656 lines
25 KiB
Python
656 lines
25 KiB
Python
################################################################################
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#
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# Copyright (c) 2017 - 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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# from typeguard import typechecked
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import ctypes
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from typing import Union
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from cuda import cuda
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import cutlass_bindings
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import numpy as np
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from cutlass.backend.arguments import ArgumentBase
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from cutlass.backend.c_types import Conv2DProblemSize, TensorRef_, get_conv2d_arguments
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from cutlass.backend.library import (
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ConvKindNames,
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ConvKindTag,
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DataTypeNames,
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DataTypeSize,
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DataTypeTag,
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IteratorAlgorithmNames,
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IteratorAlgorithmTag,
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LayoutTag,
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MathOperation,
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MathOperationTag,
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OpcodeClassNames,
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OpcodeClassTag,
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OperationKind,
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ShortDataTypeNames,
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ShortLayoutTypeNames,
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StrideSupport,
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StrideSupportTag,
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TensorDescription,
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TileDescription,
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get_complex_from_real,
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)
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from cutlass.backend.memory_manager import device_mem_alloc
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from cutlass.backend.operation import ExecutableOperation, LaunchConfiguration
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from cutlass.backend.tensor_ref import TensorRef
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from cutlass.backend.utils.software import CheckPackages, SubstituteTemplate
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if CheckPackages().check_torch():
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import torch
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# @typechecked
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class Conv2dArguments(ArgumentBase):
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"""
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Argument wrapper for Conv2d. It encodes problem information and
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user-provide tensors into the kernel's argument.
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:param operation: the Conv2d operation to take the argument
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:type operation: :class:`cutlass.backend.Conv2dOperation`
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:param problem_size: the Conv2d problem size
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:type problem_size: :class:`cutlass_bindings.conv.Conv2dProblemSize`
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:param A: tensor A
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:type A: cuda.CUdeviceptr | numpy.ndarray | torch.Tensor | cupy.ndarray
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:param B: tensor B
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:type B: cuda.CUdeviceptr | numpy.ndarray | torch.Tensor | cupy.ndarray
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:param C: tensor C
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:type C: cuda.CUdeviceptr | numpy.ndarray | torch.Tensor | cupy.ndarray
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:param D: tensor D
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:type D: cuda.CUdeviceptr | numpy.ndarray | torch.Tensor | cupy.ndarray
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:param split_k_mode: conv2d split K mode, defaults to cutlass_bindings.conv.SplitKMode.Serial
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:type split_k_mode: cutlass_bindings.conv.SplitKMode, optional
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:param output_op: output operator, optional
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:type output_op: :class:`cutlass.backend.LinearCombinationFunctorArguments`
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"""
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def __init__(
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self,
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operation: "Conv2dOperation",
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problem_size: "cutlass_bindings.conv.Conv2dProblemSize",
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A: "Union[cuda.CUdeviceptr, np.ndarray, torch.Tensor]",
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B: "Union[cuda.CUdeviceptr, np.ndarray, torch.Tensor]",
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C: "Union[cuda.CUdeviceptr, np.ndarray, torch.Tensor]",
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D: "Union[cuda.CUdeviceptr, np.ndarray, torch.Tensor]",
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split_k_mode: "cutlass_bindings.conv.SplitKMode" = cutlass_bindings.conv.SplitKMode.Serial,
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**kwargs,
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) -> None:
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self.operation = operation
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#: convolution kind
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self.conv_kind: cutlass_bindings.conv.Operator = operation.conv_kind
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self.layout_A: cutlass_bindings.layout = operation.A.layout
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self.layout_B: cutlass_bindings.layout = operation.B.layout
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self.layout_C: cutlass_bindings.layout = operation.C.layout
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self.element_A = operation.A.element
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self.element_B = operation.B.element
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self.element_C = operation.C.element
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if self.layout_C == cutlass_bindings.TensorNC32HW32:
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B = self.reorder_tensor_B(B, problem_size)
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super().__init__(A, B, C, D, **kwargs)
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# preprocessing output ops
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if "output_op" in kwargs.keys() and split_k_mode != cutlass_bindings.conv.SplitKMode.Parallel:
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self.output_op = kwargs["output_op"]
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else:
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self.output_op = self.operation.epilogue_type(1.0, 0.0)
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if "split_k_slices" in kwargs.keys():
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self.split_k_mode = split_k_mode
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self.split_k_slices = kwargs["split_k_slices"]
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else:
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self.split_k_mode = cutlass_bindings.conv.SplitKMode.Serial
