178 lines
7.1 KiB
Python
178 lines
7.1 KiB
Python
################################################################################
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#
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# Copyright (c) 2017 - 2024 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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Basic example of using the CUTLASS Python interface to run a 2d convolution
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"""
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import sys
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print("This example is deprecated. Please see examples/python for examples of using "
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"the CUTLASS Python interface.")
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sys.exit(0)
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import argparse
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import numpy as np
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import torch
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import cutlass_bindings
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import cutlass.backend as pycutlass
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from cutlass.backend import *
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from cutlass.backend.utils.reference_model import Conv2dReferenceModule
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from cutlass.backend.utils.device import device_cc
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parser = argparse.ArgumentParser(
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description=("Launch a 2d convolution kernel from Python. "
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"See https://docs.nvidia.com/deeplearning/performance/dl-performance-convolutional/index.html#convo-intro for notation."))
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parser.add_argument("--n", default=1, type=int, help="N dimension of the convolution")
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parser.add_argument("--c", default=64, type=int, help="C dimension of the convolution")
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parser.add_argument("--h", default=32, type=int, help="H dimension of the convolution")
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parser.add_argument("--w", default=32, type=int, help="W dimension of the convolution")
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parser.add_argument("--k", default=32, type=int, help="N dimension of the convolution")
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parser.add_argument("--r", default=3, type=int, help="R dimension of the convolution")
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parser.add_argument("--s", default=3, type=int, help="S dimension of the convolution")
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parser.add_argument('--print_cuda', action="store_true", help="Print the underlying CUDA kernel")
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try:
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args = parser.parse_args()
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except:
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sys.exit(0)
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# Check that the device is of a sufficient compute capability
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cc = device_cc()
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assert cc >= 70, "The CUTLASS Python Conv2d example requires compute capability greater than or equal to 70."
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alignment = 1
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np.random.seed(0)
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# Allocate a pool of device memory to be used by the kernel
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pycutlass.get_memory_pool(init_pool_size=2**30, max_pool_size=2**32)
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# Set the compiler to use to NVCC
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pycutlass.compiler.nvcc()
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# Set up A, B, C and accumulator
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A = TensorDescription(cutlass_bindings.float16, cutlass_bindings.TensorNHWC, alignment)
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B = TensorDescription(cutlass_bindings.float16, cutlass_bindings.TensorNHWC, alignment)
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C = TensorDescription(cutlass_bindings.float32, cutlass_bindings.TensorNHWC, alignment)
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element_acc = cutlass_bindings.float32
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element_epilogue = cutlass_bindings.float32
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# Select instruction shape based on the Tensor Core instructions supported
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# by the device on which we are running
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if cc == 70:
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instruction_shape = [8, 8, 4]
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elif cc == 75:
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instruction_shape = [16, 8, 8]
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else:
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# Use CUTLASS kernels for CC 80 by default (e.g., for cases in which SM86 is used)
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cc = 80
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instruction_shape = [16, 8, 16]
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math_inst = MathInstruction(
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instruction_shape,
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A.element, B.element, element_acc,
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cutlass_bindings.OpClass.TensorOp,
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MathOperation.multiply_add
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)
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tile_description = TileDescription(
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[128, 128, 32], # Threadblock shape
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2, # Number of stages
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[2, 2, 1], # Number of warps within each dimension of the threadblock shape
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math_inst
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)
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epilogue_functor = pycutlass.LinearCombination(C.element, C.alignment, element_acc, element_epilogue)
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operation = Conv2dOperation(
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conv_kind=cutlass_bindings.conv.Operator.fprop,
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iterator_algorithm=cutlass_bindings.conv.IteratorAlgorithm.optimized,
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arch=cc, tile_description=tile_description,
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A=A, B=B, C=C, stride_support=StrideSupport.Strided,
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epilogue_functor=epilogue_functor
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)
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if args.print_cuda:
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print(operation.rt_module.emit())
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operations = [operation, ]
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# Compile the operation
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pycutlass.compiler.add_module(operations)
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# Randomly initialize tensors
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problem_size = cutlass_bindings.conv.Conv2dProblemSize(
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cutlass_bindings.Tensor4DCoord(args.n, args.h, args.c, args.w),
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cutlass_bindings.Tensor4DCoord(args.k, args.r, args.s, args.c),
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cutlass_bindings.Tensor4DCoord(0, 0, 0, 0), # Padding
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cutlass_bindings.MatrixCoord(1, 1), # Strides
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cutlass_bindings.MatrixCoord(1, 1), # Dilation
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cutlass_bindings.conv.Mode.cross_correlation,
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1, # Split k slices
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1 # Groups
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)
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tensor_A_size = cutlass_bindings.conv.implicit_gemm_tensor_a_size(operation.conv_kind, problem_size)
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tensor_B_size = cutlass_bindings.conv.implicit_gemm_tensor_b_size(operation.conv_kind, problem_size)
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tensor_C_size = cutlass_bindings.conv.implicit_gemm_tensor_c_size(operation.conv_kind, problem_size)
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tensor_A = torch.ceil(torch.empty(size=(tensor_A_size,), dtype=torch.float16, device="cuda").uniform_(-8.5, 7.5))
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tensor_B = torch.ceil(torch.empty(size=(tensor_B_size,), dtype=torch.float16, device="cuda").uniform_(-8.5, 7.5))
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tensor_C = torch.ceil(torch.empty(size=(tensor_C_size,), dtype=torch.float32, device="cuda").uniform_(-8.5, 7.5))
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tensor_D = torch.ones(size=(tensor_C_size,), dtype=torch.float32, device="cuda")
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alpha = 1.
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beta = 0.
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arguments = Conv2dArguments(
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operation=operation, problem_size=problem_size,
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A=tensor_A, B=tensor_B, C=tensor_C, D=tensor_D,
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output_op=operation.epilogue_type(alpha, beta)
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)
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# Run the operation
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operation.run(arguments)
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arguments.sync()
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# Run the host reference module and compare to the CUTLASS result
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reference = Conv2dReferenceModule(A, B, C, operation.conv_kind)
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tensor_D_ref = reference.run(tensor_A, tensor_B, tensor_C, problem_size, alpha, beta)
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try:
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assert torch.equal(tensor_D, tensor_D_ref)
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except:
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assert torch.allclose(tensor_D, tensor_D_ref, rtol=1e-2)
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print("Passed.")
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