-			this.queue( optall.queue, doAnimation );
-	},
-	stop: function( type, clearQueue, gotoEnd ) {
-		var stopQueue = function( hooks ) {
-			var stop = hooks.stop;
-			delete hooks.stop;
-			stop( gotoEnd );
-		};
-
-		if ( typeof type !== "string" ) {
-			gotoEnd = clearQueue;
-			clearQueue = type;
-			type = undefined;
-		}
-		if ( clearQueue ) {
-			this.queue( type || "fx", [] );
-		}
-
-		return this.each( function() {
-			var dequeue = true,
-				index = type != null && type + "queueHooks",
-				timers = jQuery.timers,
-				data = dataPriv.get( this );
-
-			if ( index ) {
-				if ( data[ index ] && data[ index ].stop ) {
-					stopQueue( data[ index ] );
-				}
-			} else {
-				for ( index in data ) {
-					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
-						stopQueue( data[ index ] );
-					}
-				}
-			}
-
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this &&
-					( type == null || timers[ index ].queue === type ) ) {
-
-					timers[ index ].anim.stop( gotoEnd );
-					dequeue = false;
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Start the next in the queue if the last step wasn't forced.
-			// Timers currently will call their complete callbacks, which
-			// will dequeue but only if they were gotoEnd.
-			if ( dequeue || !gotoEnd ) {
-				jQuery.dequeue( this, type );
-			}
-		} );
-	},
-	finish: function( type ) {
-		if ( type !== false ) {
-			type = type || "fx";
-		}
-		return this.each( function() {
-			var index,
-				data = dataPriv.get( this ),
-				queue = data[ type + "queue" ],
-				hooks = data[ type + "queueHooks" ],
-				timers = jQuery.timers,
-				length = queue ? queue.length : 0;
-
-			// Enable finishing flag on private data
-			data.finish = true;
-
-			// Empty the queue first
-			jQuery.queue( this, type, [] );
-
-			if ( hooks && hooks.stop ) {
-				hooks.stop.call( this, true );
-			}
-
-			// Look for any active animations, and finish them
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
-					timers[ index ].anim.stop( true );
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Look for any animations in the old queue and finish them
-			for ( index = 0; index < length; index++ ) {
-				if ( queue[ index ] && queue[ index ].finish ) {
-					queue[ index ].finish.call( this );
-				}
-			}
-
-			// Turn off finishing flag
-			delete data.finish;
-		} );
-	}
-} );
-
-jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
-	var cssFn = jQuery.fn[ name ];
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return speed == null || typeof speed === "boolean" ?
-			cssFn.apply( this, arguments ) :
-			this.animate( genFx( name, true ), speed, easing, callback );
-	};
-} );
-
-// Generate shortcuts for custom animations
-jQuery.each( {
-	slideDown: genFx( "show" ),
-	slideUp: genFx( "hide" ),
-	slideToggle: genFx( "toggle" ),
-	fadeIn: { opacity: "show" },
-	fadeOut: { opacity: "hide" },
-	fadeToggle: { opacity: "toggle" }
-}, function( name, props ) {
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return this.animate( props, speed, easing, callback );
-	};
-} );
-
-jQuery.timers = [];
-jQuery.fx.tick = function() {
-	var timer,
-		i = 0,
-		timers = jQuery.timers;
-
-	fxNow = Date.now();
-
-	for ( ; i < timers.length; i++ ) {
-		timer = timers[ i ];
-
-		// Run the timer and safely remove it when done (allowing for external removal)
-		if ( !timer() && timers[ i ] === timer ) {
-			timers.splice( i--, 1 );
-		}
-	}
-
-	if ( !timers.length ) {
-		jQuery.fx.stop();
-	}
-	fxNow = undefined;
-};
-
-jQuery.fx.timer = function( timer ) {
-	jQuery.timers.push( timer );
-	jQuery.fx.start();
-};
-
-jQuery.fx.interval = 13;
-jQuery.fx.start = function() {
-	if ( inProgress ) {
-		return;
-	}
-
-	inProgress = true;
-	schedule();
-};
-
-jQuery.fx.stop = function() {
-	inProgress = null;
-};
-
-jQuery.fx.speeds = {
-	slow: 600,
-	fast: 200,
-
-	// Default speed
-	_default: 400
-};
-
-
-// Based off of the plugin by Clint Helfers, with permission.
-// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
-jQuery.fn.delay = function( time, type ) {
-	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
-	type = type || "fx";
-
-	return this.queue( type, function( next, hooks ) {
-		var timeout = window.setTimeout( next, time );
-		hooks.stop = function() {
-			window.clearTimeout( timeout );
-		};
-	} );
-};
-
-
-( function() {
-	var input = document.createElement( "input" ),
-		select = document.createElement( "select" ),
-		opt = select.appendChild( document.createElement( "option" ) );
-
-	input.type = "checkbox";
-
-	// Support: Android <=4.3 only
-	// Default value for a checkbox should be "on"
-	support.checkOn = input.value !== "";
-
-	// Support: IE <=11 only
-	// Must access selectedIndex to make default options select
-	support.optSelected = opt.selected;
-
-	// Support: IE <=11 only
-	// An input loses its value after becoming a radio
-	input = document.createElement( "input" );
-	input.value = "t";
-	input.type = "radio";
-	support.radioValue = input.value === "t";
-} )();
-
-
-var boolHook,
-	attrHandle = jQuery.expr.attrHandle;
-
-jQuery.fn.extend( {
-	attr: function( name, value ) {
-		return access( this, jQuery.attr, name, value, arguments.length > 1 );
-	},
-
-	removeAttr: function( name ) {
-		return this.each( function() {
-			jQuery.removeAttr( this, name );
-		} );
-	}
-} );
-
-jQuery.extend( {
-	attr: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set attributes on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		// Fallback to prop when attributes are not supported
-		if ( typeof elem.getAttribute === "undefined" ) {
-			return jQuery.prop( elem, name, value );
-		}
-
-		// Attribute hooks are determined by the lowercase version
-		// Grab necessary hook if one is defined
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
-				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
-		}
-
-		if ( value !== undefined ) {
-			if ( value === null ) {
-				jQuery.removeAttr( elem, name );
-				return;
-			}
-
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			elem.setAttribute( name, value + "" );
-			return value;
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		ret = jQuery.find.attr( elem, name );
-
-		// Non-existent attributes return null, we normalize to undefined
-		return ret == null ? undefined : ret;
-	},
-
-	attrHooks: {
-		type: {
-			set: function( elem, value ) {
-				if ( !support.radioValue && value === "radio" &&
-					nodeName( elem, "input" ) ) {
-					var val = elem.value;
-					elem.setAttribute( "type", value );
-					if ( val ) {
-						elem.value = val;
-					}
-					return value;
-				}
-			}
-		}
-	},
-
-	removeAttr: function( elem, value ) {
-		var name,
-			i = 0,
-
-			// Attribute names can contain non-HTML whitespace characters
-			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
-			attrNames = value && value.match( rnothtmlwhite );
-
-		if ( attrNames && elem.nodeType === 1 ) {
-			while ( ( name = attrNames[ i++ ] ) ) {
-				elem.removeAttribute( name );
-			}
-		}
-	}
-} );
-
-// Hooks for boolean attributes
-boolHook = {
-	set: function( elem, value, name ) {
-		if ( value === false ) {
-
-			// Remove boolean attributes when set to false
-			jQuery.removeAttr( elem, name );
-		} else {
-			elem.setAttribute( name, name );
-		}
-		return name;
-	}
-};
-
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
-	var getter = attrHandle[ name ] || jQuery.find.attr;
-
-	attrHandle[ name ] = function( elem, name, isXML ) {
-		var ret, handle,
-			lowercaseName = name.toLowerCase();
-
-		if ( !isXML ) {
-
-			// Avoid an infinite loop by temporarily removing this function from the getter
-			handle = attrHandle[ lowercaseName ];
-			attrHandle[ lowercaseName ] = ret;
-			ret = getter( elem, name, isXML ) != null ?
-				lowercaseName :
-				null;
-			attrHandle[ lowercaseName ] = handle;
-		}
-		return ret;
-	};
-} );
-
-
-
-
-var rfocusable = /^(?:input|select|textarea|button)$/i,
-	rclickable = /^(?:a|area)$/i;
-
-jQuery.fn.extend( {
-	prop: function( name, value ) {
-		return access( this, jQuery.prop, name, value, arguments.length > 1 );
-	},
-
-	removeProp: function( name ) {
-		return this.each( function() {
-			delete this[ jQuery.propFix[ name ] || name ];
-		} );
-	}
-} );
-
-jQuery.extend( {
-	prop: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set properties on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-
-			// Fix name and attach hooks
-			name = jQuery.propFix[ name ] || name;
-			hooks = jQuery.propHooks[ name ];
-		}
-
-		if ( value !== undefined ) {
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			return ( elem[ name ] = value );
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		return elem[ name ];
-	},
-
-	propHooks: {
-		tabIndex: {
-			get: function( elem ) {
-
-				// Support: IE <=9 - 11 only
-				// elem.tabIndex doesn't always return the
-				// correct value when it hasn't been explicitly set
-				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
-				// Use proper attribute retrieval(#12072)
-				var tabindex = jQuery.find.attr( elem, "tabindex" );
-
-				if ( tabindex ) {
-					return parseInt( tabindex, 10 );
-				}
-
-				if (
-					rfocusable.test( elem.nodeName ) ||
-					rclickable.test( elem.nodeName ) &&
-					elem.href
-				) {
-					return 0;
-				}
-
-				return -1;
-			}
-		}
-	},
-
-	propFix: {
-		"for": "htmlFor",
-		"class": "className"
-	}
-} );
-
-// Support: IE <=11 only
-// Accessing the selectedIndex property
-// forces the browser to respect setting selected
-// on the option
-// The getter ensures a default option is selected
-// when in an optgroup
-// eslint rule "no-unused-expressions" is disabled for this code
-// since it considers such accessions noop
-if ( !support.optSelected ) {
-	jQuery.propHooks.selected = {
-		get: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent && parent.parentNode ) {
-				parent.parentNode.selectedIndex;
-			}
-			return null;
-		},
-		set: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent ) {
-				parent.selectedIndex;
-
-				if ( parent.parentNode ) {
-					parent.parentNode.selectedIndex;
-				}
-			}
-		}
-	};
-}
-
-jQuery.each( [
-	"tabIndex",
-	"readOnly",
-	"maxLength",
-	"cellSpacing",
-	"cellPadding",
-	"rowSpan",
-	"colSpan",
-	"useMap",
-	"frameBorder",
-	"contentEditable"
-], function() {
-	jQuery.propFix[ this.toLowerCase() ] = this;
-} );
-
-
-
-
-	// Strip and collapse whitespace according to HTML spec
-	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
-	function stripAndCollapse( value ) {
-		var tokens = value.match( rnothtmlwhite ) || [];
-		return tokens.join( " " );
-	}
-
-
-function getClass( elem ) {
-	return elem.getAttribute && elem.getAttribute( "class" ) || "";
-}
-
-function classesToArray( value ) {
-	if ( Array.isArray( value ) ) {
-		return value;
-	}
-	if ( typeof value === "string" ) {
-		return value.match( rnothtmlwhite ) || [];
-	}
-	return [];
-}
-
-jQuery.fn.extend( {
-	addClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
-							cur += clazz + " ";
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	removeClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		if ( !arguments.length ) {
-			return this.attr( "class", "" );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-
-				// This expression is here for better compressibility (see addClass)
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-
-						// Remove *all* instances
-						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
-							cur = cur.replace( " " + clazz + " ", " " );
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	toggleClass: function( value, stateVal ) {
-		var type = typeof value,
-			isValidValue = type === "string" || Array.isArray( value );
-
-		if ( typeof stateVal === "boolean" && isValidValue ) {
-			return stateVal ? this.addClass( value ) : this.removeClass( value );
-		}
-
-		if ( isFunction( value ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).toggleClass(
-					value.call( this, i, getClass( this ), stateVal ),
-					stateVal
-				);
-			} );
-		}
-
-		return this.each( function() {
-			var className, i, self, classNames;
-
-			if ( isValidValue ) {
-
-				// Toggle individual class names
-				i = 0;
-				self = jQuery( this );
-				classNames = classesToArray( value );
-
-				while ( ( className = classNames[ i++ ] ) ) {
-
-					// Check each className given, space separated list
-					if ( self.hasClass( className ) ) {
-						self.removeClass( className );
-					} else {
-						self.addClass( className );
-					}
-				}
-
-			// Toggle whole class name
-			} else if ( value === undefined || type === "boolean" ) {
-				className = getClass( this );
-				if ( className ) {
-
-					// Store className if set
-					dataPriv.set( this, "__className__", className );
-				}
-
-				// If the element has a class name or if we're passed `false`,
-				// then remove the whole classname (if there was one, the above saved it).
-				// Otherwise bring back whatever was previously saved (if anything),
-				// falling back to the empty string if nothing was stored.
-				if ( this.setAttribute ) {
-					this.setAttribute( "class",
-						className || value === false ?
-						"" :
-						dataPriv.get( this, "__className__" ) || ""
-					);
-				}
-			}
-		} );
-	},
-
-	hasClass: function( selector ) {
-		var className, elem,
-			i = 0;
-
-		className = " " + selector + " ";
-		while ( ( elem = this[ i++ ] ) ) {
-			if ( elem.nodeType === 1 &&
-				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
-					return true;
-			}
-		}
-
-		return false;
-	}
-} );
-
-
-
-
-var rreturn = /\r/g;
-
-jQuery.fn.extend( {
-	val: function( value ) {
-		var hooks, ret, valueIsFunction,
-			elem = this[ 0 ];
-
-		if ( !arguments.length ) {
-			if ( elem ) {
-				hooks = jQuery.valHooks[ elem.type ] ||
-					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
-
-				if ( hooks &&
-					"get" in hooks &&
-					( ret = hooks.get( elem, "value" ) ) !== undefined
-				) {
-					return ret;
-				}
-
-				ret = elem.value;
-
-				// Handle most common string cases
-				if ( typeof ret === "string" ) {
-					return ret.replace( rreturn, "" );
-				}
-
-				// Handle cases where value is null/undef or number
-				return ret == null ? "" : ret;
-			}
-
-			return;
-		}
-
-		valueIsFunction = isFunction( value );
-
-		return this.each( function( i ) {
-			var val;
-
-			if ( this.nodeType !== 1 ) {
-				return;
-			}
-
-			if ( valueIsFunction ) {
-				val = value.call( this, i, jQuery( this ).val() );
-			} else {
-				val = value;
-			}
-
-			// Treat null/undefined as ""; convert numbers to string
-			if ( val == null ) {
-				val = "";
-
-			} else if ( typeof val === "number" ) {
-				val += "";
-
-			} else if ( Array.isArray( val ) ) {
-				val = jQuery.map( val, function( value ) {
-					return value == null ? "" : value + "";
-				} );
-			}
-
-			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
-
-			// If set returns undefined, fall back to normal setting
-			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
-				this.value = val;
-			}
-		} );
-	}
-} );
-
-jQuery.extend( {
-	valHooks: {
-		option: {
-			get: function( elem ) {
-
-				var val = jQuery.find.attr( elem, "value" );
-				return val != null ?
-					val :
-
-					// Support: IE <=10 - 11 only
-					// option.text throws exceptions (#14686, #14858)
-					// Strip and collapse whitespace
-					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
-					stripAndCollapse( jQuery.text( elem ) );
-			}
-		},
-		select: {
-			get: function( elem ) {
-				var value, option, i,
-					options = elem.options,
-					index = elem.selectedIndex,
-					one = elem.type === "select-one",
-					values = one ? null : [],
-					max = one ? index + 1 : options.length;
-
-				if ( index < 0 ) {
-					i = max;
-
-				} else {
-					i = one ? index : 0;
-				}
-
-				// Loop through all the selected options
-				for ( ; i < max; i++ ) {
-					option = options[ i ];
-
-					// Support: IE <=9 only
-					// IE8-9 doesn't update selected after form reset (#2551)
-					if ( ( option.selected || i === index ) &&
-
-							// Don't return options that are disabled or in a disabled optgroup
-							!option.disabled &&
-							( !option.parentNode.disabled ||
-								!nodeName( option.parentNode, "optgroup" ) ) ) {
-
-						// Get the specific value for the option
-						value = jQuery( option ).val();
-
-						// We don't need an array for one selects
-						if ( one ) {
-							return value;
-						}
-
-						// Multi-Selects return an array
-						values.push( value );
-					}
-				}
-
-				return values;
-			},
-
-			set: function( elem, value ) {
-				var optionSet, option,
-					options = elem.options,
-					values = jQuery.makeArray( value ),
-					i = options.length;
-
-				while ( i-- ) {
-					option = options[ i ];
-
-					/* eslint-disable no-cond-assign */
-
-					if ( option.selected =
-						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
-					) {
-						optionSet = true;
-					}
-
-					/* eslint-enable no-cond-assign */
-				}
-
-				// Force browsers to behave consistently when non-matching value is set
-				if ( !optionSet ) {
-					elem.selectedIndex = -1;
-				}
-				return values;
-			}
-		}
-	}
-} );
-
-// Radios and checkboxes getter/setter
-jQuery.each( [ "radio", "checkbox" ], function() {
-	jQuery.valHooks[ this ] = {
-		set: function( elem, value ) {
-			if ( Array.isArray( value ) ) {
-				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
-			}
-		}
-	};
-	if ( !support.checkOn ) {
-		jQuery.valHooks[ this ].get = function( elem ) {
-			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
-		};
-	}
-} );
-
-
-
-
-// Return jQuery for attributes-only inclusion
-
-
-support.focusin = "onfocusin" in window;
-
-
-var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
-	stopPropagationCallback = function( e ) {
-		e.stopPropagation();
-	};
-
-jQuery.extend( jQuery.event, {
-
-	trigger: function( event, data, elem, onlyHandlers ) {
-
-		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
-			eventPath = [ elem || document ],
-			type = hasOwn.call( event, "type" ) ? event.type : event,
-			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
-
-		cur = lastElement = tmp = elem = elem || document;
-
-		// Don't do events on text and comment nodes
-		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
-			return;
-		}
-
-		// focus/blur morphs to focusin/out; ensure we're not firing them right now
-		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
-			return;
-		}
-
-		if ( type.indexOf( "." ) > -1 ) {
-
-			// Namespaced trigger; create a regexp to match event type in handle()
-			namespaces = type.split( "." );
-			type = namespaces.shift();
-			namespaces.sort();
-		}
-		ontype = type.indexOf( ":" ) < 0 && "on" + type;
-
-		// Caller can pass in a jQuery.Event object, Object, or just an event type string
-		event = event[ jQuery.expando ] ?
-			event :
-			new jQuery.Event( type, typeof event === "object" && event );
-
-		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
-		event.isTrigger = onlyHandlers ? 2 : 3;
-		event.namespace = namespaces.join( "." );
-		event.rnamespace = event.namespace ?
-			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
-			null;
-
-		// Clean up the event in case it is being reused
-		event.result = undefined;
-		if ( !event.target ) {
-			event.target = elem;
-		}
-
-		// Clone any incoming data and prepend the event, creating the handler arg list
-		data = data == null ?
-			[ event ] :
-			jQuery.makeArray( data, [ event ] );
-
-		// Allow special events to draw outside the lines
-		special = jQuery.event.special[ type ] || {};
-		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
-			return;
-		}
-
-		// Determine event propagation path in advance, per W3C events spec (#9951)
-		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
-		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
-
-			bubbleType = special.delegateType || type;
-			if ( !rfocusMorph.test( bubbleType + type ) ) {
-				cur = cur.parentNode;
-			}
-			for ( ; cur; cur = cur.parentNode ) {
-				eventPath.push( cur );
-				tmp = cur;
-			}
-
-			// Only add window if we got to document (e.g., not plain obj or detached DOM)
-			if ( tmp === ( elem.ownerDocument || document ) ) {
-				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
-			}
-		}
-
-		// Fire handlers on the event path
-		i = 0;
-		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
-			lastElement = cur;
-			event.type = i > 1 ?
-				bubbleType :
-				special.bindType || type;
-
-			// jQuery handler
-			handle = (
-					dataPriv.get( cur, "events" ) || Object.create( null )
-				)[ event.type ] &&
-				dataPriv.get( cur, "handle" );
-			if ( handle ) {
-				handle.apply( cur, data );
-			}
-
-			// Native handler
-			handle = ontype && cur[ ontype ];
-			if ( handle && handle.apply && acceptData( cur ) ) {
-				event.result = handle.apply( cur, data );
-				if ( event.result === false ) {
-					event.preventDefault();
-				}
-			}
-		}
-		event.type = type;
-
-		// If nobody prevented the default action, do it now
-		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
-
-			if ( ( !special._default ||
-				special._default.apply( eventPath.pop(), data ) === false ) &&
-				acceptData( elem ) ) {
-
-				// Call a native DOM method on the target with the same name as the event.
-				// Don't do default actions on window, that's where global variables be (#6170)
-				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
-
-					// Don't re-trigger an onFOO event when we call its FOO() method
-					tmp = elem[ ontype ];
-
-					if ( tmp ) {
-						elem[ ontype ] = null;
-					}
-
-					// Prevent re-triggering of the same event, since we already bubbled it above
-					jQuery.event.triggered = type;
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.addEventListener( type, stopPropagationCallback );
-					}
-
-					elem[ type ]();
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.removeEventListener( type, stopPropagationCallback );
-					}
-
-					jQuery.event.triggered = undefined;
-
-					if ( tmp ) {
-						elem[ ontype ] = tmp;
-					}
-				}
-			}
-		}
-
-		return event.result;
-	},
-
-	// Piggyback on a donor event to simulate a different one
-	// Used only for `focus(in | out)` events
-	simulate: function( type, elem, event ) {
-		var e = jQuery.extend(
-			new jQuery.Event(),
-			event,
-			{
-				type: type,
-				isSimulated: true
-			}
-		);
-
-		jQuery.event.trigger( e, null, elem );
-	}
-
-} );
-
-jQuery.fn.extend( {
-
-	trigger: function( type, data ) {
-		return this.each( function() {
-			jQuery.event.trigger( type, data, this );
-		} );
-	},
-	triggerHandler: function( type, data ) {
-		var elem = this[ 0 ];
-		if ( elem ) {
-			return jQuery.event.trigger( type, data, elem, true );
-		}
-	}
-} );
-
-
-// Support: Firefox <=44
-// Firefox doesn't have focus(in | out) events
-// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
-//
-// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
-// focus(in | out) events fire after focus & blur events,
-// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
-// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
-if ( !support.focusin ) {
-	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
-
-		// Attach a single capturing handler on the document while someone wants focusin/focusout
-		var handler = function( event ) {
-			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
-		};
-
-		jQuery.event.special[ fix ] = {
-			setup: function() {
-
-				// Handle: regular nodes (via `this.ownerDocument`), window
-				// (via `this.document`) & document (via `this`).
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix );
-
-				if ( !attaches ) {
-					doc.addEventListener( orig, handler, true );
-				}
-				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
-			},
-			teardown: function() {
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix ) - 1;
-
-				if ( !attaches ) {
-					doc.removeEventListener( orig, handler, true );
-					dataPriv.remove( doc, fix );
-
-				} else {
-					dataPriv.access( doc, fix, attaches );
-				}
-			}
-		};
-	} );
-}
-var location = window.location;
-
-var nonce = { guid: Date.now() };
-
-var rquery = ( /\?/ );
-
-
-
-// Cross-browser xml parsing
-jQuery.parseXML = function( data ) {
-	var xml;
-	if ( !data || typeof data !== "string" ) {
-		return null;
-	}
-
-	// Support: IE 9 - 11 only
-	// IE throws on parseFromString with invalid input.
-	try {
-		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
-	} catch ( e ) {
-		xml = undefined;
-	}
-
-	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
-		jQuery.error( "Invalid XML: " + data );
-	}
-	return xml;
-};
-
-
-var
-	rbracket = /\[\]$/,
-	rCRLF = /\r?\n/g,
-	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
-	rsubmittable = /^(?:input|select|textarea|keygen)/i;
-
-function buildParams( prefix, obj, traditional, add ) {
-	var name;
-
-	if ( Array.isArray( obj ) ) {
-
-		// Serialize array item.
-		jQuery.each( obj, function( i, v ) {
-			if ( traditional || rbracket.test( prefix ) ) {
-
-				// Treat each array item as a scalar.
-				add( prefix, v );
-
-			} else {
-
-				// Item is non-scalar (array or object), encode its numeric index.
-				buildParams(
-					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
-					v,
-					traditional,
-					add
-				);
-			}
-		} );
-
-	} else if ( !traditional && toType( obj ) === "object" ) {
-
-		// Serialize object item.
-		for ( name in obj ) {
-			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
-		}
-
-	} else {
-
-		// Serialize scalar item.
-		add( prefix, obj );
-	}
-}
-
-// Serialize an array of form elements or a set of
-// key/values into a query string
-jQuery.param = function( a, traditional ) {
-	var prefix,
-		s = [],
-		add = function( key, valueOrFunction ) {
-
-			// If value is a function, invoke it and use its return value
-			var value = isFunction( valueOrFunction ) ?
-				valueOrFunction() :
-				valueOrFunction;
-
-			s[ s.length ] = encodeURIComponent( key ) + "=" +
-				encodeURIComponent( value == null ? "" : value );
-		};
-
-	if ( a == null ) {
-		return "";
-	}
-
-	// If an array was passed in, assume that it is an array of form elements.
-	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
-
-		// Serialize the form elements
-		jQuery.each( a, function() {
-			add( this.name, this.value );
-		} );
-
-	} else {
-
-		// If traditional, encode the "old" way (the way 1.3.2 or older
-		// did it), otherwise encode params recursively.
-		for ( prefix in a ) {
-			buildParams( prefix, a[ prefix ], traditional, add );
-		}
-	}
-
-	// Return the resulting serialization
-	return s.join( "&" );
-};
-
-jQuery.fn.extend( {
-	serialize: function() {
-		return jQuery.param( this.serializeArray() );
-	},
-	serializeArray: function() {
-		return this.map( function() {
-
-			// Can add propHook for "elements" to filter or add form elements
-			var elements = jQuery.prop( this, "elements" );
-			return elements ? jQuery.makeArray( elements ) : this;
-		} )
-		.filter( function() {
-			var type = this.type;
-
-			// Use .is( ":disabled" ) so that fieldset[disabled] works
-			return this.name && !jQuery( this ).is( ":disabled" ) &&
-				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
-				( this.checked || !rcheckableType.test( type ) );
-		} )
-		.map( function( _i, elem ) {
-			var val = jQuery( this ).val();
-
-			if ( val == null ) {
-				return null;
-			}
-
-			if ( Array.isArray( val ) ) {
-				return jQuery.map( val, function( val ) {
-					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-				} );
-			}
-
-			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-		} ).get();
-	}
-} );
-
-
-var
-	r20 = /%20/g,
-	rhash = /#.*$/,
-	rantiCache = /([?&])_=[^&]*/,
-	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
-
-	// #7653, #8125, #8152: local protocol detection
-	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
-	rnoContent = /^(?:GET|HEAD)$/,
-	rprotocol = /^\/\//,
-
-	/* Prefilters
-	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
-	 * 2) These are called:
-	 *    - BEFORE asking for a transport
-	 *    - AFTER param serialization (s.data is a string if s.processData is true)
-	 * 3) key is the dataType
-	 * 4) the catchall symbol "*" can be used
-	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
-	 */
-	prefilters = {},
-
-	/* Transports bindings
-	 * 1) key is the dataType
-	 * 2) the catchall symbol "*" can be used
-	 * 3) selection will start with transport dataType and THEN go to "*" if needed
-	 */
-	transports = {},
-
-	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
-	allTypes = "*/".concat( "*" ),
-
-	// Anchor tag for parsing the document origin
-	originAnchor = document.createElement( "a" );
-	originAnchor.href = location.href;
-
-// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
-function addToPrefiltersOrTransports( structure ) {
-
-	// dataTypeExpression is optional and defaults to "*"
-	return function( dataTypeExpression, func ) {
-
-		if ( typeof dataTypeExpression !== "string" ) {
-			func = dataTypeExpression;
-			dataTypeExpression = "*";
-		}
-
-		var dataType,
-			i = 0,
-			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
-
-		if ( isFunction( func ) ) {
-
-			// For each dataType in the dataTypeExpression
-			while ( ( dataType = dataTypes[ i++ ] ) ) {
-
-				// Prepend if requested
-				if ( dataType[ 0 ] === "+" ) {
-					dataType = dataType.slice( 1 ) || "*";
-					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
-
-				// Otherwise append
-				} else {
-					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
-				}
-			}
-		}
-	};
-}
-
-// Base inspection function for prefilters and transports
-function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
-
-	var inspected = {},
-		seekingTransport = ( structure === transports );
-
-	function inspect( dataType ) {
-		var selected;
-		inspected[ dataType ] = true;
-		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
-			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
-			if ( typeof dataTypeOrTransport === "string" &&
-				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
-
-				options.dataTypes.unshift( dataTypeOrTransport );
-				inspect( dataTypeOrTransport );
-				return false;
-			} else if ( seekingTransport ) {
-				return !( selected = dataTypeOrTransport );
-			}
-		} );
-		return selected;
-	}
-
-	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
-}
-
-// A special extend for ajax options
-// that takes "flat" options (not to be deep extended)
-// Fixes #9887
-function ajaxExtend( target, src ) {
-	var key, deep,
-		flatOptions = jQuery.ajaxSettings.flatOptions || {};
-
-	for ( key in src ) {
-		if ( src[ key ] !== undefined ) {
-			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
-		}
-	}
-	if ( deep ) {
-		jQuery.extend( true, target, deep );
-	}
-
-	return target;
-}
-
-/* Handles responses to an ajax request:
- * - finds the right dataType (mediates between content-type and expected dataType)
- * - returns the corresponding response
- */
-function ajaxHandleResponses( s, jqXHR, responses ) {
-
-	var ct, type, finalDataType, firstDataType,
-		contents = s.contents,
-		dataTypes = s.dataTypes;
-
-	// Remove auto dataType and get content-type in the process
-	while ( dataTypes[ 0 ] === "*" ) {
-		dataTypes.shift();
-		if ( ct === undefined ) {
-			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
-		}
-	}
-
-	// Check if we're dealing with a known content-type
-	if ( ct ) {
-		for ( type in contents ) {
-			if ( contents[ type ] && contents[ type ].test( ct ) ) {
-				dataTypes.unshift( type );
-				break;
-			}
-		}
-	}
-
-	// Check to see if we have a response for the expected dataType
-	if ( dataTypes[ 0 ] in responses ) {
-		finalDataType = dataTypes[ 0 ];
-	} else {
-
-		// Try convertible dataTypes
-		for ( type in responses ) {
-			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
-				finalDataType = type;
-				break;
-			}
-			if ( !firstDataType ) {
-				firstDataType = type;
-			}
-		}
-
-		// Or just use first one
-		finalDataType = finalDataType || firstDataType;
-	}
-
-	// If we found a dataType
-	// We add the dataType to the list if needed
-	// and return the corresponding response
-	if ( finalDataType ) {
-		if ( finalDataType !== dataTypes[ 0 ] ) {
-			dataTypes.unshift( finalDataType );
-		}
-		return responses[ finalDataType ];
-	}
-}
-
-/* Chain conversions given the request and the original response
- * Also sets the responseXXX fields on the jqXHR instance
- */
-function ajaxConvert( s, response, jqXHR, isSuccess ) {
-	var conv2, current, conv, tmp, prev,
-		converters = {},
-
-		// Work with a copy of dataTypes in case we need to modify it for conversion
-		dataTypes = s.dataTypes.slice();
-
-	// Create converters map with lowercased keys
-	if ( dataTypes[ 1 ] ) {
-		for ( conv in s.converters ) {
-			converters[ conv.toLowerCase() ] = s.converters[ conv ];
-		}
-	}
-
-	current = dataTypes.shift();
-
-	// Convert to each sequential dataType
-	while ( current ) {
-
-		if ( s.responseFields[ current ] ) {
-			jqXHR[ s.responseFields[ current ] ] = response;
-		}
-
-		// Apply the dataFilter if provided
-		if ( !prev && isSuccess && s.dataFilter ) {
-			response = s.dataFilter( response, s.dataType );
-		}
-
-		prev = current;
-		current = dataTypes.shift();
-
-		if ( current ) {
-
-			// There's only work to do if current dataType is non-auto
-			if ( current === "*" ) {
-
-				current = prev;
-
-			// Convert response if prev dataType is non-auto and differs from current
-			} else if ( prev !== "*" && prev !== current ) {
-
-				// Seek a direct converter
-				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
-
-				// If none found, seek a pair
-				if ( !conv ) {
-					for ( conv2 in converters ) {
-
-						// If conv2 outputs current
-						tmp = conv2.split( " " );
-						if ( tmp[ 1 ] === current ) {
-
-							// If prev can be converted to accepted input
-							conv = converters[ prev + " " + tmp[ 0 ] ] ||
-								converters[ "* " + tmp[ 0 ] ];
-							if ( conv ) {
-
-								// Condense equivalence converters
-								if ( conv === true ) {
-									conv = converters[ conv2 ];
-
-								// Otherwise, insert the intermediate dataType
-								} else if ( converters[ conv2 ] !== true ) {
-									current = tmp[ 0 ];
-									dataTypes.unshift( tmp[ 1 ] );
-								}
-								break;
-							}
-						}
-					}
-				}
-
-				// Apply converter (if not an equivalence)
-				if ( conv !== true ) {
-
-					// Unless errors are allowed to bubble, catch and return them
-					if ( conv && s.throws ) {
-						response = conv( response );
-					} else {
-						try {
-							response = conv( response );
-						} catch ( e ) {
-							return {
-								state: "parsererror",
-								error: conv ? e : "No conversion from " + prev + " to " + current
-							};
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return { state: "success", data: response };
-}
-
-jQuery.extend( {
-
-	// Counter for holding the number of active queries
-	active: 0,
-
-	// Last-Modified header cache for next request
-	lastModified: {},
-	etag: {},
-
-	ajaxSettings: {
-		url: location.href,
-		type: "GET",
-		isLocal: rlocalProtocol.test( location.protocol ),
-		global: true,
-		processData: true,
-		async: true,
-		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
-
-		/*
-		timeout: 0,
-		data: null,
-		dataType: null,
-		username: null,
-		password: null,
-		cache: null,
-		throws: false,
-		traditional: false,
-		headers: {},
-		*/
-
-		accepts: {
-			"*": allTypes,
-			text: "text/plain",
-			html: "text/html",
-			xml: "application/xml, text/xml",
-			json: "application/json, text/javascript"
-		},
-
-		contents: {
-			xml: /\bxml\b/,
-			html: /\bhtml/,
-			json: /\bjson\b/
-		},
-
-		responseFields: {
-			xml: "responseXML",
-			text: "responseText",
-			json: "responseJSON"
-		},
-
-		// Data converters
-		// Keys separate source (or catchall "*") and destination types with a single space
-		converters: {
-
-			// Convert anything to text
-			"* text": String,
-
-			// Text to html (true = no transformation)
-			"text html": true,
-
-			// Evaluate text as a json expression
-			"text json": JSON.parse,
-
-			// Parse text as xml
-			"text xml": jQuery.parseXML
-		},
-
-		// For options that shouldn't be deep extended:
-		// you can add your own custom options here if
-		// and when you create one that shouldn't be
-		// deep extended (see ajaxExtend)
-		flatOptions: {
-			url: true,
-			context: true
-		}
-	},
-
-	// Creates a full fledged settings object into target
-	// with both ajaxSettings and settings fields.
-	// If target is omitted, writes into ajaxSettings.
-	ajaxSetup: function( target, settings ) {
-		return settings ?
-
-			// Building a settings object
-			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
-
-			// Extending ajaxSettings
-			ajaxExtend( jQuery.ajaxSettings, target );
-	},
-
-	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
-	ajaxTransport: addToPrefiltersOrTransports( transports ),
-
-	// Main method
-	ajax: function( url, options ) {
-
-		// If url is an object, simulate pre-1.5 signature
-		if ( typeof url === "object" ) {
-			options = url;
-			url = undefined;
-		}
-
-		// Force options to be an object
-		options = options || {};
-
-		var transport,
-
-			// URL without anti-cache param
-			cacheURL,
-
-			// Response headers
-			responseHeadersString,
-			responseHeaders,
-
-			// timeout handle
-			timeoutTimer,
-
-			// Url cleanup var
-			urlAnchor,
-
-			// Request state (becomes false upon send and true upon completion)
-			completed,
-
-			// To know if global events are to be dispatched
-			fireGlobals,
-
-			// Loop variable
-			i,
-
-			// uncached part of the url
-			uncached,
-
-			// Create the final options object
-			s = jQuery.ajaxSetup( {}, options ),
-
-			// Callbacks context
-			callbackContext = s.context || s,
-
-			// Context for global events is callbackContext if it is a DOM node or jQuery collection
-			globalEventContext = s.context &&
-				( callbackContext.nodeType || callbackContext.jquery ) ?
-					jQuery( callbackContext ) :
-					jQuery.event,
-
-			// Deferreds
-			deferred = jQuery.Deferred(),
-			completeDeferred = jQuery.Callbacks( "once memory" ),
-
-			// Status-dependent callbacks
-			statusCode = s.statusCode || {},
-
-			// Headers (they are sent all at once)
-			requestHeaders = {},
-			requestHeadersNames = {},
-
-			// Default abort message
-			strAbort = "canceled",
-
-			// Fake xhr
-			jqXHR = {
-				readyState: 0,
-
-				// Builds headers hashtable if needed
-				getResponseHeader: function( key ) {
-					var match;
-					if ( completed ) {
-						if ( !responseHeaders ) {
-							responseHeaders = {};
-							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
-								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
-									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
-										.concat( match[ 2 ] );
-							}
-						}
-						match = responseHeaders[ key.toLowerCase() + " " ];
-					}
-					return match == null ? null : match.join( ", " );
-				},
-
-				// Raw string
-				getAllResponseHeaders: function() {
-					return completed ? responseHeadersString : null;
-				},
-
-				// Caches the header
-				setRequestHeader: function( name, value ) {
-					if ( completed == null ) {
-						name = requestHeadersNames[ name.toLowerCase() ] =
-							requestHeadersNames[ name.toLowerCase() ] || name;
-						requestHeaders[ name ] = value;
-					}
-					return this;
-				},
-
-				// Overrides response content-type header
-				overrideMimeType: function( type ) {
-					if ( completed == null ) {
-						s.mimeType = type;
-					}
-					return this;
-				},
-
-				// Status-dependent callbacks
-				statusCode: function( map ) {
-					var code;
-					if ( map ) {
-						if ( completed ) {
-
-							// Execute the appropriate callbacks
-							jqXHR.always( map[ jqXHR.status ] );
-						} else {
-
-							// Lazy-add the new callbacks in a way that preserves old ones
-							for ( code in map ) {
-								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
-							}
-						}
-					}
-					return this;
-				},
-
-				// Cancel the request
-				abort: function( statusText ) {
-					var finalText = statusText || strAbort;
-					if ( transport ) {
-						transport.abort( finalText );
-					}
-					done( 0, finalText );
-					return this;
-				}
-			};
-
-		// Attach deferreds
-		deferred.promise( jqXHR );
-
-		// Add protocol if not provided (prefilters might expect it)
-		// Handle falsy url in the settings object (#10093: consistency with old signature)
-		// We also use the url parameter if available
-		s.url = ( ( url || s.url || location.href ) + "" )
-			.replace( rprotocol, location.protocol + "//" );
-
-		// Alias method option to type as per ticket #12004
-		s.type = options.method || options.type || s.method || s.type;
-
-		// Extract dataTypes list
-		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
-
-		// A cross-domain request is in order when the origin doesn't match the current origin.
-		if ( s.crossDomain == null ) {
-			urlAnchor = document.createElement( "a" );
-
-			// Support: IE <=8 - 11, Edge 12 - 15
-			// IE throws exception on accessing the href property if url is malformed,
-			// e.g. http://example.com:80x/
-			try {
-				urlAnchor.href = s.url;
-
-				// Support: IE <=8 - 11 only
-				// Anchor's host property isn't correctly set when s.url is relative
-				urlAnchor.href = urlAnchor.href;
-				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
-					urlAnchor.protocol + "//" + urlAnchor.host;
-			} catch ( e ) {
-
-				// If there is an error parsing the URL, assume it is crossDomain,
-				// it can be rejected by the transport if it is invalid
-				s.crossDomain = true;
-			}
-		}
-
-		// Convert data if not already a string
-		if ( s.data && s.processData && typeof s.data !== "string" ) {
-			s.data = jQuery.param( s.data, s.traditional );
-		}
-
-		// Apply prefilters
-		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
-
-		// If request was aborted inside a prefilter, stop there
-		if ( completed ) {
-			return jqXHR;
-		}
-
-		// We can fire global events as of now if asked to
-		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
-		fireGlobals = jQuery.event && s.global;
-
-		// Watch for a new set of requests
-		if ( fireGlobals && jQuery.active++ === 0 ) {
-			jQuery.event.trigger( "ajaxStart" );
-		}
-
-		// Uppercase the type
-		s.type = s.type.toUpperCase();
-
-		// Determine if request has content
-		s.hasContent = !rnoContent.test( s.type );
-
-		// Save the URL in case we're toying with the If-Modified-Since
-		// and/or If-None-Match header later on
-		// Remove hash to simplify url manipulation
-		cacheURL = s.url.replace( rhash, "" );
-
-		// More options handling for requests with no content
-		if ( !s.hasContent ) {
-
-			// Remember the hash so we can put it back
-			uncached = s.url.slice( cacheURL.length );
-
-			// If data is available and should be processed, append data to url
-			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
-				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
-
-				// #9682: remove data so that it's not used in an eventual retry
-				delete s.data;
-			}
-
-			// Add or update anti-cache param if needed
-			if ( s.cache === false ) {
-				cacheURL = cacheURL.replace( rantiCache, "$1" );
-				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
-					uncached;
-			}
-
-			// Put hash and anti-cache on the URL that will be requested (gh-1732)
-			s.url = cacheURL + uncached;
-
-		// Change '%20' to '+' if this is encoded form body content (gh-2658)
-		} else if ( s.data && s.processData &&
-			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
-			s.data = s.data.replace( r20, "+" );
-		}
-
-		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-		if ( s.ifModified ) {
-			if ( jQuery.lastModified[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
-			}
-			if ( jQuery.etag[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
-			}
-		}
-
-		// Set the correct header, if data is being sent
-		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
-			jqXHR.setRequestHeader( "Content-Type", s.contentType );
-		}
-
-		// Set the Accepts header for the server, depending on the dataType
-		jqXHR.setRequestHeader(
-			"Accept",
-			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
-				s.accepts[ s.dataTypes[ 0 ] ] +
-					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
-				s.accepts[ "*" ]
-		);
-
-		// Check for headers option
-		for ( i in s.headers ) {
-			jqXHR.setRequestHeader( i, s.headers[ i ] );
-		}
-
-		// Allow custom headers/mimetypes and early abort
-		if ( s.beforeSend &&
-			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
-
-			// Abort if not done already and return
-			return jqXHR.abort();
-		}
-
-		// Aborting is no longer a cancellation
-		strAbort = "abort";
-
-		// Install callbacks on deferreds
-		completeDeferred.add( s.complete );
-		jqXHR.done( s.success );
-		jqXHR.fail( s.error );
-
-		// Get transport
-		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
-
-		// If no transport, we auto-abort
-		if ( !transport ) {
-			done( -1, "No Transport" );
-		} else {
-			jqXHR.readyState = 1;
-
-			// Send global event
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
-			}
-
-			// If request was aborted inside ajaxSend, stop there
-			if ( completed ) {
-				return jqXHR;
-			}
-
-			// Timeout
-			if ( s.async && s.timeout > 0 ) {
-				timeoutTimer = window.setTimeout( function() {
-					jqXHR.abort( "timeout" );
-				}, s.timeout );
-			}
-
-			try {
-				completed = false;
-				transport.send( requestHeaders, done );
-			} catch ( e ) {
-
-				// Rethrow post-completion exceptions
-				if ( completed ) {
-					throw e;
-				}
-
-				// Propagate others as results
-				done( -1, e );
-			}
-		}
-
-		// Callback for when everything is done
-		function done( status, nativeStatusText, responses, headers ) {
-			var isSuccess, success, error, response, modified,
-				statusText = nativeStatusText;
-
-			// Ignore repeat invocations
-			if ( completed ) {
-				return;
-			}
-
-			completed = true;
-
-			// Clear timeout if it exists
-			if ( timeoutTimer ) {
-				window.clearTimeout( timeoutTimer );
-			}
-
-			// Dereference transport for early garbage collection
-			// (no matter how long the jqXHR object will be used)
-			transport = undefined;
-
-			// Cache response headers
-			responseHeadersString = headers || "";
-
-			// Set readyState
-			jqXHR.readyState = status > 0 ? 4 : 0;
-
-			// Determine if successful
-			isSuccess = status >= 200 && status < 300 || status === 304;
-
-			// Get response data
-			if ( responses ) {
-				response = ajaxHandleResponses( s, jqXHR, responses );
-			}
-
-			// Use a noop converter for missing script
-			if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
-				s.converters[ "text script" ] = function() {};
-			}
-
-			// Convert no matter what (that way responseXXX fields are always set)
-			response = ajaxConvert( s, response, jqXHR, isSuccess );
-
-			// If successful, handle type chaining
-			if ( isSuccess ) {
-
-				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-				if ( s.ifModified ) {
-					modified = jqXHR.getResponseHeader( "Last-Modified" );
-					if ( modified ) {
-						jQuery.lastModified[ cacheURL ] = modified;
-					}
-					modified = jqXHR.getResponseHeader( "etag" );
-					if ( modified ) {
-						jQuery.etag[ cacheURL ] = modified;
-					}
-				}
-
-				// if no content
-				if ( status === 204 || s.type === "HEAD" ) {
-					statusText = "nocontent";
-
-				// if not modified
-				} else if ( status === 304 ) {
-					statusText = "notmodified";
-
-				// If we have data, let's convert it
-				} else {
-					statusText = response.state;
-					success = response.data;
-					error = response.error;
-					isSuccess = !error;
-				}
-			} else {
-
-				// Extract error from statusText and normalize for non-aborts
-				error = statusText;
-				if ( status || !statusText ) {
-					statusText = "error";
-					if ( status < 0 ) {
-						status = 0;
-					}
-				}
-			}
-
-			// Set data for the fake xhr object
-			jqXHR.status = status;
-			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
-
-			// Success/Error
-			if ( isSuccess ) {
-				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
-			} else {
-				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
-			}
-
-			// Status-dependent callbacks
-			jqXHR.statusCode( statusCode );
-			statusCode = undefined;
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
-					[ jqXHR, s, isSuccess ? success : error ] );
-			}
-
-			// Complete
-			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
-
-				// Handle the global AJAX counter
-				if ( !( --jQuery.active ) ) {
-					jQuery.event.trigger( "ajaxStop" );
-				}
-			}
-		}
-
-		return jqXHR;
-	},
-
-	getJSON: function( url, data, callback ) {
-		return jQuery.get( url, data, callback, "json" );
-	},
-
-	getScript: function( url, callback ) {
-		return jQuery.get( url, undefined, callback, "script" );
-	}
-} );
-
-jQuery.each( [ "get", "post" ], function( _i, method ) {
-	jQuery[ method ] = function( url, data, callback, type ) {
-
-		// Shift arguments if data argument was omitted
-		if ( isFunction( data ) ) {
-			type = type || callback;
-			callback = data;
-			data = undefined;
-		}
-
-		// The url can be an options object (which then must have .url)
-		return jQuery.ajax( jQuery.extend( {
-			url: url,
-			type: method,
-			dataType: type,
-			data: data,
-			success: callback
-		}, jQuery.isPlainObject( url ) && url ) );
-	};
-} );
-
-jQuery.ajaxPrefilter( function( s ) {
-	var i;
-	for ( i in s.headers ) {
-		if ( i.toLowerCase() === "content-type" ) {
-			s.contentType = s.headers[ i ] || "";
-		}
-	}
-} );
-
-
-jQuery._evalUrl = function( url, options, doc ) {
-	return jQuery.ajax( {
-		url: url,
-
-		// Make this explicit, since user can override this through ajaxSetup (#11264)
-		type: "GET",
-		dataType: "script",
-		cache: true,
-		async: false,
-		global: false,
-
-		// Only evaluate the response if it is successful (gh-4126)
-		// dataFilter is not invoked for failure responses, so using it instead
-		// of the default converter is kludgy but it works.
-		converters: {
-			"text script": function() {}
-		},
-		dataFilter: function( response ) {
-			jQuery.globalEval( response, options, doc );
-		}
-	} );
-};
-
-
-jQuery.fn.extend( {
-	wrapAll: function( html ) {
-		var wrap;
-
-		if ( this[ 0 ] ) {
-			if ( isFunction( html ) ) {
-				html = html.call( this[ 0 ] );
-			}
-
-			// The elements to wrap the target around
-			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
-
-			if ( this[ 0 ].parentNode ) {
-				wrap.insertBefore( this[ 0 ] );
-			}
-
-			wrap.map( function() {
-				var elem = this;
-
-				while ( elem.firstElementChild ) {
-					elem = elem.firstElementChild;
-				}
-
-				return elem;
-			} ).append( this );
-		}
-
-		return this;
-	},
-
-	wrapInner: function( html ) {
-		if ( isFunction( html ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).wrapInner( html.call( this, i ) );
-			} );
-		}
-
-		return this.each( function() {
-			var self = jQuery( this ),
-				contents = self.contents();
-
-			if ( contents.length ) {
-				contents.wrapAll( html );
-
-			} else {
-				self.append( html );
-			}
-		} );
-	},
-
-	wrap: function( html ) {
-		var htmlIsFunction = isFunction( html );
-
-		return this.each( function( i ) {
-			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
-		} );
-	},
-
-	unwrap: function( selector ) {
-		this.parent( selector ).not( "body" ).each( function() {
-			jQuery( this ).replaceWith( this.childNodes );
-		} );
-		return this;
-	}
-} );
-
-
-jQuery.expr.pseudos.hidden = function( elem ) {
-	return !jQuery.expr.pseudos.visible( elem );
-};
-jQuery.expr.pseudos.visible = function( elem ) {
-	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
-};
-
-
-
-
-jQuery.ajaxSettings.xhr = function() {
-	try {
-		return new window.XMLHttpRequest();
-	} catch ( e ) {}
-};
-
-var xhrSuccessStatus = {
-
-		// File protocol always yields status code 0, assume 200
-		0: 200,
-
-		// Support: IE <=9 only
-		// #1450: sometimes IE returns 1223 when it should be 204
-		1223: 204
-	},
-	xhrSupported = jQuery.ajaxSettings.xhr();
-
-support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
-support.ajax = xhrSupported = !!xhrSupported;
-
-jQuery.ajaxTransport( function( options ) {
-	var callback, errorCallback;
-
-	// Cross domain only allowed if supported through XMLHttpRequest
-	if ( support.cors || xhrSupported && !options.crossDomain ) {
-		return {
-			send: function( headers, complete ) {
-				var i,
-					xhr = options.xhr();
-
-				xhr.open(
-					options.type,
-					options.url,
-					options.async,
-					options.username,
-					options.password
-				);
-
-				// Apply custom fields if provided
-				if ( options.xhrFields ) {
-					for ( i in options.xhrFields ) {
-						xhr[ i ] = options.xhrFields[ i ];
-					}
-				}
-
-				// Override mime type if needed
-				if ( options.mimeType && xhr.overrideMimeType ) {
-					xhr.overrideMimeType( options.mimeType );
-				}
-
-				// X-Requested-With header
-				// For cross-domain requests, seeing as conditions for a preflight are
-				// akin to a jigsaw puzzle, we simply never set it to be sure.
-				// (it can always be set on a per-request basis or even using ajaxSetup)
-				// For same-domain requests, won't change header if already provided.
-				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
-					headers[ "X-Requested-With" ] = "XMLHttpRequest";
-				}
-
-				// Set headers
-				for ( i in headers ) {
-					xhr.setRequestHeader( i, headers[ i ] );
-				}
-
-				// Callback
-				callback = function( type ) {
-					return function() {
-						if ( callback ) {
-							callback = errorCallback = xhr.onload =
-								xhr.onerror = xhr.onabort = xhr.ontimeout =
-									xhr.onreadystatechange = null;
-
-							if ( type === "abort" ) {
-								xhr.abort();
-							} else if ( type === "error" ) {
-
-								// Support: IE <=9 only
-								// On a manual native abort, IE9 throws
-								// errors on any property access that is not readyState
-								if ( typeof xhr.status !== "number" ) {
-									complete( 0, "error" );
-								} else {
-									complete(
-
-										// File: protocol always yields status 0; see #8605, #14207
-										xhr.status,
-										xhr.statusText
-									);
-								}
-							} else {
-								complete(
-									xhrSuccessStatus[ xhr.status ] || xhr.status,
-									xhr.statusText,
-
-									// Support: IE <=9 only
-									// IE9 has no XHR2 but throws on binary (trac-11426)
-									// For XHR2 non-text, let the caller handle it (gh-2498)
-									( xhr.responseType || "text" ) !== "text"  ||
-									typeof xhr.responseText !== "string" ?
-										{ binary: xhr.response } :
-										{ text: xhr.responseText },
-									xhr.getAllResponseHeaders()
-								);
-							}
-						}
-					};
-				};
-
-				// Listen to events
-				xhr.onload = callback();
-				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
-
-				// Support: IE 9 only
-				// Use onreadystatechange to replace onabort
-				// to handle uncaught aborts
-				if ( xhr.onabort !== undefined ) {
-					xhr.onabort = errorCallback;
-				} else {
-					xhr.onreadystatechange = function() {
-
-						// Check readyState before timeout as it changes
-						if ( xhr.readyState === 4 ) {
-
-							// Allow onerror to be called first,
-							// but that will not handle a native abort
-							// Also, save errorCallback to a variable
-							// as xhr.onerror cannot be accessed
-							window.setTimeout( function() {
-								if ( callback ) {
-									errorCallback();
-								}
-							} );
-						}
-					};
-				}
-
-				// Create the abort callback
-				callback = callback( "abort" );
-
-				try {
-
-					// Do send the request (this may raise an exception)
-					xhr.send( options.hasContent && options.data || null );
-				} catch ( e ) {
-
-					// #14683: Only rethrow if this hasn't been notified as an error yet
-					if ( callback ) {
-						throw e;
-					}
-				}
-			},
-
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
-jQuery.ajaxPrefilter( function( s ) {
-	if ( s.crossDomain ) {
-		s.contents.script = false;
-	}
-} );
-
-// Install script dataType
-jQuery.ajaxSetup( {
-	accepts: {
-		script: "text/javascript, application/javascript, " +
-			"application/ecmascript, application/x-ecmascript"
-	},
-	contents: {
-		script: /\b(?:java|ecma)script\b/
-	},
-	converters: {
-		"text script": function( text ) {
-			jQuery.globalEval( text );
-			return text;
-		}
-	}
-} );
-
-// Handle cache's special case and crossDomain
-jQuery.ajaxPrefilter( "script", function( s ) {
