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convolution.h
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/***************************************************************************************************
* Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * 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.
* * Neither the name of the NVIDIA CORPORATION 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 NVIDIA CORPORATION 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 Reference implementation for convolution in host-side code.
*/
#pragma once
#include "cutlass/coord.h"
#include "cutlass/functional.h"
#include "cutlass/layout/tensor.h"
#include "cutlass/numeric_conversion.h"
#include "cutlass/numeric_types.h"
#include "cutlass/tensor_ref.h"
#include "cutlass/tensor_view.h"
#include "cutlass/conv/convolution.h"
#include "cutlass/conv/conv2d_problem_size.h"
#include "cutlass/conv/conv3d_problem_size.h"
#include <iostream>
namespace cutlass {
namespace reference {
namespace host {
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Forward propagation
////////////////////////////////////////////////////////////////////////////////////////////////////
/// y = conv2d(x, w)
template <
typename ElementA,
typename LayoutA,
typename ElementB,
typename LayoutB,
typename ElementC,
typename LayoutC,
typename ElementCompute,
typename ElementAccumulator = ElementCompute,
typename ConvertOp = NumericConverter<ElementC, ElementCompute>,
typename InnerProductOp = multiply_add<ElementAccumulator>
>
void Conv2dFprop(
conv::Conv2dProblemSize problem_size,
TensorRef<ElementA, LayoutA> tensor_x,
TensorRef<ElementB, LayoutB> tensor_w,
TensorRef<ElementC, LayoutC> tensor_y_in,
TensorRef<ElementC, LayoutC> tensor_y_out,
ElementCompute alpha,
ElementCompute beta) {
ConvertOp convert_op;
InnerProductOp inner_product_op;
// Apply MMA and accumulate ElementAccumulator
for (int n = 0; n < problem_size.N; ++n) {
for (int p = 0; p < problem_size.P; ++p) {
for (int q = 0; q < problem_size.Q; ++q) {
for (int k = 0; k < problem_size.K; ++k) {
ElementAccumulator acc = ElementAccumulator();
for (int r = 0; r < problem_size.R; ++r) {
for (int s = 0; s < problem_size.S; ++s) {
for (int c = 0; c < problem_size.C; ++c) {
int filter_r = r;
int filter_s = s;
if (problem_size.mode == cutlass::conv::Mode::kConvolution) {
filter_r = problem_size.R - 1 - r;
filter_s = problem_size.S - 1 - s;
}
int h = p * problem_size.stride_h - problem_size.pad_h + filter_r * problem_size.dilation_h;
int w = q * problem_size.stride_w - problem_size.pad_w + filter_s * problem_size.dilation_w;
if (h >= 0 && h < problem_size.H && w >= 0 && w < problem_size.W) {
ElementA a = tensor_x.at({n, h, w, c});
ElementB b = tensor_w.at({k, r, s, c});
acc = inner_product_op(ElementAccumulator(a), ElementAccumulator(b), acc);
}
}
}
}
// Apply Epilogue, compute ElementCompute, convert and store ElementC
ElementC c_ref = ElementC();
if (beta != ElementCompute()) {
c_ref = tensor_y_in.at(cutlass::make_Coord(n, p, q, k));
}
tensor_y_out.at(cutlass::make_Coord(n, p, q, k)) =
convert_op(alpha * ElementCompute(acc) + beta * ElementCompute(c_ref));
}
}
}
}
}
/// Depthwise-separable convolution
template <typename ElementA,
typename LayoutA,
typename ElementB,
typename LayoutB,
typename ElementC,
typename LayoutC,
typename ElementAccumulator,
typename ElementCompute,
typename ConvertOp = NumericConverter<ElementC, ElementCompute>,
