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triton/_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt
Philippe Tillet 1eee2d4137 [GH-PAGES] Updated website
2021-08-06 20:03:44 +00:00

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.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "getting-started/tutorials/03-matrix-multiplication.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py>`
to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_getting-started_tutorials_03-matrix-multiplication.py:
Matrix Multiplication
======================
In this tutorial, you will write a 25-lines high-performance FP16 matrix multiplication
kernel that achieves performance on par with cuBLAS.
You will specifically learn about:
- Block-level matrix multiplications
- Multi-dimensional pointer arithmetic
- Program re-ordering for improved L2 cache hit rate
- Automatic performance tuning
.. GENERATED FROM PYTHON SOURCE LINES 15-42
Motivations
-------------
Matrix multiplications are a key building block of most modern high-performance computing systems.
They are notoriously hard to optimize, hence their implementation is generally done by
hardware vendors themselves as part of so-called "kernel libraries" (e.g., cuBLAS).
Unfortunately, these libraries are often proprietary and cannot be easily customized
to accomodate the needs of modern deep learning workloads (e.g., fused activation functions).
In this tutorial, you will learn how to implement efficient matrix multiplications by
yourself with Triton, in a way that is easy to customize and extend.
Roughly speaking, the kernel that we will write will implement the following blocked
algorithm to multiply a (MxK) by a (KxN) matrix:
.. code-block:: python
# do in parallel
for m in range(0, M, BLOCK_SIZE_M):
# do in parallel
for n in range(0, N, BLOCK_SIZE_N):
acc = zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=float32)
for k in range(0, K, BLOCK_SIZE_K):
a = A[m : m+BLOCK_SIZE_M, k : k+BLOCK_SIZE_K]
b = B[k : k+BLOCK_SIZE_K, n : n+BLOCK_SIZE_N]
acc += dot(a, b)
C[m : m+BLOCK_SIZE_M, n : n+BLOCK_SIZE_N] = acc;
where each iteration of the doubly-nested for-loop corresponds to a Triton program instance.
.. GENERATED FROM PYTHON SOURCE LINES 44-129
Compute Kernel
----------------
The above algorithm is, actually, fairly straightforward to implement in Triton.
The main difficulty comes from the computation of the memory locations at which blocks
of :code:`A` and :code:`B` must be read in the inner loop. For that, we need
multi-dimensional pointer arithmetics.
Pointer Arithmetics
~~~~~~~~~~~~~~~~~~~~
For a row-major 2D tensor :code:`X`, the memory location of :code:`X[i, j]` is given b
y :code:`&X[i, j] = X + i*stride_x_0 + j*stride_x_1`.
Therefore, blocks of pointers for :code:`A[m : m+BLOCK_SIZE_M, k:k+BLOCK_SIZE_K]` and
:code:`B[k : k+BLOCK_SIZE_K, n : n+BLOCK_SIZE_N]` can be defined in pseudo-code as:
.. code-block:: python
&A[m : m+BLOCK_SIZE_M, k:k+BLOCK_SIZE_K] = A + (m : m+BLOCK_SIZE_M)[:, None]*A.stride(0) + (k : k+BLOCK_SIZE_K)[None, :]*A.stride(1);
&B[k : k+BLOCK_SIZE_K, n:n+BLOCK_SIZE_N] = B + (k : k+BLOCK_SIZE_K)[:, None]*B.stride(0) + (n : n+BLOCK_SIZE_N)[None, :]*B.stride(1);
Which means that pointers for blocks of A and B can be initialized (i.e., :code:`k=0`) in Triton as:
.. code-block:: python
pid_m = triton.program_id(0)
pid_n = triton.program_id(1)
rm = pid_m * BLOCK_SIZE_M + triton.arange(0, BLOCK_SIZE_M)
rn = pid_n * BLOCK_SIZE_N + triton.arange(0, BLOCK_SIZE_N)
rk = triton.arange(0, BLOCK_SIZE_K)
// pointer for A operand
pa = A + (rm[:, None] * stride_a_0 + rk[None, :] * stride_a_1);
// pointer for B operand
pb = B + (rk[:, None] * stride_b_0 + rn[None, :] * stride_b_1);
And then updated in the inner loop as follows:
.. code-block:: python
pa += BLOCK_SIZE_K * stride_a_1;
pb += BLOCK_SIZE_K * stride_b_0;
L2 Cache Optimizations
~~~~~~~~~~~~~~~~~~~~~~~~
As mentioned above, each program instance computes a :code:`[BLOCK_SIZE_M, BLOCK_SIZE_N]`
block of :code:`C`.