-	if ( s.cache === undefined ) {
-		s.cache = false;
-	}
-	if ( s.crossDomain ) {
-		s.type = "GET";
-	}
-} );
-
-// Bind script tag hack transport
-jQuery.ajaxTransport( "script", function( s ) {
-
-	// This transport only deals with cross domain or forced-by-attrs requests
-	if ( s.crossDomain || s.scriptAttrs ) {
-		var script, callback;
-		return {
-			send: function( _, complete ) {
-				script = jQuery( "<script>" )
-					.attr( s.scriptAttrs || {} )
-					.prop( { charset: s.scriptCharset, src: s.url } )
-					.on( "load error", callback = function( evt ) {
-						script.remove();
-						callback = null;
-						if ( evt ) {
-							complete( evt.type === "error" ? 404 : 200, evt.type );
-						}
-					} );
-
-				// Use native DOM manipulation to avoid our domManip AJAX trickery
-				document.head.appendChild( script[ 0 ] );
-			},
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-var oldCallbacks = [],
-	rjsonp = /(=)\?(?=&|$)|\?\?/;
-
-// Default jsonp settings
-jQuery.ajaxSetup( {
-	jsonp: "callback",
-	jsonpCallback: function() {
-		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
-		this[ callback ] = true;
-		return callback;
-	}
-} );
-
-// Detect, normalize options and install callbacks for jsonp requests
-jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
-
-	var callbackName, overwritten, responseContainer,
-		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
-			"url" :
-			typeof s.data === "string" &&
-				( s.contentType || "" )
-					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
-				rjsonp.test( s.data ) && "data"
-		);
-
-	// Handle iff the expected data type is "jsonp" or we have a parameter to set
-	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
-
-		// Get callback name, remembering preexisting value associated with it
-		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
-			s.jsonpCallback() :
-			s.jsonpCallback;
-
-		// Insert callback into url or form data
-		if ( jsonProp ) {
-			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
-		} else if ( s.jsonp !== false ) {
-			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
-		}
-
-		// Use data converter to retrieve json after script execution
-		s.converters[ "script json" ] = function() {
-			if ( !responseContainer ) {
-				jQuery.error( callbackName + " was not called" );
-			}
-			return responseContainer[ 0 ];
-		};
-
-		// Force json dataType
-		s.dataTypes[ 0 ] = "json";
-
-		// Install callback
-		overwritten = window[ callbackName ];
-		window[ callbackName ] = function() {
-			responseContainer = arguments;
-		};
-
-		// Clean-up function (fires after converters)
-		jqXHR.always( function() {
-
-			// If previous value didn't exist - remove it
-			if ( overwritten === undefined ) {
-				jQuery( window ).removeProp( callbackName );
-
-			// Otherwise restore preexisting value
-			} else {
-				window[ callbackName ] = overwritten;
-			}
-
-			// Save back as free
-			if ( s[ callbackName ] ) {
-
-				// Make sure that re-using the options doesn't screw things around
-				s.jsonpCallback = originalSettings.jsonpCallback;
-
-				// Save the callback name for future use
-				oldCallbacks.push( callbackName );
-			}
-
-			// Call if it was a function and we have a response
-			if ( responseContainer && isFunction( overwritten ) ) {
-				overwritten( responseContainer[ 0 ] );
-			}
-
-			responseContainer = overwritten = undefined;
-		} );
-
-		// Delegate to script
-		return "script";
-	}
-} );
-
-
-
-
-// Support: Safari 8 only
-// In Safari 8 documents created via document.implementation.createHTMLDocument
-// collapse sibling forms: the second one becomes a child of the first one.
-// Because of that, this security measure has to be disabled in Safari 8.
-// https://bugs.webkit.org/show_bug.cgi?id=137337
-support.createHTMLDocument = ( function() {
-	var body = document.implementation.createHTMLDocument( "" ).body;
-	body.innerHTML = "<form></form><form></form>";
-	return body.childNodes.length === 2;
-} )();
-
-
-// Argument "data" should be string of html
-// context (optional): If specified, the fragment will be created in this context,
-// defaults to document
-// keepScripts (optional): If true, will include scripts passed in the html string
-jQuery.parseHTML = function( data, context, keepScripts ) {
-	if ( typeof data !== "string" ) {
-		return [];
-	}
-	if ( typeof context === "boolean" ) {
-		keepScripts = context;
-		context = false;
-	}
-
-	var base, parsed, scripts;
-
-	if ( !context ) {
-
-		// Stop scripts or inline event handlers from being executed immediately
-		// by using document.implementation
-		if ( support.createHTMLDocument ) {
-			context = document.implementation.createHTMLDocument( "" );
-
-			// Set the base href for the created document
-			// so any parsed elements with URLs
-			// are based on the document's URL (gh-2965)
-			base = context.createElement( "base" );
-			base.href = document.location.href;
-			context.head.appendChild( base );
-		} else {
-			context = document;
-		}
-	}
-
-	parsed = rsingleTag.exec( data );
-	scripts = !keepScripts && [];
-
-	// Single tag
-	if ( parsed ) {
-		return [ context.createElement( parsed[ 1 ] ) ];
-	}
-
-	parsed = buildFragment( [ data ], context, scripts );
-
-	if ( scripts && scripts.length ) {
-		jQuery( scripts ).remove();
-	}
-
-	return jQuery.merge( [], parsed.childNodes );
-};
-
-
-/**
- * Load a url into a page
- */
-jQuery.fn.load = function( url, params, callback ) {
-	var selector, type, response,
-		self = this,
-		off = url.indexOf( " " );
-
-	if ( off > -1 ) {
-		selector = stripAndCollapse( url.slice( off ) );
-		url = url.slice( 0, off );
-	}
-
-	// If it's a function
-	if ( isFunction( params ) ) {
-
-		// We assume that it's the callback
-		callback = params;
-		params = undefined;
-
-	// Otherwise, build a param string
-	} else if ( params && typeof params === "object" ) {
-		type = "POST";
-	}
-
-	// If we have elements to modify, make the request
-	if ( self.length > 0 ) {
-		jQuery.ajax( {
-			url: url,
-
-			// If "type" variable is undefined, then "GET" method will be used.
-			// Make value of this field explicit since
-			// user can override it through ajaxSetup method
-			type: type || "GET",
-			dataType: "html",
-			data: params
-		} ).done( function( responseText ) {
-
-			// Save response for use in complete callback
-			response = arguments;
-
-			self.html( selector ?
-
-				// If a selector was specified, locate the right elements in a dummy div
-				// Exclude scripts to avoid IE 'Permission Denied' errors
-				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
-
-				// Otherwise use the full result
-				responseText );
-
-		// If the request succeeds, this function gets "data", "status", "jqXHR"
-		// but they are ignored because response was set above.
-		// If it fails, this function gets "jqXHR", "status", "error"
-		} ).always( callback && function( jqXHR, status ) {
-			self.each( function() {
-				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
-			} );
-		} );
-	}
-
-	return this;
-};
-
-
-
-
-jQuery.expr.pseudos.animated = function( elem ) {
-	return jQuery.grep( jQuery.timers, function( fn ) {
-		return elem === fn.elem;
-	} ).length;
-};
-
-
-
-
-jQuery.offset = {
-	setOffset: function( elem, options, i ) {
-		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
-			position = jQuery.css( elem, "position" ),
-			curElem = jQuery( elem ),
-			props = {};
-
-		// Set position first, in-case top/left are set even on static elem
-		if ( position === "static" ) {
-			elem.style.position = "relative";
-		}
-
-		curOffset = curElem.offset();
-		curCSSTop = jQuery.css( elem, "top" );
-		curCSSLeft = jQuery.css( elem, "left" );
-		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
-			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
-
-		// Need to be able to calculate position if either
-		// top or left is auto and position is either absolute or fixed
-		if ( calculatePosition ) {
-			curPosition = curElem.position();
-			curTop = curPosition.top;
-			curLeft = curPosition.left;
-
-		} else {
-			curTop = parseFloat( curCSSTop ) || 0;
-			curLeft = parseFloat( curCSSLeft ) || 0;
-		}
-
-		if ( isFunction( options ) ) {
-
-			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
-			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
-		}
-
-		if ( options.top != null ) {
-			props.top = ( options.top - curOffset.top ) + curTop;
-		}
-		if ( options.left != null ) {
-			props.left = ( options.left - curOffset.left ) + curLeft;
-		}
-
-		if ( "using" in options ) {
-			options.using.call( elem, props );
-
-		} else {
-			if ( typeof props.top === "number" ) {
-				props.top += "px";
-			}
-			if ( typeof props.left === "number" ) {
-				props.left += "px";
-			}
-			curElem.css( props );
-		}
-	}
-};
-
-jQuery.fn.extend( {
-
-	// offset() relates an element's border box to the document origin
-	offset: function( options ) {
-
-		// Preserve chaining for setter
-		if ( arguments.length ) {
-			return options === undefined ?
-				this :
-				this.each( function( i ) {
-					jQuery.offset.setOffset( this, options, i );
-				} );
-		}
-
-		var rect, win,
-			elem = this[ 0 ];
-
-		if ( !elem ) {
-			return;
-		}
-
-		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
-		// Support: IE <=11 only
-		// Running getBoundingClientRect on a
-		// disconnected node in IE throws an error
-		if ( !elem.getClientRects().length ) {
-			return { top: 0, left: 0 };
-		}
-
-		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
-		rect = elem.getBoundingClientRect();
-		win = elem.ownerDocument.defaultView;
-		return {
-			top: rect.top + win.pageYOffset,
-			left: rect.left + win.pageXOffset
-		};
-	},
-
-	// position() relates an element's margin box to its offset parent's padding box
-	// This corresponds to the behavior of CSS absolute positioning
-	position: function() {
-		if ( !this[ 0 ] ) {
-			return;
-		}
-
-		var offsetParent, offset, doc,
-			elem = this[ 0 ],
-			parentOffset = { top: 0, left: 0 };
-
-		// position:fixed elements are offset from the viewport, which itself always has zero offset
-		if ( jQuery.css( elem, "position" ) === "fixed" ) {
-
-			// Assume position:fixed implies availability of getBoundingClientRect
-			offset = elem.getBoundingClientRect();
-
-		} else {
-			offset = this.offset();
-
-			// Account for the *real* offset parent, which can be the document or its root element
-			// when a statically positioned element is identified
-			doc = elem.ownerDocument;
-			offsetParent = elem.offsetParent || doc.documentElement;
-			while ( offsetParent &&
-				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
-				jQuery.css( offsetParent, "position" ) === "static" ) {
-
-				offsetParent = offsetParent.parentNode;
-			}
-			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
-
-				// Incorporate borders into its offset, since they are outside its content origin
-				parentOffset = jQuery( offsetParent ).offset();
-				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
-				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
-			}
-		}
-
-		// Subtract parent offsets and element margins
-		return {
-			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
-			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
-		};
-	},
-
-	// This method will return documentElement in the following cases:
-	// 1) For the element inside the iframe without offsetParent, this method will return
-	//    documentElement of the parent window
-	// 2) For the hidden or detached element
-	// 3) For body or html element, i.e. in case of the html node - it will return itself
-	//
-	// but those exceptions were never presented as a real life use-cases
-	// and might be considered as more preferable results.
-	//
-	// This logic, however, is not guaranteed and can change at any point in the future
-	offsetParent: function() {
-		return this.map( function() {
-			var offsetParent = this.offsetParent;
-
-			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
-				offsetParent = offsetParent.offsetParent;
-			}
-
-			return offsetParent || documentElement;
-		} );
-	}
-} );
-
-// Create scrollLeft and scrollTop methods
-jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
-	var top = "pageYOffset" === prop;
-
-	jQuery.fn[ method ] = function( val ) {
-		return access( this, function( elem, method, val ) {
-
-			// Coalesce documents and windows
-			var win;
-			if ( isWindow( elem ) ) {
-				win = elem;
-			} else if ( elem.nodeType === 9 ) {
-				win = elem.defaultView;
-			}
-
-			if ( val === undefined ) {
-				return win ? win[ prop ] : elem[ method ];
-			}
-
-			if ( win ) {
-				win.scrollTo(
-					!top ? val : win.pageXOffset,
-					top ? val : win.pageYOffset
-				);
-
-			} else {
-				elem[ method ] = val;
-			}
-		}, method, val, arguments.length );
-	};
-} );
-
-// Support: Safari <=7 - 9.1, Chrome <=37 - 49
-// Add the top/left cssHooks using jQuery.fn.position
-// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
-// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
-// getComputedStyle returns percent when specified for top/left/bottom/right;
-// rather than make the css module depend on the offset module, just check for it here
-jQuery.each( [ "top", "left" ], function( _i, prop ) {
-	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
-		function( elem, computed ) {
-			if ( computed ) {
-				computed = curCSS( elem, prop );
-
-				// If curCSS returns percentage, fallback to offset
-				return rnumnonpx.test( computed ) ?
-					jQuery( elem ).position()[ prop ] + "px" :
-					computed;
-			}
-		}
-	);
-} );
-
-
-// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
-jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
-	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
-		function( defaultExtra, funcName ) {
-
-		// Margin is only for outerHeight, outerWidth
-		jQuery.fn[ funcName ] = function( margin, value ) {
-			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
-				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
-
-			return access( this, function( elem, type, value ) {
-				var doc;
-
-				if ( isWindow( elem ) ) {
-
-					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
-					return funcName.indexOf( "outer" ) === 0 ?
-						elem[ "inner" + name ] :
-						elem.document.documentElement[ "client" + name ];
-				}
-
-				// Get document width or height
-				if ( elem.nodeType === 9 ) {
-					doc = elem.documentElement;
-
-					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
-					// whichever is greatest
-					return Math.max(
-						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
-						elem.body[ "offset" + name ], doc[ "offset" + name ],
-						doc[ "client" + name ]
-					);
-				}
-
-				return value === undefined ?
-
-					// Get width or height on the element, requesting but not forcing parseFloat
-					jQuery.css( elem, type, extra ) :
-
-					// Set width or height on the element
-					jQuery.style( elem, type, value, extra );
-			}, type, chainable ? margin : undefined, chainable );
-		};
-	} );
-} );
-
-
-jQuery.each( [
-	"ajaxStart",
-	"ajaxStop",
-	"ajaxComplete",
-	"ajaxError",
-	"ajaxSuccess",
-	"ajaxSend"
-], function( _i, type ) {
-	jQuery.fn[ type ] = function( fn ) {
-		return this.on( type, fn );
-	};
-} );
-
-
-
-
-jQuery.fn.extend( {
-
-	bind: function( types, data, fn ) {
-		return this.on( types, null, data, fn );
-	},
-	unbind: function( types, fn ) {
-		return this.off( types, null, fn );
-	},
-
-	delegate: function( selector, types, data, fn ) {
-		return this.on( types, selector, data, fn );
-	},
-	undelegate: function( selector, types, fn ) {
-
-		// ( namespace ) or ( selector, types [, fn] )
-		return arguments.length === 1 ?
-			this.off( selector, "**" ) :
-			this.off( types, selector || "**", fn );
-	},
-
-	hover: function( fnOver, fnOut ) {
-		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
-	}
-} );
-
-jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
-	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
-	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
-	function( _i, name ) {
-
-		// Handle event binding
-		jQuery.fn[ name ] = function( data, fn ) {
-			return arguments.length > 0 ?
-				this.on( name, null, data, fn ) :
-				this.trigger( name );
-		};
-	} );
-
-
-
-
-// Support: Android <=4.0 only
-// Make sure we trim BOM and NBSP
-var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
-
-// Bind a function to a context, optionally partially applying any
-// arguments.
-// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
-// However, it is not slated for removal any time soon
-jQuery.proxy = function( fn, context ) {
-	var tmp, args, proxy;
-
-	if ( typeof context === "string" ) {
-		tmp = fn[ context ];
-		context = fn;
-		fn = tmp;
-	}
-
-	// Quick check to determine if target is callable, in the spec
-	// this throws a TypeError, but we will just return undefined.
-	if ( !isFunction( fn ) ) {
-		return undefined;
-	}
-
-	// Simulated bind
-	args = slice.call( arguments, 2 );
-	proxy = function() {
-		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
-	};
-
-	// Set the guid of unique handler to the same of original handler, so it can be removed
-	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
-
-	return proxy;
-};
-
-jQuery.holdReady = function( hold ) {
-	if ( hold ) {
-		jQuery.readyWait++;
-	} else {
-		jQuery.ready( true );
-	}
-};
-jQuery.isArray = Array.isArray;
-jQuery.parseJSON = JSON.parse;
-jQuery.nodeName = nodeName;
-jQuery.isFunction = isFunction;
-jQuery.isWindow = isWindow;
-jQuery.camelCase = camelCase;
-jQuery.type = toType;
-
-jQuery.now = Date.now;
-
-jQuery.isNumeric = function( obj ) {
-
-	// As of jQuery 3.0, isNumeric is limited to
-	// strings and numbers (primitives or objects)
-	// that can be coerced to finite numbers (gh-2662)
-	var type = jQuery.type( obj );
-	return ( type === "number" || type === "string" ) &&
-
-		// parseFloat NaNs numeric-cast false positives ("")
-		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
-		// subtraction forces infinities to NaN
-		!isNaN( obj - parseFloat( obj ) );
-};
-
-jQuery.trim = function( text ) {
-	return text == null ?
-		"" :
-		( text + "" ).replace( rtrim, "" );
-};
-
-
-
-// Register as a named AMD module, since jQuery can be concatenated with other
-// files that may use define, but not via a proper concatenation script that
-// understands anonymous AMD modules. A named AMD is safest and most robust
-// way to register. Lowercase jquery is used because AMD module names are
-// derived from file names, and jQuery is normally delivered in a lowercase
-// file name. Do this after creating the global so that if an AMD module wants
-// to call noConflict to hide this version of jQuery, it will work.
-
-// Note that for maximum portability, libraries that are not jQuery should
-// declare themselves as anonymous modules, and avoid setting a global if an
-// AMD loader is present. jQuery is a special case. For more information, see
-// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
-
-if ( typeof define === "function" && define.amd ) {
-	define( "jquery", [], function() {
-		return jQuery;
-	} );
-}
-
-
-
-
-var
-
-	// Map over jQuery in case of overwrite
-	_jQuery = window.jQuery,
-
-	// Map over the $ in case of overwrite
-	_$ = window.$;
-
-jQuery.noConflict = function( deep ) {
-	if ( window.$ === jQuery ) {
-		window.$ = _$;
-	}
-
-	if ( deep && window.jQuery === jQuery ) {
-		window.jQuery = _jQuery;
-	}
-
-	return jQuery;
-};
-
-// Expose jQuery and $ identifiers, even in AMD
-// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
-// and CommonJS for browser emulators (#13566)
-if ( typeof noGlobal === "undefined" ) {
-	window.jQuery = window.$ = jQuery;
-}
-
-
-
-
-return jQuery;
-} );
diff --git a/master/_static/jquery.js b/master/_static/jquery.js
deleted file mode 100644
index b0614034a..000000000
--- a/master/_static/jquery.js
+++ /dev/null
@@ -1,2 +0,0 @@
-/*! jQuery v3.5.1 | (c) JS Foundation and other contributors | jquery.org/license */
-!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var 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diff --git a/master/_static/language_data.js b/master/_static/language_data.js
deleted file mode 100644
index 863704b31..000000000
--- a/master/_static/language_data.js
+++ /dev/null
@@ -1,297 +0,0 @@
-/*
- * language_data.js
- * ~~~~~~~~~~~~~~~~
- *
- * This script contains the language-specific data used by searchtools.js,
- * namely the list of stopwords, stemmer, scorer and splitter.
- *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
-
-
-/* Non-minified version is copied as a separate JS file, is available */
-
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
-  var step2list = {
-    ational: 'ate',
-    tional: 'tion',
-    enci: 'ence',
-    anci: 'ance',
-    izer: 'ize',
-    bli: 'ble',
-    alli: 'al',
-    entli: 'ent',
-    eli: 'e',
-    ousli: 'ous',
-    ization: 'ize',
-    ation: 'ate',
-    ator: 'ate',
-    alism: 'al',
-    iveness: 'ive',
-    fulness: 'ful',
-    ousness: 'ous',
-    aliti: 'al',
-    iviti: 'ive',
-    biliti: 'ble',
-    logi: 'log'
-  };
-
-  var step3list = {
-    icate: 'ic',
-    ative: '',
-    alize: 'al',
-    iciti: 'ic',
-    ical: 'ic',
-    ful: '',
-    ness: ''
-  };
-
-  var c = "[^aeiou]";          // consonant
-  var v = "[aeiouy]";          // vowel
-  var C = c + "[^aeiouy]*";    // consonant sequence
-  var V = v + "[aeiou]*";      // vowel sequence
-
-  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
-  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
-  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
-  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
-
-  this.stemWord = function (w) {
-    var stem;
-    var suffix;
-    var firstch;
-    var origword = w;
-
-    if (w.length < 3)
-      return w;
-
-    var re;
-    var re2;
-    var re3;
-    var re4;
-
-    firstch = w.substr(0,1);
-    if (firstch == "y")
-      w = firstch.toUpperCase() + w.substr(1);
-
-    // Step 1a
-    re = /^(.+?)(ss|i)es$/;
-    re2 = /^(.+?)([^s])s$/;
-
-    if (re.test(w))
-      w = w.replace(re,"$1$2");
-    else if (re2.test(w))
-      w = w.replace(re2,"$1$2");
-
-    // Step 1b
-    re = /^(.+?)eed$/;
-    re2 = /^(.+?)(ed|ing)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      re = new RegExp(mgr0);
-      if (re.test(fp[1])) {
-        re = /.$/;
-        w = w.replace(re,"");
-      }
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1];
-      re2 = new RegExp(s_v);
-      if (re2.test(stem)) {
-        w = stem;
-        re2 = /(at|bl|iz)$/;
-        re3 = new RegExp("([^aeiouylsz])\\1$");
-        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-        if (re2.test(w))
-          w = w + "e";
-        else if (re3.test(w)) {
-          re = /.$/;
-          w = w.replace(re,"");
-        }
-        else if (re4.test(w))
-          w = w + "e";
-      }
-    }
-
-    // Step 1c
-    re = /^(.+?)y$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(s_v);
-      if (re.test(stem))
-        w = stem + "i";
-    }
-
-    // Step 2
-    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step2list[suffix];
-    }
-
-    // Step 3
-    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step3list[suffix];
-    }
-
-    // Step 4
-    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
-    re2 = /^(.+?)(s|t)(ion)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      if (re.test(stem))
-        w = stem;
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1] + fp[2];
-      re2 = new RegExp(mgr1);
-      if (re2.test(stem))
-        w = stem;
-    }
-
-    // Step 5
-    re = /^(.+?)e$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      re2 = new RegExp(meq1);
-      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
-        w = stem;
-    }
-    re = /ll$/;
-    re2 = new RegExp(mgr1);
-    if (re.test(w) && re2.test(w)) {
-      re = /.$/;
-      w = w.replace(re,"");
-    }
-
-    // and turn initial Y back to y
-    if (firstch == "y")
-      w = firstch.toLowerCase() + w.substr(1);
-    return w;
-  }
-}
-
-
-
-
-var splitChars = (function() {
-    var result = {};
-    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
-         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
-         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
-         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
-         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
-         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
-         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
-         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
-         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
-         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
-    var i, j, start, end;
-    for (i = 0; i < singles.length; i++) {
-        result[singles[i]] = true;
-    }
-    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
-         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
-         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
-         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
-         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
-         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
-         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
-         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
-         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
-         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
-         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
-         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
-         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
-         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
-         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
-         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
-         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
-         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
-         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
-         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
-         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
-         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
-         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
-         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
-         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
-         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
-         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
-         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
-         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
-         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
-         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
-         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
-         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
-         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
-         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
-         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
-         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
-         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
-         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
-         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
-         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
-         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
-         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
-         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
-         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
-         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
-         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
-         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
-         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
-    for (i = 0; i < ranges.length; i++) {
-        start = ranges[i][0];
-        end = ranges[i][1];
-        for (j = start; j <= end; j++) {
-            result[j] = true;
-        }
-    }
-    return result;
-})();
-
-function splitQuery(query) {
-    var result = [];
-    var start = -1;
-    for (var i = 0; i < query.length; i++) {
-        if (splitChars[query.charCodeAt(i)]) {
-            if (start !== -1) {
-                result.push(query.slice(start, i));
-                start = -1;
-            }
-        } else if (start === -1) {
-            start = i;
-        }
-    }
-    if (start !== -1) {
-        result.push(query.slice(start));
-    }
-    return result;
-}
-
-
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diff --git a/master/_static/no_image.png b/master/_static/no_image.png
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diff --git a/master/_static/searchtools.js b/master/_static/searchtools.js
deleted file mode 100644
index 8eb14218b..000000000
--- a/master/_static/searchtools.js
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-/*
- * searchtools.js
- * ~~~~~~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for the full-text search.
- *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-if (!Scorer) {
-  /**
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-   */
-  var Scorer = {
-    // Implement the following function to further tweak the score for each result
-    // The function takes a result array [filename, title, anchor, descr, score]
-    // and returns the new score.
-    /*
-    score: function(result) {
-      return result[4];
-    },
-    */
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-    objNameMatch: 11,
-    // or matches in the last dotted part of the object name
-    objPartialMatch: 6,
-    // Additive scores depending on the priority of the object
-    objPrio: {0:  15,   // used to be importantResults
-              1:  5,   // used to be objectResults
-              2: -5},  // used to be unimportantResults
-    //  Used when the priority is not in the mapping.
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-
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-    partialTitle: 7,
-    // query found in terms
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-    partialTerm: 2
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-  _index : null,
-  _queued_query : null,
-  _pulse_status : -1,
-
-  htmlToText : function(htmlString) {
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-      var htmlElement = $(htmlString, virtualDocument);
-      htmlElement.find('.headerlink').remove();
-      docContent = htmlElement.find('[role=main]')[0];
-      if(docContent === undefined) {
-          console.warn("Content block not found. Sphinx search tries to obtain it " +
-                       "via '[role=main]'. Could you check your theme or template.");
-          return "";
-      }
-      return docContent.textContent || docContent.innerText;
-  },
-
-  init : function() {
-      var params = $.getQueryParameters();
-      if (params.q) {
-          var query = params.q[0];
-          $('input[name="q"]')[0].value = query;
-          this.performSearch(query);
-      }
-  },
-
-  loadIndex : function(url) {
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-              }
-            }});
-  },
-
-  setIndex : function(index) {
-    var q;
-    this._index = index;
-    if ((q = this._queued_query) !== null) {
-      this._queued_query = null;
-      Search.query(q);
-    }
-  },
-
-  hasIndex : function() {
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-  },
-
-  deferQuery : function(query) {
-      this._queued_query = query;
-  },
-
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-      this._pulse_status = 0;
-  },
-
-  startPulse : function() {
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-      var dotString = '';
-      for (i = 0; i < Search._pulse_status; i++)
-        dotString += '.';
-      Search.dots.text(dotString);
-      if (Search._pulse_status > -1)
-        window.setTimeout(pulse, 500);
-    }
-    pulse();
-  },
-
-  /**
-   * perform a search for something (or wait until index is loaded)
-   */
-  performSearch : function(query) {
-    // create the required interface elements
-    this.out = $('#search-results');
-    this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
-    this.dots = $('<span></span>').appendTo(this.title);
-    this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
-    this.output = $('<ul class="search"/>').appendTo(this.out);
-
-    $('#search-progress').text(_('Preparing search...'));
-    this.startPulse();
-
-    // index already loaded, the browser was quick!
-    if (this.hasIndex())
-      this.query(query);
-    else
-      this.deferQuery(query);
-  },
-
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  query : function(query) {
-    var i;
-
-    // stem the searchterms and add them to the correct list
-    var stemmer = new Stemmer();
-    var searchterms = [];
-    var excluded = [];
-    var hlterms = [];
-    var tmp = splitQuery(query);
-    var objectterms = [];
-    for (i = 0; i < tmp.length; i++) {
-      if (tmp[i] !== "") {
-          objectterms.push(tmp[i].toLowerCase());
-      }
-
-      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
-        // skip this "word"
-        continue;
-      }
-      // stem the word
-      var word = stemmer.stemWord(tmp[i].toLowerCase());
-      // prevent stemmer from cutting word smaller than two chars
-      if(word.length < 3 && tmp[i].length >= 3) {
-        word = tmp[i];
-      }
-      var toAppend;
-      // select the correct list
-      if (word[0] == '-') {
-        toAppend = excluded;
-        word = word.substr(1);
-      }
-      else {
-        toAppend = searchterms;
-        hlterms.push(tmp[i].toLowerCase());
-      }
-      // only add if not already in the list
-      if (!$u.contains(toAppend, word))
-        toAppend.push(word);
-    }
-    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
-
-    // console.debug('SEARCH: searching for:');
-    // console.info('required: ', searchterms);
-    // console.info('excluded: ', excluded);
-
-    // prepare search
-    var terms = this._index.terms;
-    var titleterms = this._index.titleterms;
-
-    // array of [filename, title, anchor, descr, score]
-    var results = [];
-    $('#search-progress').empty();
-
-    // lookup as object
-    for (i = 0; i < objectterms.length; i++) {
-      var others = [].concat(objectterms.slice(0, i),
-                             objectterms.slice(i+1, objectterms.length));
-      results = results.concat(this.performObjectSearch(objectterms[i], others));
-    }
-
-    // lookup as search terms in fulltext
-    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
-
-    // let the scorer override scores with a custom scoring function
-    if (Scorer.score) {
-      for (i = 0; i < results.length; i++)
-        results[i][4] = Scorer.score(results[i]);
-    }
-
-    // now sort the results by score (in opposite order of appearance, since the
-    // display function below uses pop() to retrieve items) and then
-    // alphabetically
-    results.sort(function(a, b) {
-      var left = a[4];
-      var right = b[4];
-      if (left > right) {
-        return 1;
-      } else if (left < right) {
-        return -1;
-      } else {
-        // same score: sort alphabetically
-        left = a[1].toLowerCase();
-        right = b[1].toLowerCase();
-        return (left > right) ? -1 : ((left < right) ? 1 : 0);
-      }
-    });
-
-    // for debugging
-    //Search.lastresults = results.slice();  // a copy
-    //console.info('search results:', Search.lastresults);
-
-    // print the results
-    var resultCount = results.length;
-    function displayNextItem() {
-      // results left, load the summary and display it
-      if (results.length) {
-        var item = results.pop();
-        var listItem = $('<li></li>');
-        var requestUrl = "";
-        var linkUrl = "";
-        if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
-          // dirhtml builder
-          var dirname = item[0] + '/';
-          if (dirname.match(/\/index\/$/)) {
-            dirname = dirname.substring(0, dirname.length-6);
-          } else if (dirname == 'index/') {
-            dirname = '';
-          }
-          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
-          linkUrl = requestUrl;
-
-        } else {
-          // normal html builders
-          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
-          linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
-        }
-        listItem.append($('<a/>').attr('href',
-            linkUrl +
-            highlightstring + item[2]).html(item[1]));
-        if (item[3]) {
-          listItem.append($('<span> (' + item[3] + ')</span>'));
-          Search.output.append(listItem);
-          setTimeout(function() {
-            displayNextItem();
-          }, 5);
-        } else {
-          $.ajax({url: requestUrl,
-                  dataType: "text",
-                  complete: function(jqxhr, textstatus) {
-                    var data = jqxhr.responseText;
-                    if (data !== '' && data !== undefined) {
-                      listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
-                    }
-                    Search.output.append(listItem);
-                    setTimeout(function() {
-                      displayNextItem();
-                    }, 5);
-                  }});
-        }
-      }
-      // search finished, update title and status message
-      else {
-        Search.stopPulse();
-        Search.title.text(_('Search Results'));
-        if (!resultCount)
-          Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
-        else
-            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
-        Search.status.fadeIn(500);
-      }
-    }
-    displayNextItem();
-  },
-
-  /**
-   * search for object names
-   */
-  performObjectSearch : function(object, otherterms) {
-    var filenames = this._index.filenames;
-    var docnames = this._index.docnames;
-    var objects = this._index.objects;
-    var objnames = this._index.objnames;
-    var titles = this._index.titles;
-
-    var i;
-    var results = [];
-
-    for (var prefix in objects) {
-      for (var name in objects[prefix]) {
-        var fullname = (prefix ? prefix + '.' : '') + name;
-        var fullnameLower = fullname.toLowerCase()
-        if (fullnameLower.indexOf(object) > -1) {
-          var score = 0;
-          var parts = fullnameLower.split('.');
-          // check for different match types: exact matches of full name or
-          // "last name" (i.e. last dotted part)
-          if (fullnameLower == object || parts[parts.length - 1] == object) {
-            score += Scorer.objNameMatch;
-          // matches in last name
-          } else if (parts[parts.length - 1].indexOf(object) > -1) {
-            score += Scorer.objPartialMatch;
-          }
-          var match = objects[prefix][name];
-          var objname = objnames[match[1]][2];
-          var title = titles[match[0]];
-          // If more than one term searched for, we require other words to be
-          // found in the name/title/description
-          if (otherterms.length > 0) {
-            var haystack = (prefix + ' ' + name + ' ' +
-                            objname + ' ' + title).toLowerCase();
-            var allfound = true;
-            for (i = 0; i < otherterms.length; i++) {
-              if (haystack.indexOf(otherterms[i]) == -1) {
-                allfound = false;
-                break;
-              }
-            }
-            if (!allfound) {
-              continue;
-            }
-          }
-          var descr = objname + _(', in ') + title;
-
-          var anchor = match[3];
-          if (anchor === '')
-            anchor = fullname;
-          else if (anchor == '-')
-            anchor = objnames[match[1]][1] + '-' + fullname;
-          // add custom score for some objects according to scorer
-          if (Scorer.objPrio.hasOwnProperty(match[2])) {
-            score += Scorer.objPrio[match[2]];
-          } else {
-            score += Scorer.objPrioDefault;
-          }
-          results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
-        }
-      }
-    }
-
-    return results;
-  },
-
-  /**
-   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
-   */
-  escapeRegExp : function(string) {
-    return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
-  },
-
-  /**
-   * search for full-text terms in the index
-   */
-  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
-    var docnames = this._index.docnames;
-    var filenames = this._index.filenames;
-    var titles = this._index.titles;
-
-    var i, j, file;
-    var fileMap = {};
-    var scoreMap = {};
-    var results = [];
-
-    // perform the search on the required terms
-    for (i = 0; i < searchterms.length; i++) {
-      var word = searchterms[i];
-      var files = [];
-      var _o = [
-        {files: terms[word], score: Scorer.term},
-        {files: titleterms[word], score: Scorer.title}
-      ];
-      // add support for partial matches
-      if (word.length > 2) {
-        var word_regex = this.escapeRegExp(word);
-        for (var w in terms) {
-          if (w.match(word_regex) && !terms[word]) {
-            _o.push({files: terms[w], score: Scorer.partialTerm})
-          }
-        }
-        for (var w in titleterms) {
-          if (w.match(word_regex) && !titleterms[word]) {
-              _o.push({files: titleterms[w], score: Scorer.partialTitle})
-          }
-        }
-      }
-
-      // no match but word was a required one
-      if ($u.every(_o, function(o){return o.files === undefined;})) {
-        break;
-      }
-      // found search word in contents
-      $u.each(_o, function(o) {
-        var _files = o.files;
-        if (_files === undefined)
-          return
-
-        if (_files.length === undefined)
-          _files = [_files];
-        files = files.concat(_files);
-
-        // set score for the word in each file to Scorer.term
-        for (j = 0; j < _files.length; j++) {
-          file = _files[j];
-          if (!(file in scoreMap))
-            scoreMap[file] = {};
-          scoreMap[file][word] = o.score;
-        }
-      });
-
-      // create the mapping
-      for (j = 0; j < files.length; j++) {
-        file = files[j];
-        if (file in fileMap && fileMap[file].indexOf(word) === -1)
-          fileMap[file].push(word);
-        else
-          fileMap[file] = [word];
-      }
-    }
-
-    // now check if the files don't contain excluded terms
-    for (file in fileMap) {
-      var valid = true;
-
-      // check if all requirements are matched
-      var filteredTermCount = // as search terms with length < 3 are discarded: ignore
-        searchterms.filter(function(term){return term.length > 2}).length
-      if (
-        fileMap[file].length != searchterms.length &&
-        fileMap[file].length != filteredTermCount
-      ) continue;
-
-      // ensure that none of the excluded terms is in the search result
-      for (i = 0; i < excluded.length; i++) {
-        if (terms[excluded[i]] == file ||
-            titleterms[excluded[i]] == file ||
-            $u.contains(terms[excluded[i]] || [], file) ||
-            $u.contains(titleterms[excluded[i]] || [], file)) {
-          valid = false;
-          break;
-        }
-      }
-
-      // if we have still a valid result we can add it to the result list
-      if (valid) {
-        // select one (max) score for the file.
-        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
-        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
-        results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
-      }
-    }
-    return results;
-  },
-
-  /**
-   * helper function to return a node containing the
-   * search summary for a given text. keywords is a list
-   * of stemmed words, hlwords is the list of normal, unstemmed
-   * words. the first one is used to find the occurrence, the
-   * latter for highlighting it.
-   */
-  makeSearchSummary : function(htmlText, keywords, hlwords) {
-    var text = Search.htmlToText(htmlText);
-    var textLower = text.toLowerCase();
-    var start = 0;
-    $.each(keywords, function() {
-      var i = textLower.indexOf(this.toLowerCase());
-      if (i > -1)
-        start = i;
-    });
-    start = Math.max(start - 120, 0);
-    var excerpt = ((start > 0) ? '...' : '') +
-      $.trim(text.substr(start, 240)) +
-      ((start + 240 - text.length) ? '...' : '');
-    var rv = $('<p class="context"></p>').text(excerpt);
-    $.each(hlwords, function() {
-      rv = rv.highlightText(this, 'highlighted');
-    });
-    return rv;
-  }
-};
-
-$(document).ready(function() {
-  Search.init();
-});
diff --git a/master/_static/underscore-1.13.1.js b/master/_static/underscore-1.13.1.js
deleted file mode 100644
index ffd77af96..000000000
--- a/master/_static/underscore-1.13.1.js
+++ /dev/null
@@ -1,2042 +0,0 @@
-(function (global, factory) {
-  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
-  typeof define === 'function' && define.amd ? define('underscore', factory) :
-  (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
-    var current = global._;
-    var exports = global._ = factory();
-    exports.noConflict = function () { global._ = current; return exports; };
-  }()));
-}(this, (function () {
-  //     Underscore.js 1.13.1
-  //     https://underscorejs.org
-  //     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
-  //     Underscore may be freely distributed under the MIT license.
-
-  // Current version.
-  var VERSION = '1.13.1';
-
-  // Establish the root object, `window` (`self`) in the browser, `global`
-  // on the server, or `this` in some virtual machines. We use `self`
-  // instead of `window` for `WebWorker` support.
-  var root = typeof self == 'object' && self.self === self && self ||
-            typeof global == 'object' && global.global === global && global ||
-            Function('return this')() ||
-            {};
-
-  // Save bytes in the minified (but not gzipped) version:
-  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
-  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
-
-  // Create quick reference variables for speed access to core prototypes.
-  var push = ArrayProto.push,
-      slice = ArrayProto.slice,
-      toString = ObjProto.toString,
-      hasOwnProperty = ObjProto.hasOwnProperty;
-
-  // Modern feature detection.
-  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
-      supportsDataView = typeof DataView !== 'undefined';
-
-  // All **ECMAScript 5+** native function implementations that we hope to use
-  // are declared here.
-  var nativeIsArray = Array.isArray,
-      nativeKeys = Object.keys,
-      nativeCreate = Object.create,
-      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
-
-  // Create references to these builtin functions because we override them.
-  var _isNaN = isNaN,
-      _isFinite = isFinite;
-
-  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
-  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
-  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
-    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
-
-  // The largest integer that can be represented exactly.
-  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
-
-  // Some functions take a variable number of arguments, or a few expected
-  // arguments at the beginning and then a variable number of values to operate
-  // on. This helper accumulates all remaining arguments past the function’s
-  // argument length (or an explicit `startIndex`), into an array that becomes
-  // the last argument. Similar to ES6’s "rest parameter".
-  function restArguments(func, startIndex) {
-    startIndex = startIndex == null ? func.length - 1 : +startIndex;
-    return function() {
-      var length = Math.max(arguments.length - startIndex, 0),
-          rest = Array(length),
-          index = 0;
-      for (; index < length; index++) {
-        rest[index] = arguments[index + startIndex];
-      }
-      switch (startIndex) {
-        case 0: return func.call(this, rest);
-        case 1: return func.call(this, arguments[0], rest);
-        case 2: return func.call(this, arguments[0], arguments[1], rest);
-      }
-      var args = Array(startIndex + 1);
-      for (index = 0; index < startIndex; index++) {
-        args[index] = arguments[index];
-      }
-      args[startIndex] = rest;
-      return func.apply(this, args);
-    };
-  }
-
-  // Is a given variable an object?
-  function isObject(obj) {
-    var type = typeof obj;
-    return type === 'function' || type === 'object' && !!obj;
-  }
-
-  // Is a given value equal to null?
-  function isNull(obj) {
-    return obj === null;
-  }
-
-  // Is a given variable undefined?
-  function isUndefined(obj) {
-    return obj === void 0;
-  }
-
-  // Is a given value a boolean?
-  function isBoolean(obj) {
-    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
-  }
-
-  // Is a given value a DOM element?
-  function isElement(obj) {
-    return !!(obj && obj.nodeType === 1);
-  }
-
-  // Internal function for creating a `toString`-based type tester.
-  function tagTester(name) {
-    var tag = '[object ' + name + ']';
-    return function(obj) {
-      return toString.call(obj) === tag;
-    };
-  }
-
-  var isString = tagTester('String');
-
-  var isNumber = tagTester('Number');
-
-  var isDate = tagTester('Date');
-
-  var isRegExp = tagTester('RegExp');
-
-  var isError = tagTester('Error');
-
-  var isSymbol = tagTester('Symbol');
-
-  var isArrayBuffer = tagTester('ArrayBuffer');
-
-  var isFunction = tagTester('Function');
-
-  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
-  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
-  var nodelist = root.document && root.document.childNodes;
-  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
-    isFunction = function(obj) {
-      return typeof obj == 'function' || false;
-    };
-  }
-
-  var isFunction$1 = isFunction;
-
-  var hasObjectTag = tagTester('Object');
-
-  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
-  // In IE 11, the most common among them, this problem also applies to
-  // `Map`, `WeakMap` and `Set`.
-  var hasStringTagBug = (
-        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
-      ),
-      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
-
-  var isDataView = tagTester('DataView');
-
-  // In IE 10 - Edge 13, we need a different heuristic
-  // to determine whether an object is a `DataView`.
-  function ie10IsDataView(obj) {
-    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
-  }
-
-  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
-
-  // Is a given value an array?
-  // Delegates to ECMA5's native `Array.isArray`.
-  var isArray = nativeIsArray || tagTester('Array');
-
-  // Internal function to check whether `key` is an own property name of `obj`.
-  function has$1(obj, key) {
-    return obj != null && hasOwnProperty.call(obj, key);
-  }
-
-  var isArguments = tagTester('Arguments');
-
-  // Define a fallback version of the method in browsers (ahem, IE < 9), where
-  // there isn't any inspectable "Arguments" type.
-  (function() {
-    if (!isArguments(arguments)) {
-      isArguments = function(obj) {
-        return has$1(obj, 'callee');
-      };
-    }
-  }());
-
-  var isArguments$1 = isArguments;
-
-  // Is a given object a finite number?
-  function isFinite$1(obj) {
-    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
-  }
-
-  // Is the given value `NaN`?
-  function isNaN$1(obj) {
-    return isNumber(obj) && _isNaN(obj);
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function constant(value) {
-    return function() {
-      return value;
-    };
-  }
-
-  // Common internal logic for `isArrayLike` and `isBufferLike`.
-  function createSizePropertyCheck(getSizeProperty) {
-    return function(collection) {
-      var sizeProperty = getSizeProperty(collection);
-      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
-    }
-  }
-
-  // Internal helper to generate a function to obtain property `key` from `obj`.
-  function shallowProperty(key) {
-    return function(obj) {
-      return obj == null ? void 0 : obj[key];
-    };
-  }
-
-  // Internal helper to obtain the `byteLength` property of an object.
-  var getByteLength = shallowProperty('byteLength');
-
-  // Internal helper to determine whether we should spend extensive checks against
-  // `ArrayBuffer` et al.
-  var isBufferLike = createSizePropertyCheck(getByteLength);
-
-  // Is a given value a typed array?
-  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
-  function isTypedArray(obj) {
-    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
-    // Otherwise, fall back on the above regular expression.
-    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
-                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
-  }
-
-  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
-
-  // Internal helper to obtain the `length` property of an object.
-  var getLength = shallowProperty('length');
-
-  // Internal helper to create a simple lookup structure.
-  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
-  // circular imports. `emulatedSet` is a one-off solution that only works for
-  // arrays of strings.
-  function emulatedSet(keys) {
-    var hash = {};
-    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
-    return {
-      contains: function(key) { return hash[key]; },
-      push: function(key) {
-        hash[key] = true;
-        return keys.push(key);
-      }
-    };
-  }
-
-  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
-  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
-  // needed.
-  function collectNonEnumProps(obj, keys) {
-    keys = emulatedSet(keys);
-    var nonEnumIdx = nonEnumerableProps.length;
-    var constructor = obj.constructor;
-    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
-
-    // Constructor is a special case.
-    var prop = 'constructor';
-    if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
-
-    while (nonEnumIdx--) {
-      prop = nonEnumerableProps[nonEnumIdx];
-      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
-        keys.push(prop);
-      }
-    }
-  }
-
-  // Retrieve the names of an object's own properties.
-  // Delegates to **ECMAScript 5**'s native `Object.keys`.
-  function keys(obj) {
-    if (!isObject(obj)) return [];
-    if (nativeKeys) return nativeKeys(obj);
-    var keys = [];
-    for (var key in obj) if (has$1(obj, key)) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Is a given array, string, or object empty?
-  // An "empty" object has no enumerable own-properties.
-  function isEmpty(obj) {
-    if (obj == null) return true;
-    // Skip the more expensive `toString`-based type checks if `obj` has no
-    // `.length`.
-    var length = getLength(obj);
-    if (typeof length == 'number' && (
-      isArray(obj) || isString(obj) || isArguments$1(obj)
-    )) return length === 0;
-    return getLength(keys(obj)) === 0;
-  }
-
-  // Returns whether an object has a given set of `key:value` pairs.
-  function isMatch(object, attrs) {
-    var _keys = keys(attrs), length = _keys.length;
-    if (object == null) return !length;
-    var obj = Object(object);
-    for (var i = 0; i < length; i++) {
-      var key = _keys[i];
-      if (attrs[key] !== obj[key] || !(key in obj)) return false;
-    }
-    return true;
-  }
-
-  // If Underscore is called as a function, it returns a wrapped object that can
-  // be used OO-style. This wrapper holds altered versions of all functions added
-  // through `_.mixin`. Wrapped objects may be chained.
-  function _$1(obj) {
-    if (obj instanceof _$1) return obj;
-    if (!(this instanceof _$1)) return new _$1(obj);
-    this._wrapped = obj;
-  }
-
-  _$1.VERSION = VERSION;
-
-  // Extracts the result from a wrapped and chained object.
-  _$1.prototype.value = function() {
-    return this._wrapped;
-  };
-
-  // Provide unwrapping proxies for some methods used in engine operations
-  // such as arithmetic and JSON stringification.
-  _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
-
-  _$1.prototype.toString = function() {
-    return String(this._wrapped);
-  };
-
-  // Internal function to wrap or shallow-copy an ArrayBuffer,
-  // typed array or DataView to a new view, reusing the buffer.
-  function toBufferView(bufferSource) {
-    return new Uint8Array(
-      bufferSource.buffer || bufferSource,
