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Philippe Tillet
2021-09-03 05:18:24 +00:00
parent 5f3e8dd5be
commit 40a2ed1638
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_downloads/62d97d49a32414049819dd8bb8378080/01-vector-add.py
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@@ -43,7 +43,7 @@ def add_kernel(
y = tl.load(y_ptr + offsets, mask=mask) y = tl.load(y_ptr + offsets, mask=mask)
output = x + y output = x + y
# Write x + y back to DRAM # Write x + y back to DRAM
tl.store(output_ptr + offsets, output) tl.store(output_ptr + offsets, output, mask=mask)
# %% # %%

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{
"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\nimport triton\nimport triton.language as tl\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 **meta,\n):\n BLOCK_SIZE = meta['BLOCK_SIZE']\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# 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 **meta,\n):\n # compute memory offsets of elements handled by this instance\n BLOCK_SIZE = meta['BLOCK_SIZE']\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",
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"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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@@ -0,0 +1,164 @@
"""
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
**meta,
):
BLOCK_SIZE = meta['BLOCK_SIZE']
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,
**meta,
):
# compute memory offsets of elements handled by this instance
BLOCK_SIZE = meta['BLOCK_SIZE']
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

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@@ -33,7 +33,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"import torch\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 **meta, # Optional meta-parameters for the kernel\n):\n BLOCK_SIZE = meta['BLOCK_SIZE'] # How many inputs each program should process\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 extar 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)" "import torch\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 **meta, # Optional meta-parameters for the kernel\n):\n BLOCK_SIZE = meta['BLOCK_SIZE'] # How many inputs each program should process\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 extar 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)"
] ]
}, },
{ {

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10
_sources/getting-started/tutorials/01-vector-add.rst.txt
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@@ -67,7 +67,7 @@ Compute Kernel
y = tl.load(y_ptr + offsets, mask=mask) y = tl.load(y_ptr + offsets, mask=mask)
output = x + y output = x + y
# Write x + y back to DRAM # Write x + y back to DRAM
tl.store(output_ptr + offsets, output) tl.store(output_ptr + offsets, output, mask=mask)
@@ -231,16 +231,16 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
vector-add-performance: vector-add-performance:
size Triton Torch size Triton Torch
0 4096.0 8.000000 9.600000 0 4096.0 9.600000 9.600000
1 8192.0 19.200000 19.200000 1 8192.0 19.200000 19.200000
2 16384.0 38.400001 38.400001 2 16384.0 38.400001 38.400001
3 32768.0 76.800002 76.800002 3 32768.0 76.800002 76.800002
4 65536.0 127.999995 127.999995 4 65536.0 127.999995 127.999995
5 131072.0 219.428568 219.428568 5 131072.0 219.428568 219.428568
6 262144.0 384.000001 341.333321 6 262144.0 341.333321 384.000001
7 524288.0 472.615390 472.615390 7 524288.0 472.615390 472.615390
8 1048576.0 614.400016 614.400016 8 1048576.0 614.400016 614.400016
9 2097152.0 722.823517 722.823517 9 2097152.0 702.171410 722.823517
10 4194304.0 780.190482 780.190482 10 4194304.0 780.190482 780.190482
11 8388608.0 812.429770 812.429770 11 8388608.0 812.429770 812.429770
12 16777216.0 833.084721 833.084721 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 .. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 11.053 seconds) **Total running time of the script:** ( 0 minutes 10.972 seconds)
.. _sphx_glr_download_getting-started_tutorials_01-vector-add.py: .. _sphx_glr_download_getting-started_tutorials_01-vector-add.py:

4
_sources/getting-started/tutorials/02-fused-softmax.rst.txt
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@@ -310,7 +310,7 @@ We will then compare its performance against (1) :code:`torch.softmax` and (2) t
94 12288.0 812.429770 415.661740 199.298541 94 12288.0 812.429770 415.661740 199.298541
95 12416.0 810.840807 412.149375 198.954424 95 12416.0 810.840807 412.149375 198.954424
96 12544.0 810.925276 412.971190 199.209928 96 12544.0 810.925276 412.971190 199.209928
97 12672.0 811.007961 412.097543 199.167004 97 12672.0 811.007961 412.097543 199.264875
[98 rows x 4 columns] [98 rows x 4 columns]
@@ -328,7 +328,7 @@ In the above plot, we can see that:
.. rst-class:: sphx-glr-timing .. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 1 minutes 13.131 seconds) **Total running time of the script:** ( 1 minutes 12.586 seconds)
.. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py: .. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py:

52
_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt
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@@ -462,37 +462,37 @@ We can now compare the performance of our kernel against that of cuBLAS. Here we
matmul-performance: matmul-performance:
M cuBLAS ... Triton Triton (+ LeakyReLU) M cuBLAS ... Triton Triton (+ LeakyReLU)
0 256.0 2.978909 ... 2.978909 2.978909 0 256.0 2.978909 ... 3.276800 3.276800
1 384.0 7.372800 ... 8.507077 8.507077 1 384.0 7.372800 ... 8.507077 8.507077
2 512.0 14.563555 ... 16.384000 16.384000 2 512.0 14.563555 ... 16.384000 16.384000
3 640.0 22.260869 ... 24.380953 24.380953 3 640.0 22.260869 ... 24.380953 24.380953
4 768.0 32.768000 ... 34.028308 34.028308 4 768.0 32.768000 ... 35.389441 34.028308
5 896.0 39.025776 ... 40.140799 39.025776 5 896.0 39.025776 ... 40.140799 39.025776
6 1024.0 49.932191 ... 53.773130 52.428801 6 1024.0 49.932191 ... 52.428801 52.428801
7 1152.0 44.566925 ... 46.656000 46.656000 7 1152.0 44.566925 ... 46.656000 46.656000
8 1280.0 51.200001 ... 56.888887 56.888887 8 1280.0 51.200001 ... 56.888887 56.888887
9 1408.0 64.138541 ... 63.392744 63.392744 9 1408.0 64.138541 ... 63.392744 57.368243
10 1536.0 78.643199 ... 76.106321 76.106321 10 1536.0 79.526831 ... 75.296679 75.296679
11 1664.0 63.372618 ... 62.061463 62.061463 11 1664.0 62.929456 ... 61.217089 61.636381
12 1792.0 72.983276 ... 62.790080 62.441243 12 1792.0 72.983276 ... 62.441243 62.441243
13 1920.0 69.467336 ... 67.106797 69.818184 13 1920.0 68.776119 ... 70.172588 69.818184
14 2048.0 73.908442 ... 74.898285 74.565406 14 2048.0 73.584279 ... 74.565406 74.565406
15 2176.0 83.155572 ... 81.472263 81.143743 15 2176.0 83.155572 ... 80.494588 80.494588
16 2304.0 68.446623 ... 73.501144 73.275679 16 2304.0 68.251065 ... 73.275679 73.275679
17 2432.0 71.125224 ... 81.197876 82.147552 17 2432.0 71.125224 ... 70.766913 80.041209
18 2560.0 77.649287 ... 76.920185 77.465723 18 2560.0 77.649287 ... 76.740048 76.027843
19 2688.0 81.053536 ... 83.737433 80.537273 19 2688.0 83.922689 ... 80.880718 83.186525
20 2816.0 82.135981 ... 78.301990 79.733474 20 2816.0 83.552120 ... 78.868366 78.442822
21 2944.0 80.510553 ... 78.605729 76.435630 21 2944.0 82.102191 ... 77.385141 77.990663
22 3072.0 81.472093 ... 83.638266 84.386148 22 3072.0 79.415291 ... 81.238312 83.146995
23 3200.0 84.656085 ... 86.956520 89.635851 23 3200.0 84.321474 ... 89.012517 89.761569
24 3328.0 81.530349 ... 84.596116 86.632127 24 3328.0 83.226931 ... 85.500351 87.051143
25 3456.0 81.683457 ... 84.068369 83.980802 25 3456.0 78.655188 ... 80.300370 83.632331
26 3584.0 87.211821 ... 87.466332 91.099693 26 3584.0 85.879071 ... 91.470385 93.661869
27 3712.0 85.896254 ... 83.596102 85.822459 27 3712.0 85.822459 ... 84.802499 88.876645
28 3840.0 84.421376 ... 86.197974 86.130841 28 3840.0 85.136259 ... 87.424508 88.121115
29 3968.0 92.442373 ... 87.913500 87.787005 29 3968.0 92.864488 ... 87.284643 87.597943
30 4096.0 93.596744 ... 89.240508 89.062862 30 4096.0 93.466385 ... 90.504200 89.898012
[31 rows x 5 columns] [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 .. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 2 minutes 14.737 seconds) **Total running time of the script:** ( 2 minutes 20.017 seconds)
.. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py: .. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:

269
_sources/getting-started/tutorials/04-low-memory-dropout.rst.txt Normal file
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@@ -0,0 +1,269 @@
.. 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 29-80
.. 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
**meta,
):
BLOCK_SIZE = meta['BLOCK_SIZE']
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 81-99
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 99-147
.. code-block:: default
@triton.jit
def _seeded_dropout(
x_ptr,
output_ptr,
n_elements,
p,
seed,
**meta,
):
# compute memory offsets of elements handled by this instance
BLOCK_SIZE = meta['BLOCK_SIZE']
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 2.84284 0.298794 -1.34172 0 0 0 0
output (seed = 123) 0 0.743443 0 2.84284 0.298794 -1.34172 0 0 0 0
output (seed = 512) -1.90567 0.743443 0 2.84284 0.298794 -1.34172 0 -0.863938 0 -0.212868
------------------- --------- -------- -------- ------- -------- -------- --------- --------- --------- ---------
.. GENERATED FROM PYTHON SOURCE LINES 148-151
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 153-158
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 160-165
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 0.316 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>`_

21
_sources/getting-started/tutorials/index.rst.txt
View File

@@ -72,6 +72,27 @@ Below is a gallery of tutorials for writing various basic operations with Triton
:hidden: :hidden:
/getting-started/tutorials/03-matrix-multiplication /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 .. raw:: html
<div class="sphx-glr-clear"></div> <div class="sphx-glr-clear"></div>

10
_sources/getting-started/tutorials/sg_execution_times.rst.txt
View File

@@ -5,12 +5,14 @@
Computation times Computation times
================= =================
**03:38.920** total execution time for **getting-started_tutorials** files: **03:43.892** total execution time for **getting-started_tutorials** files:
+---------------------------------------------------------------------------------------------------------+-----------+--------+ +---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 02:14.737 | 0.0 MB | | :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 02:20.017 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+ +---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 01:13.131 | 0.0 MB | | :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 01:12.586 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+ +---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 00:11.053 | 0.0 MB | | :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 00:10.972 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+
| :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py` (``04-low-memory-dropout.py``) | 00:00.316 | 0.0 MB |
+---------------------------------------------------------------------------------------------------------+-----------+--------+ +---------------------------------------------------------------------------------------------------------+-----------+--------+

6
_sources/python-api/generated/triton.language.rand.rst.txt Normal file
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@@ -0,0 +1,6 @@
triton.language.rand
====================
.. currentmodule:: triton.language
.. autofunction:: rand

6
_sources/python-api/generated/triton.language.randint.rst.txt Normal file
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@@ -0,0 +1,6 @@
triton.language.randint
=======================
.. currentmodule:: triton.language
.. autofunction:: randint

6
_sources/python-api/generated/triton.language.randint4x.rst.txt Normal file
View File

@@ -0,0 +1,6 @@
triton.language.randint4x
=========================
.. currentmodule:: triton.language
.. autofunction:: randint4x

6
_sources/python-api/generated/triton.language.randn.rst.txt Normal file
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@@ -0,0 +1,6 @@
triton.language.randn
=====================
.. currentmodule:: triton.language
.. autofunction:: randn

15
_sources/python-api/triton.language.rst.txt
View File

@@ -121,6 +121,19 @@ Comparison ops
minimum minimum
maximum maximum
.. _Random Number Generation:
Random Number Generation
-------------------------
.. autosummary::
:toctree: generated
:nosignatures:
randint4x
randint
rand
randn
Compiler Hint Ops Compiler Hint Ops
------------------- -------------------
@@ -129,4 +142,4 @@ Compiler Hint Ops
:toctree: generated :toctree: generated
:nosignatures: :nosignatures:
multiple_of multiple_of

10
genindex.html
View File

@@ -339,10 +339,18 @@
<h2 id="R">R</h2> <h2 id="R">R</h2>
<table style="width: 100%" class="indextable genindextable"><tr> <table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="python-api/generated/triton.language.ravel.html#triton.language.ravel">ravel() (in module triton.language)</a> <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> </li>
</ul></td> </ul></td>
<td style="width: 33%; vertical-align: top;"><ul> <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><a href="python-api/generated/triton.language.reshape.html#triton.language.reshape">reshape() (in module triton.language)</a>
</li> </li>
</ul></td> </ul></td>

