312 lines
12 KiB
Python
312 lines
12 KiB
Python
#################################################################################################
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#
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# Copyright (c) 2023 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: BSD-3-Clause
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# 1. Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# 3. Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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#################################################################################################
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"""
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Tests emitting a CUTLASS kernel to a PyTorch CUDA extension
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"""
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import random
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import tempfile
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import unittest
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import cutlass
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import cutlass_bindings
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if cutlass.utils.datatypes.torch_available:
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import torch
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def _initialize(dtype, M: int, N: int, K: int):
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"""
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Utility function to initialize A, B, C, and D matrices corresponding to dimensions M, N, and K
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:param dtype: data type of tensors
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:param M: M dimension of GEMM problem
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:type M: int
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:param N: N dimension of GEMM problem
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:type N: int
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:param K: N dimension of GEMM problem
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:type K: int
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:return: initialized tensors A, B, C, and D
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:rtype: list
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"""
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sizes = [(M, K), (K, N), (M, N), (M, N)]
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return [torch.randint(-3, 3, size, device='cuda').to(dtype) for size in sizes]
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def _generate_problems(dtype, num):
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"""
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Utility function to generate `num` GEMMs of random sizes
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:param dtype: data type of tensors
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:param num: number of GEMMs to generate
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:type num: int
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:return: lists of A, B, C, and D tensors
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:rtype: list
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"""
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valid_sizes = [128, 256, 512, 1024]
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As, Bs, Cs, Ds = [], [], [], []
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for _ in range(num):
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M, N, K = [random.choice(valid_sizes) for _ in range(3)]
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A, B, C, D = _initialize(dtype, M, N, K)
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As.append(A)
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Bs.append(B)
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Cs.append(C)
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Ds.append(D)
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return As, Bs, Cs, Ds
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def _generate_conv2d_problem(conv_kind, dtype, ps):
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"""
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Utility function to generate conv2d inputs
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:param conv_kind: kind of convolution
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:type conv_kind: str
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:param dtype: data type of tensors
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:param problem_size: the conv2d problem size
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:type problem_size: cutlass_bindings.conv.Conv2dProblemSize
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:return: initialized tensors A, B, C, and D
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:rtype: list
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"""
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if conv_kind == "fprop":
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tensor_A_size = (ps.N, ps.C, ps.H, ps.W)
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tensor_B_size = (ps.K, ps.C, ps.R, ps.S)
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tensor_C_size = (ps.N, ps.K, ps.P, ps.Q)
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elif conv_kind == "dgrad":
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tensor_A_size = (ps.N, ps.K, ps.P, ps.Q)
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tensor_B_size = (ps.K, ps.C, ps.R, ps.S)
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tensor_C_size = (ps.N, ps.C, ps.H, ps.W)
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else:
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tensor_A_size = (ps.N, ps.K, ps.P, ps.Q)
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tensor_B_size = (ps.N, ps.C, ps.H, ps.W)
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tensor_C_size = (ps.K, ps.C, ps.R, ps.S)
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sizes = [tensor_A_size, tensor_B_size, tensor_C_size]
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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]
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@unittest.skipIf(not cutlass.utils.datatypes.torch_available, 'PyTorch must be available to run PyTorch extension tests')
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class PyTorchExtensionTest(unittest.TestCase):
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def test_gemm(self):
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random.seed(2023)
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dtype = torch.float16
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plan = cutlass.op.Gemm(element=dtype, layout=cutlass.LayoutType.RowMajor)
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plan.activation = cutlass.epilogue.relu
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op = plan.construct()
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with tempfile.TemporaryDirectory() as tmpdir:
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mod = cutlass.emit.pytorch(op, name='gemm_mod', cc=plan.cc, sourcedir=tmpdir, jit=True)
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A, B, C, _ = _initialize(dtype, 1024, 256, 512)
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D_ref = torch.nn.functional.relu(A @ B)
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D = mod.run(A, B)
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assert torch.allclose(D, D_ref)
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D = mod.run(A, B, C)
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assert torch.allclose(D, D_ref)
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D = mod.run(A, B, C, 1.0)
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assert torch.allclose(D, D_ref)
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D = mod.run(A, B, C, 1.0, 0.0)
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assert torch.allclose(D, D_ref)
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alpha = 2.0
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beta = -1.0
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D_ref = torch.nn.functional.relu((A @ B) * alpha + (beta * C))
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D = mod.run(A, B, C, alpha, beta)
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assert torch.allclose(D, D_ref)
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def test_grouped_gemm(self):
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random.seed(2023)
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dtype = torch.float16
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plan = cutlass.op.GroupedGemm(element=dtype, layout=cutlass.LayoutType.RowMajor)
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op = plan.construct()
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with tempfile.TemporaryDirectory() as tmpdir:
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mod = cutlass.emit.pytorch(op, name='grouped_gemm_mod', cc=plan.cc, sourcedir=tmpdir, jit=True)
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As, Bs, Cs, _ = _generate_problems(dtype, 50)
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def check_all(X, Y):
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for x, y in zip(X, Y):
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assert torch.allclose(x, y)
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Ds_ref = [a @ b for a, b in zip(As, Bs)]
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Ds = mod.run(As, Bs)
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check_all(Ds, Ds_ref)
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Ds = mod.run(As, Bs, Cs)