-      bufferSource.byteOffset || 0,
-      getByteLength(bufferSource)
-    );
-  }
-
-  // We use this string twice, so give it a name for minification.
-  var tagDataView = '[object DataView]';
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function eq(a, b, aStack, bStack) {
-    // Identical objects are equal. `0 === -0`, but they aren't identical.
-    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
-    if (a === b) return a !== 0 || 1 / a === 1 / b;
-    // `null` or `undefined` only equal to itself (strict comparison).
-    if (a == null || b == null) return false;
-    // `NaN`s are equivalent, but non-reflexive.
-    if (a !== a) return b !== b;
-    // Exhaust primitive checks
-    var type = typeof a;
-    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
-    return deepEq(a, b, aStack, bStack);
-  }
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function deepEq(a, b, aStack, bStack) {
-    // Unwrap any wrapped objects.
-    if (a instanceof _$1) a = a._wrapped;
-    if (b instanceof _$1) b = b._wrapped;
-    // Compare `[[Class]]` names.
-    var className = toString.call(a);
-    if (className !== toString.call(b)) return false;
-    // Work around a bug in IE 10 - Edge 13.
-    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
-      if (!isDataView$1(b)) return false;
-      className = tagDataView;
-    }
-    switch (className) {
-      // These types are compared by value.
-      case '[object RegExp]':
-        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
-      case '[object String]':
-        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
-        // equivalent to `new String("5")`.
-        return '' + a === '' + b;
-      case '[object Number]':
-        // `NaN`s are equivalent, but non-reflexive.
-        // Object(NaN) is equivalent to NaN.
-        if (+a !== +a) return +b !== +b;
-        // An `egal` comparison is performed for other numeric values.
-        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
-      case '[object Date]':
-      case '[object Boolean]':
-        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
-        // millisecond representations. Note that invalid dates with millisecond representations
-        // of `NaN` are not equivalent.
-        return +a === +b;
-      case '[object Symbol]':
-        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
-      case '[object ArrayBuffer]':
-      case tagDataView:
-        // Coerce to typed array so we can fall through.
-        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
-    }
-
-    var areArrays = className === '[object Array]';
-    if (!areArrays && isTypedArray$1(a)) {
-        var byteLength = getByteLength(a);
-        if (byteLength !== getByteLength(b)) return false;
-        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
-        areArrays = true;
-    }
-    if (!areArrays) {
-      if (typeof a != 'object' || typeof b != 'object') return false;
-
-      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
-      // from different frames are.
-      var aCtor = a.constructor, bCtor = b.constructor;
-      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
-                               isFunction$1(bCtor) && bCtor instanceof bCtor)
-                          && ('constructor' in a && 'constructor' in b)) {
-        return false;
-      }
-    }
-    // Assume equality for cyclic structures. The algorithm for detecting cyclic
-    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
-
-    // Initializing stack of traversed objects.
-    // It's done here since we only need them for objects and arrays comparison.
-    aStack = aStack || [];
-    bStack = bStack || [];
-    var length = aStack.length;
-    while (length--) {
-      // Linear search. Performance is inversely proportional to the number of
-      // unique nested structures.
-      if (aStack[length] === a) return bStack[length] === b;
-    }
-
-    // Add the first object to the stack of traversed objects.
-    aStack.push(a);
-    bStack.push(b);
-
-    // Recursively compare objects and arrays.
-    if (areArrays) {
-      // Compare array lengths to determine if a deep comparison is necessary.
-      length = a.length;
-      if (length !== b.length) return false;
-      // Deep compare the contents, ignoring non-numeric properties.
-      while (length--) {
-        if (!eq(a[length], b[length], aStack, bStack)) return false;
-      }
-    } else {
-      // Deep compare objects.
-      var _keys = keys(a), key;
-      length = _keys.length;
-      // Ensure that both objects contain the same number of properties before comparing deep equality.
-      if (keys(b).length !== length) return false;
-      while (length--) {
-        // Deep compare each member
-        key = _keys[length];
-        if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
-      }
-    }
-    // Remove the first object from the stack of traversed objects.
-    aStack.pop();
-    bStack.pop();
-    return true;
-  }
-
-  // Perform a deep comparison to check if two objects are equal.
-  function isEqual(a, b) {
-    return eq(a, b);
-  }
-
-  // Retrieve all the enumerable property names of an object.
-  function allKeys(obj) {
-    if (!isObject(obj)) return [];
-    var keys = [];
-    for (var key in obj) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Since the regular `Object.prototype.toString` type tests don't work for
-  // some types in IE 11, we use a fingerprinting heuristic instead, based
-  // on the methods. It's not great, but it's the best we got.
-  // The fingerprint method lists are defined below.
-  function ie11fingerprint(methods) {
-    var length = getLength(methods);
-    return function(obj) {
-      if (obj == null) return false;
-      // `Map`, `WeakMap` and `Set` have no enumerable keys.
-      var keys = allKeys(obj);
-      if (getLength(keys)) return false;
-      for (var i = 0; i < length; i++) {
-        if (!isFunction$1(obj[methods[i]])) return false;
-      }
-      // If we are testing against `WeakMap`, we need to ensure that
-      // `obj` doesn't have a `forEach` method in order to distinguish
-      // it from a regular `Map`.
-      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
-    };
-  }
-
-  // In the interest of compact minification, we write
-  // each string in the fingerprints only once.
-  var forEachName = 'forEach',
-      hasName = 'has',
-      commonInit = ['clear', 'delete'],
-      mapTail = ['get', hasName, 'set'];
-
-  // `Map`, `WeakMap` and `Set` each have slightly different
-  // combinations of the above sublists.
-  var mapMethods = commonInit.concat(forEachName, mapTail),
-      weakMapMethods = commonInit.concat(mapTail),
-      setMethods = ['add'].concat(commonInit, forEachName, hasName);
-
-  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
-
-  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
-
-  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
-
-  var isWeakSet = tagTester('WeakSet');
-
-  // Retrieve the values of an object's properties.
-  function values(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var values = Array(length);
-    for (var i = 0; i < length; i++) {
-      values[i] = obj[_keys[i]];
-    }
-    return values;
-  }
-
-  // Convert an object into a list of `[key, value]` pairs.
-  // The opposite of `_.object` with one argument.
-  function pairs(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var pairs = Array(length);
-    for (var i = 0; i < length; i++) {
-      pairs[i] = [_keys[i], obj[_keys[i]]];
-    }
-    return pairs;
-  }
-
-  // Invert the keys and values of an object. The values must be serializable.
-  function invert(obj) {
-    var result = {};
-    var _keys = keys(obj);
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      result[obj[_keys[i]]] = _keys[i];
-    }
-    return result;
-  }
-
-  // Return a sorted list of the function names available on the object.
-  function functions(obj) {
-    var names = [];
-    for (var key in obj) {
-      if (isFunction$1(obj[key])) names.push(key);
-    }
-    return names.sort();
-  }
-
-  // An internal function for creating assigner functions.
-  function createAssigner(keysFunc, defaults) {
-    return function(obj) {
-      var length = arguments.length;
-      if (defaults) obj = Object(obj);
-      if (length < 2 || obj == null) return obj;
-      for (var index = 1; index < length; index++) {
-        var source = arguments[index],
-            keys = keysFunc(source),
-            l = keys.length;
-        for (var i = 0; i < l; i++) {
-          var key = keys[i];
-          if (!defaults || obj[key] === void 0) obj[key] = source[key];
-        }
-      }
-      return obj;
-    };
-  }
-
-  // Extend a given object with all the properties in passed-in object(s).
-  var extend = createAssigner(allKeys);
-
-  // Assigns a given object with all the own properties in the passed-in
-  // object(s).
-  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
-  var extendOwn = createAssigner(keys);
-
-  // Fill in a given object with default properties.
-  var defaults = createAssigner(allKeys, true);
-
-  // Create a naked function reference for surrogate-prototype-swapping.
-  function ctor() {
-    return function(){};
-  }
-
-  // An internal function for creating a new object that inherits from another.
-  function baseCreate(prototype) {
-    if (!isObject(prototype)) return {};
-    if (nativeCreate) return nativeCreate(prototype);
-    var Ctor = ctor();
-    Ctor.prototype = prototype;
-    var result = new Ctor;
-    Ctor.prototype = null;
-    return result;
-  }
-
-  // Creates an object that inherits from the given prototype object.
-  // If additional properties are provided then they will be added to the
-  // created object.
-  function create(prototype, props) {
-    var result = baseCreate(prototype);
-    if (props) extendOwn(result, props);
-    return result;
-  }
-
-  // Create a (shallow-cloned) duplicate of an object.
-  function clone(obj) {
-    if (!isObject(obj)) return obj;
-    return isArray(obj) ? obj.slice() : extend({}, obj);
-  }
-
-  // Invokes `interceptor` with the `obj` and then returns `obj`.
-  // The primary purpose of this method is to "tap into" a method chain, in
-  // order to perform operations on intermediate results within the chain.
-  function tap(obj, interceptor) {
-    interceptor(obj);
-    return obj;
-  }
-
-  // Normalize a (deep) property `path` to array.
-  // Like `_.iteratee`, this function can be customized.
-  function toPath$1(path) {
-    return isArray(path) ? path : [path];
-  }
-  _$1.toPath = toPath$1;
-
-  // Internal wrapper for `_.toPath` to enable minification.
-  // Similar to `cb` for `_.iteratee`.
-  function toPath(path) {
-    return _$1.toPath(path);
-  }
-
-  // Internal function to obtain a nested property in `obj` along `path`.
-  function deepGet(obj, path) {
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      if (obj == null) return void 0;
-      obj = obj[path[i]];
-    }
-    return length ? obj : void 0;
-  }
-
-  // Get the value of the (deep) property on `path` from `object`.
-  // If any property in `path` does not exist or if the value is
-  // `undefined`, return `defaultValue` instead.
-  // The `path` is normalized through `_.toPath`.
-  function get(object, path, defaultValue) {
-    var value = deepGet(object, toPath(path));
-    return isUndefined(value) ? defaultValue : value;
-  }
-
-  // Shortcut function for checking if an object has a given property directly on
-  // itself (in other words, not on a prototype). Unlike the internal `has`
-  // function, this public version can also traverse nested properties.
-  function has(obj, path) {
-    path = toPath(path);
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      var key = path[i];
-      if (!has$1(obj, key)) return false;
-      obj = obj[key];
-    }
-    return !!length;
-  }
-
-  // Keep the identity function around for default iteratees.
-  function identity(value) {
-    return value;
-  }
-
-  // Returns a predicate for checking whether an object has a given set of
-  // `key:value` pairs.
-  function matcher(attrs) {
-    attrs = extendOwn({}, attrs);
-    return function(obj) {
-      return isMatch(obj, attrs);
-    };
-  }
-
-  // Creates a function that, when passed an object, will traverse that object’s
-  // properties down the given `path`, specified as an array of keys or indices.
-  function property(path) {
-    path = toPath(path);
-    return function(obj) {
-      return deepGet(obj, path);
-    };
-  }
-
-  // Internal function that returns an efficient (for current engines) version
-  // of the passed-in callback, to be repeatedly applied in other Underscore
-  // functions.
-  function optimizeCb(func, context, argCount) {
-    if (context === void 0) return func;
-    switch (argCount == null ? 3 : argCount) {
-      case 1: return function(value) {
-        return func.call(context, value);
-      };
-      // The 2-argument case is omitted because we’re not using it.
-      case 3: return function(value, index, collection) {
-        return func.call(context, value, index, collection);
-      };
-      case 4: return function(accumulator, value, index, collection) {
-        return func.call(context, accumulator, value, index, collection);
-      };
-    }
-    return function() {
-      return func.apply(context, arguments);
-    };
-  }
-
-  // An internal function to generate callbacks that can be applied to each
-  // element in a collection, returning the desired result — either `_.identity`,
-  // an arbitrary callback, a property matcher, or a property accessor.
-  function baseIteratee(value, context, argCount) {
-    if (value == null) return identity;
-    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
-    if (isObject(value) && !isArray(value)) return matcher(value);
-    return property(value);
-  }
-
-  // External wrapper for our callback generator. Users may customize
-  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
-  // This abstraction hides the internal-only `argCount` argument.
-  function iteratee(value, context) {
-    return baseIteratee(value, context, Infinity);
-  }
-  _$1.iteratee = iteratee;
-
-  // The function we call internally to generate a callback. It invokes
-  // `_.iteratee` if overridden, otherwise `baseIteratee`.
-  function cb(value, context, argCount) {
-    if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
-    return baseIteratee(value, context, argCount);
-  }
-
-  // Returns the results of applying the `iteratee` to each element of `obj`.
-  // In contrast to `_.map` it returns an object.
-  function mapObject(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = keys(obj),
-        length = _keys.length,
-        results = {};
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys[index];
-      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function noop(){}
-
-  // Generates a function for a given object that returns a given property.
-  function propertyOf(obj) {
-    if (obj == null) return noop;
-    return function(path) {
-      return get(obj, path);
-    };
-  }
-
-  // Run a function **n** times.
-  function times(n, iteratee, context) {
-    var accum = Array(Math.max(0, n));
-    iteratee = optimizeCb(iteratee, context, 1);
-    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
-    return accum;
-  }
-
-  // Return a random integer between `min` and `max` (inclusive).
-  function random(min, max) {
-    if (max == null) {
-      max = min;
-      min = 0;
-    }
-    return min + Math.floor(Math.random() * (max - min + 1));
-  }
-
-  // A (possibly faster) way to get the current timestamp as an integer.
-  var now = Date.now || function() {
-    return new Date().getTime();
-  };
-
-  // Internal helper to generate functions for escaping and unescaping strings
-  // to/from HTML interpolation.
-  function createEscaper(map) {
-    var escaper = function(match) {
-      return map[match];
-    };
-    // Regexes for identifying a key that needs to be escaped.
-    var source = '(?:' + keys(map).join('|') + ')';
-    var testRegexp = RegExp(source);
-    var replaceRegexp = RegExp(source, 'g');
-    return function(string) {
-      string = string == null ? '' : '' + string;
-      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
-    };
-  }
-
-  // Internal list of HTML entities for escaping.
-  var escapeMap = {
-    '&': '&amp;',
-    '<': '&lt;',
-    '>': '&gt;',
-    '"': '&quot;',
-    "'": '&#x27;',
-    '`': '&#x60;'
-  };
-
-  // Function for escaping strings to HTML interpolation.
-  var _escape = createEscaper(escapeMap);
-
-  // Internal list of HTML entities for unescaping.
-  var unescapeMap = invert(escapeMap);
-
-  // Function for unescaping strings from HTML interpolation.
-  var _unescape = createEscaper(unescapeMap);
-
-  // By default, Underscore uses ERB-style template delimiters. Change the
-  // following template settings to use alternative delimiters.
-  var templateSettings = _$1.templateSettings = {
-    evaluate: /<%([\s\S]+?)%>/g,
-    interpolate: /<%=([\s\S]+?)%>/g,
-    escape: /<%-([\s\S]+?)%>/g
-  };
-
-  // When customizing `_.templateSettings`, if you don't want to define an
-  // interpolation, evaluation or escaping regex, we need one that is
-  // guaranteed not to match.
-  var noMatch = /(.)^/;
-
-  // Certain characters need to be escaped so that they can be put into a
-  // string literal.
-  var escapes = {
-    "'": "'",
-    '\\': '\\',
-    '\r': 'r',
-    '\n': 'n',
-    '\u2028': 'u2028',
-    '\u2029': 'u2029'
-  };
-
-  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
-
-  function escapeChar(match) {
-    return '\\' + escapes[match];
-  }
-
-  // In order to prevent third-party code injection through
-  // `_.templateSettings.variable`, we test it against the following regular
-  // expression. It is intentionally a bit more liberal than just matching valid
-  // identifiers, but still prevents possible loopholes through defaults or
-  // destructuring assignment.
-  var bareIdentifier = /^\s*(\w|\$)+\s*$/;
-
-  // JavaScript micro-templating, similar to John Resig's implementation.
-  // Underscore templating handles arbitrary delimiters, preserves whitespace,
-  // and correctly escapes quotes within interpolated code.
-  // NB: `oldSettings` only exists for backwards compatibility.
-  function template(text, settings, oldSettings) {
-    if (!settings && oldSettings) settings = oldSettings;
-    settings = defaults({}, settings, _$1.templateSettings);
-
-    // Combine delimiters into one regular expression via alternation.
-    var matcher = RegExp([
-      (settings.escape || noMatch).source,
-      (settings.interpolate || noMatch).source,
-      (settings.evaluate || noMatch).source
-    ].join('|') + '|$', 'g');
-
-    // Compile the template source, escaping string literals appropriately.
-    var index = 0;
-    var source = "__p+='";
-    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
-      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
-      index = offset + match.length;
-
-      if (escape) {
-        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
-      } else if (interpolate) {
-        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
-      } else if (evaluate) {
-        source += "';\n" + evaluate + "\n__p+='";
-      }
-
-      // Adobe VMs need the match returned to produce the correct offset.
-      return match;
-    });
-    source += "';\n";
-
-    var argument = settings.variable;
-    if (argument) {
-      // Insure against third-party code injection. (CVE-2021-23358)
-      if (!bareIdentifier.test(argument)) throw new Error(
-        'variable is not a bare identifier: ' + argument
-      );
-    } else {
-      // If a variable is not specified, place data values in local scope.
-      source = 'with(obj||{}){\n' + source + '}\n';
-      argument = 'obj';
-    }
-
-    source = "var __t,__p='',__j=Array.prototype.join," +
-      "print=function(){__p+=__j.call(arguments,'');};\n" +
-      source + 'return __p;\n';
-
-    var render;
-    try {
-      render = new Function(argument, '_', source);
-    } catch (e) {
-      e.source = source;
-      throw e;
-    }
-
-    var template = function(data) {
-      return render.call(this, data, _$1);
-    };
-
-    // Provide the compiled source as a convenience for precompilation.
-    template.source = 'function(' + argument + '){\n' + source + '}';
-
-    return template;
-  }
-
-  // Traverses the children of `obj` along `path`. If a child is a function, it
-  // is invoked with its parent as context. Returns the value of the final
-  // child, or `fallback` if any child is undefined.
-  function result(obj, path, fallback) {
-    path = toPath(path);
-    var length = path.length;
-    if (!length) {
-      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
-    }
-    for (var i = 0; i < length; i++) {
-      var prop = obj == null ? void 0 : obj[path[i]];
-      if (prop === void 0) {
-        prop = fallback;
-        i = length; // Ensure we don't continue iterating.
-      }
-      obj = isFunction$1(prop) ? prop.call(obj) : prop;
-    }
-    return obj;
-  }
-
-  // Generate a unique integer id (unique within the entire client session).
-  // Useful for temporary DOM ids.
-  var idCounter = 0;
-  function uniqueId(prefix) {
-    var id = ++idCounter + '';
-    return prefix ? prefix + id : id;
-  }
-
-  // Start chaining a wrapped Underscore object.
-  function chain(obj) {
-    var instance = _$1(obj);
-    instance._chain = true;
-    return instance;
-  }
-
-  // Internal function to execute `sourceFunc` bound to `context` with optional
-  // `args`. Determines whether to execute a function as a constructor or as a
-  // normal function.
-  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
-    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
-    var self = baseCreate(sourceFunc.prototype);
-    var result = sourceFunc.apply(self, args);
-    if (isObject(result)) return result;
-    return self;
-  }
-
-  // Partially apply a function by creating a version that has had some of its
-  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
-  // as a placeholder by default, allowing any combination of arguments to be
-  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
-  var partial = restArguments(function(func, boundArgs) {
-    var placeholder = partial.placeholder;
-    var bound = function() {
-      var position = 0, length = boundArgs.length;
-      var args = Array(length);
-      for (var i = 0; i < length; i++) {
-        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
-      }
-      while (position < arguments.length) args.push(arguments[position++]);
-      return executeBound(func, bound, this, this, args);
-    };
-    return bound;
-  });
-
-  partial.placeholder = _$1;
-
-  // Create a function bound to a given object (assigning `this`, and arguments,
-  // optionally).
-  var bind = restArguments(function(func, context, args) {
-    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
-    var bound = restArguments(function(callArgs) {
-      return executeBound(func, bound, context, this, args.concat(callArgs));
-    });
-    return bound;
-  });
-
-  // Internal helper for collection methods to determine whether a collection
-  // should be iterated as an array or as an object.
-  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
-  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
-  var isArrayLike = createSizePropertyCheck(getLength);
-
-  // Internal implementation of a recursive `flatten` function.
-  function flatten$1(input, depth, strict, output) {
-    output = output || [];
-    if (!depth && depth !== 0) {
-      depth = Infinity;
-    } else if (depth <= 0) {
-      return output.concat(input);
-    }
-    var idx = output.length;
-    for (var i = 0, length = getLength(input); i < length; i++) {
-      var value = input[i];
-      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
-        // Flatten current level of array or arguments object.
-        if (depth > 1) {
-          flatten$1(value, depth - 1, strict, output);
-          idx = output.length;
-        } else {
-          var j = 0, len = value.length;
-          while (j < len) output[idx++] = value[j++];
-        }
-      } else if (!strict) {
-        output[idx++] = value;
-      }
-    }
-    return output;
-  }
-
-  // Bind a number of an object's methods to that object. Remaining arguments
-  // are the method names to be bound. Useful for ensuring that all callbacks
-  // defined on an object belong to it.
-  var bindAll = restArguments(function(obj, keys) {
-    keys = flatten$1(keys, false, false);
-    var index = keys.length;
-    if (index < 1) throw new Error('bindAll must be passed function names');
-    while (index--) {
-      var key = keys[index];
-      obj[key] = bind(obj[key], obj);
-    }
-    return obj;
-  });
-
-  // Memoize an expensive function by storing its results.
-  function memoize(func, hasher) {
-    var memoize = function(key) {
-      var cache = memoize.cache;
-      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
-      if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
-      return cache[address];
-    };
-    memoize.cache = {};
-    return memoize;
-  }
-
-  // Delays a function for the given number of milliseconds, and then calls
-  // it with the arguments supplied.
-  var delay = restArguments(function(func, wait, args) {
-    return setTimeout(function() {
-      return func.apply(null, args);
-    }, wait);
-  });
-
-  // Defers a function, scheduling it to run after the current call stack has
-  // cleared.
-  var defer = partial(delay, _$1, 1);
-
-  // Returns a function, that, when invoked, will only be triggered at most once
-  // during a given window of time. Normally, the throttled function will run
-  // as much as it can, without ever going more than once per `wait` duration;
-  // but if you'd like to disable the execution on the leading edge, pass
-  // `{leading: false}`. To disable execution on the trailing edge, ditto.
-  function throttle(func, wait, options) {
-    var timeout, context, args, result;
-    var previous = 0;
-    if (!options) options = {};
-
-    var later = function() {
-      previous = options.leading === false ? 0 : now();
-      timeout = null;
-      result = func.apply(context, args);
-      if (!timeout) context = args = null;
-    };
-
-    var throttled = function() {
-      var _now = now();
-      if (!previous && options.leading === false) previous = _now;
-      var remaining = wait - (_now - previous);
-      context = this;
-      args = arguments;
-      if (remaining <= 0 || remaining > wait) {
-        if (timeout) {
-          clearTimeout(timeout);
-          timeout = null;
-        }
-        previous = _now;
-        result = func.apply(context, args);
-        if (!timeout) context = args = null;
-      } else if (!timeout && options.trailing !== false) {
-        timeout = setTimeout(later, remaining);
-      }
-      return result;
-    };
-
-    throttled.cancel = function() {
-      clearTimeout(timeout);
-      previous = 0;
-      timeout = context = args = null;
-    };
-
-    return throttled;
-  }
-
-  // When a sequence of calls of the returned function ends, the argument
-  // function is triggered. The end of a sequence is defined by the `wait`
-  // parameter. If `immediate` is passed, the argument function will be
-  // triggered at the beginning of the sequence instead of at the end.
-  function debounce(func, wait, immediate) {
-    var timeout, previous, args, result, context;
-
-    var later = function() {
-      var passed = now() - previous;
-      if (wait > passed) {
-        timeout = setTimeout(later, wait - passed);
-      } else {
-        timeout = null;
-        if (!immediate) result = func.apply(context, args);
-        // This check is needed because `func` can recursively invoke `debounced`.
-        if (!timeout) args = context = null;
-      }
-    };
-
-    var debounced = restArguments(function(_args) {
-      context = this;
-      args = _args;
-      previous = now();
-      if (!timeout) {
-        timeout = setTimeout(later, wait);
-        if (immediate) result = func.apply(context, args);
-      }
-      return result;
-    });
-
-    debounced.cancel = function() {
-      clearTimeout(timeout);
-      timeout = args = context = null;
-    };
-
-    return debounced;
-  }
-
-  // Returns the first function passed as an argument to the second,
-  // allowing you to adjust arguments, run code before and after, and
-  // conditionally execute the original function.
-  function wrap(func, wrapper) {
-    return partial(wrapper, func);
-  }
-
-  // Returns a negated version of the passed-in predicate.
-  function negate(predicate) {
-    return function() {
-      return !predicate.apply(this, arguments);
-    };
-  }
-
-  // Returns a function that is the composition of a list of functions, each
-  // consuming the return value of the function that follows.
-  function compose() {
-    var args = arguments;
-    var start = args.length - 1;
-    return function() {
-      var i = start;
-      var result = args[start].apply(this, arguments);
-      while (i--) result = args[i].call(this, result);
-      return result;
-    };
-  }
-
-  // Returns a function that will only be executed on and after the Nth call.
-  function after(times, func) {
-    return function() {
-      if (--times < 1) {
-        return func.apply(this, arguments);
-      }
-    };
-  }
-
-  // Returns a function that will only be executed up to (but not including) the
-  // Nth call.
-  function before(times, func) {
-    var memo;
-    return function() {
-      if (--times > 0) {
-        memo = func.apply(this, arguments);
-      }
-      if (times <= 1) func = null;
-      return memo;
-    };
-  }
-
-  // Returns a function that will be executed at most one time, no matter how
-  // often you call it. Useful for lazy initialization.
-  var once = partial(before, 2);
-
-  // Returns the first key on an object that passes a truth test.
-  function findKey(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = keys(obj), key;
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      key = _keys[i];
-      if (predicate(obj[key], key, obj)) return key;
-    }
-  }
-
-  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
-  function createPredicateIndexFinder(dir) {
-    return function(array, predicate, context) {
-      predicate = cb(predicate, context);
-      var length = getLength(array);
-      var index = dir > 0 ? 0 : length - 1;
-      for (; index >= 0 && index < length; index += dir) {
-        if (predicate(array[index], index, array)) return index;
-      }
-      return -1;
-    };
-  }
-
-  // Returns the first index on an array-like that passes a truth test.
-  var findIndex = createPredicateIndexFinder(1);
-
-  // Returns the last index on an array-like that passes a truth test.
-  var findLastIndex = createPredicateIndexFinder(-1);
-
-  // Use a comparator function to figure out the smallest index at which
-  // an object should be inserted so as to maintain order. Uses binary search.
-  function sortedIndex(array, obj, iteratee, context) {
-    iteratee = cb(iteratee, context, 1);
-    var value = iteratee(obj);
-    var low = 0, high = getLength(array);
-    while (low < high) {
-      var mid = Math.floor((low + high) / 2);
-      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
-    }
-    return low;
-  }
-
-  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
-  function createIndexFinder(dir, predicateFind, sortedIndex) {
-    return function(array, item, idx) {
-      var i = 0, length = getLength(array);
-      if (typeof idx == 'number') {
-        if (dir > 0) {
-          i = idx >= 0 ? idx : Math.max(idx + length, i);
-        } else {
-          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
-        }
-      } else if (sortedIndex && idx && length) {
-        idx = sortedIndex(array, item);
-        return array[idx] === item ? idx : -1;
-      }
-      if (item !== item) {
-        idx = predicateFind(slice.call(array, i, length), isNaN$1);
-        return idx >= 0 ? idx + i : -1;
-      }
-      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
-        if (array[idx] === item) return idx;
-      }
-      return -1;
-    };
-  }
-
-  // Return the position of the first occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  // If the array is large and already in sort order, pass `true`
-  // for **isSorted** to use binary search.
-  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
-
-  // Return the position of the last occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  var lastIndexOf = createIndexFinder(-1, findLastIndex);
-
-  // Return the first value which passes a truth test.
-  function find(obj, predicate, context) {
-    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
-    var key = keyFinder(obj, predicate, context);
-    if (key !== void 0 && key !== -1) return obj[key];
-  }
-
-  // Convenience version of a common use case of `_.find`: getting the first
-  // object containing specific `key:value` pairs.
-  function findWhere(obj, attrs) {
-    return find(obj, matcher(attrs));
-  }
-
-  // The cornerstone for collection functions, an `each`
-  // implementation, aka `forEach`.
-  // Handles raw objects in addition to array-likes. Treats all
-  // sparse array-likes as if they were dense.
-  function each(obj, iteratee, context) {
-    iteratee = optimizeCb(iteratee, context);
-    var i, length;
-    if (isArrayLike(obj)) {
-      for (i = 0, length = obj.length; i < length; i++) {
-        iteratee(obj[i], i, obj);
-      }
-    } else {
-      var _keys = keys(obj);
-      for (i = 0, length = _keys.length; i < length; i++) {
-        iteratee(obj[_keys[i]], _keys[i], obj);
-      }
-    }
-    return obj;
-  }
-
-  // Return the results of applying the iteratee to each element.
-  function map(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length,
-        results = Array(length);
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      results[index] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Internal helper to create a reducing function, iterating left or right.
-  function createReduce(dir) {
-    // Wrap code that reassigns argument variables in a separate function than
-    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
-    var reducer = function(obj, iteratee, memo, initial) {
-      var _keys = !isArrayLike(obj) && keys(obj),
-          length = (_keys || obj).length,
-          index = dir > 0 ? 0 : length - 1;
-      if (!initial) {
-        memo = obj[_keys ? _keys[index] : index];
-        index += dir;
-      }
-      for (; index >= 0 && index < length; index += dir) {
-        var currentKey = _keys ? _keys[index] : index;
-        memo = iteratee(memo, obj[currentKey], currentKey, obj);
-      }
-      return memo;
-    };
-
-    return function(obj, iteratee, memo, context) {
-      var initial = arguments.length >= 3;
-      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
-    };
-  }
-
-  // **Reduce** builds up a single result from a list of values, aka `inject`,
-  // or `foldl`.
-  var reduce = createReduce(1);
-
-  // The right-associative version of reduce, also known as `foldr`.
-  var reduceRight = createReduce(-1);
-
-  // Return all the elements that pass a truth test.
-  function filter(obj, predicate, context) {
-    var results = [];
-    predicate = cb(predicate, context);
-    each(obj, function(value, index, list) {
-      if (predicate(value, index, list)) results.push(value);
-    });
-    return results;
-  }
-
-  // Return all the elements for which a truth test fails.
-  function reject(obj, predicate, context) {
-    return filter(obj, negate(cb(predicate)), context);
-  }
-
-  // Determine whether all of the elements pass a truth test.
-  function every(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (!predicate(obj[currentKey], currentKey, obj)) return false;
-    }
-    return true;
-  }
-
-  // Determine if at least one element in the object passes a truth test.
-  function some(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (predicate(obj[currentKey], currentKey, obj)) return true;
-    }
-    return false;
-  }
-
-  // Determine if the array or object contains a given item (using `===`).
-  function contains(obj, item, fromIndex, guard) {
-    if (!isArrayLike(obj)) obj = values(obj);
-    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
-    return indexOf(obj, item, fromIndex) >= 0;
-  }
-
-  // Invoke a method (with arguments) on every item in a collection.
-  var invoke = restArguments(function(obj, path, args) {
-    var contextPath, func;
-    if (isFunction$1(path)) {
-      func = path;
-    } else {
-      path = toPath(path);
-      contextPath = path.slice(0, -1);
-      path = path[path.length - 1];
-    }
-    return map(obj, function(context) {
-      var method = func;
-      if (!method) {
-        if (contextPath && contextPath.length) {
-          context = deepGet(context, contextPath);
-        }
-        if (context == null) return void 0;
-        method = context[path];
-      }
-      return method == null ? method : method.apply(context, args);
-    });
-  });
-
-  // Convenience version of a common use case of `_.map`: fetching a property.
-  function pluck(obj, key) {
-    return map(obj, property(key));
-  }
-
-  // Convenience version of a common use case of `_.filter`: selecting only
-  // objects containing specific `key:value` pairs.
-  function where(obj, attrs) {
-    return filter(obj, matcher(attrs));
-  }
-
-  // Return the maximum element (or element-based computation).
-  function max(obj, iteratee, context) {
-    var result = -Infinity, lastComputed = -Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value > result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Return the minimum element (or element-based computation).
-  function min(obj, iteratee, context) {
-    var result = Infinity, lastComputed = Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value < result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed < lastComputed || computed === Infinity && result === Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Sample **n** random values from a collection using the modern version of the
-  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
-  // If **n** is not specified, returns a single random element.
-  // The internal `guard` argument allows it to work with `_.map`.
-  function sample(obj, n, guard) {
-    if (n == null || guard) {
-      if (!isArrayLike(obj)) obj = values(obj);
-      return obj[random(obj.length - 1)];
-    }
-    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
-    var length = getLength(sample);
-    n = Math.max(Math.min(n, length), 0);
-    var last = length - 1;
-    for (var index = 0; index < n; index++) {
-      var rand = random(index, last);
-      var temp = sample[index];
-      sample[index] = sample[rand];
-      sample[rand] = temp;
-    }
-    return sample.slice(0, n);
-  }
-
-  // Shuffle a collection.
-  function shuffle(obj) {
-    return sample(obj, Infinity);
-  }
-
-  // Sort the object's values by a criterion produced by an iteratee.
-  function sortBy(obj, iteratee, context) {
-    var index = 0;
-    iteratee = cb(iteratee, context);
-    return pluck(map(obj, function(value, key, list) {
-      return {
-        value: value,
-        index: index++,
-        criteria: iteratee(value, key, list)
-      };
-    }).sort(function(left, right) {
-      var a = left.criteria;
-      var b = right.criteria;
-      if (a !== b) {
-        if (a > b || a === void 0) return 1;
-        if (a < b || b === void 0) return -1;
-      }
-      return left.index - right.index;
-    }), 'value');
-  }
-
-  // An internal function used for aggregate "group by" operations.
-  function group(behavior, partition) {
-    return function(obj, iteratee, context) {
-      var result = partition ? [[], []] : {};
-      iteratee = cb(iteratee, context);
-      each(obj, function(value, index) {
-        var key = iteratee(value, index, obj);
-        behavior(result, value, key);
-      });
-      return result;
-    };
-  }
-
-  // Groups the object's values by a criterion. Pass either a string attribute
-  // to group by, or a function that returns the criterion.
-  var groupBy = group(function(result, value, key) {
-    if (has$1(result, key)) result[key].push(value); else result[key] = [value];
-  });
-
-  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
-  // when you know that your index values will be unique.
-  var indexBy = group(function(result, value, key) {
-    result[key] = value;
-  });
-
-  // Counts instances of an object that group by a certain criterion. Pass
-  // either a string attribute to count by, or a function that returns the
-  // criterion.
-  var countBy = group(function(result, value, key) {
-    if (has$1(result, key)) result[key]++; else result[key] = 1;
-  });
-
-  // Split a collection into two arrays: one whose elements all pass the given
-  // truth test, and one whose elements all do not pass the truth test.
-  var partition = group(function(result, value, pass) {
-    result[pass ? 0 : 1].push(value);
-  }, true);
-
-  // Safely create a real, live array from anything iterable.
-  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
-  function toArray(obj) {
-    if (!obj) return [];
-    if (isArray(obj)) return slice.call(obj);
-    if (isString(obj)) {
-      // Keep surrogate pair characters together.
-      return obj.match(reStrSymbol);
-    }
-    if (isArrayLike(obj)) return map(obj, identity);
-    return values(obj);
-  }
-
-  // Return the number of elements in a collection.
-  function size(obj) {
-    if (obj == null) return 0;
-    return isArrayLike(obj) ? obj.length : keys(obj).length;
-  }
-
-  // Internal `_.pick` helper function to determine whether `key` is an enumerable
-  // property name of `obj`.
-  function keyInObj(value, key, obj) {
-    return key in obj;
-  }
-
-  // Return a copy of the object only containing the allowed properties.
-  var pick = restArguments(function(obj, keys) {
-    var result = {}, iteratee = keys[0];
-    if (obj == null) return result;
-    if (isFunction$1(iteratee)) {
-      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
-      keys = allKeys(obj);
-    } else {
-      iteratee = keyInObj;
-      keys = flatten$1(keys, false, false);
-      obj = Object(obj);
-    }
-    for (var i = 0, length = keys.length; i < length; i++) {
-      var key = keys[i];
-      var value = obj[key];
-      if (iteratee(value, key, obj)) result[key] = value;
-    }
-    return result;
-  });
-
-  // Return a copy of the object without the disallowed properties.
-  var omit = restArguments(function(obj, keys) {
-    var iteratee = keys[0], context;
-    if (isFunction$1(iteratee)) {
-      iteratee = negate(iteratee);
-      if (keys.length > 1) context = keys[1];
-    } else {
-      keys = map(flatten$1(keys, false, false), String);
-      iteratee = function(value, key) {
-        return !contains(keys, key);
-      };
-    }
-    return pick(obj, iteratee, context);
-  });
-
-  // Returns everything but the last entry of the array. Especially useful on
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-  // Produce an array that contains every item shared between all the
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-      for (j = 1; j < argsLength; j++) {
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-      }
-      if (j === argsLength) result.push(item);
-    }
-    return result;
-  }
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-  // Complement of zip. Unzip accepts an array of arrays and groups
-  // each array's elements on shared indices.
-  function unzip(array) {
-    var length = array && max(array, getLength).length || 0;
-    var result = Array(length);
-
-    for (var index = 0; index < length; index++) {
-      result[index] = pluck(array, index);
-    }
-    return result;
-  }
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-  // Zip together multiple lists into a single array -- elements that share
-  // an index go together.
-  var zip = restArguments(unzip);
-
-  // Converts lists into objects. Pass either a single array of `[key, value]`
-  // pairs, or two parallel arrays of the same length -- one of keys, and one of
-  // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
-  function object(list, values) {
-    var result = {};
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-      if (values) {
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-      } else {
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-      }
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-    return result;
-  }
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-  // Generate an integer Array containing an arithmetic progression. A port of
-  // the native Python `range()` function. See
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-    isArray: isArray,
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-    isSet: isSet,
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-    keys: keys,
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-    result: result,
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-    first: first,
-    head: first,
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-    last: last,
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-    tail: rest,
-    drop: rest,
-    compact: compact,
-    flatten: flatten,
-    without: without,
-    uniq: uniq,
-    unique: uniq,
-    union: union,
-    intersection: intersection,
-    difference: difference,
-    unzip: unzip,
-    transpose: unzip,
-    zip: zip,
-    object: object,
-    range: range,
-    chunk: chunk,
-    mixin: mixin,
-    'default': _$1
-  };
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-  // Default Export
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-  // Add all of the Underscore functions to the wrapper object.
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diff --git a/master/_static/underscore.js b/master/_static/underscore.js
deleted file mode 100644
index cf177d428..000000000
--- a/master/_static/underscore.js
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-<li class="toctree-l1"><a class="reference internal" href="python-api/triton.html">triton</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="python-api/triton.testing.html">triton.testing</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Programming Guide</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="programming-guide/chapter-1/introduction.html">Introduction</a></li>
-<li class="toctree-l1"><a class="reference internal" href="programming-guide/chapter-2/related-work.html">Related Work</a></li>
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- <a href="#_"><strong>_</strong></a>
- | <a href="#A"><strong>A</strong></a>
- | <a href="#B"><strong>B</strong></a>
- | <a href="#C"><strong>C</strong></a>
- | <a href="#D"><strong>D</strong></a>
- | <a href="#E"><strong>E</strong></a>
- | <a href="#H"><strong>H</strong></a>
- | <a href="#J"><strong>J</strong></a>
- | <a href="#L"><strong>L</strong></a>
- | <a href="#M"><strong>M</strong></a>
- | <a href="#N"><strong>N</strong></a>
- | <a href="#P"><strong>P</strong></a>
- | <a href="#R"><strong>R</strong></a>
- | <a href="#S"><strong>S</strong></a>
- | <a href="#W"><strong>W</strong></a>
- | <a href="#Z"><strong>Z</strong></a>
- 
-</div>
-<h2 id="_">_</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.Config.html#triton.Config.__init__">__init__() (triton.Config method)</a>
-
-      <ul>
-        <li><a href="python-api/generated/triton.testing.Benchmark.html#triton.testing.Benchmark.__init__">(triton.testing.Benchmark method)</a>
-</li>
-      </ul></li>
-  </ul></td>
-</tr></table>
-
-<h2 id="A">A</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.arange.html#triton.language.arange">arange() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.atomic_add.html#triton.language.atomic_add">atomic_add() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.atomic_cas.html#triton.language.atomic_cas">atomic_cas() (in module triton.language)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.atomic_max.html#triton.language.atomic_max">atomic_max() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.atomic_min.html#triton.language.atomic_min">atomic_min() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.atomic_xchg.html#triton.language.atomic_xchg">atomic_xchg() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.autotune.html#triton.autotune">autotune() (in module triton)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="B">B</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.testing.Benchmark.html#triton.testing.Benchmark">Benchmark (class in triton.testing)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.broadcast_to.html#triton.language.broadcast_to">broadcast_to() (in module triton.language)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="C">C</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.Config.html#triton.Config">Config (class in triton)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.cos.html#triton.language.cos">cos() (in module triton.language)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="D">D</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.testing.do_bench.html#triton.testing.do_bench">do_bench() (in module triton.testing)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.dot.html#triton.language.dot">dot() (in module triton.language)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="E">E</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.exp.html#triton.language.exp">exp() (in module triton.language)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="H">H</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.heuristics.html#triton.heuristics">heuristics() (in module triton)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="J">J</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.jit.html#triton.jit">jit() (in module triton)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="L">L</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.load.html#triton.language.load">load() (in module triton.language)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.log.html#triton.language.log">log() (in module triton.language)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="M">M</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.max.html#triton.language.max">max() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.maximum.html#triton.language.maximum">maximum() (in module triton.language)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.min.html#triton.language.min">min() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.minimum.html#triton.language.minimum">minimum() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.multiple_of.html#triton.language.multiple_of">multiple_of() (in module triton.language)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="N">N</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.num_programs.html#triton.language.num_programs">num_programs() (in module triton.language)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="P">P</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.testing.perf_report.html#triton.testing.perf_report">perf_report() (in module triton.testing)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.program_id.html#triton.language.program_id">program_id() (in module triton.language)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="R">R</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.rand.html#triton.language.rand">rand() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.randint.html#triton.language.randint">randint() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.randint4x.html#triton.language.randint4x">randint4x() (in module triton.language)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.randn.html#triton.language.randn">randn() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.ravel.html#triton.language.ravel">ravel() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.reshape.html#triton.language.reshape">reshape() (in module triton.language)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="S">S</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.sigmoid.html#triton.language.sigmoid">sigmoid() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.sin.html#triton.language.sin">sin() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.softmax.html#triton.language.softmax">softmax() (in module triton.language)</a>
-</li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.sqrt.html#triton.language.sqrt">sqrt() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.store.html#triton.language.store">store() (in module triton.language)</a>
-</li>
-      <li><a href="python-api/generated/triton.language.sum.html#triton.language.sum">sum() (in module triton.language)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="W">W</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.where.html#triton.language.where">where() (in module triton.language)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="Z">Z</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="python-api/generated/triton.language.zeros.html#triton.language.zeros">zeros() (in module triton.language)</a>
-</li>
-  </ul></td>
-</tr></table>
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-<ul>
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-<h1>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="binary-distributions">
-<h2>Binary Distributions<a class="headerlink" href="#binary-distributions" title="Permalink to this headline">¶</a></h2>
-<p>You can install the latest stable release of Triton from pip:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip install triton
-</pre></div>
-</div>
-<p>Binary wheels are available for CPython 3.6-3.9 and PyPy 3.6-3.7.</p>
-<p>And the latest nightly release:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip install -U --pre triton
-</pre></div>
-</div>
-</div>
-<div class="section" id="from-source">
-<h2>From Source<a class="headerlink" href="#from-source" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="python-package">
-<h3>Python Package<a class="headerlink" href="#python-package" title="Permalink to this headline">¶</a></h3>
-<p>You can install the Python package from source by running the following commands:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git clone https://github.com/openai/triton.git<span class="p">;</span>
-<span class="nb">cd</span> triton/python<span class="p">;</span>
-pip install cmake<span class="p">;</span> <span class="c1"># build time dependency</span>
-pip install -e .
-</pre></div>
-</div>
-<p>Note that, if llvm-11 is not present on your system, the setup.py script will download the official LLVM11 static libraries link against that.</p>
-<p>You can then test your installation by running the unit tests:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip install -e <span class="s1">&#39;.[tests]&#39;</span>
-pytest -vs test/unit/
-</pre></div>
-</div>
-<p>and the benchmarks</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> bench/
-python -m run --with-plots --result-dir /tmp/triton-bench
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-<li class="toctree-l1 current"><a class="reference internal" href="index.html">Tutorials</a><ul class="current">
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Vector Addition</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#compute-kernel">Compute Kernel</a></li>
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-getting-started-tutorials-01-vector-add-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<div class="sphx-glr-example-title section" id="vector-addition">
-<span id="sphx-glr-getting-started-tutorials-01-vector-add-py"></span><h1>Vector Addition<a class="headerlink" href="#vector-addition" title="Permalink to this headline">¶</a></h1>
-<p>In this tutorial, you will write a simple vector addition using Triton and learn about:</p>
-<ul class="simple">
-<li><p>The basic programming model of Triton</p></li>
-<li><p>The <cite>triton.jit</cite> decorator, which is used to define Triton kernels.</p></li>
-<li><p>The best practices for validating and benchmarking your custom ops against native reference implementations</p></li>
-</ul>
-<div class="section" id="compute-kernel">