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self.split_k_slices = 1
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#: problem_size
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self.problem_size: cutlass_bindings.conv.Conv2dProblemSize = problem_size
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self.problem_size.split_k_slices = self.split_k_slices
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if hasattr(self, "tensor_c_numel"):
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c_coord = cutlass_bindings.conv.implicit_gemm_tensor_c_extent(
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self.conv_kind, problem_size)
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if self.tensor_c_numel == c_coord.at(3) and self.tensor_c_numel < c_coord.size():
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self.bias = True
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#
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# initialize the argument
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#
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self.initialize()
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# @typechecked
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def reorder_tensor_B(self, tensor_B: "np.ndarray",
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problem_size: "cutlass_bindings.conv.Conv2dProblemSize"):
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"""
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Reorder tensor_B for interleaved layout
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:param tensor_B: input tensor B
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:type tensor_B: numpy.ndarray
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:param problem_size: Conv2d problem size
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:type problem_size: :class:`cutlass_bindings.conv.Conv2dProblemSize`
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:return: reordered tensor B
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:rtype: numpy.ndarray
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"""
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reordered_tensor_B = np.empty_like(tensor_B)
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tensor_ref_B = self.get_tensor_ref(
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tensor_B, self.element_B, self.layout_B, problem_size, "b")
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reordered_tensor_ref_B = self.get_tensor_ref(
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reordered_tensor_B, self.element_B, self.layout_B, problem_size, "b")
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cutlass_bindings.conv.host.reorder_convK(
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reordered_tensor_ref_B, tensor_ref_B, self.conv_kind, problem_size)
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return reordered_tensor_B
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def get_tensor_ref(
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self, tensor, dtype, tensor_layout, problem_size, operand):
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if operand == "a":
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tensor_coord = cutlass_bindings.conv.implicit_gemm_tensor_a_extent(
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self.conv_kind, problem_size)
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elif operand == "b":
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tensor_coord = cutlass_bindings.conv.implicit_gemm_tensor_b_extent(
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self.conv_kind, problem_size)
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elif operand in ["c", "d"]:
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tensor_coord = cutlass_bindings.conv.implicit_gemm_tensor_c_extent(
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self.conv_kind, problem_size)
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else:
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raise ValueError("unknown operand: " + operand)
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# Zero stride trick
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if operand == "c" and self.bias:
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tensor_coord = cutlass_bindings.Tensor4DCoord(0, 0, 0, 0)
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layout = tensor_layout.packed(tensor_coord)
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return TensorRef(tensor, dtype, layout).tensor_ref
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def get_arguments(self, semaphore):
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ref_A = TensorRef_(self.get_tensor_ref(
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self.ptr_A, self.element_A, self.layout_A, self.problem_size, "a"))
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ref_B = TensorRef_(self.get_tensor_ref(
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self.ptr_B, self.element_B, self.layout_B, self.problem_size, "b"))
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ref_C = TensorRef_(self.get_tensor_ref(
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self.ptr_C, self.element_C, self.layout_C, self.problem_size, "c"))
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ref_D = TensorRef_(self.get_tensor_ref(
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self.ptr_D, self.element_C, self.layout_C, self.problem_size, "d"))
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self.c_arguments = self.operation.argument_type(
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Conv2DProblemSize(self.problem_size),
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ref_A, ref_B, ref_C, ref_D, self.output_op, self.split_k_mode)
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self.semaphore = semaphore
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def initialize(self):
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# Get launch configuration
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self.launch_config = self.operation.rt_module.plan(self)
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# Allocate and initialize device workspace
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device_workspace_size = self.operation.rt_module.get_device_workspace_size(self)
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if device_workspace_size > 0:
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self.workspace_buffer = device_mem_alloc(device_workspace_size)
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workspace_ptr = self.workspace_buffer.ptr
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err, = cuda.cuMemsetD32(
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workspace_ptr, 0, device_workspace_size // 4)
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else:
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workspace_ptr = None
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# Get kernel params as a bytearray
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semaphore = 0
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if (workspace_ptr is not None
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and self.split_k_mode == cutlass_bindings.conv.SplitKMode.Parallel):
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self.ptr_D = workspace_ptr
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elif (workspace_ptr is not None
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and self.split_k_mode == cutlass_bindings.conv.SplitKMode.Serial):
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semaphore = workspace_ptr
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self.get_arguments(semaphore)
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params_ = self.operation.rt_module.get_args(
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ctypes.byref(self.c_arguments), ctypes.c_void_p(int(self.semaphore)))
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self.host_workspace = bytearray(params_.contents)
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self.device_workspace = None
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def sync(self):
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"""
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Synchronize the arguments. If the input tensor is in host,
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copy it from device to host.