typename InnerProductOp = multiply_add<ElementAccumulator> >
void Depsep_Fprop(
cutlass::TensorView<ElementA, LayoutA> tensor_A,
cutlass::TensorView<ElementB, LayoutB> tensor_B,
cutlass::TensorView<ElementC, LayoutC> tensor_C,
ElementCompute alpha,
ElementCompute beta,
cutlass::Tensor4DCoord padding,
cutlass::Coord<2> conv_stride,
cutlass::Coord<2> dilation,
cutlass::conv::Mode mode = cutlass::conv::Mode::kCrossCorrelation) {
ConvertOp convert_op;
InnerProductOp inner_product_op;
// Apply MMA and accumulate ElementAccumulator
for (int n = 0; n < tensor_C.extent().n(); ++n) {
for (int p = 0; p < tensor_C.extent().h(); ++p) {
for (int q = 0; q < tensor_C.extent().w(); ++q) {
for (int g = 0; g < tensor_C.extent().c(); ++g) {
ElementAccumulator acc = ElementAccumulator();
for (int r = 0; r < tensor_B.extent().h(); ++r) {
for (int s = 0; s < tensor_B.extent().w(); ++s) {
if ((p * conv_stride[0] - padding[0] + r * dilation[0]) < tensor_A.extent().h() &&
(p * conv_stride[0] - padding[0] + r * dilation[0]) >= 0 &&
(q * conv_stride[1] - padding[2] + s * dilation[1]) < tensor_A.extent().w() &&
(q * conv_stride[1] - padding[2] + s * dilation[1]) >= 0) {
ElementA a = tensor_A.at(
cutlass::make_Coord(n,
p * conv_stride[0] - padding[0] + r * dilation[0],
q * conv_stride[1] - padding[2] + s * dilation[1],
g));
ElementB b = (mode == cutlass::conv::Mode::kCrossCorrelation)
? tensor_B.at(cutlass::make_Coord(g, r, s, 0))
: tensor_B.at(cutlass::make_Coord(
g, tensor_B.extent().h() - r - 1, tensor_B.extent().w() - s - 1, 0));
acc = inner_product_op(ElementAccumulator(a), ElementAccumulator(b), acc);
}
}
}
// Apply Epilogue, compute ElementCompute, convert and store ElementC
ElementC c_ref = tensor_C.at(cutlass::make_Coord(n, p, q, g));
tensor_C.at(cutlass::make_Coord(n, p, q, g)) =
convert_op(alpha * ElementCompute(acc) + beta * ElementCompute(c_ref));
}
}
}
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Dgrad
////////////////////////////////////////////////////////////////////////////////////////////////////
/// dx = dgrad(dy, w)
template <
typename ElementA,
typename LayoutA,
typename ElementB,
typename LayoutB,
typename ElementC,
typename LayoutC,
typename ElementCompute,
typename ElementAccumulator = ElementCompute,
typename ConvertOp = NumericConverter<ElementC, ElementCompute>,
typename InnerProductOp = multiply_add<ElementAccumulator>
>
void Conv2dDgrad(
cutlass::conv::Conv2dProblemSize problem_size,
TensorRef<ElementA, LayoutA> tensor_dy,
TensorRef<ElementB, LayoutB> tensor_w,
TensorRef<ElementC, LayoutC> tensor_dx_in,
TensorRef<ElementC, LayoutC> tensor_dx_out,
ElementCompute alpha,
ElementCompute beta) {
ConvertOp convert_op;
InnerProductOp inner_product_op;
// Apply MMA and accumulate ElementAccumulator
for (int n = 0; n < problem_size.N; ++n) {
for (int h = 0; h < problem_size.H; ++h) {
for (int w = 0; w < problem_size.W; ++w) {
for (int c = 0; c < problem_size.C; ++c) {
ElementAccumulator acc = ElementAccumulator();
for (int r = 0; r < problem_size.R; ++r) {
for (int s = 0; s < problem_size.S; ++s) {
for (int k = 0; k < problem_size.K; ++k) {
int filter_r = r;
int filter_s = s;
if (problem_size.mode == cutlass::conv::Mode::kConvolution) {
filter_r = problem_size.R - 1 - r;
filter_s = problem_size.S - 1 - s;
}
int p = h + problem_size.pad_h - filter_r * problem_size.dilation_h;
int q = w + problem_size.pad_w - filter_s * problem_size.dilation_w;
if (p >= 0 && (p % problem_size.stride_h) == 0 &&