-<h2>Compute Kernel<a class="headerlink" href="#compute-kernel" title="Permalink to this headline">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">import</span> <span class="nn">triton</span>
-<span class="kn">import</span> <span class="nn">triton.language</span> <span class="k">as</span> <span class="nn">tl</span>
-
-
-<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
-<span class="k">def</span> <span class="nf">add_kernel</span><span class="p">(</span>
-    <span class="n">x_ptr</span><span class="p">,</span>  <span class="c1"># *Pointer* to first input vector</span>
-    <span class="n">y_ptr</span><span class="p">,</span>  <span class="c1"># *Pointer* to second input vector</span>
-    <span class="n">output_ptr</span><span class="p">,</span>  <span class="c1"># *Pointer* to output vector</span>
-    <span class="n">n_elements</span><span class="p">,</span>  <span class="c1"># Size of the vector</span>
-    <span class="n">BLOCK_SIZE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span>  <span class="c1"># Number of elements each program should process</span>
-                 <span class="c1"># NOTE: `constexpr` so it can be used as a shape value</span>
-<span class="p">):</span>
-    <span class="c1"># There are multiple &#39;program&#39;s processing different data. We identify which program</span>
-    <span class="c1"># we are here</span>
-    <span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># We use a 1D launch grid so axis is 0</span>
-    <span class="c1"># This program will process inputs that are offset from the initial data.</span>
-    <span class="c1"># for instance, if you had a vector of length 256 and block_size of 64, the programs</span>
-    <span class="c1"># would each access the elements [0:64, 64:128, 128:192, 192:256].</span>
-    <span class="c1"># Note that offsets is a list of pointers</span>
-    <span class="n">block_start</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">*</span> <span class="n">BLOCK_SIZE</span>
-    <span class="n">offsets</span> <span class="o">=</span> <span class="n">block_start</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="p">)</span>
-    <span class="c1"># Create a mask to guard memory operations against out-of-bounds accesses</span>
-    <span class="n">mask</span> <span class="o">=</span> <span class="n">offsets</span> <span class="o">&lt;</span> <span class="n">n_elements</span>
-    <span class="c1"># Load x and y from DRAM, masking out any extra elements in case the input is not a</span>
-    <span class="c1"># multiple of the block size</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">x_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">y_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-    <span class="n">output</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
-    <span class="c1"># Write x + y back to DRAM</span>
-    <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">output_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Let’s also declare a helper function to (1) allocate the <cite>z</cite> tensor
-and (2) enqueue the above kernel with appropriate grid/block sizes.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
-    <span class="c1"># We need to preallocate the output</span>
-    <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-    <span class="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">is_cuda</span> <span class="ow">and</span> <span class="n">y</span><span class="o">.</span><span class="n">is_cuda</span> <span class="ow">and</span> <span class="n">output</span><span class="o">.</span><span class="n">is_cuda</span>
-    <span class="n">n_elements</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
-    <span class="c1"># The SPMD launch grid denotes the number of kernel instances that run in parallel.</span>
-    <span class="c1"># It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -&gt; Tuple[int]</span>
-    <span class="c1"># In this case, we use a 1D grid where the size is the number of blocks</span>
-    <span class="n">grid</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">meta</span><span class="p">:</span> <span class="p">(</span><span class="n">triton</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">n_elements</span><span class="p">,</span> <span class="n">meta</span><span class="p">[</span><span class="s1">&#39;BLOCK_SIZE&#39;</span><span class="p">]),)</span>
-    <span class="c1"># NOTE:</span>
-    <span class="c1">#  - each torch.tensor object is implicitly converted into a pointer to its first element.</span>
-    <span class="c1">#  - `triton.jit`&#39;ed functions can be index with a launch grid to obtain a callable GPU kernel</span>
-    <span class="c1">#  - don&#39;t forget to pass meta-parameters as keywords arguments</span>
-    <span class="n">add_kernel</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">n_elements</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="mi">1024</span><span class="p">)</span>
-    <span class="c1"># We return a handle to z but, since `torch.cuda.synchronize()` hasn&#39;t been called, the kernel is still</span>
-    <span class="c1"># running asynchronously at this point.</span>
-    <span class="k">return</span> <span class="n">output</span>
-</pre></div>
-</div>
-<p>We can now use the above function to compute the element-wise sum of two <cite>torch.tensor</cite> objects and test its correctness:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">size</span> <span class="o">=</span> <span class="mi">98432</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
-<span class="n">output_torch</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
-<span class="n">output_triton</span> <span class="o">=</span> <span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">output_torch</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">output_triton</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s1">&#39;The maximum difference between torch and triton is &#39;</span>
-    <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">output_torch</span> <span class="o">-</span> <span class="n">output_triton</span><span class="p">))</span><span class="si">}</span><span class="s1">&#39;</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([1.3713, 1.3076, 0.4940,  ..., 0.6724, 1.2141, 0.9733], device=&#39;cuda:0&#39;)
-tensor([1.3713, 1.3076, 0.4940,  ..., 0.6724, 1.2141, 0.9733], device=&#39;cuda:0&#39;)
-The maximum difference between torch and triton is 0.0
-</pre></div>
-</div>
-<p>Seems like we’re good to go!</p>
-</div>
-<div class="section" id="benchmark">
-<h2>Benchmark<a class="headerlink" href="#benchmark" title="Permalink to this headline">¶</a></h2>
-<p>We can now benchmark our custom op on vectors of increasing sizes to get a sense of how it does relative to PyTorch.
-To make things easier, Triton has a set of built-in utilities that allow us to concisely plot the performance of your custom ops
-for different problem sizes.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">perf_report</span><span class="p">(</span>
-    <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">Benchmark</span><span class="p">(</span>
-        <span class="n">x_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;size&#39;</span><span class="p">],</span>  <span class="c1"># argument names to use as an x-axis for the plot</span>
-        <span class="n">x_vals</span><span class="o">=</span><span class="p">[</span>
-            <span class="mi">2</span> <span class="o">**</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="p">],</span>  <span class="c1"># different possible values for `x_name`</span>
-        <span class="n">x_log</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>  <span class="c1"># x axis is logarithmic</span>
-        <span class="n">line_arg</span><span class="o">=</span><span class="s1">&#39;provider&#39;</span><span class="p">,</span>  <span class="c1"># argument name whose value corresponds to a different line in the plot</span>
-        <span class="n">line_vals</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;triton&#39;</span><span class="p">,</span> <span class="s1">&#39;torch&#39;</span><span class="p">],</span>  <span class="c1"># possible values for `line_arg`</span>
-        <span class="n">line_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Triton&#39;</span><span class="p">,</span> <span class="s1">&#39;Torch&#39;</span><span class="p">],</span>  <span class="c1"># label name for the lines</span>
-        <span class="n">styles</span><span class="o">=</span><span class="p">[(</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">)],</span>  <span class="c1"># line styles</span>
-        <span class="n">ylabel</span><span class="o">=</span><span class="s1">&#39;GB/s&#39;</span><span class="p">,</span>  <span class="c1"># label name for the y-axis</span>
-        <span class="n">plot_name</span><span class="o">=</span><span class="s1">&#39;vector-add-performance&#39;</span><span class="p">,</span>  <span class="c1"># name for the plot. Used also as a file name for saving the plot.</span>
-        <span class="n">args</span><span class="o">=</span><span class="p">{},</span>  <span class="c1"># values for function arguments not in `x_names` and `y_name`</span>
-    <span class="p">)</span>
-<span class="p">)</span>
-<span class="k">def</span> <span class="nf">benchmark</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">provider</span><span class="p">):</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;torch&#39;</span><span class="p">:</span>
-        <span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;triton&#39;</span><span class="p">:</span>
-        <span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">))</span>
-    <span class="n">gbps</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">ms</span><span class="p">:</span> <span class="mi">12</span> <span class="o">*</span> <span class="n">size</span> <span class="o">/</span> <span class="n">ms</span> <span class="o">*</span> <span class="mf">1e-6</span>
-    <span class="k">return</span> <span class="n">gbps</span><span class="p">(</span><span class="n">ms</span><span class="p">),</span> <span class="n">gbps</span><span class="p">(</span><span class="n">max_ms</span><span class="p">),</span> <span class="n">gbps</span><span class="p">(</span><span class="n">min_ms</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>We can now run the decorated function above. Pass <cite>print_data=True</cite> to see the performance number, <cite>show_plots=True</cite> to plot them, and/or
-<a href="#id1"><span class="problematic" id="id2">`</span></a>save_path=’/path/to/results/’ to save them to disk along with raw CSV data</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">benchmark</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">print_data</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">show_plots</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<img alt="01 vector add" class="sphx-glr-single-img" src="../../_images/sphx_glr_01-vector-add_001.png" />
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vector-add-performance:
-           size      Triton       Torch
-0        4096.0    9.600000    9.600000
-1        8192.0   19.200000   19.200000
-2       16384.0   38.400001   38.400001
-3       32768.0   76.800002   76.800002
-4       65536.0  127.999995  127.999995
-5      131072.0  219.428568  219.428568
-6      262144.0  341.333321  384.000001
-7      524288.0  472.615390  472.615390
-8     1048576.0  614.400016  614.400016
-9     2097152.0  722.823517  722.823517
-10    4194304.0  780.190482  780.190482
-11    8388608.0  812.429770  812.429770
-12   16777216.0  833.084721  833.084721
-13   33554432.0  842.004273  842.004273
-14   67108864.0  847.448255  848.362445
-15  134217728.0  849.737435  850.656574
-</pre></div>
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-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="index.html">Tutorials</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="01-vector-add.html">Vector Addition</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Fused Softmax</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#motivations">Motivations</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#compute-kernel">Compute Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#unit-test">Unit Test</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#benchmark">Benchmark</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="03-matrix-multiplication.html">Matrix Multiplication</a></li>
-<li class="toctree-l2"><a class="reference internal" href="04-low-memory-dropout.html">Low-Memory Dropout</a></li>
-<li class="toctree-l2"><a class="reference internal" href="05-layer-norm.html">Layer Normalization</a></li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Python API</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../python-api/triton.html">triton</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../programming-guide/chapter-1/introduction.html">Introduction</a></li>
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-            
-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-getting-started-tutorials-02-fused-softmax-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<div class="sphx-glr-example-title section" id="fused-softmax">
-<span id="sphx-glr-getting-started-tutorials-02-fused-softmax-py"></span><h1>Fused Softmax<a class="headerlink" href="#fused-softmax" title="Permalink to this headline">¶</a></h1>
-<p>In this tutorial, you will write a fused softmax operation that is significantly faster
-than PyTorch’s native op for a particular class of matrices: those whose rows can fit in
-the GPU’s SRAM.
-You will learn about:</p>
-<ul class="simple">
-<li><p>The benefits of kernel fusion for bandwidth-bound operations.</p></li>
-<li><p>Reduction operators in Triton.</p></li>
-</ul>
-<div class="section" id="motivations">
-<h2>Motivations<a class="headerlink" href="#motivations" title="Permalink to this headline">¶</a></h2>
-<p>Custom GPU kernels for elementwise additions are educationally valuable but won’t get you very far in practice.
-Let us consider instead the case of a simple (numerically stabilized) softmax operation:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">import</span> <span class="nn">triton</span>
-<span class="kn">import</span> <span class="nn">triton.language</span> <span class="k">as</span> <span class="nn">tl</span>
-
-
-<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
-<span class="k">def</span> <span class="nf">naive_softmax</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Compute row-wise softmax of X using native pytorch</span>
-
-<span class="sd">    We subtract the maximum element in order to avoid overflows. Softmax is invariant to</span>
-<span class="sd">    this shift.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># read  MN elements ; write M  elements</span>
-    <span class="n">x_max</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="c1"># read MN + M elements ; write MN elements</span>
-    <span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">-</span> <span class="n">x_max</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span>
-    <span class="c1"># read  MN elements ; write MN elements</span>
-    <span class="n">numerator</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
-    <span class="c1"># read  MN elements ; write M  elements</span>
-    <span class="n">denominator</span> <span class="o">=</span> <span class="n">numerator</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="c1"># read MN + M elements ; write MN elements</span>
-    <span class="n">ret</span> <span class="o">=</span> <span class="n">numerator</span> <span class="o">/</span> <span class="n">denominator</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span>
-    <span class="c1"># in total: read 5MN + 2M elements ; wrote 3MN + 2M elements</span>
-    <span class="k">return</span> <span class="n">ret</span>
-</pre></div>
-</div>
-<p>When implemented naively in PyTorch, computing <code class="code docutils literal notranslate"><span class="pre">y</span> <span class="pre">=</span> <span class="pre">naive_softmax(x)</span></code> for <span class="math notranslate nohighlight">\(x \in R^{M \times N}\)</span>
-requires reading <span class="math notranslate nohighlight">\(5MN + 2M\)</span> elements from DRAM and writing back <span class="math notranslate nohighlight">\(3MN + 2M\)</span> elements.
-This is obviously wasteful; we’d prefer to have a custom “fused” kernel that only reads
-X once and does all the necessary computations on-chip.
-Doing so would require reading and writing back only <span class="math notranslate nohighlight">\(MN\)</span> bytes, so we could
-expect a theoretical speed-up of ~4x (i.e., <span class="math notranslate nohighlight">\((8MN + 4M) / 2MN\)</span>).
-The <cite>torch.jit.script</cite> flags aims to perform this kind of “kernel fusion” automatically
-but, as we will see later, it is still far from ideal.</p>
-</div>
-<div class="section" id="compute-kernel">
-<h2>Compute Kernel<a class="headerlink" href="#compute-kernel" title="Permalink to this headline">¶</a></h2>
-<p>Our softmax kernel works as follows: each program loads a row of the input matrix X,
-normalizes it and writes back the result to the output Y.
-Note that one important limitation of Triton is that each block must have a
-power-of-two number of elements, so we need to internally “pad” each row and guard the
-memory operations properly if we want to handle any possible input shapes:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
-<span class="k">def</span> <span class="nf">softmax_kernel</span><span class="p">(</span>
-    <span class="n">output_ptr</span><span class="p">,</span> <span class="n">input_ptr</span><span class="p">,</span> <span class="n">input_row_stride</span><span class="p">,</span> <span class="n">output_row_stride</span><span class="p">,</span> <span class="n">n_cols</span><span class="p">,</span>
-    <span class="n">BLOCK_SIZE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span>
-<span class="p">):</span>
-    <span class="c1"># The rows of the softmax are independent, so we parallelize across those</span>
-    <span class="n">row_idx</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-    <span class="c1"># The stride represents how much we need to increase the pointer to advance 1 row</span>
-    <span class="n">row_start_ptr</span> <span class="o">=</span> <span class="n">input_ptr</span> <span class="o">+</span> <span class="n">row_idx</span> <span class="o">*</span> <span class="n">input_row_stride</span>
-    <span class="c1"># The block size is the next power of two greater than n_cols, so we can fit each</span>
-    <span class="c1"># row in a single block</span>
-    <span class="n">col_offsets</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="p">)</span>
-    <span class="n">input_ptrs</span> <span class="o">=</span> <span class="n">row_start_ptr</span> <span class="o">+</span> <span class="n">col_offsets</span>
-    <span class="c1"># Load the row into SRAM, using a mask since BLOCK_SIZE may be &gt; than n_cols</span>
-    <span class="n">row</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">input_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">col_offsets</span> <span class="o">&lt;</span> <span class="n">n_cols</span><span class="p">,</span> <span class="n">other</span><span class="o">=-</span><span class="nb">float</span><span class="p">(</span><span class="s1">&#39;inf&#39;</span><span class="p">))</span>
-    <span class="c1"># Substract maximum for numerical stability</span>
-    <span class="n">row_minus_max</span> <span class="o">=</span> <span class="n">row</span> <span class="o">-</span> <span class="n">tl</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="c1"># Note that exponentials in Triton are fast but approximate (i.e., think __expf in CUDA)</span>
-    <span class="n">numerator</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">row_minus_max</span><span class="p">)</span>
-    <span class="n">denominator</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">numerator</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">softmax_output</span> <span class="o">=</span> <span class="n">numerator</span> <span class="o">/</span> <span class="n">denominator</span>
-    <span class="c1"># Write back output to DRAM</span>
-    <span class="n">output_row_start_ptr</span> <span class="o">=</span> <span class="n">output_ptr</span> <span class="o">+</span> <span class="n">row_idx</span> <span class="o">*</span> <span class="n">output_row_stride</span>
-    <span class="n">output_ptrs</span> <span class="o">=</span> <span class="n">output_row_start_ptr</span> <span class="o">+</span> <span class="n">col_offsets</span>
-    <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">output_ptrs</span><span class="p">,</span> <span class="n">softmax_output</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">col_offsets</span> <span class="o">&lt;</span> <span class="n">n_cols</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>We can create a helper function that enqueues the kernel and its (meta-)arguments for any given input tensor.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="n">n_rows</span><span class="p">,</span> <span class="n">n_cols</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span>
-    <span class="c1"># The block size is the smallest power of two greater than the number of columns in `x`</span>
-    <span class="n">BLOCK_SIZE</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">next_power_of_2</span><span class="p">(</span><span class="n">n_cols</span><span class="p">)</span>
-    <span class="c1"># Another trick we can use is to ask the compiler to use more threads per row by</span>
-    <span class="c1"># increasing the number of warps (`num_warps`) over which each row is distributed.</span>
-    <span class="c1"># You will see in the next tutorial how to auto-tune this value in a more natural</span>
-    <span class="c1"># way so you don&#39;t have to come up with manual heuristics yourself.</span>
-    <span class="n">num_warps</span> <span class="o">=</span> <span class="mi">4</span>
-    <span class="k">if</span> <span class="n">BLOCK_SIZE</span> <span class="o">&gt;=</span> <span class="mi">2048</span><span class="p">:</span>
-        <span class="n">num_warps</span> <span class="o">=</span> <span class="mi">8</span>
-    <span class="k">if</span> <span class="n">BLOCK_SIZE</span> <span class="o">&gt;=</span> <span class="mi">4096</span><span class="p">:</span>
-        <span class="n">num_warps</span> <span class="o">=</span> <span class="mi">16</span>
-    <span class="c1"># Allocate output</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-    <span class="c1"># Enqueue kernel. The 1D launch grid is simple: we have one kernel instance per row o</span>
-    <span class="c1"># f the input matrix</span>
-    <span class="n">softmax_kernel</span><span class="p">[(</span><span class="n">n_rows</span><span class="p">,)](</span>
-        <span class="n">y</span><span class="p">,</span>
-        <span class="n">x</span><span class="p">,</span>
-        <span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span>
-        <span class="n">y</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span>
-        <span class="n">n_cols</span><span class="p">,</span>
-        <span class="n">num_warps</span><span class="o">=</span><span class="n">num_warps</span><span class="p">,</span>
-        <span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="n">BLOCK_SIZE</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">y</span>
-</pre></div>
-</div>
-</div>
-<div class="section" id="unit-test">
-<h2>Unit Test<a class="headerlink" href="#unit-test" title="Permalink to this headline">¶</a></h2>
-<p>We make sure that we test our kernel on a matrix with an irregular number of rows and columns.
-This will allow us to verify that our padding mechanism works.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1823</span><span class="p">,</span> <span class="mi">781</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
-<span class="n">y_triton</span> <span class="o">=</span> <span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-<span class="n">y_torch</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">y_triton</span><span class="p">,</span> <span class="n">y_torch</span><span class="p">),</span> <span class="p">(</span><span class="n">y_triton</span><span class="p">,</span> <span class="n">y_torch</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>As expected, the results are identical.</p>
-</div>
-<div class="section" id="benchmark">
-<h2>Benchmark<a class="headerlink" href="#benchmark" title="Permalink to this headline">¶</a></h2>
-<p>Here we will benchmark our operation as a function of the number of columns in the input matrix – assuming 4096 rows.
-We will then compare its performance against (1) <code class="code docutils literal notranslate"><span class="pre">torch.softmax</span></code> and (2) the <code class="code docutils literal notranslate"><span class="pre">naive_softmax</span></code> defined above.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">perf_report</span><span class="p">(</span>
-    <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">Benchmark</span><span class="p">(</span>
-        <span class="n">x_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;N&#39;</span><span class="p">],</span>  <span class="c1"># argument names to use as an x-axis for the plot</span>
-        <span class="n">x_vals</span><span class="o">=</span><span class="p">[</span>
-            <span class="mi">128</span> <span class="o">*</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-        <span class="p">],</span>  <span class="c1"># different possible values for `x_name`</span>
-        <span class="n">line_arg</span><span class="o">=</span><span class="s1">&#39;provider&#39;</span><span class="p">,</span>  <span class="c1"># argument name whose value corresponds to a different line in the plot</span>
-        <span class="n">line_vals</span><span class="o">=</span><span class="p">[</span>
-            <span class="s1">&#39;triton&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;torch-native&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;torch-jit&#39;</span><span class="p">,</span>
-        <span class="p">],</span>  <span class="c1"># possible values for `line_arg``</span>
-        <span class="n">line_names</span><span class="o">=</span><span class="p">[</span>
-            <span class="s2">&quot;Triton&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;Torch (native)&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;Torch (jit)&quot;</span><span class="p">,</span>
-        <span class="p">],</span>  <span class="c1"># label name for the lines</span>
-        <span class="n">styles</span><span class="o">=</span><span class="p">[(</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="s1">&#39;--&#39;</span><span class="p">)],</span>  <span class="c1"># line styles</span>
-        <span class="n">ylabel</span><span class="o">=</span><span class="s2">&quot;GB/s&quot;</span><span class="p">,</span>  <span class="c1"># label name for the y-axis</span>
-        <span class="n">plot_name</span><span class="o">=</span><span class="s2">&quot;softmax-performance&quot;</span><span class="p">,</span>  <span class="c1"># name for the plot. Used also as a file name for saving the plot.</span>
-        <span class="n">args</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;M&#39;</span><span class="p">:</span> <span class="mi">4096</span><span class="p">},</span>  <span class="c1"># values for function arguments not in `x_names` and `y_name`</span>
-    <span class="p">)</span>
-<span class="p">)</span>
-<span class="k">def</span> <span class="nf">benchmark</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">provider</span><span class="p">):</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;torch-native&#39;</span><span class="p">:</span>
-        <span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
-    <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;triton&#39;</span><span class="p">:</span>
-        <span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-    <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;torch-jit&#39;</span><span class="p">:</span>
-        <span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">naive_softmax</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-    <span class="n">gbps</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">ms</span><span class="p">:</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">nelement</span><span class="p">()</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">element_size</span><span class="p">()</span> <span class="o">*</span> <span class="mf">1e-9</span> <span class="o">/</span> <span class="p">(</span><span class="n">ms</span> <span class="o">*</span> <span class="mf">1e-3</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">gbps</span><span class="p">(</span><span class="n">ms</span><span class="p">),</span> <span class="n">gbps</span><span class="p">(</span><span class="n">max_ms</span><span class="p">),</span> <span class="n">gbps</span><span class="p">(</span><span class="n">min_ms</span><span class="p">)</span>
-
-
-<span class="n">benchmark</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">show_plots</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">print_data</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<img alt="02 fused softmax" class="sphx-glr-single-img" src="../../_images/sphx_glr_02-fused-softmax_001.png" />
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>softmax-performance:
-          N      Triton  Torch (native)  Torch (jit)
-0     256.0  546.133347      546.133347   188.321838
-1     384.0  585.142862      585.142862   151.703707
-2     512.0  655.360017      606.814814   154.566038
-3     640.0  682.666684      640.000002   158.759699
-4     768.0  722.823517      664.216187   163.839992
-..      ...         ...             ...          ...
-93  12160.0  814.058574      405.755985   198.631953
-94  12288.0  814.111783      415.661740   198.794749
-95  12416.0  814.163950      412.149375   198.457532
-96  12544.0  814.214963      412.971190   198.716830
-97  12672.0  814.265046      411.679167   198.776477
-
-[98 rows x 4 columns]
-</pre></div>
-</div>
-<p>In the above plot, we can see that:</p>
-<blockquote>
-<div><ul class="simple">
-<li><p>Triton is 4x faster than the Torch JIT. This confirms our suspicions that the Torch JIT does not do any fusion here.</p></li>
-<li><p>Triton is noticeably faster than <code class="code docutils literal notranslate"><span class="pre">torch.softmax</span></code> – in addition to being <strong>easier to read, understand and maintain</strong>.
-Note however that the PyTorch <cite>softmax</cite> operation is more general and will works on tensors of any shape.</p></li>
-</ul>
-</div></blockquote>
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-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-getting-started-tutorials-03-matrix-multiplication-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<div class="sphx-glr-example-title section" id="matrix-multiplication">
-<span id="sphx-glr-getting-started-tutorials-03-matrix-multiplication-py"></span><h1>Matrix Multiplication<a class="headerlink" href="#matrix-multiplication" title="Permalink to this headline">¶</a></h1>
-<p>In this tutorial, you will write a 25-lines high-performance FP16 matrix multiplication
-kernel that achieves performance on par with cuBLAS.
-You will specifically learn about:</p>
-<ul class="simple">
-<li><p>Block-level matrix multiplications</p></li>
-<li><p>Multi-dimensional pointer arithmetic</p></li>
-<li><p>Program re-ordering for improved L2 cache hit rate</p></li>
-<li><p>Automatic performance tuning</p></li>
-</ul>
-<div class="section" id="motivations">
-<h2>Motivations<a class="headerlink" href="#motivations" title="Permalink to this headline">¶</a></h2>
-<p>Matrix multiplications are a key building block of most modern high-performance computing systems.
-They are notoriously hard to optimize, hence their implementation is generally done by
-hardware vendors themselves as part of so-called “kernel libraries” (e.g., cuBLAS).
-Unfortunately, these libraries are often proprietary and cannot be easily customized
-to accomodate the needs of modern deep learning workloads (e.g., fused activation functions).
-In this tutorial, you will learn how to implement efficient matrix multiplications by
-yourself with Triton, in a way that is easy to customize and extend.</p>
-<p>Roughly speaking, the kernel that we will write will implement the following blocked
-algorithm to multiply a (M, K) by a (K, N) matrix:</p>
-<blockquote>
-<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># do in parallel</span>
-<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">):</span>
-  <span class="c1"># do in parallel</span>
-  <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">):</span>
-    <span class="n">acc</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">((</span><span class="n">BLOCK_SIZE_M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">BLOCK_SIZE_K</span><span class="p">):</span>
-      <span class="n">a</span> <span class="o">=</span> <span class="n">A</span><span class="p">[</span><span class="n">m</span> <span class="p">:</span> <span class="n">m</span><span class="o">+</span><span class="n">BLOCK_SIZE_M</span><span class="p">,</span> <span class="n">k</span> <span class="p">:</span> <span class="n">k</span><span class="o">+</span><span class="n">BLOCK_SIZE_K</span><span class="p">]</span>
-      <span class="n">b</span> <span class="o">=</span> <span class="n">B</span><span class="p">[</span><span class="n">k</span> <span class="p">:</span> <span class="n">k</span><span class="o">+</span><span class="n">BLOCK_SIZE_K</span><span class="p">,</span> <span class="n">n</span> <span class="p">:</span> <span class="n">n</span><span class="o">+</span><span class="n">BLOCK_SIZE_N</span><span class="p">]</span>
-      <span class="n">acc</span> <span class="o">+=</span> <span class="n">dot</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
-    <span class="n">C</span><span class="p">[</span><span class="n">m</span> <span class="p">:</span> <span class="n">m</span><span class="o">+</span><span class="n">BLOCK_SIZE_M</span><span class="p">,</span> <span class="n">n</span> <span class="p">:</span> <span class="n">n</span><span class="o">+</span><span class="n">BLOCK_SIZE_N</span><span class="p">]</span> <span class="o">=</span> <span class="n">acc</span><span class="p">;</span>
-</pre></div>
-</div>
-</div></blockquote>
-<p>where each iteration of the doubly-nested for-loop is performed by a dedicated Triton program instance.</p>
-</div>
-<div class="section" id="compute-kernel">
-<h2>Compute Kernel<a class="headerlink" href="#compute-kernel" title="Permalink to this headline">¶</a></h2>
-<p>The above algorithm is, actually, fairly straightforward to implement in Triton.
-The main difficulty comes from the computation of the memory locations at which blocks
-of <code class="code docutils literal notranslate"><span class="pre">A</span></code> and <code class="code docutils literal notranslate"><span class="pre">B</span></code> must be read in the inner loop. For that, we need
-multi-dimensional pointer arithmetics.</p>
-<div class="section" id="pointer-arithmetics">
-<h3>Pointer Arithmetics<a class="headerlink" href="#pointer-arithmetics" title="Permalink to this headline">¶</a></h3>
-<p>For a row-major 2D tensor <code class="code docutils literal notranslate"><span class="pre">X</span></code>, the memory location of <code class="code docutils literal notranslate"><span class="pre">X[i,</span> <span class="pre">j]</span></code> is given b
-y <code class="code docutils literal notranslate"><span class="pre">&amp;X[i,</span> <span class="pre">j]</span> <span class="pre">=</span> <span class="pre">X</span> <span class="pre">+</span> <span class="pre">i*stride_xi</span> <span class="pre">+</span> <span class="pre">j*stride_xj</span></code>.
-Therefore, blocks of pointers for <code class="code docutils literal notranslate"><span class="pre">A[m</span> <span class="pre">:</span> <span class="pre">m+BLOCK_SIZE_M,</span> <span class="pre">k:k+BLOCK_SIZE_K]</span></code> and
-<code class="code docutils literal notranslate"><span class="pre">B[k</span> <span class="pre">:</span> <span class="pre">k+BLOCK_SIZE_K,</span> <span class="pre">n</span> <span class="pre">:</span> <span class="pre">n+BLOCK_SIZE_N]</span></code> can be defined in pseudo-code as:</p>
-<blockquote>
-<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="o">&amp;</span><span class="n">A</span><span class="p">[</span><span class="n">m</span> <span class="p">:</span> <span class="n">m</span><span class="o">+</span><span class="n">BLOCK_SIZE_M</span><span class="p">,</span> <span class="n">k</span><span class="p">:</span><span class="n">k</span><span class="o">+</span><span class="n">BLOCK_SIZE_K</span><span class="p">]</span> <span class="o">=</span>  <span class="n">a_ptr</span> <span class="o">+</span> <span class="p">(</span><span class="n">m</span> <span class="p">:</span> <span class="n">m</span><span class="o">+</span><span class="n">BLOCK_SIZE_M</span><span class="p">)[:,</span> <span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">A</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">k</span> <span class="p">:</span> <span class="n">k</span><span class="o">+</span><span class="n">BLOCK_SIZE_K</span><span class="p">)[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span><span class="o">*</span><span class="n">A</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-<span class="o">&amp;</span><span class="n">B</span><span class="p">[</span><span class="n">k</span> <span class="p">:</span> <span class="n">k</span><span class="o">+</span><span class="n">BLOCK_SIZE_K</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span><span class="n">n</span><span class="o">+</span><span class="n">BLOCK_SIZE_N</span><span class="p">]</span> <span class="o">=</span>  <span class="n">b_ptr</span> <span class="o">+</span> <span class="p">(</span><span class="n">k</span> <span class="p">:</span> <span class="n">k</span><span class="o">+</span><span class="n">BLOCK_SIZE_K</span><span class="p">)[:,</span> <span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">B</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">n</span> <span class="p">:</span> <span class="n">n</span><span class="o">+</span><span class="n">BLOCK_SIZE_N</span><span class="p">)[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span><span class="o">*</span><span class="n">B</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
-</pre></div>
-</div>
-</div></blockquote>
-<p>Which means that pointers for blocks of A and B can be initialized (i.e., <code class="code docutils literal notranslate"><span class="pre">k=0</span></code>) in Triton as:</p>
-<blockquote>
-<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">offs_am</span> <span class="o">=</span> <span class="n">pid_m</span> <span class="o">*</span> <span class="n">BLOCK_SIZE_M</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">)</span>
-<span class="n">offs_bn</span> <span class="o">=</span> <span class="n">pid_n</span> <span class="o">*</span> <span class="n">BLOCK_SIZE_N</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">)</span>
-<span class="n">offs_k</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_K</span><span class="p">)</span>
-<span class="n">a_ptrs</span> <span class="o">=</span> <span class="n">a_ptr</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_am</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">stride_am</span> <span class="o">+</span> <span class="n">offs_k</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span><span class="o">*</span><span class="n">stride_ak</span><span class="p">)</span>
-<span class="n">b_ptrs</span> <span class="o">=</span> <span class="n">b_ptr</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_k</span> <span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">stride_bk</span> <span class="o">+</span> <span class="n">offs_bn</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span><span class="o">*</span><span class="n">stride_bn</span><span class="p">)</span>
-</pre></div>
-</div>
-</div></blockquote>
-<p>And then updated in the inner loop as follows:</p>
-<blockquote>
-<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">pa</span> <span class="o">+=</span> <span class="n">BLOCK_SIZE_K</span> <span class="o">*</span> <span class="n">stride_ak</span><span class="p">;</span>
-<span class="n">pb</span> <span class="o">+=</span> <span class="n">BLOCK_SIZE_K</span> <span class="o">*</span> <span class="n">stride_bk</span><span class="p">;</span>
-</pre></div>
-</div>
-</div></blockquote>
-</div>
-<div class="section" id="l2-cache-optimizations">
-<h3>L2 Cache Optimizations<a class="headerlink" href="#l2-cache-optimizations" title="Permalink to this headline">¶</a></h3>
-<p>As mentioned above, each program instance computes a <code class="code docutils literal notranslate"><span class="pre">[BLOCK_SIZE_M,</span> <span class="pre">BLOCK_SIZE_N]</span></code>
-block of <code class="code docutils literal notranslate"><span class="pre">C</span></code>.
-It is important to remember that the order in which these blocks are computed does
-matter, since it affects the L2 cache hit rate of our program. and unfortunately, a
-a simple row-major ordering</p>
-<blockquote>
-<div><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">pid</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">);</span>
-<span class="n">grid_m</span> <span class="o">=</span> <span class="p">(</span><span class="n">M</span> <span class="o">+</span> <span class="n">BLOCK_SIZE_M</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">BLOCK_SIZE_M</span><span class="p">;</span>
-<span class="n">grid_n</span> <span class="o">=</span> <span class="p">(</span><span class="n">N</span> <span class="o">+</span> <span class="n">BLOCK_SIZE_N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">BLOCK_SIZE_N</span><span class="p">;</span>
-<span class="n">pid_m</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">/</span> <span class="n">grid_n</span><span class="p">;</span>
-<span class="n">pid_n</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">%</span> <span class="n">grid_n</span><span class="p">;</span>
-</pre></div>
-</div>
-</div></blockquote>
-<p>is just not going to cut it.</p>
-<p>One possible solution is to launch blocks in an order that promotes data reuse.
-This can be done by ‘super-grouping’ blocks in groups of <code class="code docutils literal notranslate"><span class="pre">GROUP_M</span></code> rows before
-switching to the next column:</p>
-<blockquote>
-<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># program ID</span>
-<span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="c1"># number of program ids along the M axis</span>
-<span class="n">num_pid_m</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">)</span>
-<span class="c1"># number of programs ids along the N axis</span>
-<span class="n">num_pid_n</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">)</span>
-<span class="c1"># number of programs in group</span>
-<span class="n">num_pid_in_group</span> <span class="o">=</span> <span class="n">GROUP_SIZE_M</span> <span class="o">*</span> <span class="n">num_pid_n</span>
-<span class="c1"># id of the group this program is in</span>
-<span class="n">group_id</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">//</span> <span class="n">num_pid_in_group</span>
-<span class="c1"># row-id of the first program in the group</span>
-<span class="n">first_pid_m</span> <span class="o">=</span> <span class="n">group_id</span> <span class="o">*</span> <span class="n">GROUP_SIZE_M</span>
-<span class="c1"># if `num_pid_m` isn&#39;t divisible by `GROUP_SIZE_M`, the last group is smaller</span>
-<span class="n">group_size_m</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">num_pid_m</span> <span class="o">-</span> <span class="n">first_pid_m</span><span class="p">,</span> <span class="n">GROUP_SIZE_M</span><span class="p">)</span>
-<span class="c1"># *within groups*, programs are ordered in a column-major order</span>
-<span class="c1"># row-id of the program in the *launch grid*</span>
-<span class="n">pid_m</span> <span class="o">=</span> <span class="n">first_pid_m</span> <span class="o">+</span> <span class="p">(</span><span class="n">pid</span> <span class="o">%</span> <span class="n">group_size_m</span><span class="p">)</span>
-<span class="c1"># col-id of the program in the *launch grid*</span>
-<span class="n">pid_n</span> <span class="o">=</span> <span class="p">(</span><span class="n">pid</span> <span class="o">%</span> <span class="n">num_pid_in_group</span><span class="p">)</span> <span class="o">//</span> <span class="n">group_size_m</span>
-</pre></div>
-</div>
-</div></blockquote>
-<p>For example, in the following matmul where each matrix is 9 blocks by 9 blocks,
-we can see that if we compute the output in row-major ordering, we need to load 90
-blocks into SRAM to compute the first 9 output blocks, but if we do it in grouped
-ordering, we only need to load 54 blocks.</p>
-<blockquote>
-<div><img alt="../../_images/grouped_vs_row_major_ordering.png" src="../../_images/grouped_vs_row_major_ordering.png" />
-</div></blockquote>
-<p>In practice, this can improve the performance of our matrix multiplication kernel by
-more than 10% on some hardware architecture (e.g., 220 to 245 TFLOPS on A100).</p>
-</div>
-</div>
-<div class="section" id="final-result">
-<h2>Final Result<a class="headerlink" href="#final-result" title="Permalink to this headline">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">import</span> <span class="nn">triton</span>
-<span class="kn">import</span> <span class="nn">triton.language</span> <span class="k">as</span> <span class="nn">tl</span>
-
-<span class="c1"># %</span>
-<span class="c1"># :code:`triton.jit`&#39;ed functions can be auto-tuned by using the `triton.autotune`</span>
-<span class="c1"># decorator, which consumes:</span>
-<span class="c1">#   - A list of :code:`triton.Config` objects that define different configurations of</span>
-<span class="c1">#       meta-parameters (e.g., BLOCK_SIZE_M) and compilation options (e.g., num_warps) to try</span>
-<span class="c1">#   - An autotuning *key* whose change in values will trigger evaluation of all the</span>
-<span class="c1">#       provided configs</span>
-
-
-<span class="nd">@triton</span><span class="o">.</span><span class="n">autotune</span><span class="p">(</span>
-    <span class="n">configs</span><span class="o">=</span><span class="p">[</span>
-        <span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">&#39;BLOCK_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_N&#39;</span><span class="p">:</span> <span class="mi">256</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_K&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">&#39;GROUP_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span>
-        <span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">&#39;BLOCK_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">256</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_N&#39;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_K&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">&#39;GROUP_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span>
-        <span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">&#39;BLOCK_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">256</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_N&#39;</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_K&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">&#39;GROUP_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
-        <span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">&#39;BLOCK_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_N&#39;</span><span class="p">:</span> <span class="mi">256</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_K&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">&#39;GROUP_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
-        <span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">&#39;BLOCK_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_N&#39;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_K&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">&#39;GROUP_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
-        <span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">&#39;BLOCK_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_N&#39;</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_K&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">&#39;GROUP_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
-        <span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">&#39;BLOCK_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_N&#39;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_K&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">&#39;GROUP_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
-        <span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">&#39;BLOCK_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_N&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_K&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">&#39;GROUP_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
-        <span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">&#39;BLOCK_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_N&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_K&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">&#39;GROUP_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
-        <span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s1">&#39;BLOCK_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_N&#39;</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span> <span class="s1">&#39;BLOCK_SIZE_K&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span> <span class="s1">&#39;GROUP_SIZE_M&#39;</span><span class="p">:</span> <span class="mi">8</span><span class="p">},</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
-    <span class="p">],</span>
-    <span class="n">key</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;M&#39;</span><span class="p">,</span> <span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;K&#39;</span><span class="p">],</span>
-<span class="p">)</span>
-<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
-<span class="k">def</span> <span class="nf">matmul_kernel</span><span class="p">(</span>
-    <span class="c1"># Pointers to matrices</span>
-    <span class="n">a_ptr</span><span class="p">,</span> <span class="n">b_ptr</span><span class="p">,</span> <span class="n">c_ptr</span><span class="p">,</span>
-    <span class="c1"># Matrix dimensions</span>
-    <span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span>
-    <span class="c1"># The stride variables represent how much to increase the ptr by when moving by 1</span>
-    <span class="c1"># element in a particular dimension. E.g. stride_am is how much to increase a_ptr</span>
-    <span class="c1"># by to get the element one row down (A has M rows)</span>
-    <span class="n">stride_am</span><span class="p">,</span> <span class="n">stride_ak</span><span class="p">,</span>
-    <span class="n">stride_bk</span><span class="p">,</span> <span class="n">stride_bn</span><span class="p">,</span>
-    <span class="n">stride_cm</span><span class="p">,</span> <span class="n">stride_cn</span><span class="p">,</span>
-    <span class="c1"># Meta-parameters</span>
-    <span class="n">BLOCK_SIZE_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">BLOCK_SIZE_K</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span>
-    <span class="n">GROUP_SIZE_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span>
-    <span class="n">ACTIVATION</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span>
-<span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Kernel for computing the matmul C = A x B.</span>
-<span class="sd">    A has shape (M, K), B has shape (K, N) and C has shape (M, N)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># -----------------------------------------------------------</span>
-    <span class="c1"># Map program ids `pid` to the block of C it should compute.</span>
-    <span class="c1"># This is done in a grouped ordering to promote L2 data reuse</span>
-    <span class="c1"># See above `L2 Cache Optimizations` section for details</span>
-    <span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">num_pid_m</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">)</span>
-    <span class="n">num_pid_n</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">)</span>
-    <span class="n">num_pid_in_group</span> <span class="o">=</span> <span class="n">GROUP_SIZE_M</span> <span class="o">*</span> <span class="n">num_pid_n</span>
-    <span class="n">group_id</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">//</span> <span class="n">num_pid_in_group</span>
-    <span class="n">first_pid_m</span> <span class="o">=</span> <span class="n">group_id</span> <span class="o">*</span> <span class="n">GROUP_SIZE_M</span>
-    <span class="n">group_size_m</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">num_pid_m</span> <span class="o">-</span> <span class="n">first_pid_m</span><span class="p">,</span> <span class="n">GROUP_SIZE_M</span><span class="p">)</span>
-    <span class="n">pid_m</span> <span class="o">=</span> <span class="n">first_pid_m</span> <span class="o">+</span> <span class="p">(</span><span class="n">pid</span> <span class="o">%</span> <span class="n">group_size_m</span><span class="p">)</span>
-    <span class="n">pid_n</span> <span class="o">=</span> <span class="p">(</span><span class="n">pid</span> <span class="o">%</span> <span class="n">num_pid_in_group</span><span class="p">)</span> <span class="o">//</span> <span class="n">group_size_m</span>
-
-    <span class="c1"># ----------------------------------------------------------</span>
-    <span class="c1"># Create pointers for the first blocks of A and B.</span>
-    <span class="c1"># We will advance this pointer as we move in the K direction</span>
-    <span class="c1"># and accumulate</span>
-    <span class="c1"># a_ptrs is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers</span>
-    <span class="c1"># b_ptrs is a block of [BLOCK_SIZE_K, BLOCK_SIZE_n] pointers</span>
-    <span class="c1"># see above `Pointer Arithmetics` section for details</span>
-    <span class="n">offs_am</span> <span class="o">=</span> <span class="n">pid_m</span> <span class="o">*</span> <span class="n">BLOCK_SIZE_M</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">)</span>
-    <span class="n">offs_bn</span> <span class="o">=</span> <span class="n">pid_n</span> <span class="o">*</span> <span class="n">BLOCK_SIZE_N</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">)</span>
-    <span class="n">offs_k</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_K</span><span class="p">)</span>
-    <span class="n">a_ptrs</span> <span class="o">=</span> <span class="n">a_ptr</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_am</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_am</span> <span class="o">+</span> <span class="n">offs_k</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">*</span> <span class="n">stride_ak</span><span class="p">)</span>
-    <span class="n">b_ptrs</span> <span class="o">=</span> <span class="n">b_ptr</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_k</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_bk</span> <span class="o">+</span> <span class="n">offs_bn</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">*</span> <span class="n">stride_bn</span><span class="p">)</span>
-
-    <span class="c1"># -----------------------------------------------------------</span>
-    <span class="c1"># Iterate to compute a block of the C matrix</span>
-    <span class="c1"># We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block</span>
-    <span class="c1"># of fp32 values for higher accuracy.</span>
-    <span class="c1"># `accumulator` will be converted back to fp16 after the loop</span>