It is important to remember that the order in which these blocks are computed does
matter, since it affects the L2 cache hit rate of our program. and unfortunately, a
a simple row-major ordering
.. code-block:: Python
pid = triton.program_id(0);
grid_m = (M + BLOCK_SIZE_M - 1) // BLOCK_SIZE_M;
grid_n = (N + BLOCK_SIZE_N - 1) // BLOCK_SIZE_N;
pid_m = pid / grid_n;
pid_n = pid % grid_n;
is just not going to cut it.
One possible solution is to launch blocks in an order that promotes data reuse.
This can be done by 'super-grouping' blocks in groups of :code:`GROUP_M` rows before
switching to the next column:
.. code-block:: python
pid = triton.program_id(0);
width = GROUP_M * grid_n;
group_id = pid // width;
# we need to handle the case where M % (GROUP_M*BLOCK_SIZE_M) != 0
group_size = min(grid_m - group_id * GROUP_M, GROUP_M);
pid_m = group_id * GROUP_M + (pid % group_size);
pid_n = (pid % width) // (group_size);
For example, in the following matmul where each matrix is 9 blocks by 9 blocks,
we can see that if we compute the output in row-major ordering, we need to load 90
blocks into SRAM to compute the first 9 output blocks, but if we do it in grouped
ordering, we only need to load 54 blocks.
.. image:: grouped_vs_row_major_ordering.png
In practice, this can improve the performance of our matrix multiplication kernel by
more than 10\% on some hardware architecture (e.g., 220 to 245 TFLOPS on A100).
.. GENERATED FROM PYTHON SOURCE LINES 131-134
Final Result
-------------
.. GENERATED FROM PYTHON SOURCE LINES 134-263
.. code-block:: default
import torch
import triton
import triton.language as tl
# %
# :code:`triton.jit`'ed functions can be auto-tuned by using the `triton.autotune`
# decorator, which consumes:
# - A list of :code:`triton.Config` objects that define different configurations of
# meta-parameters (e.g., BLOCK_SIZE_M) and compilation options (e.g., num_warps) to try
# - An autotuning *key* whose change in values will trigger evaluation of all the
# provided configs
@triton.autotune(
configs=[
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 64 , 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64 , 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 64 , 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32 , 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 64 , 'BLOCK_SIZE_N': 32 , 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
triton.Config({'BLOCK_SIZE_M': 32 , 'BLOCK_SIZE_N': 64 , 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
],
key=['M', 'N', 'K'],
)
# %
# We can now define our kernel as normal, using all the techniques presented above
@triton.jit
def matmul_kernel(
# Pointers to matrices
a_ptr,
b_ptr,
c_ptr,
# Matrix dimensions
M,
N,
K,
# The stride variables represent how much to increase the ptr by when moving by 1
# element in a particular dimension. E.g. stride_am is how much to increase a_ptr
# by to get the element one row down (A has M rows)
stride_am,
stride_ak,
stride_bk,
stride_bn,
stride_cm,
stride_cn,
**meta,
):
"""Kernel for computing the matmul AB = C
A has shape (M, K), B has shape (K, N) and C has shape (M, N)
"""
# extract meta-parameters
BLOCK_SIZE_M = meta['BLOCK_SIZE_M']
BLOCK_SIZE_N = meta['BLOCK_SIZE_N']
BLOCK_SIZE_K = meta['BLOCK_SIZE_K']
GROUP_SIZE_M = 8
pid = tl.program_id(axis=0)
# the number of blocks is the ceil(M / BLOCK_SIZE_M) since we need an extra block
# Note that this will lead to some quantization in performance where time-taken jumps
# when you need to add a new block
n_blocks_m = (M + BLOCK_SIZE_M - 1) // BLOCK_SIZE_M
n_blocks_n = (N + BLOCK_SIZE_N - 1) // BLOCK_SIZE_N
# Map PIDs to the block they should compute. This is done in a grouped ordering
# to promote L2 cache reuse.
n_output_blocks_in_group = GROUP_SIZE_M * n_blocks_n
group_id = pid // n_output_blocks_in_group
first_m_block_in_group = group_id * GROUP_SIZE_M
# If the number of blocks is not divisible by the group size, the last group is smaller
group_size_m = min(n_blocks_m - first_m_block_in_group, GROUP_SIZE_M)
# Within a group, we compute in col-major ordering, block_m and block_n are the
# output row and col that this program is computing in terms of blocks
block_m = first_m_block_in_group + (pid % group_size_m)
block_n = (pid % n_output_blocks_in_group) // group_size_m
# Convert from block indices back to element indices
m_start = block_m * BLOCK_SIZE_M
n_start = block_n * BLOCK_SIZE_N
# Expand out to all the offsets for each of the elements in this block.
m_offsets_a = (m_start + tl.arange(0, BLOCK_SIZE_M))[:, None]
n_offsets_b = (n_start + tl.arange(0, BLOCK_SIZE_N))[None, :]
k_offsets = tl.arange(0, BLOCK_SIZE_K)