11
getting-started/tutorials/01-vector-add.html
View File

@@ -103,6 +103,7 @@
</li> </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="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="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>
</ul> </ul>
</li> </li>
</ul> </ul>
@@ -231,7 +232,7 @@ to download the full example code</p>
<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">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="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="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">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> </pre></div>
</div> </div>
<p>Let’s also declare a helper function to (1) allocate the <cite>z</cite> tensor <p>Let’s also declare a helper function to (1) allocate the <cite>z</cite> tensor
@@ -319,16 +320,16 @@ for different problem sizes.</p>
<p class="sphx-glr-script-out">Out:</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: <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vector-add-performance:
size Triton Torch size Triton Torch
0 4096.0 8.000000 9.600000 0 4096.0 9.600000 9.600000
1 8192.0 19.200000 19.200000 1 8192.0 19.200000 19.200000
2 16384.0 38.400001 38.400001 2 16384.0 38.400001 38.400001
3 32768.0 76.800002 76.800002 3 32768.0 76.800002 76.800002
4 65536.0 127.999995 127.999995 4 65536.0 127.999995 127.999995
5 131072.0 219.428568 219.428568 5 131072.0 219.428568 219.428568
6 262144.0 384.000001 341.333321 6 262144.0 341.333321 384.000001
7 524288.0 472.615390 472.615390 7 524288.0 472.615390 472.615390
8 1048576.0 614.400016 614.400016 8 1048576.0 614.400016 614.400016
9 2097152.0 722.823517 722.823517 9 2097152.0 702.171410 722.823517
10 4194304.0 780.190482 780.190482 10 4194304.0 780.190482 780.190482
11 8388608.0 812.429770 812.429770 11 8388608.0 812.429770 812.429770
12 16777216.0 833.084721 833.084721 12 16777216.0 833.084721 833.084721
@@ -337,7 +338,7 @@ for different problem sizes.</p>
15 134217728.0 851.577704 850.656574 15 134217728.0 851.577704 850.656574
</pre></div> </pre></div>
</div> </div>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 11.053 seconds)</p> <p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 10.972 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-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"> <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> <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>