-    <span class="n">accumulator</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">BLOCK_SIZE_M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">BLOCK_SIZE_K</span><span class="p">):</span>
-        <span class="c1"># Note that for simplicity, we don&#39;t apply a mask here.</span>
-        <span class="c1"># This means that if K is not a multiple of BLOCK_SIZE_K,</span>
-        <span class="c1"># this will access out-of-bounds memory and produce an</span>
-        <span class="c1"># error or (worse!) incorrect results.</span>
-        <span class="n">a</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">a_ptrs</span><span class="p">)</span>
-        <span class="n">b</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">b_ptrs</span><span class="p">)</span>
-        <span class="c1"># We accumulate along the K dimension</span>
-        <span class="n">accumulator</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
-        <span class="c1"># Advance the ptrs to the next K block</span>
-        <span class="n">a_ptrs</span> <span class="o">+=</span> <span class="n">BLOCK_SIZE_K</span> <span class="o">*</span> <span class="n">stride_ak</span>
-        <span class="n">b_ptrs</span> <span class="o">+=</span> <span class="n">BLOCK_SIZE_K</span> <span class="o">*</span> <span class="n">stride_bk</span>
-    <span class="c1"># you can fuse arbitrary activation functions here</span>
-    <span class="c1"># while the accumulator is still in FP32!</span>
-    <span class="k">if</span> <span class="n">ACTIVATION</span><span class="p">:</span>
-        <span class="n">accumulator</span> <span class="o">=</span> <span class="n">ACTIVATION</span><span class="p">(</span><span class="n">accumulator</span><span class="p">)</span>
-    <span class="n">c</span> <span class="o">=</span> <span class="n">accumulator</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
-
-    <span class="c1"># -----------------------------------------------------------</span>
-    <span class="c1"># Write back the block of the output matrix C</span>
-    <span class="n">offs_cm</span> <span class="o">=</span> <span class="n">pid_m</span> <span class="o">*</span> <span class="n">BLOCK_SIZE_M</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">)</span>
-    <span class="n">offs_cn</span> <span class="o">=</span> <span class="n">pid_n</span> <span class="o">*</span> <span class="n">BLOCK_SIZE_N</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">)</span>
-    <span class="n">c_ptrs</span> <span class="o">=</span> <span class="n">c_ptr</span> <span class="o">+</span> <span class="n">stride_cm</span> <span class="o">*</span> <span class="n">offs_cm</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">+</span> <span class="n">stride_cn</span> <span class="o">*</span> <span class="n">offs_cn</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span>
-    <span class="n">c_mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">offs_cm</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">M</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_cn</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">N</span><span class="p">)</span>
-    <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">c_ptrs</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">c_mask</span><span class="p">)</span>
-
-
-<span class="c1"># we can fuse `leaky_relu` by providing it as an `ACTIVATION` meta-parameter in `_matmul`</span>
-<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
-<span class="k">def</span> <span class="nf">leaky_relu</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">x</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="mf">0.01</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>We can now create a convenience wrapper function that only takes two input tensors
-and (1) checks any shape constraint; (2) allocates the output; (3) launches the above kernel</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="c1"># checks constraints</span>
-    <span class="k">assert</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">&quot;incompatible dimensions&quot;</span>
-    <span class="k">assert</span> <span class="n">a</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(),</span> <span class="s2">&quot;matrix A must be contiguous&quot;</span>
-    <span class="k">assert</span> <span class="n">b</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(),</span> <span class="s2">&quot;matrix B must be contiguous&quot;</span>
-    <span class="n">M</span><span class="p">,</span> <span class="n">K</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span>
-    <span class="n">K</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">shape</span>
-    <span class="k">assert</span> <span class="p">(</span>
-        <span class="n">K</span> <span class="o">%</span> <span class="mi">32</span> <span class="o">==</span> <span class="mi">0</span>
-    <span class="p">),</span> <span class="s2">&quot;We don&#39;t check memory-out-of-bounds with K so K must be divisible by BLOCK_SIZE_K&quot;</span>
-    <span class="c1"># allocates output</span>
-    <span class="n">c</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">a</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">a</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="c1"># 1D launch kernel where each block gets its own program.</span>
-    <span class="n">grid</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">META</span><span class="p">:</span> <span class="p">(</span>
-        <span class="n">triton</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">META</span><span class="p">[</span><span class="s1">&#39;BLOCK_SIZE_M&#39;</span><span class="p">])</span> <span class="o">*</span> <span class="n">triton</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">META</span><span class="p">[</span><span class="s1">&#39;BLOCK_SIZE_N&#39;</span><span class="p">]),</span>
-    <span class="p">)</span>
-    <span class="n">matmul_kernel</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span>
-        <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span>
-        <span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span>
-        <span class="n">a</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span>
-        <span class="n">b</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">b</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span>
-        <span class="n">c</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">c</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span>
-        <span class="n">ACTIVATION</span><span class="o">=</span><span class="n">activation</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">c</span>
-</pre></div>
-</div>
-</div>
-<div class="section" id="unit-test">
-<h2>Unit Test<a class="headerlink" href="#unit-test" title="Permalink to this headline">¶</a></h2>
-<p>We can test our custom matrix multiplication operation against a native torch implementation (i.e., cuBLAS)</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
-<span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
-<span class="n">triton_output</span> <span class="o">=</span> <span class="n">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-<span class="n">torch_output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;triton_output=</span><span class="si">{</span><span class="n">triton_output</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;torch_output=</span><span class="si">{</span><span class="n">torch_output</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="k">if</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">triton_output</span><span class="p">,</span> <span class="n">torch_output</span><span class="p">):</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;✅ Triton and Torch match&quot;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;❌ Triton and Torch differ&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>triton_output=tensor([[  1.1045, -36.9688,  31.4688,  ..., -11.3984,  24.4531, -32.3438],
-        [  6.3555, -19.6094,  34.0938,  ...,  -5.8945,   5.2891,   6.8867],
-        [-32.0625,   5.9492,  15.3984,  ..., -21.3906, -23.9844, -10.1328],
-        ...,
-        [ -5.7031,   7.4492,   8.2656,  ..., -10.6953, -40.0000,  17.7500],
-        [ 25.5000,  24.3281,  -8.4688,  ..., -18.9375,  32.5312, -29.9219],
-        [ -5.3477,   4.9844,  11.8906,  ...,   5.5898,   6.4023, -17.3125]],
-       device=&#39;cuda:0&#39;, dtype=torch.float16)
-torch_output=tensor([[  1.1045, -36.9688,  31.4688,  ..., -11.3906,  24.4531, -32.3438],
-        [  6.3516, -19.6094,  34.0938,  ...,  -5.8906,   5.2812,   6.8828],
-        [-32.0625,   5.9531,  15.3984,  ..., -21.4062, -23.9844, -10.1328],
-        ...,
-        [ -5.7070,   7.4492,   8.2656,  ..., -10.6953, -40.0000,  17.7500],
-        [ 25.5000,  24.3438,  -8.4609,  ..., -18.9375,  32.5312, -29.9219],
-        [ -5.3477,   4.9805,  11.8828,  ...,   5.5859,   6.4023, -17.3125]],
-       device=&#39;cuda:0&#39;, dtype=torch.float16)
-✅ Triton and Torch match
-</pre></div>
-</div>
-</div>
-<div class="section" id="benchmark">
-<h2>Benchmark<a class="headerlink" href="#benchmark" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="square-matrix-performance">
-<h3>Square Matrix Performance<a class="headerlink" href="#square-matrix-performance" title="Permalink to this headline">¶</a></h3>
-<p>We can now compare the performance of our kernel against that of cuBLAS. Here we focus on square matrices, but feel free to arrange this script as you wish to benchmark any other matrix shape.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">perf_report</span><span class="p">(</span>
-    <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">Benchmark</span><span class="p">(</span>
-        <span class="n">x_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;M&#39;</span><span class="p">,</span> <span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;K&#39;</span><span class="p">],</span>  <span class="c1"># argument names to use as an x-axis for the plot</span>
-        <span class="n">x_vals</span><span class="o">=</span><span class="p">[</span>
-            <span class="mi">128</span> <span class="o">*</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">33</span><span class="p">)</span>
-        <span class="p">],</span>  <span class="c1"># different possible values for `x_name`</span>
-        <span class="n">line_arg</span><span class="o">=</span><span class="s1">&#39;provider&#39;</span><span class="p">,</span>  <span class="c1"># argument name whose value corresponds to a different line in the plot</span>
-        <span class="c1"># possible values for `line_arg``</span>
-        <span class="n">line_vals</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;cublas&#39;</span><span class="p">,</span> <span class="s1">&#39;cublas + relu&#39;</span><span class="p">,</span> <span class="s1">&#39;triton&#39;</span><span class="p">,</span> <span class="s1">&#39;triton + relu&#39;</span><span class="p">],</span>
-        <span class="c1"># label name for the lines</span>
-        <span class="n">line_names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;cuBLAS&quot;</span><span class="p">,</span> <span class="s2">&quot;cuBLAS (+ torch.nn.LeakyReLU)&quot;</span><span class="p">,</span> <span class="s2">&quot;Triton&quot;</span><span class="p">,</span> <span class="s2">&quot;Triton (+ LeakyReLU)&quot;</span><span class="p">],</span>
-        <span class="c1"># line styles</span>
-        <span class="n">styles</span><span class="o">=</span><span class="p">[(</span><span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="s1">&#39;--&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="s1">&#39;--&#39;</span><span class="p">)],</span>
-        <span class="n">ylabel</span><span class="o">=</span><span class="s2">&quot;TFLOPS&quot;</span><span class="p">,</span>  <span class="c1"># label name for the y-axis</span>
-        <span class="n">plot_name</span><span class="o">=</span><span class="s2">&quot;matmul-performance&quot;</span><span class="p">,</span>  <span class="c1"># name for the plot. Used also as a file name for saving the plot.</span>
-        <span class="n">args</span><span class="o">=</span><span class="p">{},</span>
-    <span class="p">)</span>
-<span class="p">)</span>
-<span class="k">def</span> <span class="nf">benchmark</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">provider</span><span class="p">):</span>
-    <span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="n">M</span><span class="p">,</span> <span class="n">K</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
-    <span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="n">K</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;cublas&#39;</span><span class="p">:</span>
-        <span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
-    <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;triton&#39;</span><span class="p">:</span>
-        <span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
-    <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;cublas + relu&#39;</span><span class="p">:</span>
-        <span class="n">torch_relu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span>
-            <span class="k">lambda</span><span class="p">:</span> <span class="n">torch_relu</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
-        <span class="p">)</span>
-    <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;triton + relu&#39;</span><span class="p">:</span>
-        <span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span>
-            <span class="k">lambda</span><span class="p">:</span> <span class="n">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">leaky_relu</span><span class="p">)</span>
-        <span class="p">)</span>
-    <span class="n">perf</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">ms</span><span class="p">:</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">M</span> <span class="o">*</span> <span class="n">N</span> <span class="o">*</span> <span class="n">K</span> <span class="o">*</span> <span class="mf">1e-12</span> <span class="o">/</span> <span class="p">(</span><span class="n">ms</span> <span class="o">*</span> <span class="mf">1e-3</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">perf</span><span class="p">(</span><span class="n">ms</span><span class="p">),</span> <span class="n">perf</span><span class="p">(</span><span class="n">max_ms</span><span class="p">),</span> <span class="n">perf</span><span class="p">(</span><span class="n">min_ms</span><span class="p">)</span>
-
-
-<span class="n">benchmark</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">show_plots</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">print_data</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<img alt="03 matrix multiplication" class="sphx-glr-single-img" src="../../_images/sphx_glr_03-matrix-multiplication_001.png" />
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>matmul-performance:
-         M     cuBLAS  ...     Triton  Triton (+ LeakyReLU)
-0    256.0   2.978909  ...   3.276800              2.978909
-1    384.0   7.372800  ...   7.899428              8.507077
-2    512.0  14.563555  ...  16.384000             16.384000
-3    640.0  22.260869  ...  24.380953             24.380953
-4    768.0  32.768000  ...  35.389441             34.028308
-5    896.0  37.971025  ...  40.140799             39.025776
-6   1024.0  49.932191  ...  53.773130             52.428801
-7   1152.0  45.242181  ...  48.161033             47.396572
-8   1280.0  51.200001  ...  57.690139             57.690139
-9   1408.0  64.138541  ...  68.147202             67.305878
-10  1536.0  79.526831  ...  79.526831             79.526831
-11  1664.0  62.929456  ...  63.372618             62.929456
-12  1792.0  72.512412  ...  63.142831             62.790080
-13  1920.0  69.120002  ...  71.257735             70.892307
-14  2048.0  73.262953  ...  78.033565             77.672296
-15  2176.0  83.155572  ...  87.115360             86.739860
-16  2304.0  68.251065  ...  77.810656             77.810656
-17  2432.0  71.305746  ...  75.522751             75.320281
-18  2560.0  77.833728  ...  82.539044             82.125311
-19  2688.0  83.737433  ...  91.185232             90.748936
-20  2816.0  84.523664  ...  81.445766             84.523664
-21  2944.0  81.298583  ...  83.899046             84.040530
-22  3072.0  82.540970  ...  90.164177             89.735509
-23  3200.0  84.656085  ...  96.385543             96.240602
-24  3328.0  83.226931  ...  85.602017             84.249616
-25  3456.0  81.849303  ...  85.133652             84.864807
-26  3584.0  87.466332  ...  99.684470             99.354022
-27  3712.0  81.548851  ...  84.159518             87.706180
-28  3840.0  84.874902  ...  86.535214             91.398346
-29  3968.0  88.040360  ...  92.442373             86.480463
-30  4096.0  93.760204  ...  91.992956             90.871857
-
-[31 rows x 5 columns]
-</pre></div>
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deleted file mode 100644
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-<!DOCTYPE html>
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-<li class="toctree-l1 current"><a class="reference internal" href="index.html">Tutorials</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="01-vector-add.html">Vector Addition</a></li>
-<li class="toctree-l2"><a class="reference internal" href="02-fused-softmax.html">Fused Softmax</a></li>
-<li class="toctree-l2"><a class="reference internal" href="03-matrix-multiplication.html">Matrix Multiplication</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Low-Memory Dropout</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#baseline">Baseline</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#seeded-dropout">Seeded dropout</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="#references">References</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="05-layer-norm.html">Layer Normalization</a></li>
-</ul>
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-</ul>
-<p class="caption" role="heading"><span class="caption-text">Python API</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../python-api/triton.html">triton</a></li>
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-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-getting-started-tutorials-04-low-memory-dropout-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<div class="sphx-glr-example-title section" id="low-memory-dropout">
-<span id="sphx-glr-getting-started-tutorials-04-low-memory-dropout-py"></span><h1>Low-Memory Dropout<a class="headerlink" href="#low-memory-dropout" title="Permalink to this headline">¶</a></h1>
-<p>In this tutorial, you will write a memory-efficient implementation of dropout whose state
-will be composed of a single int32 seed. This differs from more traditional implementations of dropout,
-whose state is generally composed of a bit mask tensor of the same shape as the input. You will learn about:</p>
-<ul class="simple">
-<li><p>The limitations of naive implementations of Dropout with PyTorch</p></li>
-<li><p>Parallel pseudo-random number generation in Triton</p></li>
-</ul>
-<div class="section" id="baseline">
-<h2>Baseline<a class="headerlink" href="#baseline" title="Permalink to this headline">¶</a></h2>
-<p>The <em>dropout</em> operator was first introduced in <a class="reference internal" href="#srivastava2014" id="id1"><span>[SRIVASTAVA2014]</span></a> as a way to improve the performance
-of deep neural networks in low-data regime (i.e. regularization).</p>
-<p>It takes a vector as input and produces a vector of the same shape as output. Each scalar in the
-output has a probability <span class="math notranslate nohighlight">\(p\)</span> of being changed to zero and otherwise it is copied from the input.
-This forces the network to perform well even when only <span class="math notranslate nohighlight">\(1 - p\)</span> scalars from the input are available.</p>
-<p>At evaluation time we want to use the full power of the network so we set <span class="math notranslate nohighlight">\(p=0\)</span>. Naively this would
-increase the norm of the output (which can be a bad thing, e.g. it can lead to artificial decrease
-in the output softmax temperature). To prevent this we multiply the output by <span class="math notranslate nohighlight">\(\frac{1}{1 - p}\)</span>, which
-keeps the norm consistent regardless of the dropout probability.</p>
-<p>Let’s first take a look at the baseline implementation.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tabulate</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">import</span> <span class="nn">triton</span>
-<span class="kn">import</span> <span class="nn">triton.language</span> <span class="k">as</span> <span class="nn">tl</span>
-
-
-<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
-<span class="k">def</span> <span class="nf">_dropout</span><span class="p">(</span>
-        <span class="n">x_ptr</span><span class="p">,</span>  <span class="c1"># pointer to the input</span>
-        <span class="n">x_keep_ptr</span><span class="p">,</span>  <span class="c1"># pointer to a mask of 0s and 1s</span>
-        <span class="n">output_ptr</span><span class="p">,</span>  <span class="c1"># pointer to the output</span>
-        <span class="n">n_elements</span><span class="p">,</span>  <span class="c1"># number of elements in the `x` tensor</span>
-        <span class="n">p</span><span class="p">,</span>  <span class="c1"># probability that an element of `x` is changed to zero</span>
-        <span class="n">BLOCK_SIZE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span>
-<span class="p">):</span>
-    <span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">block_start</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">*</span> <span class="n">BLOCK_SIZE</span>
-    <span class="n">offsets</span> <span class="o">=</span> <span class="n">block_start</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="p">)</span>
-    <span class="n">mask</span> <span class="o">=</span> <span class="n">offsets</span> <span class="o">&lt;</span> <span class="n">n_elements</span>
-    <span class="c1"># Load data</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">x_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-    <span class="n">x_keep</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">x_keep_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-    <span class="c1"># The line below is the crucial part, described in the paragraph above!</span>
-    <span class="n">output</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">x_keep</span><span class="p">,</span> <span class="n">x</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">p</span><span class="p">),</span> <span class="mf">0.0</span><span class="p">)</span>
-    <span class="c1"># Write-back output</span>
-    <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">output_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x_keep</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span>
-    <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-    <span class="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">()</span>
-    <span class="n">n_elements</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
-    <span class="n">grid</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">meta</span><span class="p">:</span> <span class="p">(</span><span class="n">triton</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">n_elements</span><span class="p">,</span> <span class="n">meta</span><span class="p">[</span><span class="s1">&#39;BLOCK_SIZE&#39;</span><span class="p">]),)</span>
-    <span class="n">_dropout</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span><span class="n">x</span><span class="p">,</span> <span class="n">x_keep</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">n_elements</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="mi">1024</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">output</span>
-
-
-<span class="c1"># Input tensor</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,))</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
-<span class="c1"># Dropout mask</span>
-<span class="n">p</span> <span class="o">=</span> <span class="mf">0.5</span>
-<span class="n">x_keep</span> <span class="o">=</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,))</span> <span class="o">&gt;</span> <span class="n">p</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
-<span class="c1">#</span>
-<span class="n">output</span> <span class="o">=</span> <span class="n">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x_keep</span><span class="o">=</span><span class="n">x_keep</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">tabulate</span><span class="o">.</span><span class="n">tabulate</span><span class="p">([</span>
-    <span class="p">[</span><span class="s2">&quot;input&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
-    <span class="p">[</span><span class="s2">&quot;keep mask&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">x_keep</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
-    <span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">output</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-<span class="p">]))</span>
-</pre></div>
-</div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>---------  -------  ---------  --------  --------  --------  --------  --------  --------  ---------  ---------
-input      1.541    -0.293429  -2.17879  0.568431  -1.08452  -1.3986   0.403347  0.838026  -0.719258  -0.403344
-keep mask  1         1          0        1          0         1        1         0          0          0
-output     3.08199  -0.586858   0        1.13686    0        -2.79719  0.806694  0          0          0
----------  -------  ---------  --------  --------  --------  --------  --------  --------  ---------  ---------
-</pre></div>
-</div>
-</div>
-<div class="section" id="seeded-dropout">
-<h2>Seeded dropout<a class="headerlink" href="#seeded-dropout" title="Permalink to this headline">¶</a></h2>
-<p>Above implementation of dropout works fine, but it can be a bit awkward to deal with. Firstly
-we need to store the dropout mask for backpropagation. Secondly, dropout state management can get
-very tricky when using recompute/checkpointing (e.g. see all the notes about <cite>preserve_rng_state</cite> in
-<a class="reference external" href="https://pytorch.org/docs/1.9.0/checkpoint.html">https://pytorch.org/docs/1.9.0/checkpoint.html</a>). In this tutorial we’ll describe an alternative implementation
-that (1) has a smaller memory footprint; (2) requires less data movement; and (3) simplifies the management
-of persisting randomness across multiple invocations of the kernel.</p>
-<p>Pseudorandom number generation in Triton is simple! In this tutorial we will use the
-<code class="code docutils literal notranslate"><span class="pre">triton.language.rand</span></code> function which generates a block of uniformly distributed <code class="code docutils literal notranslate"><span class="pre">float32</span></code>
-values in [0, 1), given a seed and a block of <code class="code docutils literal notranslate"><span class="pre">int32</span></code> offsets. But if you need it, Triton also provides
-other <a class="reference internal" href="../../python-api/triton.language.html#random-number-generation"><span class="std std-ref">random number generation strategies</span></a>.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Triton’s implementation of PRNG is based on the Philox algorithm (described on <a class="reference internal" href="#salmon2011" id="id2"><span>[SALMON2011]</span></a>).</p>
-</div>
-<p>Let’s put it all together.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
-<span class="k">def</span> <span class="nf">_seeded_dropout</span><span class="p">(</span>
-        <span class="n">x_ptr</span><span class="p">,</span>
-        <span class="n">output_ptr</span><span class="p">,</span>
-        <span class="n">n_elements</span><span class="p">,</span>
-        <span class="n">p</span><span class="p">,</span>
-        <span class="n">seed</span><span class="p">,</span>
-        <span class="n">BLOCK_SIZE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span>
-<span class="p">):</span>
-    <span class="c1"># compute memory offsets of elements handled by this instance</span>
-    <span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">block_start</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">*</span> <span class="n">BLOCK_SIZE</span>
-    <span class="n">offsets</span> <span class="o">=</span> <span class="n">block_start</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="p">)</span>
-    <span class="c1"># load data from x</span>
-    <span class="n">mask</span> <span class="o">=</span> <span class="n">offsets</span> <span class="o">&lt;</span> <span class="n">n_elements</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">x_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-    <span class="c1"># randomly prune it</span>
-    <span class="n">random</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">seed</span><span class="p">,</span> <span class="n">offsets</span><span class="p">)</span>
-    <span class="n">x_keep</span> <span class="o">=</span> <span class="n">random</span> <span class="o">&gt;</span> <span class="n">p</span>
-    <span class="c1"># write-back</span>
-    <span class="n">output</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">x_keep</span><span class="p">,</span> <span class="n">x</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">p</span><span class="p">),</span> <span class="mf">0.0</span><span class="p">)</span>
-    <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">output_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">seeded_dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="p">):</span>
-    <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-    <span class="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">()</span>
-    <span class="n">n_elements</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
-    <span class="n">grid</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">meta</span><span class="p">:</span> <span class="p">(</span><span class="n">triton</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">n_elements</span><span class="p">,</span> <span class="n">meta</span><span class="p">[</span><span class="s1">&#39;BLOCK_SIZE&#39;</span><span class="p">]),)</span>
-    <span class="n">_seeded_dropout</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span><span class="n">x</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">n_elements</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="mi">1024</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">output</span>
-
-
-<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,))</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
-<span class="c1"># Compare this to the baseline - dropout mask is never instantiated!</span>
-<span class="n">output</span> <span class="o">=</span> <span class="n">seeded_dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">123</span><span class="p">)</span>
-<span class="n">output2</span> <span class="o">=</span> <span class="n">seeded_dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">123</span><span class="p">)</span>
-<span class="n">output3</span> <span class="o">=</span> <span class="n">seeded_dropout</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">512</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">tabulate</span><span class="o">.</span><span class="n">tabulate</span><span class="p">([</span>
-    <span class="p">[</span><span class="s2">&quot;input&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
-    <span class="p">[</span><span class="s2">&quot;output (seed = 123)&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">output</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
-    <span class="p">[</span><span class="s2">&quot;output (seed = 123)&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">output2</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
-    <span class="p">[</span><span class="s2">&quot;output (seed = 512)&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">output3</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-<span class="p">]))</span>
-</pre></div>
-</div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>-------------------  ---------  --------  --------  -------  --------  --------  ---------  ---------  ---------  ---------
-input                -0.952835  0.371721  0.408716  1.42142  0.149397  -0.67086  -0.214186  -0.431969  -0.707878  -0.106434
-output (seed = 123)   0         0.743443  0         0        0         -1.34172   0          0         -1.41576   -0.212868
-output (seed = 123)   0         0.743443  0         0        0         -1.34172   0          0         -1.41576   -0.212868
-output (seed = 512)   0         0         0.817432  2.84284  0         -1.34172  -0.428372   0          0          0
--------------------  ---------  --------  --------  -------  --------  --------  ---------  ---------  ---------  ---------
-</pre></div>
-</div>
-<p>Et Voilà! We have a triton kernel that applies the same dropout mask provided the seed is the same!
-If you’d like explore further applications of pseudorandomness in GPU programming, we encourage you
-to explore the <cite>triton/language/random</cite> folder!</p>
-</div>
-<div class="section" id="exercises">
-<h2>Exercises<a class="headerlink" href="#exercises" title="Permalink to this headline">¶</a></h2>
-<ol class="arabic simple">
-<li><p>Extend the kernel to operate over a matrix and use a vector of seeds - one per row.</p></li>
-<li><p>Add support for striding.</p></li>
-<li><p>(challenge) Implement a kernel for sparse Johnson-Lindenstrauss transform which generates the projection matrix one the fly each time using a seed.</p></li>
-</ol>
-</div>
-<div class="section" id="references">
-<h2>References<a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2>
-<dl class="citation">
-<dt class="label" id="salmon2011"><span class="brackets"><a class="fn-backref" href="#id2">SALMON2011</a></span></dt>
-<dd><p>John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw, “Parallel Random Numbers: As Easy as 1, 2, 3”, 2011</p>
-</dd>
-<dt class="label" id="srivastava2014"><span class="brackets"><a class="fn-backref" href="#id1">SRIVASTAVA2014</a></span></dt>
-<dd><p>Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, JMLR 2014</p>
-</dd>
-</dl>
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-<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-04-low-memory-dropout-py">
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-<p><a class="reference download internal" download="" href="../../_downloads/c9aed78977a4c05741d675a38dde3d7d/04-low-memory-dropout.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">04-low-memory-dropout.py</span></code></a></p>
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-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-getting-started-tutorials-05-layer-norm-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<div class="sphx-glr-example-title section" id="layer-normalization">
-<span id="sphx-glr-getting-started-tutorials-05-layer-norm-py"></span><h1>Layer Normalization<a class="headerlink" href="#layer-normalization" title="Permalink to this headline">¶</a></h1>
-<img alt="05 layer norm" class="sphx-glr-single-img" src="../../_images/sphx_glr_05-layer-norm_001.png" />
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>layer-norm-backward:
-          N      Triton       Torch        Apex
-0    1024.0  361.411758   97.912354  303.407414
-1    1536.0  409.599994  134.540150  341.333333
-2    2048.0  491.520012  161.154101  323.368435
-3    2560.0  461.954908  181.238943  326.808501
-4    3072.0  515.580429  192.501302  320.556515
-5    3584.0  554.941930  208.271186  311.652167
-6    4096.0  561.737163  220.907859  294.323343
-7    4608.0  502.690905  232.825259  291.799469
-8    5120.0  525.128191  242.366855  289.129408
-9    5632.0  540.671974  243.107920  288.820505
-10   6144.0  546.133354  248.661056  286.322318
-11   6656.0  527.207907  256.000009  285.257135
-12   7168.0  503.017523  260.063480  285.293536
-13   7680.0  483.779539  262.938666  275.928134
-14   8192.0  463.698115  266.767970  284.526763
-15   8704.0  413.655443  267.472468  284.987724
-16   9216.0  427.822068  271.724806  288.375482
-17   9728.0  436.396262  280.615388  289.667485
-18  10240.0  446.025405  286.433562  290.153487
-19  10752.0  428.651173  246.935876  290.267711
-20  11264.0  426.397479  245.536784  286.980888
-21  11776.0  420.571432  249.667843  288.981596
-22  12288.0  416.542386  254.673582  294.617366
-23  12800.0  410.695192  253.884294  288.180121
-24  13312.0  409.075539  252.959629  290.443638
-25  13824.0  404.604870  257.390218  292.056329
-26  14336.0  394.116833  254.862216  286.959121
-27  14848.0  385.662341  257.852379  289.952797
-28  15360.0  380.433442  257.970599  286.433562
-29  15872.0  370.192407  261.626369  290.562936
-</pre></div>
-</div>
-<div class="line-block">
-<div class="line"><br /></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">import</span> <span class="nn">triton</span>
-<span class="kn">import</span> <span class="nn">triton.language</span> <span class="k">as</span> <span class="nn">tl</span>
-
-<span class="k">try</span><span class="p">:</span>
-    <span class="c1"># This is https://github.com/NVIDIA/apex, NOT the apex on PyPi, so it</span>
-    <span class="c1"># should not be added to extras_require in setup.py.</span>
-    <span class="kn">import</span> <span class="nn">apex</span>
-    <span class="n">HAS_APEX</span> <span class="o">=</span> <span class="kc">True</span>
-<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
-    <span class="n">HAS_APEX</span> <span class="o">=</span> <span class="kc">False</span>
-
-
-<span class="c1"># Forward Pass</span>
-<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
-<span class="k">def</span> <span class="nf">_layer_norm_fwd_fused</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">W</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">eps</span><span class="p">,</span>
-                          <span class="n">BLOCK_SIZE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">):</span>
-    <span class="c1"># position of elements processed by this program</span>
-    <span class="n">row</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">cols</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="p">)</span>
-    <span class="n">mask</span> <span class="o">=</span> <span class="n">cols</span> <span class="o">&lt;</span> <span class="n">N</span>
-    <span class="c1"># offset data pointers to start at the row of interest</span>
-    <span class="n">X</span> <span class="o">+=</span> <span class="n">row</span> <span class="o">*</span> <span class="n">stride</span>
-    <span class="n">Y</span> <span class="o">+=</span> <span class="n">row</span> <span class="o">*</span> <span class="n">stride</span>
-    <span class="c1"># load data and cast to float32</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">X</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-    <span class="c1"># compute mean</span>
-    <span class="n">mean</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">N</span>
-    <span class="c1"># compute std</span>
-    <span class="n">xmean</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">x</span> <span class="o">-</span> <span class="n">mean</span><span class="p">,</span> <span class="mf">0.</span><span class="p">)</span>
-    <span class="n">var</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">xmean</span> <span class="o">*</span> <span class="n">xmean</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">N</span>
-    <span class="n">rstd</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">tl</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">var</span> <span class="o">+</span> <span class="n">eps</span><span class="p">)</span>
-    <span class="n">xhat</span> <span class="o">=</span> <span class="n">xmean</span> <span class="o">*</span> <span class="n">rstd</span>
-    <span class="c1"># write-back mean/rstd</span>
-    <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">M</span> <span class="o">+</span> <span class="n">row</span><span class="p">,</span> <span class="n">mean</span><span class="p">)</span>
-    <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">V</span> <span class="o">+</span> <span class="n">row</span><span class="p">,</span> <span class="n">rstd</span><span class="p">)</span>
-    <span class="c1"># multiply by weight and add bias</span>
-    <span class="n">w</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">W</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-    <span class="n">b</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">B</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">xhat</span> <span class="o">*</span> <span class="n">w</span> <span class="o">+</span> <span class="n">b</span>
-    <span class="c1"># write-back</span>
-    <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">Y</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-
-
-<span class="c1"># Backward pass (DX + partial DW + partial DB)</span>
-<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
-<span class="k">def</span> <span class="nf">_layer_norm_bwd_dx_fused</span><span class="p">(</span><span class="n">DX</span><span class="p">,</span> <span class="n">DY</span><span class="p">,</span> <span class="n">DW</span><span class="p">,</span> <span class="n">DB</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">Lock</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">eps</span><span class="p">,</span>
-                             <span class="n">GROUP_SIZE_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">):</span>
-    <span class="c1"># position of elements processed by this program</span>
-    <span class="n">row</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">cols</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">)</span>
-    <span class="n">mask</span> <span class="o">=</span> <span class="n">cols</span> <span class="o">&lt;</span> <span class="n">N</span>
-    <span class="c1"># offset data pointers to start at the row of interest</span>
-    <span class="n">X</span> <span class="o">+=</span> <span class="n">row</span> <span class="o">*</span> <span class="n">stride</span>
-    <span class="n">DY</span> <span class="o">+=</span> <span class="n">row</span> <span class="o">*</span> <span class="n">stride</span>
-    <span class="n">DX</span> <span class="o">+=</span> <span class="n">row</span> <span class="o">*</span> <span class="n">stride</span>
-    <span class="c1"># offset locks and weight/bias gradient pointer</span>
-    <span class="c1"># each kernel instance accumulates partial sums for</span>
-    <span class="c1"># DW and DB into one of GROUP_SIZE_M independent buffers</span>
-    <span class="c1"># these buffers stay in the L2, which allow this kernel</span>
-    <span class="c1"># to be fast</span>
-    <span class="n">lock_id</span> <span class="o">=</span> <span class="n">row</span> <span class="o">%</span> <span class="n">GROUP_SIZE_M</span>
-    <span class="n">Lock</span> <span class="o">+=</span> <span class="n">lock_id</span>
-    <span class="n">Count</span> <span class="o">=</span> <span class="n">Lock</span> <span class="o">+</span> <span class="n">GROUP_SIZE_M</span>
-    <span class="n">DW</span> <span class="o">=</span> <span class="n">DW</span> <span class="o">+</span> <span class="n">lock_id</span> <span class="o">*</span> <span class="n">N</span> <span class="o">+</span> <span class="n">cols</span>
-    <span class="n">DB</span> <span class="o">=</span> <span class="n">DB</span> <span class="o">+</span> <span class="n">lock_id</span> <span class="o">*</span> <span class="n">N</span> <span class="o">+</span> <span class="n">cols</span>
-    <span class="c1"># load data to SRAM</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">X</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-    <span class="n">dy</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">DY</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-    <span class="n">w</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">W</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-    <span class="n">mean</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">M</span> <span class="o">+</span> <span class="n">row</span><span class="p">)</span>
-    <span class="n">rstd</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">V</span> <span class="o">+</span> <span class="n">row</span><span class="p">)</span>
-    <span class="c1"># compute dx</span>
-    <span class="n">xhat</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span> <span class="o">*</span> <span class="n">rstd</span>
-    <span class="n">wdy</span> <span class="o">=</span> <span class="n">w</span> <span class="o">*</span> <span class="n">dy</span>
-    <span class="n">xhat</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">xhat</span><span class="p">,</span> <span class="mf">0.</span><span class="p">)</span>
-    <span class="n">wdy</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">wdy</span><span class="p">,</span> <span class="mf">0.</span><span class="p">)</span>
-    <span class="n">mean1</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">xhat</span> <span class="o">*</span> <span class="n">wdy</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">N</span>
-    <span class="n">mean2</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">wdy</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">N</span>
-    <span class="n">dx</span> <span class="o">=</span> <span class="p">(</span><span class="n">wdy</span> <span class="o">-</span> <span class="p">(</span><span class="n">xhat</span> <span class="o">*</span> <span class="n">mean1</span> <span class="o">+</span> <span class="n">mean2</span><span class="p">))</span> <span class="o">*</span> <span class="n">rstd</span>
-    <span class="c1"># write-back dx</span>
-    <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">DX</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-    <span class="c1"># accumulate partial sums for dw/db</span>
-    <span class="n">partial_dw</span> <span class="o">=</span> <span class="p">(</span><span class="n">dy</span> <span class="o">*</span> <span class="n">xhat</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="n">partial_db</span> <span class="o">=</span> <span class="p">(</span><span class="n">dy</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="k">while</span> <span class="n">tl</span><span class="o">.</span><span class="n">atomic_cas</span><span class="p">(</span><span class="n">Lock</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="k">pass</span>
-    <span class="n">count</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">Count</span><span class="p">)</span>
-    <span class="c1"># first store doesn&#39;t accumulate</span>
-    <span class="k">if</span> <span class="n">count</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">tl</span><span class="o">.</span><span class="n">atomic_xchg</span><span class="p">(</span><span class="n">Count</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">partial_dw</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">DW</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-        <span class="n">partial_db</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">DB</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-    <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">DW</span><span class="p">,</span> <span class="n">partial_dw</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-    <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">DB</span><span class="p">,</span> <span class="n">partial_db</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
-    <span class="c1"># release lock</span>
-    <span class="n">tl</span><span class="o">.</span><span class="n">atomic_xchg</span><span class="p">(</span><span class="n">Lock</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-<span class="c1"># Backward pass (total DW + total DB)</span>
-
-
-<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
-<span class="k">def</span> <span class="nf">_layer_norm_bwd_dwdb</span><span class="p">(</span><span class="n">DW</span><span class="p">,</span> <span class="n">DB</span><span class="p">,</span> <span class="n">FINAL_DW</span><span class="p">,</span> <span class="n">FINAL_DB</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span>
-                         <span class="n">BLOCK_SIZE_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">):</span>
-    <span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">cols</span> <span class="o">=</span> <span class="n">pid</span> <span class="o">*</span> <span class="n">BLOCK_SIZE_N</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">)</span>
-    <span class="n">dw</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">BLOCK_SIZE_M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-    <span class="n">db</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">BLOCK_SIZE_M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">):</span>
-        <span class="n">rows</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_SIZE_M</span><span class="p">)</span>
-        <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">rows</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">M</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">cols</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">N</span><span class="p">)</span>
-        <span class="n">offs</span> <span class="o">=</span> <span class="n">rows</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">N</span> <span class="o">+</span> <span class="n">cols</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span>
-        <span class="n">dw</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">DW</span> <span class="o">+</span> <span class="n">offs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.</span><span class="p">)</span>
-        <span class="n">db</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">DB</span> <span class="o">+</span> <span class="n">offs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.</span><span class="p">)</span>
-    <span class="n">sum_dw</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dw</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">sum_db</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">db</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">FINAL_DW</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">sum_dw</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">cols</span> <span class="o">&lt;</span> <span class="n">N</span><span class="p">)</span>
-    <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">FINAL_DB</span> <span class="o">+</span> <span class="n">cols</span><span class="p">,</span> <span class="n">sum_db</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">cols</span> <span class="o">&lt;</span> <span class="n">N</span><span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">LayerNorm</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">normalized_shape</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">eps</span><span class="p">):</span>
-        <span class="c1"># allocate output</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="c1"># reshape input data into 2D tensor</span>
-        <span class="n">x_arg</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">x_arg</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">mean</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">M</span><span class="p">,</span> <span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
-        <span class="n">rstd</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">M</span><span class="p">,</span> <span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
-        <span class="c1"># Less than 64KB per feature: enqueue fused kernel</span>
-        <span class="n">MAX_FUSED_SIZE</span> <span class="o">=</span> <span class="mi">65536</span> <span class="o">//</span> <span class="n">x</span><span class="o">.</span><span class="n">element_size</span><span class="p">()</span>
-        <span class="n">BLOCK_SIZE</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">MAX_FUSED_SIZE</span><span class="p">,</span> <span class="n">triton</span><span class="o">.</span><span class="n">next_power_of_2</span><span class="p">(</span><span class="n">N</span><span class="p">))</span>
-        <span class="k">if</span> <span class="n">N</span> <span class="o">&gt;</span> <span class="n">BLOCK_SIZE</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;This layer norm doesn&#39;t support feature dim &gt;= 64KB.&quot;</span><span class="p">)</span>
-        <span class="c1"># heuristics for number of warps</span>
-        <span class="n">num_warps</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">BLOCK_SIZE</span> <span class="o">//</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="mi">8</span><span class="p">)</span>
-        <span class="c1"># enqueue kernel</span>
-        <span class="n">_layer_norm_fwd_fused</span><span class="p">[(</span><span class="n">M</span><span class="p">,)](</span><span class="n">x_arg</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">rstd</span><span class="p">,</span>
-                                    <span class="n">x_arg</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">N</span><span class="p">,</span> <span class="n">eps</span><span class="p">,</span>
-                                    <span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="n">BLOCK_SIZE</span><span class="p">,</span> <span class="n">num_warps</span><span class="o">=</span><span class="n">num_warps</span><span class="p">)</span>
-        <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">rstd</span><span class="p">)</span>
-        <span class="n">ctx</span><span class="o">.</span><span class="n">BLOCK_SIZE</span> <span class="o">=</span> <span class="n">BLOCK_SIZE</span>
-        <span class="n">ctx</span><span class="o">.</span><span class="n">num_warps</span> <span class="o">=</span> <span class="n">num_warps</span>
-        <span class="n">ctx</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span>
-        <span class="k">return</span> <span class="n">y</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dy</span><span class="p">):</span>
-        <span class="n">x</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span>
-        <span class="c1"># heuristics for amount of parallel reduction stream for DG/DB</span>
-        <span class="n">N</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">GROUP_SIZE_M</span> <span class="o">=</span> <span class="mi">64</span>
-        <span class="k">if</span> <span class="n">N</span> <span class="o">&lt;=</span> <span class="mi">8192</span><span class="p">:</span> <span class="n">GROUP_SIZE_M</span> <span class="o">=</span> <span class="mi">96</span>
-        <span class="k">if</span> <span class="n">N</span> <span class="o">&lt;=</span> <span class="mi">4096</span><span class="p">:</span> <span class="n">GROUP_SIZE_M</span> <span class="o">=</span> <span class="mi">128</span>
-        <span class="k">if</span> <span class="n">N</span> <span class="o">&lt;=</span> <span class="mi">1024</span><span class="p">:</span> <span class="n">GROUP_SIZE_M</span> <span class="o">=</span> <span class="mi">256</span>
-        <span class="c1"># allocate output</span>
-        <span class="n">locks</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">GROUP_SIZE_M</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
-        <span class="n">_dw</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">GROUP_SIZE_M</span><span class="p">,</span> <span class="n">w</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-        <span class="n">_db</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">GROUP_SIZE_M</span><span class="p">,</span> <span class="n">w</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-        <span class="n">dw</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">w</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-        <span class="n">db</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">w</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-        <span class="n">dx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">dy</span><span class="p">)</span>