# Get the pointers for the first block of each. We will advance this pointer
# as we move in the K direction and accumulate.
# a_ptrs should contain BLOCK_SIZE_M * BLOCK_SIZE_K pointers
a_ptrs = a_ptr + (stride_am * m_offsets_a + stride_ak * k_offsets[None, :])
# b_ptrs should contain BLOCK_SIZE_K * BLOCK_SIZE_N pointers
b_ptrs = b_ptr + (stride_bk * k_offsets[:, None] + stride_bn * n_offsets_b)
# We accumulate internally in fp32, but the output is written out in the dtype
# of the tensor when it is stored
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, K, BLOCK_SIZE_K):
# Note that for simplicity, we don't apply a mask here. This means that if K is
# not a multiple of BLOCK_SIZE_K, this will access out-of-bounds memory and
# accumulate it incorrectly.
a = tl.load(a_ptrs)
b = tl.load(b_ptrs)
# We accumulate along the K dimension
accumulator += tl.dot(a, b)
# Advance the ptrs to the next K block
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
# triton can accept arbitrary activation function via metaparameters!
if meta['ACTIVATION']:
accumulator = meta['ACTIVATION'](accumulator)
m_offsets_c = (m_start + tl.arange(0, BLOCK_SIZE_M))[:, None]
n_offsets_c = (n_start + tl.arange(0, BLOCK_SIZE_N))[None, :]
c_ptrs = c_ptr + stride_cm * m_offsets_c + stride_cn * n_offsets_c
mask = (m_offsets_c < M) & (n_offsets_c < N)
tl.store(c_ptrs, accumulator, mask=mask)
# we can fuse `leaky_relu` by providing it as an `ACTIVATION` meta-parameter in `_matmul`
@triton.jit
def leaky_relu(x):
return tl.where(x >= 0, x, 0.01 * x)
.. GENERATED FROM PYTHON SOURCE LINES 264-266
We can now create a convenience wrapper function that only takes two input tensors
and (1) checks any shape constraint; (2) allocates the output; (3) launches the above kernel
.. GENERATED FROM PYTHON SOURCE LINES 266-302
.. code-block:: default
def matmul(a, b, activation=None):
# checks constraints
assert a.shape[1] == b.shape[0], "incompatible dimensions"
assert a.is_contiguous(), "matrix A must be contiguous"
assert b.is_contiguous(), "matrix B must be contiguous"
M, K = a.shape
K, N = b.shape
assert (
K % 32 == 0
), "We don't check memory-out-of-bounds with K so K must be divisible by BLOCK_SIZE_K"
# allocates output
c = torch.empty((M, N), device=a.device, dtype=a.dtype)
# 1D launch kernel where each block gets its own program.
grid = lambda META: (
triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),
)
matmul_kernel[grid](
a,
b,
c,
M,
N,
K,
a.stride(0),
a.stride(1),
b.stride(0),
b.stride(1),
c.stride(0),
c.stride(1),
ACTIVATION=activation,
)
return c
.. GENERATED FROM PYTHON SOURCE LINES 303-307
Unit Test
-----------
We can test our custom matrix multiplication operation against a native torch implementation (i.e., cuBLAS)
.. GENERATED FROM PYTHON SOURCE LINES 307-320
.. code-block:: default
torch.manual_seed(0)
a = torch.randn((512, 512), device='cuda', dtype=torch.float16)
b = torch.randn((512, 512), device='cuda', dtype=torch.float16)
triton_output = matmul(a, b, activation=None)
torch_output = torch.matmul(a, b)
print(f"triton_output={triton_output}")
print(f"torch_output={torch_output}")
if triton.testing.allclose(triton_output, torch_output):
print("✅ Triton and Torch match")
else:
print("❌ Triton and Torch differ")
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
triton_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3984, 24.4531, -32.3438],
[ 6.3555, -19.6094, 34.0938, ..., -5.8945, 5.2891, 6.8867],
[-32.0625, 5.9492, 15.3984, ..., -21.3906, -23.9844, -10.1328],
...,
[ -5.7031, 7.4492, 8.2656, ..., -10.6953, -40.0000, 17.7500],
[ 25.5000, 24.3281, -8.4688, ..., -18.9375, 32.5312, -29.9219],
[ -5.3477, 4.9844, 11.8906, ..., 5.5898, 6.4023, -17.3125]],
device='cuda:0', dtype=torch.float16)
torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -32.3438],
[ 6.3516, -19.6094, 34.0938, ..., -5.8906, 5.2812, 6.8828],
[-32.0625, 5.9531, 15.3984, ..., -21.4062, -23.9844, -10.1328],
...,
[ -5.7070, 7.4492, 8.2656, ..., -10.6953, -40.0000, 17.7500],
[ 25.5000, 24.3438, -8.4609, ..., -18.9375, 32.5312, -29.9219],
[ -5.3477, 4.9805, 11.8828, ..., 5.5859, 6.4023, -17.3125]],
device='cuda:0', dtype=torch.float16)
✅ Triton and Torch match
.. GENERATED FROM PYTHON SOURCE LINES 321-327
Benchmark
--------------
Square Matrix Performance
~~~~~~~~~~~~~~~~~~~~~~~~~~
We can now compare the performance of our kernel against that of cuBLAS. Here we focus on square matrices, but feel free to arrange this script as you wish to benchmark any other matrix shape.