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check_all(Ds, Ds_ref)
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Ds = mod.run(As, Bs, Cs, 1.0)
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check_all(Ds, Ds_ref)
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Ds = mod.run(As, Bs, Cs, 1.0, 0.0)
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check_all(Ds, Ds_ref)
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alpha = 2.0
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beta = -1.0
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Ds_ref = [(a @ b) * alpha + (beta * c) for a, b, c in zip(As, Bs, Cs)]
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Ds = mod.run(As, Bs, Cs, alpha, beta)
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check_all(Ds, Ds_ref)
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def test_conv2d_fprop(self):
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torch.manual_seed(2023)
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dtype = torch.float16
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plan = cutlass.op.Conv2d(kind="fprop", element=dtype, element_accumulator=torch.float32)
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plan.activation = "relu"
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op = plan.construct()
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with tempfile.TemporaryDirectory() as tmpdir:
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mod = cutlass.emit.pytorch(op, name="conv2d_mod", cc=plan.cc, sourcedir=tmpdir, jit=True)
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problem_size = cutlass_bindings.conv.Conv2dProblemSize(
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cutlass_bindings.Tensor4DCoord(1, 4, 4, 16),
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cutlass_bindings.Tensor4DCoord(8, 3, 3, 16),
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cutlass_bindings.Tensor4DCoord(0, 0, 0, 0),
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cutlass_bindings.MatrixCoord(3, 3),
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cutlass_bindings.MatrixCoord(1, 1),
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cutlass_bindings.conv.Mode.cross_correlation,
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1, 1
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)
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A, B, C = _generate_conv2d_problem("fprop", dtype, problem_size)
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stride = (problem_size.stride_h, problem_size.stride_w)
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padding = (problem_size.pad_h, problem_size.pad_w)
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alpha = 1.0
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beta = 0.5
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D_ref = alpha * torch.ops.aten.conv2d(
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A, B, stride=stride, padding=padding
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) + beta * C
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D_ref = torch.nn.functional.relu(D_ref)
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D = mod.run(A, B, C, stride, padding, alpha=alpha, beta=beta)
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assert torch.allclose(D, D_ref)
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# Test serial split-K
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D_serial_split_k = mod.run(A, B, C, stride, padding, alpha=alpha, beta=beta, split_k_mode="serial", split_k_slices=3)
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assert torch.allclose(D, D_serial_split_k)
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# Test parallel split-K
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D_parallel_split_k = mod.run(A, B, C, stride, padding, alpha=alpha, beta=beta, split_k_mode="parallel", split_k_slices=7)
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assert torch.allclose(D, D_parallel_split_k)
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def test_conv2d_dgrad(self):
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torch.manual_seed(2023)
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dtype = torch.float16
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plan = cutlass.op.Conv2d(kind="dgrad", element=dtype, element_accumulator=torch.float32)
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op = plan.construct()
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with tempfile.TemporaryDirectory() as tmpdir:
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mod = cutlass.emit.pytorch(op, name="conv2d_dgrad_mod", cc=plan.cc, sourcedir=tmpdir, jit=True)
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problem_size = cutlass_bindings.conv.Conv2dProblemSize(
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cutlass_bindings.Tensor4DCoord(1, 4, 4, 16),
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cutlass_bindings.Tensor4DCoord(8, 3, 3, 16),
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cutlass_bindings.Tensor4DCoord(0, 0, 0, 0),
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cutlass_bindings.MatrixCoord(3, 3),
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cutlass_bindings.MatrixCoord(1, 1),
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cutlass_bindings.conv.Mode.cross_correlation,
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1, 1
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)
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A, B, C = _generate_conv2d_problem("dgrad", dtype, problem_size)
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stride = (problem_size.stride_h, problem_size.stride_w)
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padding = (problem_size.pad_h, problem_size.pad_w)
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alpha = 1.0
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beta = 0.5
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input_size = (problem_size.N, problem_size.C, problem_size.H, problem_size.W)
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D_ref = alpha * torch.nn.grad.conv2d_input(
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input_size, B, A,
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stride=stride, padding=padding
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) + beta * C
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D = mod.run(input_size, A, B, C, stride, padding, alpha=alpha, beta=beta, )
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assert torch.allclose(D, D_ref)
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def test_conv2d_wgrad(self):
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torch.manual_seed(2023)
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dtype = torch.float16
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plan = cutlass.op.Conv2d(kind="wgrad", element=dtype, element_accumulator=torch.float32)
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op = plan.construct()
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with tempfile.TemporaryDirectory() as tmpdir:
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mod = cutlass.emit.pytorch(op, name="conv2d_wgrad_mod", cc=plan.cc, sourcedir=tmpdir, jit=True)
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problem_size = cutlass_bindings.conv.Conv2dProblemSize(
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cutlass_bindings.Tensor4DCoord(1, 4, 4, 16),
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cutlass_bindings.Tensor4DCoord(8, 3, 3, 16),
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cutlass_bindings.Tensor4DCoord(0, 0, 0, 0),
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cutlass_bindings.MatrixCoord(3, 3),
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cutlass_bindings.MatrixCoord(1, 1),
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cutlass_bindings.conv.Mode.cross_correlation,
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1, 1
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)
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A, B, C = _generate_conv2d_problem("wgrad", dtype, problem_size)
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stride = (problem_size.stride_h, problem_size.stride_w)
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padding = (problem_size.pad_h, problem_size.pad_w)
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alpha = 1.0
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beta = 0.5
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weight_size = (problem_size.K, problem_size.C, problem_size.R, problem_size.S)
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D_ref = alpha * torch.nn.grad.conv2d_weight(
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B, weight_size, A,
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stride=stride, padding=padding
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) + beta * C
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D = mod.run(weight_size, A, B, C, stride, padding, alpha=alpha, beta=beta)
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assert torch.allclose(D, D_ref)
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# Test serial split-K
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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)
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assert torch.allclose(D, D_serial_split_k)
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# Test parallel split-K
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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)
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assert torch.allclose(D, D_parallel_split_k)
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if __name__ == '__main__':
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unittest.main()
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