5
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@@ -106,6 +106,7 @@
</ul> </ul>
</li> </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="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>
</ul> </ul>
</li> </li>
</ul> </ul>
@@ -395,7 +396,7 @@ We will then compare its performance against (1) <code class="code docutils lite
94 12288.0 812.429770 415.661740 199.298541 94 12288.0 812.429770 415.661740 199.298541
95 12416.0 810.840807 412.149375 198.954424 95 12416.0 810.840807 412.149375 198.954424
96 12544.0 810.925276 412.971190 199.209928 96 12544.0 810.925276 412.971190 199.209928
97 12672.0 811.007961 412.097543 199.167004 97 12672.0 811.007961 412.097543 199.264875
[98 rows x 4 columns] [98 rows x 4 columns]
</pre></div> </pre></div>
@@ -408,7 +409,7 @@ 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> Note however that the PyTorch <cite>softmax</cite> operation is more general and will works on tensors of any shape.</p></li>
</ul> </ul>
</div></blockquote> </div></blockquote>
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@@ -46,7 +46,7 @@
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@@ -113,6 +113,7 @@
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@@ -566,42 +567,42 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
<p class="sphx-glr-script-out">Out:</p> <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: <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>matmul-performance:
M cuBLAS ... Triton Triton (+ LeakyReLU) M cuBLAS ... Triton Triton (+ LeakyReLU)
0 256.0 2.978909 ... 2.978909 2.978909 0 256.0 2.978909 ... 3.276800 3.276800
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2 512.0 14.563555 ... 16.384000 16.384000 2 512.0 14.563555 ... 16.384000 16.384000
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5 896.0 39.025776 ... 40.140799 39.025776 5 896.0 39.025776 ... 40.140799 39.025776
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7 1152.0 44.566925 ... 46.656000 46.656000 7 1152.0 44.566925 ... 46.656000 46.656000
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9 1408.0 64.138541 ... 63.392744 63.392744 9 1408.0 64.138541 ... 63.392744 57.368243
10 1536.0 78.643199 ... 76.106321 76.106321 10 1536.0 79.526831 ... 75.296679 75.296679
11 1664.0 63.372618 ... 62.061463 62.061463 11 1664.0 62.929456 ... 61.217089 61.636381
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13 1920.0 69.467336 ... 67.106797 69.818184 13 1920.0 68.776119 ... 70.172588 69.818184
14 2048.0 73.908442 ... 74.898285 74.565406 14 2048.0 73.584279 ... 74.565406 74.565406
15 2176.0 83.155572 ... 81.472263 81.143743 15 2176.0 83.155572 ... 80.494588 80.494588
16 2304.0 68.446623 ... 73.501144 73.275679 16 2304.0 68.251065 ... 73.275679 73.275679
17 2432.0 71.125224 ... 81.197876 82.147552 17 2432.0 71.125224 ... 70.766913 80.041209
18 2560.0 77.649287 ... 76.920185 77.465723 18 2560.0 77.649287 ... 76.740048 76.027843
19 2688.0 81.053536 ... 83.737433 80.537273 19 2688.0 83.922689 ... 80.880718 83.186525
20 2816.0 82.135981 ... 78.301990 79.733474 20 2816.0 83.552120 ... 78.868366 78.442822
21 2944.0 80.510553 ... 78.605729 76.435630 21 2944.0 82.102191 ... 77.385141 77.990663
22 3072.0 81.472093 ... 83.638266 84.386148 22 3072.0 79.415291 ... 81.238312 83.146995
23 3200.0 84.656085 ... 86.956520 89.635851 23 3200.0 84.321474 ... 89.012517 89.761569
24 3328.0 81.530349 ... 84.596116 86.632127 24 3328.0 83.226931 ... 85.500351 87.051143
25 3456.0 81.683457 ... 84.068369 83.980802 25 3456.0 78.655188 ... 80.300370 83.632331
26 3584.0 87.211821 ... 87.466332 91.099693 26 3584.0 85.879071 ... 91.470385 93.661869
27 3712.0 85.896254 ... 83.596102 85.822459 27 3712.0 85.822459 ... 84.802499 88.876645
28 3840.0 84.421376 ... 86.197974 86.130841 28 3840.0 85.136259 ... 87.424508 88.121115
29 3968.0 92.442373 ... 87.913500 87.787005 29 3968.0 92.864488 ... 87.284643 87.597943
30 4096.0 93.596744 ... 89.240508 89.062862 30 4096.0 93.466385 ... 90.504200 89.898012
[31 rows x 5 columns] [31 rows x 5 columns]
</pre></div> </pre></div>
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@@ -621,7 +622,7 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
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<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="o">**</span><span class="n">meta</span><span class="p">,</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="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="o">**</span><span class="n">meta</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">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="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 2.84284 0.298794 -1.34172 0 0 0 0
output (seed = 123) 0 0.743443 0 2.84284 0.298794 -1.34172 0 0 0 0
output (seed = 512) -1.90567 0.743443 0 2.84284 0.298794 -1.34172 0 -0.863938 0 -0.212868
------------------- --------- -------- -------- ------- -------- -------- --------- --------- --------- ---------
</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>
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<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="01-vector-add.html">Vector Addition</a></li>
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<div class="section" id="computation-times"> <div class="section" id="computation-times">
<span id="sphx-glr-getting-started-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline"></a></h1> <span id="sphx-glr-getting-started-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline"></a></h1>
<p><strong>03:38.920</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p> <p><strong>03:43.892</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
<table class="docutils align-default"> <table class="docutils align-default">
<colgroup> <colgroup>
<col style="width: 85%" /> <col style="width: 85%" />
@@ -183,15 +183,19 @@
</colgroup> </colgroup>
<tbody> <tbody>
<tr class="row-odd"><td><p><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> (<code class="docutils literal notranslate"><span class="pre">03-matrix-multiplication.py</span></code>)</p></td> <tr class="row-odd"><td><p><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> (<code class="docutils literal notranslate"><span class="pre">03-matrix-multiplication.py</span></code>)</p></td>
<td><p>02:14.737</p></td> <td><p>02:20.017</p></td>
<td><p>0.0 MB</p></td> <td><p>0.0 MB</p></td>
</tr> </tr>
<tr class="row-even"><td><p><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> (<code class="docutils literal notranslate"><span class="pre">02-fused-softmax.py</span></code>)</p></td> <tr class="row-even"><td><p><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> (<code class="docutils literal notranslate"><span class="pre">02-fused-softmax.py</span></code>)</p></td>
<td><p>01:13.131</p></td> <td><p>01:12.586</p></td>
<td><p>0.0 MB</p></td> <td><p>0.0 MB</p></td>
</tr> </tr>
<tr class="row-odd"><td><p><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> (<code class="docutils literal notranslate"><span class="pre">01-vector-add.py</span></code>)</p></td> <tr class="row-odd"><td><p><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> (<code class="docutils literal notranslate"><span class="pre">01-vector-add.py</span></code>)</p></td>
<td><p>00:11.053</p></td> <td><p>00:10.972</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><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> (<code class="docutils literal notranslate"><span class="pre">04-low-memory-dropout.py</span></code>)</p></td>
<td><p>00:00.316</p></td>
<td><p>0.0 MB</p></td> <td><p>0.0 MB</p></td>
</tr> </tr>
</tbody> </tbody>