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"""
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return super().sync()
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# @typechecked
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class Conv2dRT(ExecutableOperation):
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"""
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Conv2dRT manages the CUTLASS runtime components
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"""
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KernelTemplate = r"""
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extern "C"
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__global__ void
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${operation_name}(${operation_name}${operation_suffix}::Params params) {
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// Dynamic shared memory base pointer
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extern __shared__ int SharedStorageBase[];
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// Declare pointer to dynamic shared memory.
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${operation_name}${operation_suffix}::SharedStorage *shared_storage =
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reinterpret_cast<${operation_name}${operation_suffix}::SharedStorage *>(SharedStorageBase);
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${operation_name}${operation_suffix} op;
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op(params, *shared_storage);
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}
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"""
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HostTemplate = r"""
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extern "C" {
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// Get the size of params in bytes
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int ${operation_name}_get_param_size(){
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return sizeof(${operation_name}${operation_suffix}::Params);
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}
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// Get the size of dynamic shared memory in bytes
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int ${operation_name}_shared_memory_size() {
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return int(sizeof(${operation_name}${operation_suffix}::SharedStorage));
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}
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// Get the params as byte array
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char* ${operation_name}_get_params(${operation_name}${operation_suffix}::Arguments* arguments, int *semaphore=nullptr){
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typename ${operation_name}${operation_suffix}::Params* params;
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params = new ${operation_name}${operation_suffix}::Params(*arguments, semaphore);
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char *bytes = ((char*)(params));
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char *output = new char[sizeof(${operation_name}${operation_suffix}::Params)];
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for (unsigned int i = 0; i < sizeof(${operation_name}${operation_suffix}::Params); i ++)
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output[i] = bytes[i];
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return output;
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}
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}
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"""
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def __init__(self, operation: "Conv2dOperation"):
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super().__init__(operation)
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self.argument_type, self.epilogue_type = get_conv2d_arguments(operation.epilogue_functor)
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self.argtype = [ctypes.POINTER(self.argument_type), ctypes.c_void_p]
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self.conv_kind = operation.conv_kind
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self.operation: Conv2dOperation = operation
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self.emitter = EmitConv2dInstance("_type")
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self.threads: int = operation.tile_description.num_threads
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self.swizzle_functor = operation.swizzling_functor
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def emit(self):
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return self.emitter.emit(self.operation)
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def get_device_workspace_size(self, arguments: Conv2dArguments):
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workspace_bytes = 0
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launch_config = arguments.launch_config
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self.conv_kind = self.operation.conv_kind
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if arguments.split_k_mode == cutlass_bindings.conv.SplitKMode.Parallel:
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problem_size = arguments.problem_size
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workspace_bytes = DataTypeSize[self.operation.C.element] \
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* launch_config.grid[2] * cutlass_bindings.conv.implicit_gemm_tensor_c_size(
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self.conv_kind, problem_size
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) // 8
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elif arguments.split_k_mode == cutlass_bindings.conv.SplitKMode.Serial and \
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arguments.split_k_slices > 1:
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workspace_bytes = launch_config.grid[0] * launch_config.grid[1] * 4
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return workspace_bytes
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# @typechecked
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def plan(self, arguments: Conv2dArguments):
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tile_size = cutlass_bindings.gemm.GemmCoord(
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self.operation.tile_description.threadblock_shape[0],
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self.operation.tile_description.threadblock_shape[1],
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self.operation.tile_description.threadblock_shape[2],
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)
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grid = self.swizzle_functor.get_grid_shape(
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self.swizzle_functor.get_tiled_shape(
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self.conv_kind, arguments.problem_size,
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tile_size, arguments.split_k_slices
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)
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)
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return LaunchConfiguration(
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[grid.x, grid.y, grid.z], [self.threads, 1, 1],
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self.shared_memory_capacity)
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def initialize(self):
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err, = cuda.cuFuncSetAttribute(
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self.kernel,
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attrib=cuda.CUfunction_attribute.CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES,
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value=self.shared_memory_capacity)
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if err != cuda.CUresult.CUDA_SUCCESS:
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raise RuntimeError("Cuda Error: {}".format(err))
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class Conv2dOperation:
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"""
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CUTLASS Conv2d operation description.