q >= 0 && (q % problem_size.stride_w) == 0) {
p = p / problem_size.stride_h;
q = q / problem_size.stride_w;
#if 0
std::cout << "row:"
<< n * problem_size.H * problem_size.W +
h * problem_size.W +
w << " "
<< "n, p, q: ("
<< n << ", "
<< p << ", "
<< q << ") * "
<< "r, s: ("
<< r << ", "
<< s << ") ["
<< ((p < problem_size.P && q < problem_size.Q) ? "true":"false") << "]"
<< std::endl;
#endif
if (p < problem_size.P && q < problem_size.Q) {
ElementA a = tensor_dy.at(cutlass::make_Coord(n, p, q, k));
ElementB b = tensor_w.at(cutlass::make_Coord(k, r, s, c));
acc = inner_product_op(ElementAccumulator(a), ElementAccumulator(b), acc);
}
}
} // for (K)
} // for (S)
} // for (R)
// Apply Epilogue, compute ElementCompute, convert and store ElementC
ElementC c_ref = ElementC();
if (beta != ElementCompute()) {
c_ref = tensor_dx_in.at(cutlass::make_Coord(n, h, w, c));
}
tensor_dx_out.at(cutlass::make_Coord(n, h, w, c)) =
convert_op(alpha * ElementCompute(acc) + beta * ElementCompute(c_ref));
} // for (C)
} // for (W)
} // for (H)
} // for (N)
}
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Wgrad
////////////////////////////////////////////////////////////////////////////////////////////////////
/// dw = wgrad(dy, x)
template <
typename ElementA,
typename LayoutA,
typename ElementB,
typename LayoutB,
typename ElementC,
typename LayoutC,
typename ElementCompute,
typename ElementAccumulator = ElementCompute,
typename ConvertOp = NumericConverter<ElementC, ElementCompute>,
typename InnerProductOp = multiply_add<ElementAccumulator>
>
void Conv2dWgrad(
cutlass::conv::Conv2dProblemSize problem_size,
TensorRef<ElementA, LayoutA> tensor_dy,
TensorRef<ElementB, LayoutB> tensor_x,
TensorRef<ElementC, LayoutC> tensor_dw_in,
TensorRef<ElementC, LayoutC> tensor_dw_out,
ElementCompute alpha,
ElementCompute beta) {
InnerProductOp inner_product_op;
ConvertOp convert_op;
// Apply MMA and accumulate ElementAccumulator
for (int k = 0; k < problem_size.K; ++k) {
for (int r = 0; r < problem_size.R; ++r) {
for (int s = 0; s < problem_size.S; ++s) {
for (int c = 0; c < problem_size.C; ++c) {
ElementAccumulator acc = ElementAccumulator();
for (int n = 0; n < problem_size.N; ++n) {
for (int p = 0; p < problem_size.P; ++p) {
for (int q = 0; q < problem_size.Q; ++q) {
cutlass::Tensor4DCoord b_coord;
int filter_r = r;
int filter_s = s;
if (problem_size.mode == cutlass::conv::Mode::kConvolution) {
filter_r = problem_size.R - 1 - r;
filter_s = problem_size.S - 1 - s;
}
b_coord = make_Coord(
n,
p * problem_size.stride_h - problem_size.pad_h + filter_r * problem_size.dilation_h,
q * problem_size.stride_w - problem_size.pad_w + filter_s * problem_size.dilation_w,
c);
if (b_coord.h() < problem_size.H && b_coord.h() >= 0 &&
b_coord.w() < problem_size.W && b_coord.w() >= 0) {
ElementAccumulator a = ElementAccumulator(tensor_dy.at(cutlass::make_Coord(n, p, q, k)));
ElementAccumulator b = ElementAccumulator(tensor_x.at(b_coord));
acc = inner_product_op(a, b, acc);
}
}
}
}
// Apply Epilogue, compute ElementCompute, convert and store ElementC
ElementC c_ref = ElementC();
if (beta != ElementCompute()) {
c_ref = tensor_dw_in.at(cutlass::make_Coord(k, r, s, c));
}
tensor_dw_out.at(cutlass::make_Coord(k, r, s, c)) =
convert_op(alpha * ElementCompute(acc) + beta * ElementCompute(c_ref));
} // for (C)
} // for (S)
} // for (R)
} // for (K)
}
/// Generic 2D convolution targeting Conv2dFprop, Conv2dDgrad, and Conv2dWgrad.