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python-api/generated/triton.language.arange.html
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@@ -115,6 +115,7 @@
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.atomic_add.html
View File

@@ -117,6 +117,7 @@
</ul> </ul>
</li> </li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.atomic_cas.html
View File

@@ -123,6 +123,7 @@
</ul> </ul>
</li> </li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.atomic_max.html
View File

@@ -117,6 +117,7 @@
</ul> </ul>
</li> </li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.atomic_min.html
View File

@@ -117,6 +117,7 @@
</ul> </ul>
</li> </li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.atomic_xchg.html
View File

@@ -117,6 +117,7 @@
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.broadcast_to.html
View File

@@ -116,6 +116,7 @@
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.cos.html
View File

@@ -120,6 +120,7 @@
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.dot.html
View File

@@ -114,6 +114,7 @@
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.exp.html
View File

@@ -120,6 +120,7 @@
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.load.html
View File

@@ -117,6 +117,7 @@
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.log.html
View File

@@ -120,6 +120,7 @@
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.max.html
View File

@@ -116,6 +116,7 @@
</li> </li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

5
python-api/generated/triton.language.maximum.html
View File

@@ -46,7 +46,7 @@
<link rel="index" title="Index" href="../../genindex.html" /> <link rel="index" title="Index" href="../../genindex.html" />
<link rel="search" title="Search" href="../../search.html" /> <link rel="search" title="Search" href="../../search.html" />
<link rel="next" title="triton.language.multiple_of" href="triton.language.multiple_of.html" /> <link rel="next" title="triton.language.randint4x" href="triton.language.randint4x.html" />
<link rel="prev" title="triton.language.minimum" href="triton.language.minimum.html" /> <link rel="prev" title="triton.language.minimum" href="triton.language.minimum.html" />
</head> </head>
@@ -115,6 +115,7 @@
<li class="toctree-l3 current"><a class="current reference internal" href="#">triton.language.maximum</a></li> <li class="toctree-l3 current"><a class="current reference internal" href="#">triton.language.maximum</a></li>
</ul> </ul>
</li> </li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>
@@ -217,7 +218,7 @@
</div> </div>
<footer> <footer>
<div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
<a href="triton.language.multiple_of.html" class="btn btn-neutral float-right" title="triton.language.multiple_of" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a> <a href="triton.language.randint4x.html" class="btn btn-neutral float-right" title="triton.language.randint4x" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
<a href="triton.language.minimum.html" class="btn btn-neutral float-left" title="triton.language.minimum" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a> <a href="triton.language.minimum.html" class="btn btn-neutral float-left" title="triton.language.minimum" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
</div> </div>

1
python-api/generated/triton.language.min.html
View File

@@ -116,6 +116,7 @@
</li> </li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.minimum.html
View File

@@ -115,6 +115,7 @@
<li class="toctree-l3"><a class="reference internal" href="triton.language.maximum.html">triton.language.maximum</a></li> <li class="toctree-l3"><a class="reference internal" href="triton.language.maximum.html">triton.language.maximum</a></li>
</ul> </ul>
</li> </li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

5
python-api/generated/triton.language.multiple_of.html
View File

@@ -47,7 +47,7 @@
<link rel="index" title="Index" href="../../genindex.html" /> <link rel="index" title="Index" href="../../genindex.html" />
<link rel="search" title="Search" href="../../search.html" /> <link rel="search" title="Search" href="../../search.html" />
<link rel="next" title="triton.testing" href="../triton.testing.html" /> <link rel="next" title="triton.testing" href="../triton.testing.html" />
<link rel="prev" title="triton.language.maximum" href="triton.language.maximum.html" /> <link rel="prev" title="triton.language.randn" href="triton.language.randn.html" />
</head> </head>
<body class="wy-body-for-nav"> <body class="wy-body-for-nav">
@@ -111,6 +111,7 @@
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2 current"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a><ul class="current"> <li class="toctree-l2 current"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a><ul class="current">
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<div class="section" id="triton-language-rand">
<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><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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<div class="section" id="triton-language-randint">
<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><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>
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<div class="section" id="triton-language-randint4x">
<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><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>
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<div class="section" id="triton-language-randn">
<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><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>
</ul>
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@@ -120,6 +120,7 @@
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.store.html
View File