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:param conv_kind: convolution operator
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:type conv_kind: :class:`cutlass_bindings.conv.Operator`
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:param iterator_algorithm: Selects among several implementation
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variants trading off performance with simplicity
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:type iterator_algorithm: :class:`cutlass_bindings.conv.IteratorAlgorithm`
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:param arch: GPU compute capability (sm_xx)
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:type arch: int
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:param tile_description: tile description
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:type tile_description: :class:`cutlass.backend.TileDescription`
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:param A: tensor A description
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:type A: :class:`cutlass.backend.TensorDescription`
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:param B: tensor B description
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:type B: :class:`cutlass.backend.TensorDescription`
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:param C: tensor C description
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:type C: :class:`cutlass.backend.TensorDescription`
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:param D: tensor D description
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:type D: :class:`cutlass.backend.TensorDescription`
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:param element_epilogue: element type for computation in epilogue \
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:type element_epilogue: cutlass_bindings.int8 | cutlass_bindings.int32 | cutlass_bindings.float16 | \
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cutlass_bindings.bfloat16 | cutlass_bindings.float32 | cutlass_bindings.float64
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:param stride_support: distinguish among partial specializations that \
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accelerate certain problems where convolution stride is unit \
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:type stride_support: :class:`cutlass_bindings.conv.StrideSupport`
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:param epilogue_functor: convolution epilogue functor
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:type epilogue_functor: :class:`EpilogueFunctor`
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:param swizzling_functor: threadblock swizzling functor
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"""
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def __init__(
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self,
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conv_kind: cutlass_bindings.conv.Operator,
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iterator_algorithm: cutlass_bindings.conv.IteratorAlgorithm,
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arch: int,
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tile_description: TileDescription,
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A: TensorDescription,
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B: TensorDescription,
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C: TensorDescription,
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stride_support,
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epilogue_functor,
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swizzling_functor=cutlass_bindings.IdentitySwizzle1
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):
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self.operation_kind: OperationKind = OperationKind.Conv2d
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self.arch: int = arch
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self.tile_description: TileDescription = tile_description
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self.conv_kind = conv_kind
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self.A: TensorDescription = A
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self.B: TensorDescription = B
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self.C: TensorDescription = C
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self.epilogue_functor = epilogue_functor
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self.iterator_algorithm = iterator_algorithm
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self.stride_support = stride_support
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self.swizzling_functor = swizzling_functor()
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self.rt_module: Conv2dRT = Conv2dRT(self)
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self.argument_type = self.rt_module.argument_type
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self.epilogue_type = self.rt_module.epilogue_type
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def run(self, arguments: Conv2dArguments) -> cuda.CUresult:
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"""
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Launch the cuda kernel with input arguments
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:param arguments: conv2d arguments
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:type arguments: :class:`cutlass.backend.Conv2dArguments`
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"""
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# launch the kernel
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err = self.rt_module.run(
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arguments.host_workspace,
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arguments.device_workspace,
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arguments.launch_config,
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)
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if err != cuda.CUresult.CUDA_SUCCESS:
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raise RuntimeError("CUDA Error %s" % str(err))
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return err
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#
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# Get function name
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#
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def procedural_name(self):
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"""The full procedural name indicates architecture, extended name, tile size, and layout."""
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return self.configuration_name()
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#
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def configuration_name(self):
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"""The full procedural name indicates architecture, extended name, tile size, and layout."""