.. GENERATED FROM PYTHON SOURCE LINES 327-368
.. code-block:: default
@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=['M', 'N', 'K'], # argument names to use as an x-axis for the plot
x_vals=[
128 * i for i in range(1, 33)
], # different possible values for `x_name`
line_arg='provider', # argument name whose value corresponds to a different line in the plot
# possible values for `line_arg``
line_vals=['cublas', 'cublas + relu', 'triton', 'triton + relu'],
# label name for the lines
line_names=["cuBLAS", "cuBLAS (+ torch.nn.LeakyReLU)", "Triton", "Triton (+ LeakyReLU)"],
# line styles
styles=[('green', '-'), ('green', '--'), ('blue', '-'), ('blue', '--')],
ylabel="TFLOPS", # label name for the y-axis
plot_name="matmul-performance", # name for the plot. Used also as a file name for saving the plot.
args={},
)
)
def benchmark(M, N, K, provider):
a = torch.randn((M, K), device='cuda', dtype=torch.float16)
b = torch.randn((K, N), device='cuda', dtype=torch.float16)
if provider == 'cublas':
ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a, b))
if provider == 'triton':
ms, min_ms, max_ms = triton.testing.do_bench(lambda: matmul(a, b))
if provider == 'cublas + relu':
torch_relu = torch.nn.ReLU(inplace=True)
ms, min_ms, max_ms = triton.testing.do_bench(
lambda: torch_relu(torch.matmul(a, b))
)
if provider == 'triton + relu':
ms, min_ms, max_ms = triton.testing.do_bench(
lambda: matmul(a, b, activation=leaky_relu)
)
perf = lambda ms: 2 * M * N * K * 1e-12 / (ms * 1e-3)
return perf(ms), perf(max_ms), perf(min_ms)
benchmark.run(show_plots=True, print_data=True)
.. image:: /getting-started/tutorials/images/sphx_glr_03-matrix-multiplication_001.png
:alt: 03 matrix multiplication
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
matmul-performance:
M cuBLAS ... Triton Triton (+ LeakyReLU)
0 128.0 0.455111 ... 0.512000 0.512000
1 256.0 2.730667 ... 3.276800 2.978909
2 384.0 7.372800 ... 7.899428 7.899428
3 512.0 14.563555 ... 16.384000 16.384000
4 640.0 22.260869 ... 24.380953 24.380953
5 768.0 32.768000 ... 34.028308 34.028308
6 896.0 39.025776 ... 40.140799 35.150663
7 1024.0 49.932191 ... 52.428801 52.428801
8 1152.0 44.566925 ... 46.656000 46.656000
9 1280.0 51.200001 ... 56.109587 56.109587
10 1408.0 64.138541 ... 64.902096 64.138541
11 1536.0 80.430545 ... 76.106321 75.296679
12 1664.0 62.929456 ... 62.061463 62.061463
13 1792.0 72.983276 ... 69.810085 69.379162
14 1920.0 69.120002 ... 70.892307 69.120002
15 2048.0 73.584279 ... 74.898285 74.565406
16 2176.0 83.155572 ... 80.817862 79.855747
17 2304.0 68.446623 ... 72.828879 73.275679
18 2432.0 71.305746 ... 82.388456 81.908060
19 2560.0 78.019048 ... 77.283019 75.676673
20 2688.0 83.552988 ... 83.552988 83.922689
21 2816.0 81.827785 ... 77.330158 79.154642
22 2944.0 81.166173 ... 77.747321 79.483304
23 3072.0 79.863336 ... 82.661468 82.420822
24 3200.0 83.660130 ... 90.395483 85.906037
25 3328.0 83.226931 ... 87.368079 83.613586
26 3456.0 80.220468 ... 81.600781 83.459178
27 3584.0 87.466332 ... 92.887804 84.983685
28 3712.0 84.159518 ... 83.178475 83.666116
29 3840.0 83.591840 ... 84.228485 85.663823
30 3968.0 91.885495 ... 84.680037 84.154440
31 4096.0 89.181212 ... 90.260743 90.200084
[32 rows x 5 columns]
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 2 minutes 29.710 seconds)
.. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: 03-matrix-multiplication.py <03-matrix-multiplication.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: 03-matrix-multiplication.ipynb <03-matrix-multiplication.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
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