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CUTLASS 3.2.1 (#1113)

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* Updates for 3.2.1 release.

* Minor fix in gemm op profiler for raster order.

* Add scheduler mapping for raster order in the kernels.
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ANIKET SHIVAM
2023-09-26 14:24:26 -07:00
committed by GitHub
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commit 90d3b0fb18
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test/python/cutlass/emit/pytorch.py Normal file
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#################################################################################################
#
# Copyright (c) 2023 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
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# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
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# this list of conditions and the following disclaimer in the documentation
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# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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#################################################################################################
"""
Tests emitting a CUTLASS kernel to a PyTorch CUDA extension
"""
import random
import tempfile
import unittest
import cutlass
if cutlass.utils.datatypes.torch_available:
import torch
def _initialize(dtype, M: int, N: int, K: int):
"""
Utility function to initialize A, B, C, and D matrices corresponding to dimensions M, N, and K
:param dtype: data type of tensors
:param M: M dimension of GEMM problem
:type M: int
:param N: N dimension of GEMM problem
:type N: int
:param K: N dimension of GEMM problem
:type K: int
:return: initialized tensors A, B, C, and D
:rtype: list
"""
sizes = [(M, K), (K, N), (M, N), (M, N)]
return [torch.randint(-3, 3, size, device='cuda').to(dtype) for size in sizes]
def _generate_problems(dtype, num):
"""
Utility function to generate `num` GEMMs of random sizes
:param dtype: data type of tensors
:param num: number of GEMMs to generate
:type num: int
:return: lists of A, B, C, and D tensors
:rtype: list
"""
valid_sizes = [128, 256, 512, 1024]
As, Bs, Cs, Ds = [], [], [], []
for _ in range(num):
M, N, K = [random.choice(valid_sizes) for _ in range(3)]
A, B, C, D = _initialize(dtype, M, N, K)
As.append(A)
Bs.append(B)
Cs.append(C)
Ds.append(D)
return As, Bs, Cs, Ds
def _generate_conv2d_problem(conv_kind, dtype, ps):
"""
Utility function to generate conv2d inputs
:param conv_kind: kind of convolution
:type conv_kind: str
:param dtype: data type of tensors
:param problem_size: the conv2d problem size
:type problem_size: cutlass.shape.Conv2DProblemSize
:return: initialized tensors A, B, C, and D
:rtype: list
"""
if conv_kind == "fprop":
tensor_A_size = (ps.N, ps.C, ps.H, ps.W)
tensor_B_size = (ps.K, ps.C, ps.R, ps.S)
tensor_C_size = (ps.N, ps.K, ps.P, ps.Q)
elif conv_kind == "dgrad":
tensor_A_size = (ps.N, ps.K, ps.P, ps.Q)
tensor_B_size = (ps.K, ps.C, ps.R, ps.S)
tensor_C_size = (ps.N, ps.C, ps.H, ps.W)
else:
tensor_A_size = (ps.N, ps.K, ps.P, ps.Q)
tensor_B_size = (ps.N, ps.C, ps.H, ps.W)
tensor_C_size = (ps.K, ps.C, ps.R, ps.S)
sizes = [tensor_A_size, tensor_B_size, tensor_C_size]
return [torch.ceil(torch.empty(size, dtype=dtype, device='cuda').uniform_(-4.5, 3.5)).to(memory_format=torch.channels_last) for size in sizes]
@unittest.skipIf(not cutlass.utils.datatypes.torch_available, 'PyTorch must be available to run PyTorch extension tests')
class PyTorchExtensionTest(unittest.TestCase):
def test_gemm(self):
random.seed(2023)
dtype = torch.float16
plan = cutlass.op.Gemm(element=dtype, layout=cutlass.LayoutType.RowMajor)
plan.activation = cutlass.epilogue.relu
op = plan.construct()
with tempfile.TemporaryDirectory() as tmpdir:
mod = cutlass.emit.pytorch(op, name='gemm_mod', cc=plan.cc, sourcedir=tmpdir, jit=True)
A, B, C, _ = _initialize(dtype, 1024, 256, 512)
D_ref = torch.nn.functional.relu(A @ B)
D = mod.run(A, B)
assert torch.allclose(D, D_ref)
D = mod.run(A, B, C)
assert torch.allclose(D, D_ref)
D = mod.run(A, B, C, 1.0)
assert torch.allclose(D, D_ref)
D = mod.run(A, B, C, 1.0, 0.0)
assert torch.allclose(D, D_ref)
alpha = 2.0
beta = -1.0
D_ref = torch.nn.functional.relu((A @ B) * alpha + (beta * C))
D = mod.run(A, B, C, alpha, beta)
assert torch.allclose(D, D_ref)