@@ -117,6 +117,7 @@
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.sum.html
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@@ -116,6 +116,7 @@
</li> </li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.where.html
View File

@@ -114,6 +114,7 @@
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

1
python-api/generated/triton.language.zeros.html
View File

@@ -115,6 +115,7 @@
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#atomic-ops">Atomic Ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#comparison-ops">Comparison ops</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#random-number-generation">Random Number Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li> <li class="toctree-l2"><a class="reference internal" href="../triton.language.html#compiler-hint-ops">Compiler Hint Ops</a></li>
</ul> </ul>
</li> </li>

4
python-api/triton.html
View File

@@ -47,7 +47,7 @@
<link rel="index" title="Index" href="../genindex.html" /> <link rel="index" title="Index" href="../genindex.html" />
<link rel="search" title="Search" href="../search.html" /> <link rel="search" title="Search" href="../search.html" />
<link rel="next" title="triton.jit" href="generated/triton.jit.html" /> <link rel="next" title="triton.jit" href="generated/triton.jit.html" />
<link rel="prev" title="Matrix Multiplication" href="../getting-started/tutorials/03-matrix-multiplication.html" /> <link rel="prev" title="Low-Memory Dropout" href="../getting-started/tutorials/04-low-memory-dropout.html" />
</head> </head>
<body class="wy-body-for-nav"> <body class="wy-body-for-nav">
@@ -211,7 +211,7 @@
<footer> <footer>
<div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
<a href="generated/triton.jit.html" class="btn btn-neutral float-right" title="triton.jit" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a> <a href="generated/triton.jit.html" class="btn btn-neutral float-right" title="triton.jit" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
<a href="../getting-started/tutorials/03-matrix-multiplication.html" class="btn btn-neutral float-left" title="Matrix Multiplication" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a> <a href="../getting-started/tutorials/04-low-memory-dropout.html" class="btn btn-neutral float-left" title="Low-Memory Dropout" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
</div> </div>
<hr/> <hr/>

31
python-api/triton.language.html
View File

@@ -40,6 +40,7 @@
<script src="../_static/jquery.js"></script> <script src="../_static/jquery.js"></script>
<script src="../_static/underscore.js"></script> <script src="../_static/underscore.js"></script>
<script src="../_static/doctools.js"></script> <script src="../_static/doctools.js"></script>
<script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
<script type="text/javascript" src="../_static/js/theme.js"></script> <script type="text/javascript" src="../_static/js/theme.js"></script>
@@ -160,6 +161,13 @@
<li class="toctree-l3"><a class="reference internal" href="generated/triton.language.maximum.html">triton.language.maximum</a></li> <li class="toctree-l3"><a class="reference internal" href="generated/triton.language.maximum.html">triton.language.maximum</a></li>
</ul> </ul>
</li> </li>
<li class="toctree-l2"><a class="reference internal" href="#random-number-generation">Random Number Generation</a><ul>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.language.randint4x.html">triton.language.randint4x</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.language.randint.html">triton.language.randint</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.language.rand.html">triton.language.rand</a></li>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.language.randn.html">triton.language.randn</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#compiler-hint-ops">Compiler Hint Ops</a><ul> <li class="toctree-l2"><a class="reference internal" href="#compiler-hint-ops">Compiler Hint Ops</a><ul>
<li class="toctree-l3"><a class="reference internal" href="generated/triton.language.multiple_of.html">triton.language.multiple_of</a></li> <li class="toctree-l3"><a class="reference internal" href="generated/triton.language.multiple_of.html">triton.language.multiple_of</a></li>
</ul> </ul>
@@ -438,6 +446,29 @@
</tbody> </tbody>
</table> </table>
</div> </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"> <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> <h2>Compiler Hint Ops<a class="headerlink" href="#compiler-hint-ops" title="Permalink to this headline"></a></h2>
<table class="longtable docutils align-default"> <table class="longtable docutils align-default">

2
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