template <
typename ElementA,
typename LayoutA,
typename ElementB,
typename LayoutB,
typename ElementC,
typename LayoutC,
typename ElementCompute,
typename ElementAccumulator = ElementCompute,
typename ConvertOp = NumericConverter<ElementC, ElementCompute>,
typename InnerProductOp = multiply_add<ElementAccumulator>
>
void Conv2d(
conv::Operator convolutional_operator,
conv::Conv2dProblemSize problem_size,
TensorRef<ElementA, LayoutA> tensor_A,
TensorRef<ElementB, LayoutB> tensor_B,
TensorRef<ElementC, LayoutC> tensor_C,
TensorRef<ElementC, LayoutC> tensor_D,
ElementCompute alpha,
ElementCompute beta) {
switch (convolutional_operator) {
case conv::Operator::kFprop:
Conv2dFprop<
ElementA, LayoutA,
ElementB, LayoutB,
ElementC, LayoutC,
ElementCompute,
ElementAccumulator,
ConvertOp, InnerProductOp
>(problem_size, tensor_A, tensor_B, tensor_C, tensor_D, alpha, beta);
break;
case conv::Operator::kDgrad:
Conv2dDgrad<
ElementA, LayoutA,
ElementB, LayoutB,
ElementC, LayoutC,
ElementCompute,
ElementAccumulator,
ConvertOp, InnerProductOp
>(problem_size, tensor_A, tensor_B, tensor_C, tensor_D, alpha, beta);
break;
case conv::Operator::kWgrad:
Conv2dWgrad<
ElementA, LayoutA,
ElementB, LayoutB,
ElementC, LayoutC,
ElementCompute,
ElementAccumulator,
ConvertOp, InnerProductOp
>(problem_size, tensor_A, tensor_B, tensor_C, tensor_D, alpha, beta);
break;
default:
break;
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
/// 3D convolution
////////////////////////////////////////////////////////////////////////////////////////////////////
/// y = conv3d(x, w)
template <
typename ElementA,
typename LayoutA,
typename ElementB,
typename LayoutB,
typename ElementC,
typename LayoutC,
typename ElementCompute,
typename ElementAccumulator = ElementCompute,
typename ConvertOp = NumericConverter<ElementC, ElementCompute>,
typename InnerProductOp = multiply_add<ElementAccumulator>
>
void Conv3dFprop(
conv::Conv3dProblemSize problem_size,
TensorRef<ElementA, LayoutA> tensor_x,
TensorRef<ElementB, LayoutB> tensor_w,
TensorRef<ElementC, LayoutC> tensor_y_in,
TensorRef<ElementC, LayoutC> tensor_y_out,
ElementCompute alpha,
ElementCompute beta) {
ConvertOp convert_op;
InnerProductOp inner_product_op;
// Apply MMA and accumulate ElementAccumulator
for (int n = 0; n < problem_size.N; ++n) {
for (int z = 0; z < problem_size.Z; ++z) {
for (int p = 0; p < problem_size.P; ++p) {
for (int q = 0; q < problem_size.Q; ++q) {
for (int k = 0; k < problem_size.K; ++k) {
ElementAccumulator acc = ElementAccumulator();
for (int t = 0; t < problem_size.T; ++t) {
for (int r = 0; r < problem_size.R; ++r) {
for (int s = 0; s < problem_size.S; ++s) {
for (int c = 0; c < problem_size.C; ++c) {
int filter_t = t;
int filter_r = r;
int filter_s = s;
if (problem_size.mode == cutlass::conv::Mode::kConvolution) {
filter_t = problem_size.T - 1 - t;
filter_r = problem_size.R - 1 - r;
filter_s = problem_size.S - 1 - s;
}
int d = z * problem_size.stride_d - problem_size.pad_d + filter_t * problem_size.dilation_d;
int h = p * problem_size.stride_h - problem_size.pad_h + filter_r * problem_size.dilation_h;
int w = q * problem_size.stride_w - problem_size.pad_w + filter_s * problem_size.dilation_w;
if (d >= 0 && d < problem_size.D &&
h >=0 && h < problem_size.H &&
w >= 0 && w < problem_size.W) {
ElementA a = tensor_x.at({n, d, h, w, c});
ElementB b = tensor_w.at({k, t, r, s, c});
acc = inner_product_op(ElementAccumulator(a), ElementAccumulator(b), acc);
}
}
}
}
}
// Apply Epilogue, compute ElementCompute, convert and store ElementC
ElementC c_ref = ElementC();
if (beta != ElementCompute()) {