-        <span class="c1"># enqueue kernel using forward pass heuristics</span>
-        <span class="c1"># also compute partial sums for DW and DB</span>
-        <span class="n">x_arg</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">x_arg</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">_layer_norm_bwd_dx_fused</span><span class="p">[(</span><span class="n">M</span><span class="p">,)](</span><span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="p">,</span> <span class="n">_dw</span><span class="p">,</span> <span class="n">_db</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">locks</span><span class="p">,</span>
-                                       <span class="n">x_arg</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">N</span><span class="p">,</span> <span class="n">ctx</span><span class="o">.</span><span class="n">eps</span><span class="p">,</span>
-                                       <span class="n">BLOCK_SIZE_N</span><span class="o">=</span><span class="n">ctx</span><span class="o">.</span><span class="n">BLOCK_SIZE</span><span class="p">,</span>
-                                       <span class="n">GROUP_SIZE_M</span><span class="o">=</span><span class="n">GROUP_SIZE_M</span><span class="p">,</span>
-                                       <span class="n">num_warps</span><span class="o">=</span><span class="n">ctx</span><span class="o">.</span><span class="n">num_warps</span><span class="p">)</span>
-        <span class="n">grid</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">meta</span><span class="p">:</span> <span class="p">[</span><span class="n">triton</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">meta</span><span class="p">[</span><span class="s1">&#39;BLOCK_SIZE_N&#39;</span><span class="p">])]</span>
-        <span class="c1"># accumulate partial sums in separate kernel</span>
-        <span class="n">_layer_norm_bwd_dwdb</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span><span class="n">_dw</span><span class="p">,</span> <span class="n">_db</span><span class="p">,</span> <span class="n">dw</span><span class="p">,</span> <span class="n">db</span><span class="p">,</span> <span class="n">GROUP_SIZE_M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span>
-                                   <span class="n">BLOCK_SIZE_M</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
-                                   <span class="n">BLOCK_SIZE_N</span><span class="o">=</span><span class="mi">128</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">dx</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">dw</span><span class="p">,</span> <span class="n">db</span><span class="p">,</span> <span class="kc">None</span>
-
-
-<span class="n">layer_norm</span> <span class="o">=</span> <span class="n">LayerNorm</span><span class="o">.</span><span class="n">apply</span>
-
-
-<span class="k">def</span> <span class="nf">test_layer_norm</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">):</span>
-    <span class="c1"># create data</span>
-    <span class="n">x_shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span>
-    <span class="n">w_shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">x_shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">)</span>
-    <span class="n">weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">w_shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">bias</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">w_shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="o">-</span><span class="mf">2.3</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">x_shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
-    <span class="n">dy</span> <span class="o">=</span> <span class="mf">.1</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-    <span class="c1"># forward pass</span>
-    <span class="n">y_tri</span> <span class="o">=</span> <span class="n">layer_norm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w_shape</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
-    <span class="n">y_ref</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">layer_norm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w_shape</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="c1"># backward pass (triton)</span>
-    <span class="n">y_tri</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">dy</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">dx_tri</span><span class="p">,</span> <span class="n">dw_tri</span><span class="p">,</span> <span class="n">db_tri</span> <span class="o">=</span> <span class="p">[</span><span class="n">_</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">]]</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="n">bias</span><span class="o">.</span><span class="n">grad</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
-    <span class="c1"># backward pass (torch)</span>
-    <span class="n">y_ref</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">dy</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">dx_ref</span><span class="p">,</span> <span class="n">dw_ref</span><span class="p">,</span> <span class="n">db_ref</span> <span class="o">=</span> <span class="p">[</span><span class="n">_</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">]]</span>
-    <span class="c1"># compare</span>
-    <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">y_tri</span><span class="p">,</span> <span class="n">y_ref</span><span class="p">)</span>
-    <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">dx_tri</span><span class="p">,</span> <span class="n">dx_ref</span><span class="p">)</span>
-    <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">db_tri</span><span class="p">,</span> <span class="n">db_ref</span><span class="p">,</span> <span class="n">decimal</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">dw_tri</span><span class="p">,</span> <span class="n">dw_ref</span><span class="p">,</span> <span class="n">decimal</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-
-<span class="nd">@triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">perf_report</span><span class="p">(</span>
-    <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">Benchmark</span><span class="p">(</span>
-        <span class="n">x_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;N&#39;</span><span class="p">],</span>
-        <span class="n">x_vals</span><span class="o">=</span><span class="p">[</span><span class="mi">512</span> <span class="o">*</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">32</span><span class="p">)],</span>
-        <span class="n">line_arg</span><span class="o">=</span><span class="s1">&#39;provider&#39;</span><span class="p">,</span>
-        <span class="n">line_vals</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;triton&#39;</span><span class="p">,</span> <span class="s1">&#39;torch&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="p">([</span><span class="s1">&#39;apex&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="n">HAS_APEX</span> <span class="k">else</span> <span class="p">[]),</span>
-        <span class="n">line_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Triton&#39;</span><span class="p">,</span> <span class="s1">&#39;Torch&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="p">([</span><span class="s1">&#39;Apex&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="n">HAS_APEX</span> <span class="k">else</span> <span class="p">[]),</span>
-        <span class="n">styles</span><span class="o">=</span><span class="p">[(</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;orange&#39;</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">)],</span>
-        <span class="n">ylabel</span><span class="o">=</span><span class="s1">&#39;GB/s&#39;</span><span class="p">,</span>
-        <span class="n">plot_name</span><span class="o">=</span><span class="s1">&#39;layer-norm-backward&#39;</span><span class="p">,</span>
-        <span class="n">args</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;M&#39;</span><span class="p">:</span> <span class="mi">4096</span><span class="p">,</span> <span class="s1">&#39;dtype&#39;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">,</span> <span class="s1">&#39;mode&#39;</span><span class="p">:</span> <span class="s1">&#39;backward&#39;</span><span class="p">}</span>
-    <span class="p">)</span>
-<span class="p">)</span>
-<span class="k">def</span> <span class="nf">bench_layer_norm</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">provider</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;backward&#39;</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">):</span>
-    <span class="c1"># create data</span>
-    <span class="n">x_shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span>
-    <span class="n">w_shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">x_shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">)</span>
-    <span class="n">weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">w_shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">bias</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">w_shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="o">-</span><span class="mf">2.3</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">x_shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
-    <span class="n">dy</span> <span class="o">=</span> <span class="mf">.1</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-    <span class="n">x</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-    <span class="c1"># utility functions</span>
-    <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;triton&#39;</span><span class="p">:</span>
-        <span class="n">y_fwd</span> <span class="o">=</span> <span class="k">lambda</span><span class="p">:</span> <span class="n">layer_norm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w_shape</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;torch&#39;</span><span class="p">:</span>
-        <span class="n">y_fwd</span> <span class="o">=</span> <span class="k">lambda</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">layer_norm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w_shape</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">provider</span> <span class="o">==</span> <span class="s1">&#39;apex&#39;</span><span class="p">:</span>
-        <span class="n">apex_layer_norm</span> <span class="o">=</span> <span class="n">apex</span><span class="o">.</span><span class="n">normalization</span><span class="o">.</span><span class="n">FusedLayerNorm</span><span class="p">(</span><span class="n">w_shape</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-        <span class="n">y_fwd</span> <span class="o">=</span> <span class="k">lambda</span><span class="p">:</span> <span class="n">apex_layer_norm</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-    <span class="c1"># forward pass</span>
-    <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;forward&#39;</span><span class="p">:</span>
-        <span class="n">gbps</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">ms</span><span class="p">:</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">element_size</span><span class="p">()</span> <span class="o">/</span> <span class="n">ms</span> <span class="o">*</span> <span class="mf">1e-6</span>
-        <span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="n">y_fwd</span><span class="p">,</span> <span class="n">rep</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
-    <span class="c1"># backward pass</span>
-    <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;backward&#39;</span><span class="p">:</span>
-        <span class="n">gbps</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">ms</span><span class="p">:</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">element_size</span><span class="p">()</span> <span class="o">/</span> <span class="n">ms</span> <span class="o">*</span> <span class="mf">1e-6</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">y_fwd</span><span class="p">()</span>
-        <span class="n">ms</span><span class="p">,</span> <span class="n">min_ms</span><span class="p">,</span> <span class="n">max_ms</span> <span class="o">=</span> <span class="n">triton</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">do_bench</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">y</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">dy</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-                                                     <span class="n">grad_to_none</span><span class="o">=</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">rep</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">gbps</span><span class="p">(</span><span class="n">ms</span><span class="p">),</span> <span class="n">gbps</span><span class="p">(</span><span class="n">max_ms</span><span class="p">),</span> <span class="n">gbps</span><span class="p">(</span><span class="n">min_ms</span><span class="p">)</span>
-
-
-<span class="n">bench_layer_norm</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">save_path</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">print_data</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
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-<li class="toctree-l1 current"><a class="current reference internal" href="#">Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="01-vector-add.html">Vector Addition</a></li>
-<li class="toctree-l2"><a class="reference internal" href="02-fused-softmax.html">Fused Softmax</a></li>
-<li class="toctree-l2"><a class="reference internal" href="03-matrix-multiplication.html">Matrix Multiplication</a></li>
-<li class="toctree-l2"><a class="reference internal" href="04-low-memory-dropout.html">Low-Memory Dropout</a></li>
-<li class="toctree-l2"><a class="reference internal" href="05-layer-norm.html">Layer Normalization</a></li>
-</ul>
-</li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Python API</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../python-api/triton.html">triton</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../python-api/triton.testing.html">triton.testing</a></li>
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-  <div class="section" id="tutorials">
-<span id="sphx-glr-getting-started-tutorials"></span><h1>Tutorials<a class="headerlink" href="#tutorials" title="Permalink to this headline">¶</a></h1>
-<p>Below is a gallery of tutorials for writing various basic operations with Triton. It is recommended that you read through the tutorials in order, starting with the simplest one.</p>
-<p>To install the dependencies for the tutorials:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> triton
-pip install -e <span class="s1">&#39;./python[tutorials]&#39;</span>
-</pre></div>
-</div>
-<div class="sphx-glr-thumbcontainer" tooltip="- The basic programming model of Triton - The triton.jit decorator, which is used to define Tri..."><div class="figure align-default" id="id1">
-<img alt="Vector Addition" src="../../_images/sphx_glr_01-vector-add_thumb.png" />
-<p class="caption"><span class="caption-text"><a class="reference internal" href="01-vector-add.html#sphx-glr-getting-started-tutorials-01-vector-add-py"><span class="std std-ref">Vector Addition</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
-</div>
-</div><div class="toctree-wrapper compound">
-</div>
-<div class="sphx-glr-thumbcontainer" tooltip="- The benefits of kernel fusion for bandwidth-bound operations. - Reduction operators in Triton..."><div class="figure align-default" id="id2">
-<img alt="Fused Softmax" src="../../_images/sphx_glr_02-fused-softmax_thumb.png" />
-<p class="caption"><span class="caption-text"><a class="reference internal" href="02-fused-softmax.html#sphx-glr-getting-started-tutorials-02-fused-softmax-py"><span class="std std-ref">Fused Softmax</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
-</div>
-</div><div class="toctree-wrapper compound">
-</div>
-<div class="sphx-glr-thumbcontainer" tooltip="- Block-level matrix multiplications - Multi-dimensional pointer arithmetic - Program re-orderi..."><div class="figure align-default" id="id3">
-<img alt="Matrix Multiplication" src="../../_images/sphx_glr_03-matrix-multiplication_thumb.png" />
-<p class="caption"><span class="caption-text"><a class="reference internal" href="03-matrix-multiplication.html#sphx-glr-getting-started-tutorials-03-matrix-multiplication-py"><span class="std std-ref">Matrix Multiplication</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
-</div>
-</div><div class="toctree-wrapper compound">
-</div>
-<div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, you will write a memory-efficient implementation of dropout whose state will ..."><div class="figure align-default" id="id4">
-<img alt="Low-Memory Dropout" src="../../_images/sphx_glr_04-low-memory-dropout_thumb.png" />
-<p class="caption"><span class="caption-text"><a class="reference internal" href="04-low-memory-dropout.html#sphx-glr-getting-started-tutorials-04-low-memory-dropout-py"><span class="std std-ref">Low-Memory Dropout</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
-</div>
-</div><div class="toctree-wrapper compound">
-</div>
-<div class="sphx-glr-thumbcontainer" tooltip="Layer Normalization"><div class="figure align-default" id="id5">
-<img alt="Layer Normalization" src="../../_images/sphx_glr_05-layer-norm_thumb.png" />
-<p class="caption"><span class="caption-text"><a class="reference internal" href="05-layer-norm.html#sphx-glr-getting-started-tutorials-05-layer-norm-py"><span class="std std-ref">Layer Normalization</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
-</div>
-</div><div class="toctree-wrapper compound">
-</div>
-<div class="sphx-glr-clear"></div><div class="sphx-glr-footer class sphx-glr-footer-gallery docutils container">
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-<p><a class="reference download internal" download="" href="../../_downloads/763344228ae6bc253ed1a6cf586aa30d/tutorials_python.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">all</span> <span class="pre">examples</span> <span class="pre">in</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tutorials_python.zip</span></code></a></p>
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-  <div class="section" id="introduction">
-<h1>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="motivations">
-<h2>Motivations<a class="headerlink" href="#motivations" title="Permalink to this headline">¶</a></h2>
-<p>Over the past decade, Deep Neural Networks (DNNs) have emerged as an important class of Machine Learning (ML) models, capable of  achieving state-of-the-art performance across many domains ranging from natural language processing <a class="reference internal" href="#sutskever2014" id="id1"><span>[SUTSKEVER2014]</span></a> to computer vision <a class="reference internal" href="#redmon2016" id="id2"><span>[REDMON2016]</span></a> to computational neuroscience <a class="reference internal" href="#lee2017" id="id3"><span>[LEE2017]</span></a>. The strength of these models lies in their hierarchical structure, composed of a sequence of parametric (e.g., convolutional) and non-parametric (e.g., rectified linearity) <em>layers</em>. This pattern, though notoriously computationally expensive, also generates a large amount of highly parallelizable work particularly well suited for multi- and many- core processors.</p>
-<p>As a consequence, Graphics Processing Units (GPUs) have become a cheap and accessible resource for exploring and/or deploying novel research ideas in the field. This trend has been accelerated by the release of several frameworks for General-Purpose GPU (GPGPU) computing, such as CUDA and OpenCL, which have made the development of high-performance programs easier. Yet, GPUs remain incredibly challenging to optimize for locality and parallelism, especially for computations that cannot be efficiently implemented using a combination of pre-existing optimized primitives. To make matters worse, GPU architectures are also rapidly evolving and specializing, as evidenced by the addition of tensor cores to NVIDIA (and more recently AMD) micro-architectures.</p>
-<p>This tension between the computational opportunities offered by DNNs and the practical difficulty of GPU programming has created substantial academic and industrial interest for Domain-Specific Languages (DSLs) and compilers. Regrettably, these systems – whether they be based on  polyhedral machinery (<em>e.g.</em>, Tiramisu <a class="reference internal" href="../chapter-2/related-work.html#baghdadi2021" id="id4"><span>[BAGHDADI2021]</span></a>, Tensor Comprehensions <a class="reference internal" href="../chapter-2/related-work.html#vasilache2018" id="id5"><span>[VASILACHE2018]</span></a>) or scheduling languages (<em>e.g.</em>, Halide <a class="reference internal" href="#jrk2013" id="id6"><span>[JRK2013]</span></a>, TVM <a class="reference internal" href="#chen2018" id="id7"><span>[CHEN2018]</span></a>) – remain less flexible and (for the same algorithm) markedly slower than the best handwritten compute kernels available in libraries like <a class="reference external" href="https://docs.nvidia.com/cuda/cublas/index.html">cuBLAS</a>, <a class="reference external" href="https://docs.nvidia.com/deeplearning/cudnn/api/index.html">cuDNN</a> or <a class="reference external" href="https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html">TensorRT</a>.</p>
-<p>The main premise of this project is the following: programming paradigms based on blocked algorithms <a class="reference internal" href="#lam1991" id="id8"><span>[LAM1991]</span></a> can facilitate the construction of high-performance compute kernels for neural networks.  We specifically revisit traditional “Single Program, Multiple Data” (SPMD <a class="reference internal" href="#auguin1983" id="id9"><span>[AUGUIN1983]</span></a>) execution models for GPUs, and propose a variant in which programs – rather than threads – are blocked. For example, in the case of matrix multiplication, CUDA and Triton differ as follows:</p>
-<table class="colwidths-given docutils align-default">
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>CUDA Programming Model</p>
-<p>(Scalar Program, Blocked Threads)</p>
-</th>
-<th class="head"><p>Triton Programming Model</p>
-<p>(Blocked Program, Scalar Threads)</p>
-</th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><div class="highlight-C notranslate"><div class="highlight"><pre><span></span><span class="cp">#pragma parallel</span>
-<span class="k">for</span><span class="p">(</span><span class="kt">int</span> <span class="n">m</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">M</span><span class="p">;</span> <span class="n">m</span><span class="o">++</span><span class="p">)</span>
-<span class="cp">#pragma parallel</span>
-<span class="k">for</span><span class="p">(</span><span class="kt">int</span> <span class="n">n</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">j</span> <span class="o">&lt;</span> <span class="n">N</span><span class="p">;</span> <span class="n">n</span><span class="o">++</span><span class="p">){</span>
-  <span class="kt">float</span> <span class="n">acc</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span>
-  <span class="k">for</span><span class="p">(</span><span class="kt">int</span> <span class="n">k</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">k</span> <span class="o">&lt;</span> <span class="n">K</span><span class="p">;</span><span class="n">k</span> <span class="o">++</span><span class="p">)</span>
-    <span class="n">acc</span> <span class="o">+=</span> <span class="n">A</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">k</span><span class="p">]</span><span class="o">*</span> <span class="n">B</span><span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">];</span>
-
-  <span class="n">C</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">acc</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</td>
-<td><div class="highlight-C notranslate"><div class="highlight"><pre><span></span><span class="cp">#pragma parallel</span>
-<span class="k">for</span><span class="p">(</span><span class="kt">int</span> <span class="n">m</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">m</span> <span class="o">&lt;</span> <span class="n">M</span><span class="p">;</span> <span class="n">m</span> <span class="o">+=</span> <span class="n">MB</span><span class="p">)</span>
-<span class="cp">#pragma parallel</span>
-<span class="k">for</span><span class="p">(</span><span class="kt">int</span> <span class="n">n</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">n</span> <span class="o">&lt;</span> <span class="n">N</span><span class="p">;</span> <span class="n">n</span> <span class="o">+=</span> <span class="n">NB</span><span class="p">){</span>
-  <span class="kt">float</span> <span class="n">acc</span><span class="p">[</span><span class="n">MB</span><span class="p">,</span> <span class="n">NB</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span>
-  <span class="k">for</span><span class="p">(</span><span class="kt">int</span> <span class="n">k</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">k</span> <span class="o">&lt;</span> <span class="n">K</span><span class="p">;</span> <span class="n">k</span> <span class="o">+=</span> <span class="n">KB</span><span class="p">)</span>
-    <span class="n">acc</span> <span class="o">+=</span>  <span class="n">A</span><span class="p">[</span><span class="nl">m</span><span class="p">:</span><span class="n">m</span><span class="o">+</span><span class="n">MB</span><span class="p">,</span> <span class="nl">k</span><span class="p">:</span><span class="n">k</span><span class="o">+</span><span class="n">KB</span><span class="p">]</span>
-          <span class="err">@</span> <span class="n">B</span><span class="p">[</span><span class="nl">k</span><span class="p">:</span><span class="n">k</span><span class="o">+</span><span class="n">KB</span><span class="p">,</span> <span class="nl">n</span><span class="p">:</span><span class="n">n</span><span class="o">+</span><span class="n">NB</span><span class="p">];</span>
-  <span class="n">C</span><span class="p">[</span><span class="nl">m</span><span class="p">:</span><span class="n">m</span><span class="o">+</span><span class="n">MB</span><span class="p">,</span> <span class="nl">n</span><span class="p">:</span><span class="n">n</span><span class="o">+</span><span class="n">NB</span><span class="p">]</span> <span class="o">=</span> <span class="n">acc</span><span class="p">;</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</td>
-</tr>
-<tr class="row-odd"><td><p><img alt="pic1" src="../../_images/cuda-parallel-matmul.png" /></p></td>
-<td><p><img alt="pic2" src="../../_images/triton-parallel-matmul.png" /></p></td>
-</tr>
-</tbody>
-</table>
-<p>A key benefit of this approach is that it leads to block-structured iteration spaces that offer programmers more flexibility than existing DSLs when implementing sparse operations, all while allowing compilers to aggressively optimize programs for data locality and parallelism.</p>
-</div>
-<div class="section" id="challenges">
-<h2>Challenges<a class="headerlink" href="#challenges" title="Permalink to this headline">¶</a></h2>
-<p>The main challenge posed by our proposed paradigm is that of work scheduling, i.e., how the work done by each program instance should be partitioned for efficient execution on modern GPUs. To address this issue, the Triton compiler makes heavy use of <em>block-level data-flow analysis</em>, a technique for scheduling iteration blocks statically based on the control- and data-flow structure of the target program. The resulting system actually works surprisingly well: our compiler manages to apply a broad range of interesting optimization automatically (e.g., automatic coalescing, thread swizzling, pre-fetching, automatic vectorization, tensor core-aware instruction selection, shared memory allocation/synchronization, asynchronous copy scheduling). Of course doing all this is not trivial; one of the purposes of this guide is to give you a sense of how it works.</p>
-</div>
-<div class="section" id="references">
-<h2>References<a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2>
-<dl class="citation">
-<dt class="label" id="sutskever2014"><span class="brackets"><a class="fn-backref" href="#id1">SUTSKEVER2014</a></span></dt>
-<dd><ol class="upperroman simple">
-<li><p>Sutskever et al., “Sequence to Sequence Learning with Neural Networks”, NIPS 2014</p></li>
-</ol>
-</dd>
-<dt class="label" id="redmon2016"><span class="brackets"><a class="fn-backref" href="#id2">REDMON2016</a></span></dt>
-<dd><ol class="upperalpha simple" start="10">
-<li><p>Redmon et al., “You Only Look Once: Unified, Real-Time Object Detection”, CVPR 2016</p></li>
-</ol>
-</dd>
-<dt class="label" id="lee2017"><span class="brackets"><a class="fn-backref" href="#id3">LEE2017</a></span></dt>
-<dd><ol class="upperalpha simple" start="11">
-<li><p>Lee et al., “Superhuman Accuracy on the SNEMI3D Connectomics Challenge”, ArXiV 2017</p></li>
-</ol>
-</dd>
-<dt class="label" id="baghdadi2021"><span class="brackets"><a class="fn-backref" href="#id4">BAGHDADI2021</a></span></dt>
-<dd><ol class="upperalpha simple" start="18">
-<li><p>Baghdadi et al., “Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code”, CGO 2021</p></li>
-</ol>
-</dd>
-<dt class="label" id="vasilache2018"><span class="brackets"><a class="fn-backref" href="#id5">VASILACHE2018</a></span></dt>
-<dd><ol class="upperalpha simple" start="14">
-<li><p>Vasilache et al., “Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions”, ArXiV 2018</p></li>
-</ol>
-</dd>
-<dt class="label" id="jrk2013"><span class="brackets"><a class="fn-backref" href="#id6">JRK2013</a></span></dt>
-<dd><ol class="upperalpha simple" start="10">
-<li><p>Ragan-Kelley et al., “Halide: A Language and Compiler for Optimizing Parallelism, Locality, and Recomputation in Image Processing Pipelines”, PLDI 2013</p></li>
-</ol>
-</dd>
-<dt class="label" id="chen2018"><span class="brackets"><a class="fn-backref" href="#id7">CHEN2018</a></span></dt>
-<dd><ol class="upperalpha simple" start="20">
-<li><p>Chen et al., “TVM: An Automated End-to-End Optimizing Compiler for Deep Learning”, OSDI 2018</p></li>
-</ol>
-</dd>
-<dt class="label" id="lam1991"><span class="brackets"><a class="fn-backref" href="#id8">LAM1991</a></span></dt>
-<dd><ol class="upperalpha simple" start="13">
-<li><p>Lam et al., “The Cache Performance and Optimizations of Blocked Algorithms”, ASPLOS 1991</p></li>
-</ol>
-</dd>
-<dt class="label" id="auguin1983"><span class="brackets"><a class="fn-backref" href="#id9">AUGUIN1983</a></span></dt>
-<dd><ol class="upperalpha simple" start="13">
-<li><p>Auguin et al., “Opsila: an advanced SIMD for numerical analysis and signal processing”, EUROMICRO 1983</p></li>
-</ol>
-</dd>
-</dl>
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-  <div class="section" id="related-work">
-<h1>Related Work<a class="headerlink" href="#related-work" title="Permalink to this headline">¶</a></h1>
-<p>At first sight, Triton may seem like just yet another DSL for DNNs. The purpose of this section is to contextualize Triton and highlight its differences with the two leading approaches in this domain: polyhedral compilation and scheduling languages.</p>
-<div class="section" id="polyhedral-compilation">
-<h2>Polyhedral Compilation<a class="headerlink" href="#polyhedral-compilation" title="Permalink to this headline">¶</a></h2>
-<p>Traditional compilers typically rely on intermediate representations, such as LLVM-IR <a class="reference internal" href="#lattner2004" id="id1"><span>[LATTNER2004]</span></a>, that encode control flow information using (un)conditional branches. This relatively low-level format makes it difficult to statically analyze the runtime behavior (e.g., cache misses) of input programs, and to  automatically optimize loops accordingly through the use of tiling <a class="reference internal" href="#wolfe1989" id="id2"><span>[WOLFE1989]</span></a>, fusion <a class="reference internal" href="#darte1999" id="id3"><span>[DARTE1999]</span></a> and interchange <a class="reference internal" href="#allen1984" id="id4"><span>[ALLEN1984]</span></a>. To solve this issue, polyhedral compilers <a class="reference internal" href="#ancourt1991" id="id5"><span>[ANCOURT1991]</span></a> rely on program representations that have statically predictable control flow, thereby enabling aggressive compile-time program transformations for data locality and parallelism. Though this strategy has been adopted by many languages and compilers for DNNs such as Tiramisu <a class="reference internal" href="#baghdadi2021" id="id6"><span>[BAGHDADI2021]</span></a>, Tensor Comprehensions <a class="reference internal" href="#vasilache2018" id="id7"><span>[VASILACHE2018]</span></a>, Diesel <a class="reference internal" href="#elango2018" id="id8"><span>[ELANGO2018]</span></a> and the Affine dialect in MLIR <a class="reference internal" href="#lattner2019" id="id9"><span>[LATTNER2019]</span></a>, it also comes with a number of limitations that will be described later in this section.</p>
-<div class="section" id="program-representation">
-<h3>Program Representation<a class="headerlink" href="#program-representation" title="Permalink to this headline">¶</a></h3>
-<p>Polyhedral compilation is a vast area of research. In this section we only outline the most basic aspects of this topic, but readers interested in the solid mathematical foundations underneath may refer to the ample litterature on linear and integer programming.</p>
-<table class="colwidths-given docutils align-default">
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><div class="highlight-C notranslate"><div class="highlight"><pre><span></span><span class="k">for</span><span class="p">(</span><span class="kt">int</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">3</span><span class="p">;</span> <span class="n">i</span><span class="o">++</span><span class="p">)</span>
-<span class="k">for</span><span class="p">(</span><span class="kt">int</span> <span class="n">j</span> <span class="o">=</span> <span class="n">i</span><span class="p">;</span> <span class="n">j</span> <span class="o">&lt;</span> <span class="mi">5</span><span class="p">;</span> <span class="n">j</span><span class="o">++</span><span class="p">)</span>
-  <span class="n">A</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span>
-</pre></div>
-</div>
-</td>
-<td><p><a class="reference internal" href="../../_images/polyhedral-iteration.png"><img alt="pic1" src="../../_images/polyhedral-iteration.png" style="width: 300px;" /></a></p></td>
-</tr>
-</tbody>
-</table>
-<p>Polyhedral compilers focus on a class of programs commonly known as <strong>Static Control Parts</strong> (SCoP), <em>i.e.</em>, maximal sets of consecutive statements in which conditionals and loop bounds are affine functions of surrounding loop indices and global invariant parameters. As shown above, programs in this format always lead to iteration domains that are bounded by affine inequalities, i.e., polyhedral. These polyhedra can also be defined algebraically; for the above example:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\mathcal{P} = \{ i, j \in \mathbb{Z}^2
-~|~
-\begin{pmatrix}
-1 &amp; 0 \\
--1 &amp; 0 \\
--1 &amp; 1 \\
-0 &amp; -1 \\
-\end{pmatrix}
-\begin{pmatrix}
-i \\
-j
-\end{pmatrix}
-+
-\begin{pmatrix}
-0 \\
-2 \\
-0 \\
-4
-\end{pmatrix}
-\geq
-0
-\}\end{split}\]</div>
-<p>Each point <span class="math notranslate nohighlight">\((i, j)\)</span> in <span class="math notranslate nohighlight">\(\mathcal{P}\)</span> represents a <em>polyhedral statement</em>, that is a program statement which (1) does not induce control-flow side effects (e.g., <code class="code docutils literal notranslate"><span class="pre">for</span></code>, <code class="code docutils literal notranslate"><span class="pre">if</span></code>, <code class="code docutils literal notranslate"><span class="pre">break</span></code>) and (2) contains only affine functions of loop indices and global parameters in array accesses. To facilitate alias analysis, array accesses are also mathematically abstracted, using so-called <em>access function</em>. In other words, <code class="code docutils literal notranslate"><span class="pre">A[i][j]</span></code> is simply <code class="code docutils literal notranslate"><span class="pre">A[f(i,j)]</span></code> where the access function <span class="math notranslate nohighlight">\(f\)</span> is defined by:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}f(i, j) = \begin{pmatrix}
-1 &amp; 0\\
-0 &amp; 1\\
-\end{pmatrix}
-\begin{pmatrix}
-i\\
-j
-\end{pmatrix}
-=
-(i, j)\end{split}\]</div>
-<p>Note that the iteration domains of an SCoP does not specify the order in which its statements shall execute. In fact, this iteration domain may be traversed in many different possible legal orders, i.e. <em>schedules</em>. Formally, a schedule is defined as a p-dimensional affine transformation <span class="math notranslate nohighlight">\(\Theta\)</span> of loop indices <span class="math notranslate nohighlight">\(\mathbf{x}\)</span> and global invariant parameters <span class="math notranslate nohighlight">\(\mathbf{g}\)</span>:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\Theta_S(\mathbf{x}) = T_S \begin{pmatrix}
-\vec{x}\\
-\vec{g}\\
-1
-\end{pmatrix}
-\qquad
-T_S \in \mathbb{Z} ^{p \times (\text{dim}(\mathbf{x}) + \text{dim}(\mathbf{g}) + 1)}\end{split}\]</div>
-<p>Where <span class="math notranslate nohighlight">\(\Theta_S(\mathbf{x})\)</span> is a p-dimensional vector representing the slowest to fastest growing indices (from left to right) when traversing the loop nest surrounding <span class="math notranslate nohighlight">\(S\)</span>. For the code shown above, the original schedule defined by the loop nest in C can be retrieved by using:</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\Theta_S(\mathbf{x}) = \begin{pmatrix}
-1 &amp; 0 \\
-0 &amp; 1 \\
-\end{pmatrix}
-\begin{pmatrix}
-i &amp; j
-\end{pmatrix}^T
-=
-\begin{pmatrix}
-i &amp; j
-\end{pmatrix}^T\end{split}\]</div>
-<p>where <span class="math notranslate nohighlight">\(i\)</span> and <span class="math notranslate nohighlight">\(j\)</span> are respectively the slowest and fastest growing loop indices in the nest. If <span class="math notranslate nohighlight">\(T_S\)</span> is a vector (resp. tensor), then <span class="math notranslate nohighlight">\(\Theta_S\)</span> is a said to be one-dimensional (resp. multi-dimensional).</p>
-</div>
-<div class="section" id="advantages">
-<h3>Advantages<a class="headerlink" href="#advantages" title="Permalink to this headline">¶</a></h3>
-<p>Programs amenable to polyhedral compilation can be aggressively transformed and optimized. Most of these transformations actually boil down to the production of  schedules and iteration domains that enable loop transformations promoting parallelism and spatial/temporal data locality (e.g., fusion, interchange, tiling, parallelization).</p>
-<p>Polyhedral compilers can also automatically go through complex verification processes to ensure that the semantics of their input program is preserved throughout this optimization phase. Note that polyhedral optimizers are not incompatible with more standard optimization techniques. In fact, it is not uncommon for these systems to be implemented as a set of LLVM passes that can be run ahead of more traditional compilation techniques <a class="reference internal" href="#grosser2012" id="id10"><span>[GROSSER2012]</span></a>.</p>
-<p>All in all, polyhedral machinery is extremely powerful, when applicable. It has been shown to support most common loop transformations, and has indeed achieved performance comparable to state-of-the-art GPU libraries for dense matrix multiplication <a class="reference internal" href="#elango2018" id="id11"><span>[ELANGO2018]</span></a>. Additionally, it is also fully automatic and doesn’t require any hint from programmers apart from source-code in a C-like format.</p>
-</div>
-<div class="section" id="limitations">
-<h3>Limitations<a class="headerlink" href="#limitations" title="Permalink to this headline">¶</a></h3>
-<p>Unfortunately, polyhedral compilers suffer from two major limitations that have prevented its adoption as a universal method for code generation in neural networks.</p>
-<p>First, the set of possible program transformations <span class="math notranslate nohighlight">\(\Omega = \{ \Theta_S ~|~ S \in \text{program} \}\)</span> is large, and grows with the number of statements in the program as well as with the size of their iteration domain. Verifying the legality of each transformation can also require the resolution of complex integer linear programs, making polyhedral compilation very computationally expensive. To make matters worse, hardware properties (e.g., cache size, number of SMs) and contextual characteristics (e.g., input tensor shapes) also have to be taken into account by this framework, leading to expensive auto-tuning procedures <a class="reference internal" href="#sato2019" id="id12"><span>[SATO2019]</span></a>.</p>
-<p>Second, the polyhedral framework is not very generally applicable; SCoPs are relatively common <a class="reference internal" href="#girbal2006" id="id13"><span>[GIRBAL2006]</span></a> but require loop bounds and array subscripts to be affine functions of loop indices, which typically only occurs in regular, dense computations. For this reason, this framework still has to be successfully applied to sparse – or even structured-sparse – neural networks, whose importance has been rapidly rising over the past few years.</p>
-<p>On the other hand, blocked program representations advocated by this dissertation are less restricted in scope and can achieve close to peak performance using standard dataflow analysis.</p>
-</div>
-</div>
-<div class="section" id="scheduling-languages">
-<h2>Scheduling Languages<a class="headerlink" href="#scheduling-languages" title="Permalink to this headline">¶</a></h2>
-<p>Separation of concerns <a class="reference internal" href="#dijkstra82" id="id14"><span>[DIJKSTRA82]</span></a> is a well-known design principle in computer science: programs should be decomposed into modular layers of abstraction that separate the semantics of their algorithms from the details of their implementation. Systems like Halide and TVM push this philosophy one step further, and enforce this separation at the grammatical level through the use of a  <strong>scheduling language</strong>. The benefits of this methodology are particularly visible in the case of matrix multiplication, where, as one can see below, the definition of the algorithm (Line 1-7) is completely disjoint from its implementation (Line 8-16), meaning that both can be maintained, optimized and distributed independently.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="o">//</span> <span class="n">algorithm</span>
-<span class="linenos"> 2</span><span class="n">Var</span> <span class="n">x</span><span class="p">(</span><span class="s2">&quot;x&quot;</span><span class="p">),</span> <span class="n">y</span><span class="p">(</span><span class="s2">&quot;y&quot;</span><span class="p">);</span>
-<span class="linenos"> 3</span><span class="n">Func</span> <span class="n">matmul</span><span class="p">(</span><span class="s2">&quot;matmul&quot;</span><span class="p">);</span>
-<span class="linenos"> 4</span><span class="n">RDom</span> <span class="n">k</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">matrix_size</span><span class="p">);</span>
-<span class="linenos"> 5</span><span class="n">RVar</span> <span class="n">ki</span><span class="p">;</span>
-<span class="linenos"> 6</span><span class="n">matmul</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="o">=</span> <span class="mf">0.0</span><span class="n">f</span><span class="p">;</span>
-<span class="linenos"> 7</span><span class="n">matmul</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="o">+=</span> <span class="n">A</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="o">*</span> <span class="n">B</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="p">);</span>
-<span class="linenos"> 8</span><span class="o">//</span> <span class="n">schedule</span>
-<span class="linenos"> 9</span><span class="n">Var</span> <span class="n">xi</span><span class="p">(</span><span class="s2">&quot;xi&quot;</span><span class="p">),</span> <span class="n">xo</span><span class="p">(</span><span class="s2">&quot;xo&quot;</span><span class="p">),</span> <span class="n">yo</span><span class="p">(</span><span class="s2">&quot;yo&quot;</span><span class="p">),</span> <span class="n">yi</span><span class="p">(</span><span class="s2">&quot;yo&quot;</span><span class="p">),</span> <span class="n">yii</span><span class="p">(</span><span class="s2">&quot;yii&quot;</span><span class="p">),</span> <span class="n">xii</span><span class="p">(</span><span class="s2">&quot;xii&quot;</span><span class="p">);</span>
-<span class="linenos">10</span><span class="n">matmul</span><span class="o">.</span><span class="n">vectorize</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">8</span><span class="p">);</span>
-<span class="linenos">11</span><span class="n">matmul</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="linenos">12</span>    <span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">xi</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">xi</span><span class="p">,</span> <span class="n">xi</span><span class="p">,</span> <span class="n">xii</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
-<span class="linenos">13</span>    <span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">yi</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">yi</span><span class="p">,</span> <span class="n">yi</span><span class="p">,</span> <span class="n">yii</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="linenos">14</span>    <span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">ki</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span>
-<span class="linenos">15</span>    <span class="o">.</span><span class="n">reorder</span><span class="p">(</span><span class="n">xii</span><span class="p">,</span> <span class="n">yii</span><span class="p">,</span> <span class="n">xi</span><span class="p">,</span> <span class="n">ki</span><span class="p">,</span> <span class="n">yi</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="linenos">16</span>    <span class="o">.</span><span class="n">parallel</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">vectorize</span><span class="p">(</span><span class="n">xii</span><span class="p">)</span><span class="o">.</span><span class="n">unroll</span><span class="p">(</span><span class="n">xi</span><span class="p">)</span><span class="o">.</span><span class="n">unroll</span><span class="p">(</span><span class="n">yii</span><span class="p">);</span>
-</pre></div>
-</div>
-<p>The resulting code may however not be completely portable, as schedules can sometimes rely on execution models (e.g., SPMD) or hardware intrinsics (e.g., matrix-multiply-accumulate) that are not widely available. This issue can be mitigated by auto-scheduling mechanisms <a class="reference internal" href="#mullapudi2016" id="id15"><span>[MULLAPUDI2016]</span></a>.</p>
-<div class="section" id="id16">
-<h3>Advantages<a class="headerlink" href="#id16" title="Permalink to this headline">¶</a></h3>
-<p>The main advantage of this approach is that it allows programmers to write an algorithm <em>only once</em>, and focus on performance optimization separately. It makes it possible to manually specify optimizations that a polyhedral compiler wouldn’t be able to figure out automatically using static data-flow analysis.</p>
-<p>Scheduling languages are, without a doubt, one of the most popular approaches for neural network code generation. The most popular system for this purpose is probably TVM, which provides good performance across a wide range of platforms as well as built-in automatic scheduling mechanisms.</p>
-</div>
-<div class="section" id="id17">
-<h3>Limitations<a class="headerlink" href="#id17" title="Permalink to this headline">¶</a></h3>
-<p>This ease-of-development comes at a cost. First of all, existing systems that follow this paradigm tend to be noticeably slower than Triton on modern hardware when applicable (e.g., V100/A100 tensor cores w/ equal tile sizes). I do believe that this is not a fundamental issue of scheduling languages – in the sense that it could probably be solved with more efforts – but it could mean that these systems are harder to engineer. More importantly, existing scheduling languages generate loops whose bounds and increments cannot depend on surrounding loop indice without at least imposing severe constraints on possible schedules – if not breaking the system entirely. This is problematic for sparse computations, whose iteration spaces may be irregular.</p>
-<table class="colwidths-given docutils align-default">
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><div class="highlight-C notranslate"><div class="highlight"><pre><span></span><span class="k">for</span><span class="p">(</span><span class="kt">int</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">4</span><span class="p">;</span> <span class="n">i</span><span class="o">++</span><span class="p">)</span>
-<span class="k">for</span><span class="p">(</span><span class="kt">int</span> <span class="n">j</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">j</span> <span class="o">&lt;</span> <span class="mi">4</span><span class="p">;</span> <span class="n">j</span><span class="o">++</span><span class="p">)</span>
-  <span class="kt">float</span> <span class="n">acc</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span>
-  <span class="k">for</span><span class="p">(</span><span class="kt">int</span> <span class="n">k</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">k</span> <span class="o">&lt;</span> <span class="n">K</span><span class="p">[</span><span class="n">i</span><span class="p">];</span> <span class="n">k</span><span class="o">++</span><span class="p">)</span>
-    <span class="n">acc</span> <span class="o">+=</span> <span class="n">A</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">col</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">k</span><span class="p">]]</span><span class="o">*</span><span class="n">B</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">j</span><span class="p">]</span>
-  <span class="n">C</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">acc</span><span class="p">;</span>
-</pre></div>
-</div>
-</td>
-<td><p><a class="reference internal" href="../../_images/halide-iteration.png"><img alt="pic2" src="../../_images/halide-iteration.png" style="width: 300px;" /></a></p></td>
-</tr>
-</tbody>
-</table>
-<p>On the other hand, the block-based program representation that we advocate for through this work allows for block-structured iteration spaces and allows programmers to manually handle load-balancing as they wish.</p>
-</div>
-</div>
-<div class="section" id="references">
-<h2>References<a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2>
-<dl class="citation">
-<dt class="label" id="lattner2004"><span class="brackets"><a class="fn-backref" href="#id1">LATTNER2004</a></span></dt>
-<dd><ol class="upperalpha simple" start="3">
-<li><p>Lattner et al., “LLVM: a compilation framework for lifelong program analysis transformation”, CGO 2004</p></li>
-</ol>
-</dd>
-<dt class="label" id="wolfe1989"><span class="brackets"><a class="fn-backref" href="#id2">WOLFE1989</a></span></dt>
-<dd><ol class="upperalpha simple" start="13">
-<li><p>Wolfe, “More Iteration Space Tiling”, SC 1989</p></li>
-</ol>
-</dd>
-<dt class="label" id="darte1999"><span class="brackets"><a class="fn-backref" href="#id3">DARTE1999</a></span></dt>
-<dd><ol class="upperalpha simple">
-<li><p>Darte, “On the Complexity of Loop Fusion”, PACT 1999</p></li>
-</ol>
-</dd>
-<dt class="label" id="allen1984"><span class="brackets"><a class="fn-backref" href="#id4">ALLEN1984</a></span></dt>
-<dd><ol class="upperalpha simple" start="10">
-<li><p>Allen et al., “Automatic Loop Interchange”, SIGPLAN Notices 1984</p></li>
-</ol>
-</dd>
-<dt class="label" id="ancourt1991"><span class="brackets"><a class="fn-backref" href="#id5">ANCOURT1991</a></span></dt>
-<dd><ol class="upperalpha simple" start="3">
-<li><p>Ancourt et al., “Scanning Polyhedra with DO Loops”, PPoPP 1991</p></li>
-</ol>
-</dd>
-<dt class="label" id="baghdadi2021"><span class="brackets"><a class="fn-backref" href="#id6">BAGHDADI2021</a></span></dt>
-<dd><ol class="upperalpha simple" start="18">
-<li><p>Baghdadi et al., “Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code”, CGO 2021</p></li>
-</ol>
-</dd>
-<dt class="label" id="vasilache2018"><span class="brackets"><a class="fn-backref" href="#id7">VASILACHE2018</a></span></dt>
-<dd><ol class="upperalpha simple" start="14">
-<li><p>Vasilache et al., “Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions”, ArXiV 2018</p></li>
-</ol>
-</dd>
-<dt class="label" id="elango2018"><span class="brackets">ELANGO2018</span><span class="fn-backref">(<a href="#id8">1</a>,<a href="#id11">2</a>)</span></dt>
-<dd><ol class="upperalpha simple" start="22">
-<li><p>Elango et al. “Diesel: DSL for Linear Algebra and Neural Net Computations on GPUs”, MAPL 2018</p></li>
-</ol>
-</dd>
-<dt class="label" id="lattner2019"><span class="brackets"><a class="fn-backref" href="#id9">LATTNER2019</a></span></dt>
-<dd><ol class="upperalpha simple" start="3">
-<li><p>Lattner et al., “MLIR Primer: A Compiler Infrastructure for the End of Moore’s Law”, Arxiv 2019</p></li>
-</ol>
-</dd>
-<dt class="label" id="grosser2012"><span class="brackets"><a class="fn-backref" href="#id10">GROSSER2012</a></span></dt>
-<dd><ol class="upperalpha simple" start="20">
-<li><p>Grosser et al., “Polly - Performing Polyhedral Optimizations on a Low-Level Intermediate Representation”, Parallel Processing Letters 2012</p></li>
-</ol>
-</dd>
-<dt class="label" id="sato2019"><span class="brackets"><a class="fn-backref" href="#id12">SATO2019</a></span></dt>
-<dd><ol class="upperalpha simple" start="25">
-<li><p>Sato et al., “An Autotuning Framework for Scalable Execution of Tiled Code via Iterative Polyhedral Compilation”, TACO 2019</p></li>
-</ol>
-</dd>
-<dt class="label" id="girbal2006"><span class="brackets"><a class="fn-backref" href="#id13">GIRBAL2006</a></span></dt>
-<dd><ol class="upperalpha simple" start="19">
-<li><p>Girbal et al., “Semi-Automatic Composition of Loop Transformations for Deep Parallelism and Memory Hierarchies”, International Journal of Parallel Programming 2006</p></li>
-</ol>
-</dd>
-<dt class="label" id="dijkstra82"><span class="brackets"><a class="fn-backref" href="#id14">DIJKSTRA82</a></span></dt>
-<dd><ol class="upperalpha simple" start="5">
-<li><ol class="upperalpha simple" start="23">
-<li><p>Dijkstra et al., “On the role of scientific thought”, Selected writings on computing: a personal perspective 1982</p></li>
-</ol>
-</li>
-</ol>
-</dd>
-<dt class="label" id="mullapudi2016"><span class="brackets"><a class="fn-backref" href="#id15">MULLAPUDI2016</a></span></dt>
-<dd><ol class="upperalpha simple" start="18">
-<li><p>Mullapudi et al., “Automatically scheduling halide image processing pipelines”, TOG 2016</p></li>
-</ol>
-</dd>
-</dl>
-</div>
-</div>
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-<h1>triton.Config<a class="headerlink" href="#triton-config" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt class="sig sig-object py" id="triton.Config">
-<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">triton.</span></span><span class="sig-name descname"><span class="pre">Config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_warps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_stages</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_hook</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.Config" title="Permalink to this definition">¶</a></dt>
-<dd><p>An object that represents a possible kernel configuration for the auto-tuner to try.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Variables</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>meta</strong> – a dictionary of meta-parameters to pass to the kernel as keyword arguments.</p></li>
-<li><p><strong>num_warps</strong> – the number of warps to use for the kernel when compiled for GPUs. For example, if
-<cite>num_warps=8</cite>, then each kernel instance will be automatically parallelized to