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opcode_class_name = OpcodeClassNames[
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self.tile_description.math_instruction.opcode_class
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]
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threadblock = "%dx%d_%dx%d" % (
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self.tile_description.threadblock_shape[0],
|
|
self.tile_description.threadblock_shape[1],
|
|
self.tile_description.threadblock_shape[2],
|
|
self.tile_description.stages,
|
|
)
|
|
|
|
if self.stride_support == StrideSupport.Unity:
|
|
configuration_name = "cutlass_sm${arch}_${opcode_class}_${extended_name}_${threadblock}_${layout}_unity_stride_align${alignment}"
|
|
else:
|
|
configuration_name = "cutlass_sm${arch}_${opcode_class}_${extended_name}_${threadblock}_${layout}_align${alignment}"
|
|
|
|
return SubstituteTemplate(
|
|
configuration_name,
|
|
{
|
|
"arch": str(self.arch),
|
|
"opcode_class": opcode_class_name,
|
|
"extended_name": self.extended_name(),
|
|
"threadblock": threadblock,
|
|
"layout": self.layout_name(),
|
|
"alignment": "%d" % self.A.alignment
|
|
},
|
|
)
|
|
|
|
#
|
|
def extended_name(self):
|
|
"""Append data types if they differ from compute type."""
|
|
if self.C.element != self.tile_description.math_instruction.element_accumulator and \
|
|
self.A.element != self.tile_description.math_instruction.element_accumulator:
|
|
extended_name = "${element_c}_${core_name}_${element_a}"
|
|
elif self.C.element == self.tile_description.math_instruction.element_accumulator and \
|
|
self.A.element != self.tile_description.math_instruction.element_accumulator:
|
|
extended_name = "${core_name}_${element_a}"
|
|
else:
|
|
extended_name = "${core_name}"
|
|
|
|
extended_name = SubstituteTemplate(extended_name, {
|
|
"element_a": DataTypeNames[self.A.element],
|
|
"element_c": DataTypeNames[self.C.element],
|
|
"core_name": self.core_name(),
|
|
})
|
|
|
|
return extended_name
|
|
|
|
#
|
|
def layout_name(self):
|
|
return "%s" % (ShortLayoutTypeNames[self.A.layout])
|
|
|
|
#
|
|
def core_name(self):
|
|
"""The basic operation kind is prefixed with a letter indicating the accumulation type."""
|
|
|
|
intermediate_type = ""
|
|
|
|
if self.tile_description.math_instruction.opcode_class == cutlass_bindings.OpClass.TensorOp:
|
|
inst_shape = "%dx%dx%d" % tuple(
|
|
self.tile_description.math_instruction.instruction_shape)
|
|
if self.tile_description.math_instruction.element_a != self.A.element and \
|
|
self.tile_description.math_instruction.element_a != self.accumulator_type():
|
|
intermediate_type = DataTypeNames[self.tile_description.math_instruction.element_a]
|
|
else:
|
|
inst_shape = ""
|
|
|
|
return "%s%s%s%s_%s" % (
|
|
ShortDataTypeNames[self.accumulator_type()],
|
|
inst_shape,
|
|
intermediate_type,
|
|
ConvKindNames[self.conv_kind],
|
|
IteratorAlgorithmNames[self.iterator_algorithm]
|
|
)
|
|
|
|
#
|
|
def is_complex(self):
|
|
complex_operators = [
|
|
MathOperation.multiply_add_complex,
|
|
MathOperation.multiply_add_complex_gaussian,
|
|
]
|
|
return self.tile_description.math_instruction.math_operation in complex_operators
|
|
|
|
#
|
|
def accumulator_type(self):
|
|
accum = self.tile_description.math_instruction.element_accumulator
|
|
|
|
if self.is_complex():
|
|
return get_complex_from_real(accum)
|
|
|
|
return accum
|
|
|
|
|
|
###################################################################################################
|
|
#
|
|
# Emits single instances of a CUTLASS device-wide operator
|
|
#
|
|
###################################################################################################
|
|
|
|
|
|
class EmitConv2dInstance:
|
|
def __init__(self, operation_suffix=""):
|
|
