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/***************************************************************************************************
* Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
* 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.
*
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* 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
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Implements several possible threadblock-swizzling functions mapping blockIdx to
Convolution problems.
*/
#pragma once
#include "cutlass/cutlass.h"
#include "cutlass/layout/matrix.h"
#include "cutlass/platform/platform.h"
#include "cutlass/gemm/gemm.h"
#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
#include "cutlass/conv/convolution.h"
#include "cutlass/conv/conv2d_problem_size.h"
/////////////////////////////////////////////////////////////////////////////////////////////////
namespace cutlass {
namespace conv {
namespace threadblock {
/////////////////////////////////////////////////////////////////////////////////////////////////
CUTLASS_HOST_DEVICE
static int get_strided_dgrad_tile_m(
cutlass::conv::Conv2dProblemSize const &problem_size,
int tile_size_m) {
// CTAs in M dimension per starting filter position
int tile_m_per_filter = strided_dgrad_tile_m_per_filter(problem_size, tile_size_m);
// Inflate number of CTAs in M dimension to cover every strating filter position even those that
// may fall out of valid MMA (Dy * w) but are needed to apply epilogue (beta * Dx_source)
// and point-wise fusion
int tile_m = tile_m_per_filter * int(problem_size.stride().product());
// There is a possible performance optimization here that leads up to 2x speeds than the current
// CUTLASS strided dgrad performance for stride > filter, i.e., stride={2x2} and filter={1x1})
//
// * Optimization *
// Only launch CTAs in M dimension which contribute to a row in Dx output
//
//
// * Constraints *
// (A) stride <= filter, for example, stride={2x2} and filter={3x3}:
// - (A.1): There are no constraints for this case and the optimization does
// affect this case functionality or performance.
// (B) stride > filter, for example, stride={2x2} and filter={1x1}:
// - (B.1): Dx output tensor should be zero initialized
// - (B.2): The kernel epilogue cannot apply beta. Thus, beta should be zero
return tile_m;
}
/////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////////////////////
/// Threadblock swizzling function for strided dgrad convolution
struct StridedDgradHorizontalThreadblockSwizzle :
public gemm::threadblock::GemmHorizontalThreadblockSwizzle {
using Base = gemm::threadblock::GemmHorizontalThreadblockSwizzle;
CUTLASS_HOST_DEVICE
StridedDgradHorizontalThreadblockSwizzle() { }
/// Returns the shape of the problem in units of logical tiles
/// For ImplicitGemmConvolution Conv2d problem size: conv_operator(NPQK, NHWC, KRSC)
CUTLASS_HOST_DEVICE
static gemm::GemmCoord get_tiled_shape(
cutlass::conv::Operator conv_operator,
cutlass::conv::Conv2dProblemSize const &problem_size,
gemm::GemmCoord tile_size,
int split_k_slices) {
gemm::GemmCoord implicit_gemm_problem_size =
cutlass::conv::implicit_gemm_problem_size(conv_operator, problem_size);
// compute number of tiles in m dimension
int tile_m = get_strided_dgrad_tile_m(problem_size, tile_size.m());
// compute number of tiles in n dimension
int tile_n = (implicit_gemm_problem_size.n() + tile_size.n() - 1) / tile_size.n();
return gemm::GemmCoord(
tile_m,
tile_n,
split_k_slices);
}
/// Returns the shape of the problem in units of logical tiles
/// For GEMM problem size (MxNxK) (Do not use base class get_tiled_shape())
private:
using Base::get_tiled_shape;
};
/////////////////////////////////////////////////////////////////////////////////////////////////
/// Threadblock swizzling function for strided dgrad convolution
template <int N = 1>
struct StridedDgradIdentityThreadblockSwizzle :
public gemm::threadblock::GemmIdentityThreadblockSwizzle<N> {
using Base = gemm::threadblock::GemmIdentityThreadblockSwizzle<N>;
CUTLASS_HOST_DEVICE
StridedDgradIdentityThreadblockSwizzle() { }
/// Returns the shape of the problem in units of logical tiles
/// For ImplicitGemmConvolution Conv2d problem size: conv_operator(NPQK, NHWC, KRSC)
CUTLASS_HOST_DEVICE
static gemm::GemmCoord get_tiled_shape(
cutlass::conv::Operator conv_operator,
cutlass::conv::Conv2dProblemSize const &problem_size,
gemm::GemmCoord tile_size,
int split_k_slices) {
gemm::GemmCoord implicit_gemm_problem_size =
cutlass::conv::implicit_gemm_problem_size(conv_operator, problem_size);
// compute number of tiles in m dimension
int tile_m = get_strided_dgrad_tile_m(problem_size, tile_size.m());
// compute number of tiles in n dimension
int tile_n = (implicit_gemm_problem_size.n() + tile_size.n() - 1) / tile_size.n();
return gemm::GemmCoord(
tile_m,
tile_n,
split_k_slices);
}
/// Returns the shape of the problem in units of logical tiles
/// For GEMM problem size (MxNxK) (Do not use base class get_tiled_shape())
private:
using Base::get_tiled_shape;
};
/////////////////////////////////////////////////////////////////////////////////////////////////
/// Threadblock swizzling function for GEMMs
template <int N = 1, int Output_N = 1, int Output_P = 1, int Output_Q = 1>
struct DepthwiseDirect2dConvIdentityThreadblockSwizzle
: public gemm::threadblock::GemmIdentityThreadblockSwizzle<N> {
CUTLASS_HOST_DEVICE
DepthwiseDirect2dConvIdentityThreadblockSwizzle() {}
/// Returns the shape of the problem in units of logical tiles
CUTLASS_HOST_DEVICE
static gemm::GemmCoord get_tiled_shape(cutlass::conv::Operator conv_operator,
cutlass::conv::Conv2dProblemSize const &problem_size,
gemm::GemmCoord tile_size,
int split_k_slices) {
gemm::GemmCoord implicit_gemm_problem_size =
cutlass::conv::implicit_gemm_problem_size(conv_operator, problem_size);
return gemm::GemmCoord(1,
(implicit_gemm_problem_size.n() + tile_size.n() - 1) / tile_size.n(),
split_k_slices);
}
};
} // namespace threadblock
} // namespace conv
} // namespace cutlass
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