-cooperatively execute using <cite>8 * 32 = 256</cite> threads.</p></li>
-<li><p><strong>num_stages</strong> – the number of stages that the compiler should use when software-pipelining loops.
-Mostly useful for matrix multiplication workloads on SM80+ GPUs.</p></li>
-<li><p><strong>pre_hook</strong> – a function that will be called before the kernel is called. Parameters of this
-function are args.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="triton.Config.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_warps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_stages</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_hook</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.Config.__init__" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#triton.Config.__init__" title="triton.Config.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(self, kwargs[, num_warps, …])</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
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-  <div class="section" id="triton-autotune">
-<h1>triton.autotune<a class="headerlink" href="#triton-autotune" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.autotune">
-<span class="sig-prename descclassname"><span class="pre">triton.</span></span><span class="sig-name descname"><span class="pre">autotune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">configs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prune_configs_by</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reset_to_zero</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.autotune" title="Permalink to this definition">¶</a></dt>
-<dd><p>Decorator for auto-tuning a <code class="code docutils literal notranslate"><span class="pre">triton.jit</span></code>’d function.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@triton</span><span class="o">.</span><span class="n">autotune</span><span class="p">(</span><span class="n">configs</span><span class="o">=</span><span class="p">[</span>
-    <span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">(</span><span class="n">meta</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;BLOCK_SIZE&#39;</span><span class="p">:</span> <span class="mi">128</span><span class="p">},</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
-    <span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">(</span><span class="n">meta</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;BLOCK_SIZE&#39;</span><span class="p">:</span> <span class="mi">1024</span><span class="p">},</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span>
-  <span class="p">],</span>
-  <span class="n">key</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;x_size&#39;</span><span class="p">]</span> <span class="c1"># the two above configs will be evaluated anytime</span>
-                 <span class="c1"># the value of x_size changes</span>
-<span class="p">)</span>
-<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">x_ptr</span><span class="p">,</span> <span class="n">x_size</span><span class="p">,</span> <span class="o">**</span><span class="n">META</span><span class="p">):</span>
-    <span class="n">BLOCK_SIZE</span> <span class="o">=</span> <span class="n">META</span><span class="p">[</span><span class="s1">&#39;BLOCK_SIZE&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Note</dt>
-<dd class="field-odd"><p>When all the configurations are evaluated, the kernel will run multiple time.
-This means that whatever value the kernel updates will be updated multiple times.
-To avoid this undesired behavior, you can use the <cite>reset_to_zero</cite> argument, which
-reset the value of the provided tensor to <cite>zero</cite> before running any configuration.</p>
-</dd>
-<dt class="field-even">Parameters</dt>
-<dd class="field-even"><ul class="simple">
-<li><p><strong>configs</strong> (<em>list</em><em>[</em><a class="reference internal" href="triton.Config.html#triton.Config" title="triton.Config"><em>triton.Config</em></a><em>]</em>) – a list of <code class="code docutils literal notranslate"><span class="pre">triton.Config</span></code> objects</p></li>
-<li><p><strong>key</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – a list of argument names whose change in value will trigger the evaluation of all provided configs.</p></li>
-<li><p><strong>prune_configs_by</strong> – a dict of functions that are used to prune configs, fields:
-‘perf_model’: performance model used to predicate running time with different configs, returns running time
-‘top_k’: number of configs to bench
-‘early_config_prune’(optional): a function used to do early prune (eg, num_stages). It take configs:List[Config] as its input, and returns pruned configs.</p></li>
-<li><p><strong>reset_to_zero</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – a list of argument names whose value will be reset to zero before evaluating any configs.</p></li>
-</ul>
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-  <div class="section" id="triton-heuristics">
-<h1>triton.heuristics<a class="headerlink" href="#triton-heuristics" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.heuristics">
-<span class="sig-prename descclassname"><span class="pre">triton.</span></span><span class="sig-name descname"><span class="pre">heuristics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.heuristics" title="Permalink to this definition">¶</a></dt>
-<dd><p>Decorator for specifying how the values of certain meta-parameters may be computed.
-This is useful for cases where auto-tuning is prohibitevely expensive, or just not applicable.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@triton</span><span class="o">.</span><span class="n">heuristics</span><span class="p">(</span><span class="n">values</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;BLOCK_SIZE&#39;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">args</span><span class="p">:</span> <span class="mi">2</span> <span class="o">**</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">])))})</span>
-<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span>
-<span class="k">def</span> <span class="nf">kernel</span><span class="p">(</span><span class="n">x_ptr</span><span class="p">,</span> <span class="n">x_size</span><span class="p">,</span> <span class="o">**</span><span class="n">META</span><span class="p">):</span>
-    <span class="n">BLOCK_SIZE</span> <span class="o">=</span> <span class="n">META</span><span class="p">[</span><span class="s1">&#39;BLOCK_SIZE&#39;</span><span class="p">]</span> <span class="c1"># smallest power-of-two &gt;= x_size</span>
-</pre></div>
-</div>
-<dl class="simple">
-<dt>.param values: a dictionary of meta-parameter names and functions that compute the value of the meta-parameter.</dt><dd><p>each such function takes a list of positional arguments as input.</p>
-</dd>
-</dl>
-<p>.type values: dict[str, Callable[[list[Any]], Any]]</p>
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-<h1>triton.jit<a class="headerlink" href="#triton-jit" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.jit">
-<span class="sig-prename descclassname"><span class="pre">triton.</span></span><span class="sig-name descname"><span class="pre">jit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.jit" title="Permalink to this definition">¶</a></dt>
-<dd><p>Decorator for JIT-compiling a function using the Triton compiler.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Note</dt>
-<dd class="field-odd"><p>When a jit’d function is called, <code class="code docutils literal notranslate"><span class="pre">torch.tensor</span></code> arguments are implicitly converted to pointers using the <code class="code docutils literal notranslate"><span class="pre">.data_ptr()</span></code> method.</p>
-</dd>
-<dt class="field-even">Note</dt>
-<dd class="field-even"><p>This function will be compiled and run on the GPU. It will only have access to:</p>
-<ul class="simple">
-<li><p>python primitives,</p></li>
-<li><p>objects within the triton.language package,</p></li>
-<li><p>arguments to this function,</p></li>
-<li><p>other jit’d functions</p></li>
-</ul>
-</dd>
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>fn</strong> (<em>Callable</em>) – the function to be jit-compiled</p>
-</dd>
-</dl>
-</dd></dl>
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-<dl class="py function">
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-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">arange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.arange" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns contiguous values within the open interval [<code class="code docutils literal notranslate"><span class="pre">start</span></code>, <code class="code docutils literal notranslate"><span class="pre">end</span></code>).</p>
-<dl class="field-list simple">
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-<dd class="field-odd"><ul class="simple">
-<li><p><strong>start</strong> (<em>int</em>) – Start of the interval. Must be a power of two.</p></li>
-<li><p><strong>stop</strong> (<em>int</em>) – End of the interval. Must be a power of two &gt;= start.</p></li>
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-<h1>triton.language.atomic_add<a class="headerlink" href="#triton-language-atomic-add" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.atomic_add">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">atomic_add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pointer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.atomic_add" title="Permalink to this definition">¶</a></dt>
-<dd><p>Performs an atomic add at the memory location specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p>
-<p>Return the data stored at <code class="code docutils literal notranslate"><span class="pre">pointer</span></code> before the atomic operation.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>pointer</strong> (<em>Block of dtype=triton.PointerDType</em>) – The memory locations to compare-and-swap.</p></li>
-<li><p><strong>cmp</strong> (<em>Block of dtype=`pointer.dtype.element_ty`</em>) – The values expected to be found in the atomic object</p></li>
-<li><p><strong>val</strong> (<em>Block of dtype=`pointer.dtype.element_ty`</em>) – The values to copy in case the expected value matches the contained value.</p></li>
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-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.atomic_cas">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">atomic_cas</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pointer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.atomic_cas" title="Permalink to this definition">¶</a></dt>
-<dd><p>Performs an atomic compare-and-swap at the memory location specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p>
-<p>Return the data stored at <code class="code docutils literal notranslate"><span class="pre">pointer</span></code> before the atomic operation.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>pointer</strong> (<em>Block of dtype=triton.PointerDType</em>) – The memory locations to compare-and-swap.</p></li>
-<li><p><strong>cmp</strong> (<em>Block of dtype=`pointer.dtype.element_ty`</em>) – The values expected to be found in the atomic object</p></li>
-<li><p><strong>val</strong> (<em>Block of dtype=`pointer.dtype.element_ty`</em>) – The values to copy in case the expected value matches the contained value.</p></li>
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-<h1>triton.language.atomic_max<a class="headerlink" href="#triton-language-atomic-max" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.atomic_max">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">atomic_max</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pointer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.atomic_max" title="Permalink to this definition">¶</a></dt>
-<dd><p>Performs an atomic max at the memory location specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p>
-<p>Return the data stored at <code class="code docutils literal notranslate"><span class="pre">pointer</span></code> before the atomic operation.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>pointer</strong> (<em>Block of dtype=triton.PointerDType</em>) – The memory locations to compare-and-swap.</p></li>
-<li><p><strong>cmp</strong> (<em>Block of dtype=`pointer.dtype.element_ty`</em>) – The values expected to be found in the atomic object</p></li>
-<li><p><strong>val</strong> (<em>Block of dtype=`pointer.dtype.element_ty`</em>) – The values to copy in case the expected value matches the contained value.</p></li>
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-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">atomic_min</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pointer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.atomic_min" title="Permalink to this definition">¶</a></dt>
-<dd><p>Performs an atomic min at the memory location specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p>
-<p>Return the data stored at <code class="code docutils literal notranslate"><span class="pre">pointer</span></code> before the atomic operation.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>pointer</strong> (<em>Block of dtype=triton.PointerDType</em>) – The memory locations to compare-and-swap.</p></li>
-<li><p><strong>cmp</strong> (<em>Block of dtype=`pointer.dtype.element_ty`</em>) – The values expected to be found in the atomic object</p></li>
-<li><p><strong>val</strong> (<em>Block of dtype=`pointer.dtype.element_ty`</em>) – The values to copy in case the expected value matches the contained value.</p></li>
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-<h1>triton.language.atomic_xchg<a class="headerlink" href="#triton-language-atomic-xchg" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.atomic_xchg">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">atomic_xchg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pointer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.atomic_xchg" title="Permalink to this definition">¶</a></dt>
-<dd><p>Performs an atomic exchange at the memory location specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p>
-<p>Return the data stored at <code class="code docutils literal notranslate"><span class="pre">pointer</span></code> before the atomic operation.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>pointer</strong> (<em>Block of dtype=triton.PointerDType</em>) – The memory locations to compare-and-swap.</p></li>
-<li><p><strong>cmp</strong> (<em>Block of dtype=`pointer.dtype.element_ty`</em>) – The values expected to be found in the atomic object</p></li>
-<li><p><strong>val</strong> (<em>Block of dtype=`pointer.dtype.element_ty`</em>) – The values to copy in case the expected value matches the contained value.</p></li>
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-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.broadcast_to">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">broadcast_to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.broadcast_to" title="Permalink to this definition">¶</a></dt>
-<dd><p>Tries to broadcast the given tensor to a new <code class="code docutils literal notranslate"><span class="pre">shape</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>input</strong> (<em>Block</em>) – The input tensor.</p></li>
-<li><p><strong>shape</strong> (<em>Tuple</em><em>[</em><em>int</em><em>]</em>) – The desired shape.</p></li>
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-<h1>triton.language.cos<a class="headerlink" href="#triton-language-cos" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.cos">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">cos</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.cos" title="Permalink to this definition">¶</a></dt>
-<dd><p>Computes the element-wise cosine of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>x</strong> (<em>Block</em>) – the input values</p>
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-<dl class="py function">
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-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">dot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_tf32</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.dot" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the matrix product of two blocks.</p>
-<p>The two blocks must be two dimensionals and have compatible inner dimensions.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>input</strong> (2D tensor of scalar-type in {<code class="code docutils literal notranslate"><span class="pre">float16</span></code>, <code class="code docutils literal notranslate"><span class="pre">bfloat16</span></code>, <code class="code docutils literal notranslate"><span class="pre">float32</span></code>}) – The first tensor to be multiplied.</p></li>
-<li><p><strong>other</strong> (2D tensor of scalar-type in {<code class="code docutils literal notranslate"><span class="pre">float16</span></code>, <code class="code docutils literal notranslate"><span class="pre">bfloat16</span></code>, <code class="code docutils literal notranslate"><span class="pre">float32</span></code>}) – The second tensor to be multiplied.</p></li>
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-<h1>triton.language.exp<a class="headerlink" href="#triton-language-exp" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.exp">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">exp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.exp" title="Permalink to this definition">¶</a></dt>
-<dd><p>Computes the element-wise exponential of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>x</strong> (<em>Block</em>) – the input values</p>
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-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.load">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pointer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_modifier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eviction_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">volatile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.load" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return a tensor of data whose values are, elementwise, loaded from memory at location defined by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p>
-<p><code class="code docutils literal notranslate"><span class="pre">mask</span></code> and <code class="code docutils literal notranslate"><span class="pre">other</span></code> are implicitly broadcast to <code class="code docutils literal notranslate"><span class="pre">pointer.shape</span></code>.</p>
-<p><code class="code docutils literal notranslate"><span class="pre">other</span></code> is implicitly typecast to <code class="code docutils literal notranslate"><span class="pre">pointer.dtype.element_ty</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>pointer</strong> (<em>Block of dtype=triton.PointerDType</em>) – Pointers to the data to be loaded.</p></li>
-<li><p><strong>mask</strong> (<em>Block of triton.int1</em><em>, </em><em>optional</em>) – if mask[idx] is false, do not load the data at address <code class="code docutils literal notranslate"><span class="pre">pointer[idx]</span></code>.</p></li>
-<li><p><strong>other</strong> (<em>Block</em><em>, </em><em>optional</em>) – if mask[idx] is false, return other[idx]</p></li>
-<li><p><strong>cache_modifier</strong> – changes cache option in nvidia ptx</p></li>
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-<h1>triton.language.log<a class="headerlink" href="#triton-language-log" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.log">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">log</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.log" title="Permalink to this definition">¶</a></dt>
-<dd><p>Computes the element-wise natural logarithm of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>x</strong> (<em>Block</em>) – the input values</p>
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-<h1>triton.language.max<a class="headerlink" href="#triton-language-max" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.max">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">max</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.max" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the maximum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> tensor along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>input</strong> – the input values</p></li>
-<li><p><strong>axis</strong> – the dimension along which the reduction should be done</p></li>
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-<h1>triton.language.maximum<a class="headerlink" href="#triton-language-maximum" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
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-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">maximum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.maximum" title="Permalink to this definition">¶</a></dt>
-<dd><p>Computes the element-wise maximum of <code class="code docutils literal notranslate"><span class="pre">x</span></code> and <code class="code docutils literal notranslate"><span class="pre">y</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>input</strong> (<em>Block</em>) – the first input tensor</p></li>
-<li><p><strong>other</strong> (<em>Block</em>) – the second input tensor</p></li>
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-<h1>triton.language.min<a class="headerlink" href="#triton-language-min" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.min">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">min</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.min" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the minimum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> tensor along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>input</strong> – the input values</p></li>
-<li><p><strong>axis</strong> – the dimension along which the reduction should be done</p></li>
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-<h1>triton.language.minimum<a class="headerlink" href="#triton-language-minimum" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.minimum">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">minimum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.minimum" title="Permalink to this definition">¶</a></dt>
-<dd><p>Computes the element-wise minimum of <code class="code docutils literal notranslate"><span class="pre">x</span></code> and <code class="code docutils literal notranslate"><span class="pre">y</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>input</strong> (<em>Block</em>) – the first input tensor</p></li>
-<li><p><strong>other</strong> (<em>Block</em>) – the second input tensor</p></li>
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-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">num_programs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.num_programs" title="Permalink to this definition">¶</a></dt>
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-<dd class="field-odd"><p><strong>axis</strong> (<em>int</em>) – The axis of the 3D launch grid. Has to be either 0, 1 or 2.</p>
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-<dl class="py function">
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-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">program_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.program_id" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the id of the current program instance along the given <code class="code docutils literal notranslate"><span class="pre">axis</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>axis</strong> (<em>int</em>) – The axis of the 3D launch grid. Has to be either 0, 1 or 2.</p>
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-<h1>triton.language.rand<a class="headerlink" href="#triton-language-rand" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
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-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">rand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_rounds</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">triton.language.core.constexpr</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.rand" title="Permalink to this definition">¶</a></dt>
-<dd><p>Given a <code class="code docutils literal notranslate"><span class="pre">seed</span></code> scalar and an <code class="code docutils literal notranslate"><span class="pre">offset</span></code> block,
-returns a block of random <code class="code docutils literal notranslate"><span class="pre">float32</span></code> in <span class="math notranslate nohighlight">\(U(0, 1)\)</span></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>seed</strong> – The seed for generating random numbers.</p></li>
-<li><p><strong>offsets</strong> – The offsets to generate random numbers for.</p></li>
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-<h1>triton.language.randint<a class="headerlink" href="#triton-language-randint" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.randint">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">randint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_rounds</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">triton.language.core.constexpr</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.randint" title="Permalink to this definition">¶</a></dt>
-<dd><p>Given a <code class="code docutils literal notranslate"><span class="pre">seed</span></code> scalar and an <code class="code docutils literal notranslate"><span class="pre">offset</span></code> block, returns a single
-block of random <code class="code docutils literal notranslate"><span class="pre">int32</span></code>.</p>
-<p>If you need multiple streams of random numbers,
-using <cite>randint4x</cite> is likely to be faster than calling <cite>randint</cite> 4 times.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>seed</strong> – The seed for generating random numbers.</p></li>
-<li><p><strong>offsets</strong> – The offsets to generate random numbers for.</p></li>
-</ul>
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-<h1>triton.language.randint4x<a class="headerlink" href="#triton-language-randint4x" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.randint4x">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">randint4x</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_rounds</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">triton.language.core.constexpr</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.randint4x" title="Permalink to this definition">¶</a></dt>
-<dd><p>Given a <code class="code docutils literal notranslate"><span class="pre">seed</span></code> scalar and an <code class="code docutils literal notranslate"><span class="pre">offset</span></code> block, returns four
-blocks of random <code class="code docutils literal notranslate"><span class="pre">int32</span></code>.</p>
-<p>This is the maximally efficient entry point
-to Triton’s Philox pseudo-random number generator.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>seed</strong> – The seed for generating random numbers.</p></li>
-<li><p><strong>offsets</strong> – The offsets to generate random numbers for.</p></li>
-</ul>
-</dd>
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-<h1>triton.language.randn<a class="headerlink" href="#triton-language-randn" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.randn">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">randn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_rounds</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">triton.language.core.constexpr</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.randn" title="Permalink to this definition">¶</a></dt>
-<dd><p>Given a <code class="code docutils literal notranslate"><span class="pre">seed</span></code> scalar and an <code class="code docutils literal notranslate"><span class="pre">offset</span></code> block,
-returns a block of random <code class="code docutils literal notranslate"><span class="pre">float32</span></code> in <span class="math notranslate nohighlight">\(\mathcal{N}(0, 1)\)</span></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>seed</strong> – The seed for generating random numbers.</p></li>
-<li><p><strong>offsets</strong> – The offsets to generate random numbers for.</p></li>
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-<h1>triton.language.ravel<a class="headerlink" href="#triton-language-ravel" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.ravel">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">ravel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.ravel" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a contiguous flattened view of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>x</strong> (<em>Block</em>) – the input tensor</p>
-</dd>
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-<dl class="py function">
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-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">reshape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.reshape" title="Permalink to this definition">¶</a></dt>
-<dd><p>Tries to reshape the given tensor to a new shape.</p>
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-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>input</strong> – The input tensor.</p></li>
-<li><p><strong>shape</strong> (<em>Tuple</em><em>[</em><em>int</em><em>]</em>) – The desired shape.</p></li>
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-<h1>triton.language.sigmoid<a class="headerlink" href="#triton-language-sigmoid" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.sigmoid">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">sigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.sigmoid" title="Permalink to this definition">¶</a></dt>
-<dd><p>Computes the element-wise sigmoid of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>x</strong> (<em>Block</em>) – the input values</p>
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-<h1>triton.language.sin<a class="headerlink" href="#triton-language-sin" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.sin">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">sin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.sin" title="Permalink to this definition">¶</a></dt>
-<dd><p>Computes the element-wise sine of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>x</strong> (<em>Block</em>) – the input values</p>
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-<h1>triton.language.softmax<a class="headerlink" href="#triton-language-softmax" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.softmax">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ieee_rounding</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">triton.language.core.constexpr</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.softmax" title="Permalink to this definition">¶</a></dt>
-<dd><p>Computes the element-wise softmax of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>x</strong> (<em>Block</em>) – the input values</p>
-</dd>
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-<h1>triton.language.sqrt<a class="headerlink" href="#triton-language-sqrt" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.sqrt">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">sqrt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.sqrt" title="Permalink to this definition">¶</a></dt>
-<dd><p>Computes the element-wise square root of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>x</strong> (<em>Block</em>) – the input values</p>
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-<h1>triton.language.store<a class="headerlink" href="#triton-language-store" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.store">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">store</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pointer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.store" title="Permalink to this definition">¶</a></dt>
-<dd><p>Stores <code class="code docutils literal notranslate"><span class="pre">value</span></code> tensor of elements in memory, element-wise, at the memory locations specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p>
-<p><code class="code docutils literal notranslate"><span class="pre">value</span></code> is implicitly broadcast to <code class="code docutils literal notranslate"><span class="pre">pointer.shape</span></code> and typecast to <code class="code docutils literal notranslate"><span class="pre">pointer.dtype.element_ty</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>pointer</strong> (<em>Block of dtype=triton.PointerDType</em>) – The memory locations where the elements of <code class="code docutils literal notranslate"><span class="pre">value</span></code> are stored.</p></li>
-<li><p><strong>value</strong> (<em>Block</em>) – The tensor of elements to be stored.</p></li>
-<li><p><strong>mask</strong> (<em>Block of triton.int1</em><em>, </em><em>optional</em>) – If mask[idx] is false, do not store <code class="code docutils literal notranslate"><span class="pre">value[idx]</span></code> at <code class="code docutils literal notranslate"><span class="pre">pointer[idx]</span></code>.</p></li>
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-<h1>triton.language.sum<a class="headerlink" href="#triton-language-sum" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.sum">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.sum" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns the sum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> tensor along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>input</strong> – the input values</p></li>
-<li><p><strong>axis</strong> – the dimension along which the reduction should be done</p></li>
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-<h1>triton.language.where<a class="headerlink" href="#triton-language-where" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.language.where">
-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">where</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">condition</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.where" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a tensor of elements from either <code class="code docutils literal notranslate"><span class="pre">x</span></code> or <code class="code docutils literal notranslate"><span class="pre">y</span></code>, depending on <code class="code docutils literal notranslate"><span class="pre">condition</span></code>.</p>
-<p>Note that <code class="code docutils literal notranslate"><span class="pre">x</span></code> and <code class="code docutils literal notranslate"><span class="pre">y</span></code> are always evaluated regardless of the value of <code class="code docutils literal notranslate"><span class="pre">condition</span></code>.</p>
-<p>If you want to avoid unintented memory operations, use the <code class="code docutils literal notranslate"><span class="pre">mask</span></code> arguments in <cite>triton.load</cite> and <cite>triton.store</cite> instead.</p>
-<p>The shape of <code class="code docutils literal notranslate"><span class="pre">x</span></code> and <code class="code docutils literal notranslate"><span class="pre">y</span></code> are both broadcast to the shape of <code class="code docutils literal notranslate"><span class="pre">condition</span></code>.
-<code class="code docutils literal notranslate"><span class="pre">x</span></code> and <code class="code docutils literal notranslate"><span class="pre">y</span></code> must have the data type.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>condition</strong> (<em>Block of triton.bool</em>) – When True (nonzero), yield x, otherwise yield y.</p></li>
-<li><p><strong>x</strong> – values selected at indices where condition is True.</p></li>
-<li><p><strong>y</strong> – values selected at indices where condition is False.</p></li>
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-<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">zeros</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.zeros" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a tensor filled with the scalar value 0 for the given <code class="code docutils literal notranslate"><span class="pre">shape</span></code> and <code class="code docutils literal notranslate"><span class="pre">dtype</span></code>.</p>
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-<li><p><strong>shape</strong> (<em>tuple of ints</em>) – Shape of the new array, e.g., (8, 16) or (8, )</p></li>
-<li><p><strong>dtype</strong> (<em>DType</em>) – Data-type of the new array, e.g., <code class="code docutils literal notranslate"><span class="pre">tl.float16</span></code></p></li>
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-<h1>triton.testing.Benchmark<a class="headerlink" href="#triton-testing-benchmark" title="Permalink to this headline">¶</a></h1>
-<dl class="py class">
-<dt class="sig sig-object py" id="triton.testing.Benchmark">
-<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">triton.testing.</span></span><span class="sig-name descname"><span class="pre">Benchmark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">styles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.testing.Benchmark" title="Permalink to this definition">¶</a></dt>
-<dd><p>This class is used by the <code class="code docutils literal notranslate"><span class="pre">perf_report</span></code> function to generate line plots with a concise API.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="triton.testing.Benchmark.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">styles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.testing.Benchmark.__init__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Constructor</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>x_names</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – Name of the arguments that should appear on the x axis of the plot. If the list contains more than one element, all the arguments are assumed to have the same value.</p></li>
-<li><p><strong>x_vals</strong> (<em>List</em><em>[</em><em>Any</em><em>]</em>) – List of values to use for the arguments in <code class="code docutils literal notranslate"><span class="pre">x_names</span></code>.</p></li>
-<li><p><strong>line_arg</strong> (<em>str</em>) – Argument name for which different values correspond to different lines in the plot.</p></li>
-<li><p><strong>line_vals</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of values to use for the arguments in <code class="code docutils literal notranslate"><span class="pre">line_arg</span></code>.</p></li>
-<li><p><strong>line_names</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – Label names for the different lines.</p></li>
-<li><p><strong>plot_name</strong> (<em>str</em>) – Name of the plot.</p></li>
-<li><p><strong>args</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of arguments to remain fixed throughout the benchmark.</p></li>
-<li><p><strong>xlabel</strong> (<em>str</em><em>, </em><em>optional</em>) – Label for the x axis of the plot.</p></li>
-<li><p><strong>ylabel</strong> (<em>str</em><em>, </em><em>optional</em>) – Label for the y axis of the plot.</p></li>
-<li><p><strong>x_log</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether the x axis should be log scale.</p></li>
-<li><p><strong>y_log</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether the y axis should be log scale.</p></li>
-</ul>
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-<td><p>Constructor</p></td>
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-<h1>triton.testing.do_bench<a class="headerlink" href="#triton-testing-do-bench" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.testing.do_bench">
-<span class="sig-prename descclassname"><span class="pre">triton.testing.</span></span><span class="sig-name descname"><span class="pre">do_bench</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warmup</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">percentiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.5,</span> <span class="pre">0.2,</span> <span class="pre">0.8]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_clocks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.testing.do_bench" title="Permalink to this definition">¶</a></dt>
-<dd><p>Benchmark the runtime of the provided function. By default, return the median runtime of <code class="code docutils literal notranslate"><span class="pre">fn</span></code> along with
-the 20-th and 80-th performance percentile.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>fn</strong> (<em>Callable</em>) – Function to benchmark</p></li>
-<li><p><strong>warmup</strong> (<em>int</em>) – Warmup time (in ms)</p></li>
-<li><p><strong>rep</strong> (<em>int</em>) – Repetition time (in ms)</p></li>
-<li><p><strong>grad_to_none</strong> (<em>torch.tensor</em><em>, </em><em>optional</em>) – Reset the gradient of the provided tensor to None</p></li>
-<li><p><strong>percentiles</strong> (<em>list</em><em>[</em><em>float</em><em>]</em>) – Performance percentile to return in addition to the median.</p></li>
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-<h1>triton.testing.perf_report<a class="headerlink" href="#triton-testing-perf-report" title="Permalink to this headline">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="triton.testing.perf_report">
-<span class="sig-prename descclassname"><span class="pre">triton.testing.</span></span><span class="sig-name descname"><span class="pre">perf_report</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">benchmarks</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.testing.perf_report" title="Permalink to this definition">¶</a></dt>
-<dd><p>Mark a function for benchmarking. The benchmark can then be executed by using the <code class="code docutils literal notranslate"><span class="pre">.run</span></code> method on the return value.</p>
-<dl class="field-list simple">
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-<h1>triton<a class="headerlink" href="#triton" title="Permalink to this headline">¶</a></h1>
-<table class="longtable docutils align-default">
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diff --git a/master/python-api/triton.language.html b/master/python-api/triton.language.html
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-            
-  <div class="section" id="triton-language">
-<h1>triton.language<a class="headerlink" href="#triton-language" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="programming-model">
-<h2>Programming Model<a class="headerlink" href="#programming-model" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.program_id.html#triton.language.program_id" title="triton.language.program_id"><code class="xref py py-obj docutils literal notranslate"><span class="pre">program_id</span></code></a></p></td>
-<td><p>Returns the id of the current program instance along the given <code class="code docutils literal notranslate"><span class="pre">axis</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.num_programs.html#triton.language.num_programs" title="triton.language.num_programs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_programs</span></code></a></p></td>
-<td><p>Returns the number of program instances launched along the given <code class="code docutils literal notranslate"><span class="pre">axis</span></code>.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="creation-ops">
-<h2>Creation Ops<a class="headerlink" href="#creation-ops" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.arange.html#triton.language.arange" title="triton.language.arange"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arange</span></code></a></p></td>
-<td><p>Returns contiguous values within the open interval [<code class="code docutils literal notranslate"><span class="pre">start</span></code>, <code class="code docutils literal notranslate"><span class="pre">end</span></code>).</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.zeros.html#triton.language.zeros" title="triton.language.zeros"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zeros</span></code></a></p></td>
-<td><p>Returns a tensor filled with the scalar value 0 for the given <code class="code docutils literal notranslate"><span class="pre">shape</span></code> and <code class="code docutils literal notranslate"><span class="pre">dtype</span></code>.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="shape-manipulation-ops">
-<h2>Shape Manipulation Ops<a class="headerlink" href="#shape-manipulation-ops" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.broadcast_to.html#triton.language.broadcast_to" title="triton.language.broadcast_to"><code class="xref py py-obj docutils literal notranslate"><span class="pre">broadcast_to</span></code></a></p></td>
-<td><p>Tries to broadcast the given tensor to a new <code class="code docutils literal notranslate"><span class="pre">shape</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.reshape.html#triton.language.reshape" title="triton.language.reshape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reshape</span></code></a></p></td>
-<td><p>Tries to reshape the given tensor to a new shape.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.ravel.html#triton.language.ravel" title="triton.language.ravel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ravel</span></code></a></p></td>
-<td><p>Returns a contiguous flattened view of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="linear-algebra-ops">
-<h2>Linear Algebra Ops<a class="headerlink" href="#linear-algebra-ops" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.dot.html#triton.language.dot" title="triton.language.dot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dot</span></code></a></p></td>
-<td><p>Returns the matrix product of two blocks.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="memory-ops">
-<h2>Memory Ops<a class="headerlink" href="#memory-ops" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.load.html#triton.language.load" title="triton.language.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a></p></td>
-<td><p>Return a tensor of data whose values are, elementwise, loaded from memory at location defined by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.store.html#triton.language.store" title="triton.language.store"><code class="xref py py-obj docutils literal notranslate"><span class="pre">store</span></code></a></p></td>
-<td><p>Stores <code class="code docutils literal notranslate"><span class="pre">value</span></code> tensor of elements in memory, element-wise, at the memory locations specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.atomic_cas.html#triton.language.atomic_cas" title="triton.language.atomic_cas"><code class="xref py py-obj docutils literal notranslate"><span class="pre">atomic_cas</span></code></a></p></td>
-<td><p>Performs an atomic compare-and-swap at the memory location specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.atomic_xchg.html#triton.language.atomic_xchg" title="triton.language.atomic_xchg"><code class="xref py py-obj docutils literal notranslate"><span class="pre">atomic_xchg</span></code></a></p></td>
-<td><p>Performs an atomic exchange at the memory location specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="indexing-ops">
-<h2>Indexing Ops<a class="headerlink" href="#indexing-ops" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.where.html#triton.language.where" title="triton.language.where"><code class="xref py py-obj docutils literal notranslate"><span class="pre">where</span></code></a></p></td>
-<td><p>Returns a tensor of elements from either <code class="code docutils literal notranslate"><span class="pre">x</span></code> or <code class="code docutils literal notranslate"><span class="pre">y</span></code>, depending on <code class="code docutils literal notranslate"><span class="pre">condition</span></code>.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="math-ops">
-<h2>Math Ops<a class="headerlink" href="#math-ops" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.exp.html#triton.language.exp" title="triton.language.exp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exp</span></code></a></p></td>
-<td><p>Computes the element-wise exponential of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.log.html#triton.language.log" title="triton.language.log"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log</span></code></a></p></td>
-<td><p>Computes the element-wise natural logarithm of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.cos.html#triton.language.cos" title="triton.language.cos"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cos</span></code></a></p></td>
-<td><p>Computes the element-wise cosine of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.sin.html#triton.language.sin" title="triton.language.sin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sin</span></code></a></p></td>
-<td><p>Computes the element-wise sine of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.sqrt.html#triton.language.sqrt" title="triton.language.sqrt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sqrt</span></code></a></p></td>
-<td><p>Computes the element-wise square root of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.sigmoid.html#triton.language.sigmoid" title="triton.language.sigmoid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sigmoid</span></code></a></p></td>
-<td><p>Computes the element-wise sigmoid of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.softmax.html#triton.language.softmax" title="triton.language.softmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">softmax</span></code></a></p></td>
-<td><p>Computes the element-wise softmax of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="reduction-ops">
-<h2>Reduction Ops<a class="headerlink" href="#reduction-ops" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.max.html#triton.language.max" title="triton.language.max"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max</span></code></a></p></td>
-<td><p>Returns the maximum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> tensor along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.min.html#triton.language.min" title="triton.language.min"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min</span></code></a></p></td>
-<td><p>Returns the minimum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> tensor along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.sum.html#triton.language.sum" title="triton.language.sum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sum</span></code></a></p></td>
-<td><p>Returns the sum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> tensor along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="atomic-ops">
-<h2>Atomic Ops<a class="headerlink" href="#atomic-ops" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.atomic_cas.html#triton.language.atomic_cas" title="triton.language.atomic_cas"><code class="xref py py-obj docutils literal notranslate"><span class="pre">atomic_cas</span></code></a></p></td>
-<td><p>Performs an atomic compare-and-swap at the memory location specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.atomic_add.html#triton.language.atomic_add" title="triton.language.atomic_add"><code class="xref py py-obj docutils literal notranslate"><span class="pre">atomic_add</span></code></a></p></td>
-<td><p>Performs an atomic add at the memory location specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.atomic_max.html#triton.language.atomic_max" title="triton.language.atomic_max"><code class="xref py py-obj docutils literal notranslate"><span class="pre">atomic_max</span></code></a></p></td>
-<td><p>Performs an atomic max at the memory location specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.atomic_min.html#triton.language.atomic_min" title="triton.language.atomic_min"><code class="xref py py-obj docutils literal notranslate"><span class="pre">atomic_min</span></code></a></p></td>
-<td><p>Performs an atomic min at the memory location specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="comparison-ops">
-<h2>Comparison ops<a class="headerlink" href="#comparison-ops" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.minimum.html#triton.language.minimum" title="triton.language.minimum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">minimum</span></code></a></p></td>
-<td><p>Computes the element-wise minimum of <code class="code docutils literal notranslate"><span class="pre">x</span></code> and <code class="code docutils literal notranslate"><span class="pre">y</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.maximum.html#triton.language.maximum" title="triton.language.maximum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">maximum</span></code></a></p></td>
-<td><p>Computes the element-wise maximum of <code class="code docutils literal notranslate"><span class="pre">x</span></code> and <code class="code docutils literal notranslate"><span class="pre">y</span></code>.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="random-number-generation">
-<span id="id1"></span><h2>Random Number Generation<a class="headerlink" href="#random-number-generation" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.randint4x.html#triton.language.randint4x" title="triton.language.randint4x"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randint4x</span></code></a></p></td>
-<td><p>Given a <code class="code docutils literal notranslate"><span class="pre">seed</span></code> scalar and an <code class="code docutils literal notranslate"><span class="pre">offset</span></code> block, returns four blocks of random <code class="code docutils literal notranslate"><span class="pre">int32</span></code>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.randint.html#triton.language.randint" title="triton.language.randint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randint</span></code></a></p></td>
-<td><p>Given a <code class="code docutils literal notranslate"><span class="pre">seed</span></code> scalar and an <code class="code docutils literal notranslate"><span class="pre">offset</span></code> block, returns a single block of random <code class="code docutils literal notranslate"><span class="pre">int32</span></code>.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.rand.html#triton.language.rand" title="triton.language.rand"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rand</span></code></a></p></td>
-<td><p>Given a <code class="code docutils literal notranslate"><span class="pre">seed</span></code> scalar and an <code class="code docutils literal notranslate"><span class="pre">offset</span></code> block, returns a block of random <code class="code docutils literal notranslate"><span class="pre">float32</span></code> in <span class="math notranslate nohighlight">\(U(0, 1)\)</span></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.randn.html#triton.language.randn" title="triton.language.randn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randn</span></code></a></p></td>
-<td><p>Given a <code class="code docutils literal notranslate"><span class="pre">seed</span></code> scalar and an <code class="code docutils literal notranslate"><span class="pre">offset</span></code> block, returns a block of random <code class="code docutils literal notranslate"><span class="pre">float32</span></code> in <span class="math notranslate nohighlight">\(\mathcal{N}(0, 1)\)</span></p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="section" id="compiler-hint-ops">
-<h2>Compiler Hint Ops<a class="headerlink" href="#compiler-hint-ops" title="Permalink to this headline">¶</a></h2>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.multiple_of.html#triton.language.multiple_of" title="triton.language.multiple_of"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multiple_of</span></code></a></p></td>
-<td><p>Let the compiler knows that the values in <code class="code docutils literal notranslate"><span class="pre">input</span></code> are all multiples of <code class="code docutils literal notranslate"><span class="pre">value</span></code>.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
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diff --git a/v1.1.2/.doctrees/getting-started/installation.doctree b/v1.1.2/.doctrees/getting-started/installation.doctree
index bf6019ca81cd2b3407f5a083af5e96701ce3ba16..a99ccda7b146de987ca80e4a3125fdcfb68f3365 100644
GIT binary patch
delta 21
ccmZ3ew@`0GAR|YnSy53zRblq#NX8SQ09I=U+yDRo