self.operation_suffix = operation_suffix
|
|
self.includes = [
|
|
"cutlass/cutlass.h",
|
|
"cutlass/conv/kernel/default_conv2d_fprop.h",
|
|
"cutlass/conv/kernel/default_conv2d_dgrad.h",
|
|
"cutlass/conv/kernel/default_conv2d_wgrad.h"
|
|
]
|
|
self.template = """
|
|
// Conv2d${conv_kind_name} ${iterator_algorithm_name} kernel instance "${operation_name}"
|
|
using ${operation_name}_base =
|
|
typename cutlass::conv::kernel::DefaultConv2d${conv_kind_name}<
|
|
${element_a},
|
|
${layout_a},
|
|
${element_b},
|
|
${layout_b},
|
|
${element_c},
|
|
${layout_c},
|
|
${element_accumulator},
|
|
${opcode_class},
|
|
${arch},
|
|
cutlass::gemm::GemmShape<${threadblock_shape_m}, ${threadblock_shape_n}, ${threadblock_shape_k}>,
|
|
cutlass::gemm::GemmShape<${warp_shape_m}, ${warp_shape_n}, ${warp_shape_k} >,
|
|
cutlass::gemm::GemmShape<${instruction_shape_m}, ${instruction_shape_n}, ${instruction_shape_k}>,
|
|
${epilogue_functor},
|
|
${swizzling_functor}, // cutlass::gemm::threadblock::GemmSplitKIdentityThreadblockSwizzle<>,
|
|
${stages},
|
|
${math_operator},
|
|
${iterator_algorithm},
|
|
${stride_support},
|
|
${align_a},
|
|
${align_b}
|
|
>::Kernel;
|
|
|
|
struct ${operation_name}${operation_suffix}:
|
|
public ${operation_name}_base { };
|
|
|
|
"""
|
|
|
|
def emit(self, operation):
|
|
warp_shape = [int(operation.tile_description.threadblock_shape[idx] /
|
|
operation.tile_description.warp_count[idx]) for idx in range(3)]
|
|
|
|
epilogue_vector_length = int(min(
|
|
operation.C.alignment * DataTypeSize[operation.C.element], 128) / DataTypeSize[operation.C.element])
|
|
|
|
values = {
|
|
"operation_name": operation.procedural_name(),
|
|
"operation_suffix": self.operation_suffix,
|
|
"conv_kind": ConvKindTag[operation.conv_kind],
|
|
"conv_kind_name": ConvKindNames[operation.conv_kind].capitalize(),
|
|
"element_a": DataTypeTag[operation.A.element],
|
|
"layout_a": LayoutTag[operation.A.layout],
|
|
"element_b": DataTypeTag[operation.B.element],
|
|
"layout_b": LayoutTag[operation.B.layout],
|
|
"element_c": DataTypeTag[operation.C.element],
|
|
"layout_c": LayoutTag[operation.C.layout],
|
|
"element_accumulator": DataTypeTag[operation.accumulator_type()],
|
|
"opcode_class": OpcodeClassTag[operation.tile_description.math_instruction.opcode_class],
|
|
"arch": "cutlass::arch::Sm%d" % operation.arch,
|
|
"threadblock_shape_m": str(operation.tile_description.threadblock_shape[0]),
|
|
"threadblock_shape_n": str(operation.tile_description.threadblock_shape[1]),
|
|
"threadblock_shape_k": str(operation.tile_description.threadblock_shape[2]),
|
|
"warp_shape_m": str(warp_shape[0]),
|
|
"warp_shape_n": str(warp_shape[1]),
|
|
"warp_shape_k": str(warp_shape[2]),
|
|
"instruction_shape_m": str(operation.tile_description.math_instruction.instruction_shape[0]),
|
|
"instruction_shape_n": str(operation.tile_description.math_instruction.instruction_shape[1]),
|
|
"instruction_shape_k": str(operation.tile_description.math_instruction.instruction_shape[2]),
|
|
"epilogue_vector_length": str(epilogue_vector_length),
|
|
"epilogue_functor": operation.epilogue_functor.emit(),
|
|
"swizzling_functor": operation.swizzling_functor.tag(),
|
|
"stages": str(operation.tile_description.stages),
|
|
"iterator_algorithm": IteratorAlgorithmTag[operation.iterator_algorithm],
|
|
"iterator_algorithm_name": IteratorAlgorithmNames[operation.iterator_algorithm].capitalize(),
|
|
"stride_support": StrideSupportTag[operation.stride_support],
|
|
"math_operator": "cutlass::arch::OpMultiplyAddComplex" if operation.is_complex() else MathOperationTag[operation.tile_description.math_instruction.math_operation],
|
|
"align_a": str(operation.A.alignment),
|
|
"align_b": str(operation.B.alignment),
|
|
}
|
|
|
|
return SubstituteTemplate(self.template, values)
|