def test_grouped_gemm(self):
random.seed(2023)
dtype = torch.float16
plan = cutlass.op.GroupedGemm(element=dtype, layout=cutlass.LayoutType.RowMajor)
op = plan.construct()
with tempfile.TemporaryDirectory() as tmpdir:
mod = cutlass.emit.pytorch(op, name='grouped_gemm_mod', cc=plan.cc, sourcedir=tmpdir, jit=True)
As, Bs, Cs, _ = _generate_problems(dtype, 50)
def check_all(X, Y):
for x, y in zip(X, Y):
assert torch.allclose(x, y)
Ds_ref = [a @ b for a, b in zip(As, Bs)]
Ds = mod.run(As, Bs)
check_all(Ds, Ds_ref)
Ds = mod.run(As, Bs, Cs)
check_all(Ds, Ds_ref)
Ds = mod.run(As, Bs, Cs, 1.0)
check_all(Ds, Ds_ref)
Ds = mod.run(As, Bs, Cs, 1.0, 0.0)
check_all(Ds, Ds_ref)
alpha = 2.0
beta = -1.0
Ds_ref = [(a @ b) * alpha + (beta * c) for a, b, c in zip(As, Bs, Cs)]
Ds = mod.run(As, Bs, Cs, alpha, beta)
check_all(Ds, Ds_ref)
def test_conv2d_fprop(self):
torch.manual_seed(2023)
dtype = torch.float16
plan = cutlass.op.Conv2d(kind="fprop", element=dtype, element_accumulator=torch.float32)
plan.activation = "relu"
op = plan.construct()
with tempfile.TemporaryDirectory() as tmpdir:
mod = cutlass.emit.pytorch(op, name="conv2d_mod", cc=plan.cc, sourcedir=tmpdir, jit=True)
problem_size = cutlass.shape.Conv2DProblemSize(
1, 4, 4, 16,
8, 3, 3, 16,
0, 0,
3, 3,
1, 1
)
A, B, C = _generate_conv2d_problem("fprop", dtype, problem_size)
stride = (problem_size.stride_h, problem_size.stride_w)
padding = (problem_size.pad_h, problem_size.pad_w)
alpha = 1.0
beta = 0.5
D_ref = alpha * torch.ops.aten.conv2d(
A, B, stride=stride, padding=padding
) + beta * C
D_ref = torch.nn.functional.relu(D_ref)
D = mod.run(A, B, C, stride, padding, alpha=alpha, beta=beta)
assert torch.allclose(D, D_ref)
# Test serial split-K
D_serial_split_k = mod.run(A, B, C, stride, padding, alpha=alpha, beta=beta, split_k_mode="serial", split_k_slices=3)
assert torch.allclose(D, D_serial_split_k)
# Test parallel split-K
D_parallel_split_k = mod.run(A, B, C, stride, padding, alpha=alpha, beta=beta, split_k_mode="parallel", split_k_slices=7)
assert torch.allclose(D, D_parallel_split_k)
def test_conv2d_dgrad(self):
torch.manual_seed(2023)
dtype = torch.float16
plan = cutlass.op.Conv2d(kind="dgrad", element=dtype, element_accumulator=torch.float32)
op = plan.construct()
with tempfile.TemporaryDirectory() as tmpdir:
mod = cutlass.emit.pytorch(op, name="conv2d_dgrad_mod", cc=plan.cc, sourcedir=tmpdir, jit=True)
problem_size = cutlass.shape.Conv2DProblemSize(
1, 4, 4, 16,
8, 3, 3, 16,
0, 0,
3, 3,
1, 1,
cutlass.ConvMode.CrossCorrelation,
1, 1
)
A, B, C = _generate_conv2d_problem("dgrad", dtype, problem_size)
stride = (problem_size.stride_h, problem_size.stride_w)
padding = (problem_size.pad_h, problem_size.pad_w)
alpha = 1.0
beta = 0.5
input_size = (problem_size.N, problem_size.C, problem_size.H, problem_size.W)
D_ref = alpha * torch.nn.grad.conv2d_input(
input_size, B, A,
stride=stride, padding=padding
) + beta * C
D = mod.run(input_size, A, B, C, stride, padding, alpha=alpha, beta=beta, )
assert torch.allclose(D, D_ref)
def test_conv2d_wgrad(self):
torch.manual_seed(2023)
dtype = torch.float16
plan = cutlass.op.Conv2d(kind="wgrad", element=dtype, element_accumulator=torch.float32)
op = plan.construct()
with tempfile.TemporaryDirectory() as tmpdir:
mod = cutlass.emit.pytorch(op, name="conv2d_wgrad_mod", cc=plan.cc, sourcedir=tmpdir, jit=True)
problem_size = cutlass.shape.Conv2DProblemSize(
1, 4, 4, 16,
8, 3, 3, 16,
0, 0,
3, 3,
1, 1,
cutlass.ConvMode.CrossCorrelation,
1, 1
)
A, B, C = _generate_conv2d_problem("wgrad", dtype, problem_size)
stride = (problem_size.stride_h, problem_size.stride_w)
padding = (problem_size.pad_h, problem_size.pad_w)
alpha = 1.0
beta = 0.5
weight_size = (problem_size.K, problem_size.C, problem_size.R, problem_size.S)
D_ref = alpha * torch.nn.grad.conv2d_weight(
B, weight_size, A,
stride=stride, padding=padding
) + beta * C
D = mod.run(weight_size, A, B, C, stride, padding, alpha=alpha, beta=beta)
assert torch.allclose(D, D_ref)
# Test serial split-K
D_serial_split_k = mod.run(weight_size, A, B, C, stride, padding, alpha=alpha, beta=beta, split_k_mode="serial", split_k_slices=3)
assert torch.allclose(D, D_serial_split_k)
# Test parallel split-K
D_parallel_split_k = mod.run(weight_size, A, B, C, stride, padding, alpha=alpha, beta=beta, split_k_mode="parallel", split_k_slices=7)
assert torch.allclose(D, D_parallel_split_k)
if __name__ == '__main__':
unittest.main()