delta 21
ccmZ3ew@`0GAR|X=W?FJiVMf8`NX8SQ09Nz|-v9sr

diff --git a/v1.1.2/.doctrees/getting-started/tutorials/01-vector-add.doctree b/v1.1.2/.doctrees/getting-started/tutorials/01-vector-add.doctree
index 4753a21a20a3bb698c20d5bbbf271c918e0f60c5..efb3f726e62f175be862d0b244386bb900c66ab7 100644
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delta 102
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delta 102
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diff --git a/v1.1.2/.doctrees/getting-started/tutorials/02-fused-softmax.doctree b/v1.1.2/.doctrees/getting-started/tutorials/02-fused-softmax.doctree
index 83f2fe732c841f82818bb4ca361da9a15050ce3d..77ad19e625dbfc5b8fa35d602f9dd16d0f799544 100644
GIT binary patch
delta 481
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delta 481
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diff --git a/v1.1.2/.doctrees/getting-started/tutorials/03-matrix-multiplication.doctree b/v1.1.2/.doctrees/getting-started/tutorials/03-matrix-multiplication.doctree
index ad787b68f690bc4b027ee9761194a98c99cba235..1af0b6225f0f105252e5c7b0de05b66abd8363aa 100644
GIT binary patch
delta 2001
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diff --git a/v1.1.2/.doctrees/getting-started/tutorials/04-low-memory-dropout.doctree b/v1.1.2/.doctrees/getting-started/tutorials/04-low-memory-dropout.doctree
index e5625ab5ee5012a90534feb7f049eef641e08d36..c3a5c1f3e85b020bf2849dd887340fd2e0a670b0 100644
GIT binary patch
delta 48
ycmaDgo$1wdrVaLt9GPZCMFmxb*_+)NxpJ6|49zBo<emi63VEs^db3B~B3S^ah7cG4

delta 48
ycmaDgo$1wdrVaLt9I2UU$vK4?1)JR&xpJ5d4GkuT<emi63VEs^db3B~B3S^aE)WI)

diff --git a/v1.1.2/.doctrees/getting-started/tutorials/05-layer-norm.doctree b/v1.1.2/.doctrees/getting-started/tutorials/05-layer-norm.doctree
index 1dddfc1a2d77ebac56749a00271653098711dff7..abaf3ff74e46fa162ab7cb2dcf2864a78b8b36a9 100644
GIT binary patch
delta 2413
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delta 2413
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diff --git a/v1.1.2/_sources/getting-started/tutorials/01-vector-add.rst.txt b/v1.1.2/_sources/getting-started/tutorials/01-vector-add.rst.txt
index 4871595c0..ceabe9c16 100644
--- a/v1.1.2/_sources/getting-started/tutorials/01-vector-add.rst.txt
+++ b/v1.1.2/_sources/getting-started/tutorials/01-vector-add.rst.txt
@@ -231,7 +231,7 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
 
     vector-add-performance:
                size      Triton       Torch
-    0        4096.0    9.600000    9.600000
+    0        4096.0    8.000000    9.600000
     1        8192.0   19.200000   19.200000
     2       16384.0   38.400001   38.400001
     3       32768.0   76.800002   76.800002
@@ -240,7 +240,7 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
     6      262144.0  341.333321  341.333321
     7      524288.0  472.615390  472.615390
     8     1048576.0  614.400016  614.400016
-    9     2097152.0  722.823517  722.823517
+    9     2097152.0  722.823517  702.171410
     10    4194304.0  780.190482  780.190482
     11    8388608.0  812.429770  812.429770
     12   16777216.0  833.084721  833.084721
@@ -254,7 +254,7 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  48.227 seconds)
+   **Total running time of the script:** ( 1 minutes  52.927 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_01-vector-add.py:
diff --git a/v1.1.2/_sources/getting-started/tutorials/02-fused-softmax.rst.txt b/v1.1.2/_sources/getting-started/tutorials/02-fused-softmax.rst.txt
index 6fbfba470..20e09451e 100644
--- a/v1.1.2/_sources/getting-started/tutorials/02-fused-softmax.rst.txt
+++ b/v1.1.2/_sources/getting-started/tutorials/02-fused-softmax.rst.txt
@@ -286,17 +286,17 @@ We will then compare its performance against (1) :code:`torch.softmax` and (2) t
 
     softmax-performance:
               N      Triton  Torch (native)  Torch (jit)
-    0     256.0  512.000001      546.133347   190.511628
-    1     384.0  585.142862      558.545450   149.853661
-    2     512.0  655.360017      606.814814   154.566038
-    3     640.0  682.666684      640.000002   158.759699
+    0     256.0  512.000001      546.133347   188.321838
+    1     384.0  585.142862      585.142862   151.703707
+    2     512.0  655.360017      585.142849   154.566038
+    3     640.0  682.666684      640.000002   160.000000
     4     768.0  722.823517      664.216187   162.754967
     ..      ...         ...             ...          ...
-    93  12160.0  812.359066      405.755985   198.733401
-    94  12288.0  814.111783      415.661740   199.096718
-    95  12416.0  812.498981      412.149375   198.755369
-    96  12544.0  812.566838      412.546756   198.815254
-    97  12672.0  812.633240      411.888249   198.971549
+    93  12160.0  812.359066      406.179533   198.530610
+    94  12288.0  814.111783      415.661740   198.895304
+    95  12416.0  814.163950      412.149375   198.358474
+    96  12544.0  812.566838      412.971190   198.716830
+    97  12672.0  812.633240      412.097543   198.776477
 
     [98 rows x 4 columns]
 
@@ -314,7 +314,7 @@ In the above plot, we can see that:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 3 minutes  25.246 seconds)
+   **Total running time of the script:** ( 3 minutes  27.294 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py:
diff --git a/v1.1.2/_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt b/v1.1.2/_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt
index 02c9771bb..51ce0c1ee 100644
--- a/v1.1.2/_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt
+++ b/v1.1.2/_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt
@@ -463,36 +463,36 @@ We can now compare the performance of our kernel against that of cuBLAS. Here we
     matmul-performance:
              M     cuBLAS  ...     Triton  Triton (+ LeakyReLU)
     0    256.0   2.730667  ...   2.978909              2.978909
-    1    384.0   7.372800  ...   8.507077              8.507077
+    1    384.0   7.372800  ...   8.507077              7.899428
     2    512.0  14.563555  ...  16.384000             16.384000
     3    640.0  22.260869  ...  24.380953             24.380953
     4    768.0  32.768000  ...  34.028308             34.028308
-    5    896.0  37.971025  ...  39.025776             37.971025
-    6   1024.0  49.932191  ...  52.428801             52.428801
+    5    896.0  39.025776  ...  40.140799             39.025776
+    6   1024.0  51.150050  ...  53.773130             52.428801
     7   1152.0  45.242181  ...  46.656000             46.656000
     8   1280.0  51.200001  ...  56.888887             56.888887
     9   1408.0  64.138541  ...  67.305878             66.485074
     10  1536.0  80.430545  ...  79.526831             78.643199
-    11  1664.0  63.372618  ...  62.492442             62.061463
-    12  1792.0  72.983276  ...  72.047592             71.588687
-    13  1920.0  68.776119  ...  70.530615             70.530615
+    11  1664.0  62.929456  ...  62.492442             62.061463
+    12  1792.0  72.983276  ...  59.154861             71.588687
+    13  1920.0  69.120002  ...  70.172588             70.172588
     14  2048.0  73.908442  ...  76.959706             76.608294
     15  2176.0  83.155572  ...  85.998493             85.269692
-    16  2304.0  68.446623  ...  76.809875             76.563695
-    17  2432.0  71.305746  ...  83.366361             84.877538
-    18  2560.0  77.833728  ...  81.512437             81.310171
-    19  2688.0  84.295681  ...  90.102270             89.254248
-    20  2816.0  84.605647  ...  83.873477             82.602666
-    21  2944.0  81.431424  ...  82.646820             82.921853
-    22  3072.0  81.589488  ...  88.612060             88.612060
-    23  3200.0  84.044651  ...  94.814812             91.298148
-    24  3328.0  83.808259  ...  84.895397             85.500351
-    25  3456.0  81.932484  ...  86.876687             85.676480
-    26  3584.0  87.381330  ...  98.808123             92.983857
-    27  3712.0  81.415926  ...  87.937800             85.822459
-    28  3840.0  82.654712  ...  92.468225             85.201850
-    29  3968.0  92.652949  ...  80.147087             77.377257
-    30  4096.0  91.867031  ...  93.336389             89.181212
+    16  2304.0  68.251065  ...  77.057651             76.809875
+    17  2432.0  71.305746  ...  74.918570             84.877538
+    18  2560.0  77.833728  ...  81.310171             80.908642
+    19  2688.0  83.922689  ...  90.102270             89.464755
+    20  2816.0  83.074685  ...  83.074685             82.916747
+    21  2944.0  82.646820  ...  81.564701             82.921853
+    22  3072.0  81.589488  ...  88.473602             87.516392
+    23  3200.0  80.706181  ...  93.704243             95.665176
+    24  3328.0  82.796202  ...  83.857070             84.895397
+    25  3456.0  81.766291  ...  91.304157             91.719645
+    26  3584.0  87.551500  ...  96.891584             98.160909
+    27  3712.0  85.601834  ...  91.397382             87.399253
+    28  3840.0  84.874902  ...  90.798032             87.910967
+    29  3968.0  91.954739  ...  90.859224             85.871877
+    30  4096.0  93.401342  ...  92.372834             90.200084
 
     [31 rows x 5 columns]
 
@@ -502,7 +502,7 @@ We can now compare the performance of our kernel against that of cuBLAS. Here we
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  57.968 seconds)
+   **Total running time of the script:** ( 6 minutes  12.411 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:
diff --git a/v1.1.2/_sources/getting-started/tutorials/04-low-memory-dropout.rst.txt b/v1.1.2/_sources/getting-started/tutorials/04-low-memory-dropout.rst.txt
index fd1f257c9..dfdd90472 100644
--- a/v1.1.2/_sources/getting-started/tutorials/04-low-memory-dropout.rst.txt
+++ b/v1.1.2/_sources/getting-started/tutorials/04-low-memory-dropout.rst.txt
@@ -238,7 +238,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.110 seconds)
+   **Total running time of the script:** ( 0 minutes  0.216 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_04-low-memory-dropout.py:
diff --git a/v1.1.2/_sources/getting-started/tutorials/05-layer-norm.rst.txt b/v1.1.2/_sources/getting-started/tutorials/05-layer-norm.rst.txt
index 3be1d436c..eed294f0e 100644
--- a/v1.1.2/_sources/getting-started/tutorials/05-layer-norm.rst.txt
+++ b/v1.1.2/_sources/getting-started/tutorials/05-layer-norm.rst.txt
@@ -38,36 +38,36 @@ Layer Normalization
 
     layer-norm-backward:
               N      Triton       Torch        Apex
-    0    1024.0  311.088617   97.912354  303.407414
-    1    1536.0  351.085717  134.540150  341.333333
-    2    2048.0  427.408686  161.154101  334.367350
-    3    2560.0  465.454542  181.238943  330.322572
-    4    3072.0  515.580429  192.501302  323.368415
-    5    3584.0  551.384634  208.271186  311.652167
-    6    4096.0  568.231237  220.907859  297.890900
-    7    4608.0  498.162157  232.825259  287.251954
-    8    5120.0  525.128191  242.366855  285.104413
-    9    5632.0  540.671974  243.107920  290.060087
-    10   6144.0  544.118087  248.661056  286.879370
-    11   6656.0  528.953642  255.590406  285.767438
-    12   7168.0  507.469040  260.260201  284.821192
-    13   7680.0  485.052616  262.938666  280.121579
-    14   8192.0  460.440290  266.406514  284.115618
-    15   8704.0  416.127506  267.815384  284.987724
-    16   9216.0  429.483477  271.391419  288.375482
-    17   9728.0  437.213490  280.615388  290.027323
-    18  10240.0  446.025405  286.433562  290.153487
-    19  10752.0  429.721875  246.935876  290.267711
-    20  11264.0  429.786952  245.536784  286.980888
-    21  11776.0  423.724129  249.667843  289.277383
-    22  12288.0  419.504980  254.673582  294.617366
-    23  12800.0  414.016170  253.884294  289.811310
-    24  13312.0  412.242569  252.959629  290.707920
-    25  13824.0  406.090579  257.390218  292.056329
-    26  14336.0  396.387109  254.862216  287.198654
-    27  14848.0  386.918555  257.852379  289.717061
-    28  15360.0  376.163261  257.970599  287.775181
-    29  15872.0  368.046389  261.626369  290.341468
+    0    1024.0  311.088617   99.497980  311.088617
+    1    1536.0  351.085717  134.050910  341.333333
+    2    2048.0  423.724127  159.067963  332.108094
+    3    2560.0  465.454542  181.775141  330.322572
+    4    3072.0  511.999982  191.501303  313.736171
+    5    3584.0  551.384634  207.267476  310.527060
+    6    4096.0  568.231237  218.939860  298.796351
+    7    4608.0  498.162157  233.316456  289.507855
+    8    5120.0  527.381977  240.469672  285.104413
+    9    5632.0  542.843364  244.426754  290.683877
+    10   6144.0  544.118087  250.349744  287.438593
+    11   6656.0  532.479975  255.590406  286.279570
+    12   7168.0  513.528374  257.147993  282.019678
+    13   7680.0  486.332448  263.690977  282.699379
+    14   8192.0  464.794337  262.143998  282.077471
+    15   8704.0  416.127506  266.789264  285.377055
+    16   9216.0  430.319054  272.394084  289.129410
+    17   9728.0  438.857162  280.278512  289.308559
+    18  10240.0  447.650282  286.767793  288.112552
+    19  10752.0  430.079980  246.935876  290.267711
+    20  11264.0  427.746848  245.983625  288.204696
+    21  11776.0  421.826879  249.227509  288.686414
+    22  12288.0  421.302872  254.673582  295.207195
+    23  12800.0  415.135142  253.465340  290.359162
+    24  13312.0  411.181478  252.959629  290.443638
+    25  13824.0  405.594132  257.190689  292.056329
+    26  14336.0  394.568805  254.485198  288.402346
+    27  14848.0  384.829370  256.922861  289.246765
+    28  15360.0  374.253788  257.970599  287.550706
+    29  15872.0  366.982663  262.347108  291.006885
 
 
 
@@ -329,7 +329,7 @@ Layer Normalization
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  13.534 seconds)
+   **Total running time of the script:** ( 2 minutes  14.015 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_05-layer-norm.py:
diff --git a/v1.1.2/_sources/getting-started/tutorials/sg_execution_times.rst.txt b/v1.1.2/_sources/getting-started/tutorials/sg_execution_times.rst.txt
index 29b309b36..b2a8606d2 100644
--- a/v1.1.2/_sources/getting-started/tutorials/sg_execution_times.rst.txt
+++ b/v1.1.2/_sources/getting-started/tutorials/sg_execution_times.rst.txt
@@ -5,16 +5,16 @@
 
 Computation times
 =================
-**13:25.084** total execution time for **getting-started_tutorials** files:
+**13:46.864** total execution time for **getting-started_tutorials** files:
 
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 05:57.968 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:12.411 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:25.246 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``)                 | 03:27.294 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 02:13.534 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``)                       | 02:14.015 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 01:48.227 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``)                       | 01:52.927 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py` (``04-low-memory-dropout.py``)       | 00:00.110 | 0.0 MB |
+| :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py` (``04-low-memory-dropout.py``)       | 00:00.216 | 0.0 MB |
 +---------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/v1.1.2/getting-started/tutorials/01-vector-add.html b/v1.1.2/getting-started/tutorials/01-vector-add.html
index 6b1b4cacc..6ff16390c 100644
--- a/v1.1.2/getting-started/tutorials/01-vector-add.html
+++ b/v1.1.2/getting-started/tutorials/01-vector-add.html
@@ -321,7 +321,7 @@ for different problem sizes.</p>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vector-add-performance:
            size      Triton       Torch
-0        4096.0    9.600000    9.600000
+0        4096.0    8.000000    9.600000
 1        8192.0   19.200000   19.200000
 2       16384.0   38.400001   38.400001
 3       32768.0   76.800002   76.800002
@@ -330,7 +330,7 @@ for different problem sizes.</p>
 6      262144.0  341.333321  341.333321
 7      524288.0  472.615390  472.615390
 8     1048576.0  614.400016  614.400016
-9     2097152.0  722.823517  722.823517
+9     2097152.0  722.823517  702.171410
 10    4194304.0  780.190482  780.190482
 11    8388608.0  812.429770  812.429770
 12   16777216.0  833.084721  833.084721
@@ -339,7 +339,7 @@ for different problem sizes.</p>
 15  134217728.0  849.737435  850.656574
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  48.227 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  52.927 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-01-vector-add-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/62d97d49a32414049819dd8bb8378080/01-vector-add.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">01-vector-add.py</span></code></a></p>
diff --git a/v1.1.2/getting-started/tutorials/02-fused-softmax.html b/v1.1.2/getting-started/tutorials/02-fused-softmax.html
index df7eaa824..103385b24 100644
--- a/v1.1.2/getting-started/tutorials/02-fused-softmax.html
+++ b/v1.1.2/getting-started/tutorials/02-fused-softmax.html
@@ -374,17 +374,17 @@ We will then compare its performance against (1) <code class="code docutils lite
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>softmax-performance:
           N      Triton  Torch (native)  Torch (jit)
-0     256.0  512.000001      546.133347   190.511628
-1     384.0  585.142862      558.545450   149.853661
-2     512.0  655.360017      606.814814   154.566038
-3     640.0  682.666684      640.000002   158.759699
+0     256.0  512.000001      546.133347   188.321838
+1     384.0  585.142862      585.142862   151.703707
+2     512.0  655.360017      585.142849   154.566038
+3     640.0  682.666684      640.000002   160.000000
 4     768.0  722.823517      664.216187   162.754967
 ..      ...         ...             ...          ...
-93  12160.0  812.359066      405.755985   198.733401
-94  12288.0  814.111783      415.661740   199.096718
-95  12416.0  812.498981      412.149375   198.755369
-96  12544.0  812.566838      412.546756   198.815254
-97  12672.0  812.633240      411.888249   198.971549
+93  12160.0  812.359066      406.179533   198.530610
+94  12288.0  814.111783      415.661740   198.895304
+95  12416.0  814.163950      412.149375   198.358474
+96  12544.0  812.566838      412.971190   198.716830
+97  12672.0  812.633240      412.097543   198.776477
 
 [98 rows x 4 columns]
 </pre></div>
@@ -397,7 +397,7 @@ We will then compare its performance against (1) <code class="code docutils lite
 Note however that the PyTorch <cite>softmax</cite> operation is more general and will works on tensors of any shape.</p></li>
 </ul>
 </div></blockquote>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  25.246 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  27.294 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-02-fused-softmax-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/d91442ac2982c4e0cc3ab0f43534afbc/02-fused-softmax.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">02-fused-softmax.py</span></code></a></p>
diff --git a/v1.1.2/getting-started/tutorials/03-matrix-multiplication.html b/v1.1.2/getting-started/tutorials/03-matrix-multiplication.html
index 5d932603d..fe25651d5 100644
--- a/v1.1.2/getting-started/tutorials/03-matrix-multiplication.html
+++ b/v1.1.2/getting-started/tutorials/03-matrix-multiplication.html
@@ -569,41 +569,41 @@ torch_output=tensor([[  1.1045, -36.9688,  31.4688,  ..., -11.3906,  24.4531, -3
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>matmul-performance:
          M     cuBLAS  ...     Triton  Triton (+ LeakyReLU)
 0    256.0   2.730667  ...   2.978909              2.978909
-1    384.0   7.372800  ...   8.507077              8.507077
+1    384.0   7.372800  ...   8.507077              7.899428
 2    512.0  14.563555  ...  16.384000             16.384000
 3    640.0  22.260869  ...  24.380953             24.380953
 4    768.0  32.768000  ...  34.028308             34.028308
-5    896.0  37.971025  ...  39.025776             37.971025
-6   1024.0  49.932191  ...  52.428801             52.428801
+5    896.0  39.025776  ...  40.140799             39.025776
+6   1024.0  51.150050  ...  53.773130             52.428801
 7   1152.0  45.242181  ...  46.656000             46.656000
 8   1280.0  51.200001  ...  56.888887             56.888887
 9   1408.0  64.138541  ...  67.305878             66.485074
 10  1536.0  80.430545  ...  79.526831             78.643199
-11  1664.0  63.372618  ...  62.492442             62.061463
-12  1792.0  72.983276  ...  72.047592             71.588687
-13  1920.0  68.776119  ...  70.530615             70.530615
+11  1664.0  62.929456  ...  62.492442             62.061463
+12  1792.0  72.983276  ...  59.154861             71.588687
+13  1920.0  69.120002  ...  70.172588             70.172588
 14  2048.0  73.908442  ...  76.959706             76.608294
 15  2176.0  83.155572  ...  85.998493             85.269692
-16  2304.0  68.446623  ...  76.809875             76.563695
-17  2432.0  71.305746  ...  83.366361             84.877538
-18  2560.0  77.833728  ...  81.512437             81.310171
-19  2688.0  84.295681  ...  90.102270             89.254248
-20  2816.0  84.605647  ...  83.873477             82.602666
-21  2944.0  81.431424  ...  82.646820             82.921853
-22  3072.0  81.589488  ...  88.612060             88.612060
-23  3200.0  84.044651  ...  94.814812             91.298148
-24  3328.0  83.808259  ...  84.895397             85.500351
-25  3456.0  81.932484  ...  86.876687             85.676480
-26  3584.0  87.381330  ...  98.808123             92.983857
-27  3712.0  81.415926  ...  87.937800             85.822459
-28  3840.0  82.654712  ...  92.468225             85.201850
-29  3968.0  92.652949  ...  80.147087             77.377257
-30  4096.0  91.867031  ...  93.336389             89.181212
+16  2304.0  68.251065  ...  77.057651             76.809875
+17  2432.0  71.305746  ...  74.918570             84.877538
+18  2560.0  77.833728  ...  81.310171             80.908642
+19  2688.0  83.922689  ...  90.102270             89.464755
+20  2816.0  83.074685  ...  83.074685             82.916747
+21  2944.0  82.646820  ...  81.564701             82.921853
+22  3072.0  81.589488  ...  88.473602             87.516392
+23  3200.0  80.706181  ...  93.704243             95.665176
+24  3328.0  82.796202  ...  83.857070             84.895397
+25  3456.0  81.766291  ...  91.304157             91.719645
+26  3584.0  87.551500  ...  96.891584             98.160909
+27  3712.0  85.601834  ...  91.397382             87.399253
+28  3840.0  84.874902  ...  90.798032             87.910967
+29  3968.0  91.954739  ...  90.859224             85.871877
+30  4096.0  93.401342  ...  92.372834             90.200084
 
 [31 rows x 5 columns]
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes  57.968 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 6 minutes  12.411 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-03-matrix-multiplication-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/d5fee5b55a64e47f1b5724ec39adf171/03-matrix-multiplication.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">03-matrix-multiplication.py</span></code></a></p>
diff --git a/v1.1.2/getting-started/tutorials/04-low-memory-dropout.html b/v1.1.2/getting-started/tutorials/04-low-memory-dropout.html
index 996428616..a98614650 100644
--- a/v1.1.2/getting-started/tutorials/04-low-memory-dropout.html
+++ b/v1.1.2/getting-started/tutorials/04-low-memory-dropout.html
@@ -371,7 +371,7 @@ to explore the <cite>triton/language/random</cite> folder!</p>
 <dd><p>Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, JMLR 2014</p>
 </dd>
 </dl>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.110 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.216 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-04-low-memory-dropout-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/c9aed78977a4c05741d675a38dde3d7d/04-low-memory-dropout.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">04-low-memory-dropout.py</span></code></a></p>
diff --git a/v1.1.2/getting-started/tutorials/05-layer-norm.html b/v1.1.2/getting-started/tutorials/05-layer-norm.html
index f4cb9349b..5da7ad744 100644
--- a/v1.1.2/getting-started/tutorials/05-layer-norm.html
+++ b/v1.1.2/getting-started/tutorials/05-layer-norm.html
@@ -194,36 +194,36 @@ to download the full example code</p>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>layer-norm-backward:
           N      Triton       Torch        Apex
-0    1024.0  311.088617   97.912354  303.407414
-1    1536.0  351.085717  134.540150  341.333333
-2    2048.0  427.408686  161.154101  334.367350
-3    2560.0  465.454542  181.238943  330.322572
-4    3072.0  515.580429  192.501302  323.368415
-5    3584.0  551.384634  208.271186  311.652167
-6    4096.0  568.231237  220.907859  297.890900
-7    4608.0  498.162157  232.825259  287.251954
-8    5120.0  525.128191  242.366855  285.104413
-9    5632.0  540.671974  243.107920  290.060087
-10   6144.0  544.118087  248.661056  286.879370
-11   6656.0  528.953642  255.590406  285.767438
-12   7168.0  507.469040  260.260201  284.821192
-13   7680.0  485.052616  262.938666  280.121579
-14   8192.0  460.440290  266.406514  284.115618
-15   8704.0  416.127506  267.815384  284.987724
-16   9216.0  429.483477  271.391419  288.375482
-17   9728.0  437.213490  280.615388  290.027323
-18  10240.0  446.025405  286.433562  290.153487
-19  10752.0  429.721875  246.935876  290.267711
-20  11264.0  429.786952  245.536784  286.980888
-21  11776.0  423.724129  249.667843  289.277383
-22  12288.0  419.504980  254.673582  294.617366
-23  12800.0  414.016170  253.884294  289.811310
-24  13312.0  412.242569  252.959629  290.707920
-25  13824.0  406.090579  257.390218  292.056329
-26  14336.0  396.387109  254.862216  287.198654
-27  14848.0  386.918555  257.852379  289.717061
-28  15360.0  376.163261  257.970599  287.775181
-29  15872.0  368.046389  261.626369  290.341468
+0    1024.0  311.088617   99.497980  311.088617
+1    1536.0  351.085717  134.050910  341.333333
+2    2048.0  423.724127  159.067963  332.108094
+3    2560.0  465.454542  181.775141  330.322572
+4    3072.0  511.999982  191.501303  313.736171
+5    3584.0  551.384634  207.267476  310.527060
+6    4096.0  568.231237  218.939860  298.796351
+7    4608.0  498.162157  233.316456  289.507855
+8    5120.0  527.381977  240.469672  285.104413
+9    5632.0  542.843364  244.426754  290.683877
+10   6144.0  544.118087  250.349744  287.438593
+11   6656.0  532.479975  255.590406  286.279570
+12   7168.0  513.528374  257.147993  282.019678
+13   7680.0  486.332448  263.690977  282.699379
+14   8192.0  464.794337  262.143998  282.077471
+15   8704.0  416.127506  266.789264  285.377055
+16   9216.0  430.319054  272.394084  289.129410
+17   9728.0  438.857162  280.278512  289.308559
+18  10240.0  447.650282  286.767793  288.112552
+19  10752.0  430.079980  246.935876  290.267711
+20  11264.0  427.746848  245.983625  288.204696
+21  11776.0  421.826879  249.227509  288.686414
+22  12288.0  421.302872  254.673582  295.207195
+23  12800.0  415.135142  253.465340  290.359162
+24  13312.0  411.181478  252.959629  290.443638
+25  13824.0  405.594132  257.190689  292.056329
+26  14336.0  394.568805  254.485198  288.402346
+27  14848.0  384.829370  256.922861  289.246765
+28  15360.0  374.253788  257.970599  287.550706
+29  15872.0  366.982663  262.347108  291.006885
 </pre></div>
 </div>
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@@ -477,7 +477,7 @@ to download the full example code</p>
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