c_ref = tensor_y_in.at(cutlass::make_Coord(n, z, p, q, k));
}
tensor_y_out.at(cutlass::make_Coord(n, z, p, q, k)) =
convert_op(alpha * ElementCompute(acc) + beta * ElementCompute(c_ref));
}
}
}
}
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Dgrad
////////////////////////////////////////////////////////////////////////////////////////////////////
/// dx = dgrad(dy, w)
template <
typename ElementA,
typename LayoutA,
typename ElementB,
typename LayoutB,
typename ElementC,
typename LayoutC,
typename ElementCompute,
typename ElementAccumulator = ElementCompute,
typename ConvertOp = NumericConverter<ElementC, ElementCompute>,
typename InnerProductOp = multiply_add<ElementAccumulator>
>
void Conv3dDgrad(
cutlass::conv::Conv3dProblemSize problem_size,
TensorRef<ElementA, LayoutA> tensor_dy,
TensorRef<ElementB, LayoutB> tensor_w,
TensorRef<ElementC, LayoutC> tensor_dx_in,
TensorRef<ElementC, LayoutC> tensor_dx_out,
ElementCompute alpha,
ElementCompute beta) {
ConvertOp convert_op;
InnerProductOp inner_product_op;
// Apply MMA and accumulate ElementAccumulator
for (int n = 0; n < problem_size.N; ++n) {
for (int d = 0; d < problem_size.D; ++d) {
for (int h = 0; h < problem_size.H; ++h) {
for (int w = 0; w < problem_size.W; ++w) {
for (int c = 0; c < problem_size.C; ++c) {
ElementAccumulator acc = ElementAccumulator();
for (int t = 0; t < problem_size.T; ++t) {
for (int r = 0; r < problem_size.R; ++r) {
for (int s = 0; s < problem_size.S; ++s) {
for (int k = 0; k < problem_size.K; ++k) {
int filter_t = t;
int filter_r = r;
int filter_s = s;
if (problem_size.mode == cutlass::conv::Mode::kConvolution) {
filter_t = problem_size.T - 1 - t;
filter_r = problem_size.R - 1 - r;
filter_s = problem_size.S - 1 - s;
}
int z = d + problem_size.pad_d - filter_t * problem_size.dilation_d;
int p = h + problem_size.pad_h - filter_r * problem_size.dilation_h;
int q = w + problem_size.pad_w - filter_s * problem_size.dilation_w;
if (z >= 0 && (z % problem_size.stride_d) == 0 &&
p >= 0 && (p % problem_size.stride_h) == 0 &&
q >= 0 && (q % problem_size.stride_w) == 0) {
z = z / problem_size.stride_d;
p = p / problem_size.stride_h;
q = q / problem_size.stride_w;
if (z < problem_size.Z && p < problem_size.P && q < problem_size.Q) {
ElementA a = tensor_dy.at(cutlass::make_Coord(n, z, p, q, k));
ElementB b = tensor_w.at(cutlass::make_Coord(k, t, r, s, c));
acc = inner_product_op(ElementAccumulator(a), ElementAccumulator(b), acc);
}
}
} // for (K)
} // for (S)
} // for (R)
} // for (T)
// Apply Epilogue, compute ElementCompute, convert and store ElementC
ElementC c_ref = ElementC();
if (beta != ElementCompute()) {
c_ref = tensor_dx_in.at(cutlass::make_Coord(n, d, h, w, c));
}
tensor_dx_out.at(cutlass::make_Coord(n, d, h, w, c)) =
convert_op(alpha * ElementCompute(acc) + beta * ElementCompute(c_ref));
} // for (C)
} // for (W)
} // for (H)
} // for (D)
} // for (N)
}
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Wgrad
////////////////////////////////////////////////////////////////////////////////////////////////////
/// dw = wgrad(dy, x)
template <
typename ElementA,
typename LayoutA,
typename ElementB,
typename LayoutB,
typename ElementC,
typename LayoutC,
typename ElementCompute,
typename ElementAccumulator = ElementCompute,
typename ConvertOp = NumericConverter<ElementC, ElementCompute>,
typename InnerProductOp = multiply_add<ElementAccumulator>
>
void Conv3dWgrad(
cutlass::conv::Conv3dProblemSize problem_size,
TensorRef<ElementA, LayoutA> tensor_dy,
TensorRef<ElementB, LayoutB> tensor_x,
TensorRef<ElementC, LayoutC> tensor_dw_in,
TensorRef<ElementC, LayoutC> tensor_dw_out,
ElementCompute alpha,
ElementCompute beta) {
InnerProductOp inner_product_op;
ConvertOp convert_op;
// Apply MMA and accumulate ElementAccumulator
for (int k = 0; k < problem_size.K; ++k) {
for (int t = 0; t < problem_size.T; ++t) {
for (int r = 0; r < problem_size.R; ++r) {
for (int s = 0; s < problem_size.S; ++s) {
for (int c = 0; c < problem_size.C; ++c) {
ElementAccumulator acc = ElementAccumulator();
for (int n = 0; n < problem_size.N; ++n) {
for (int z = 0; z < problem_size.Z; ++z) {
for (int p = 0; p < problem_size.P; ++p) {
for (int q = 0; q < problem_size.Q; ++q) {
int filter_t = t;
int filter_r = r;
int filter_s = s;
if (problem_size.mode == cutlass::conv::Mode::kConvolution) {
filter_t = problem_size.T - 1 - t;
filter_r = problem_size.R - 1 - r;
filter_s = problem_size.S - 1 - s;
}
Tensor5DCoord b_coord = make_Coord(
n,
z * problem_size.stride_d - problem_size.pad_d + filter_t * problem_size.dilation_d,
p * problem_size.stride_h - problem_size.pad_h + filter_r * problem_size.dilation_h,
q * problem_size.stride_w - problem_size.pad_w + filter_s * problem_size.dilation_w,
c);
if (b_coord.d() < problem_size.D && b_coord.d() >= 0 &&
b_coord.h() < problem_size.H && b_coord.h() >= 0 &&
b_coord.w() < problem_size.W && b_coord.w() >= 0) {
ElementAccumulator a = ElementAccumulator(tensor_dy.at(cutlass::make_Coord(n, z, p, q, k)));
ElementAccumulator b = ElementAccumulator(tensor_x.at(b_coord));
acc = inner_product_op(a, b, acc);
}
}
}
}
}
// Apply Epilogue, compute ElementCompute, convert and store ElementC
ElementC c_ref = ElementC();
if (beta != ElementCompute()) {
c_ref = tensor_dw_in.at(cutlass::make_Coord(k, t, r, s, c));
}
tensor_dw_out.at(cutlass::make_Coord(k, t, r, s, c)) =
convert_op(alpha * ElementCompute(acc) + beta * ElementCompute(c_ref));
} // for (C)
} // for (S)
} // for (R)
} // for (T)
} // for (K)
}
///////////////////////////////////////////////////////////////////////////////////////////////////
/// Generic 3D convolution targeting Conv2dFprop, Conv2dDgrad, and Conv2dWgrad.
template <
typename ElementA,
typename LayoutA,
typename ElementB,
typename LayoutB,
typename ElementC,
typename LayoutC,
typename ElementCompute,
typename ElementAccumulator = ElementCompute,
typename ConvertOp = NumericConverter<ElementC, ElementCompute>,
typename InnerProductOp = multiply_add<ElementAccumulator>
>
void Conv3d(
conv::Operator convolutional_operator,
conv::Conv3dProblemSize problem_size,
TensorRef<ElementA, LayoutA> tensor_A,
TensorRef<ElementB, LayoutB> tensor_B,
TensorRef<ElementC, LayoutC> tensor_C,
TensorRef<ElementC, LayoutC> tensor_D,
ElementCompute alpha,
ElementCompute beta) {
switch (convolutional_operator) {
case conv::Operator::kFprop:
Conv3dFprop<
ElementA, LayoutA,
ElementB, LayoutB,
ElementC, LayoutC,
ElementCompute,
ElementAccumulator,
ConvertOp, InnerProductOp
>(problem_size, tensor_A, tensor_B, tensor_C, tensor_D, alpha, beta);
break;
case conv::Operator::kDgrad:
Conv3dDgrad<
ElementA, LayoutA,
ElementB, LayoutB,
ElementC, LayoutC,
ElementCompute,
ElementAccumulator,
ConvertOp, InnerProductOp
>(problem_size, tensor_A, tensor_B, tensor_C, tensor_D, alpha, beta);
break;
case conv::Operator::kWgrad:
Conv3dWgrad<
ElementA, LayoutA,
ElementB, LayoutB,
ElementC, LayoutC,
ElementCompute,
ElementAccumulator,
ConvertOp, InnerProductOp
>(problem_size, tensor_A, tensor_B, tensor_C, tensor_D, alpha, beta);
break;
default:
break;
}
}
/////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace host
} // namespace reference
} // namespace cutlass
/////////////////////////////////////////////////////////////////////////////////////////////////