CUTLASS 2.4 (Implicit GEMM convolution) (#147)
CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
This commit is contained in:
Manish Gupta
co-authored by
Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
parent
c2b80ad4e4
commit
6615010cd0
@@ -0,0 +1,450 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 This file contains definitions and utility functions for describing convolution problem sizes.
|
||||
|
||||
Conv2dProblem desciption:
|
||||
activation (NHWC),
|
||||
filter (KRSC),
|
||||
output (NPQK),
|
||||
pading (pad_h, pad_w),
|
||||
stride (stride_h, stride_w),
|
||||
dilation (dilation_h, dilation_w).
|
||||
|
||||
Free functions to map:
|
||||
Map tensor extents (Conv2d -> ImplicitGemm) : implicit_gemm_tensor_[a|b|c]_extent(ConvolutionOperator)
|
||||
Map tensor sizes (Conv2d -> ImplicitGemm) : implicit_gemm_tensor_[a|b|c]_size(ConvolutionOperator)
|
||||
Map tensor problem sizes (Conv2d -> ImplicitGemm): implicit_gemm_problem_size(ConvolutionOperator)
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/tensor_coord.h"
|
||||
#include "cutlass/fast_math.h"
|
||||
#include "cutlass/gemm/gemm.h"
|
||||
#include "cutlass/matrix_coord.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Problem size structure
|
||||
struct Conv2dProblemSize {
|
||||
|
||||
// Conv2d strictly problem size parameters
|
||||
int N, H, W, C, P, Q, K, R, S;
|
||||
int pad_h, pad_w;
|
||||
int stride_h, stride_w;
|
||||
int dilation_h, dilation_w;
|
||||
Mode mode;
|
||||
|
||||
// Conv2d implementation-related parameters
|
||||
int split_k_slices;
|
||||
int groups;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
public:
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dProblemSize():
|
||||
N(0), H(0), W(0), C(0), P(0), Q(0), K(0), R(0), S(0),
|
||||
pad_h(0), pad_w(0), stride_h(1), stride_w(1), dilation_h(1), dilation_w(1),
|
||||
mode(Mode::kConvolution), split_k_slices(1), groups(1) { }
|
||||
|
||||
/// Constructor for default padding, stride, dilation, and split-K
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dProblemSize(
|
||||
int N,
|
||||
int H,
|
||||
int W,
|
||||
int C,
|
||||
int P,
|
||||
int Q,
|
||||
int K,
|
||||
int R,
|
||||
int S,
|
||||
Mode mode
|
||||
):
|
||||
N(N), H(H), W(W), C(C), P(P), Q(Q), K(K), R(R), S(S),
|
||||
pad_h(R / 2), pad_w(S / 2), stride_h(1), stride_w(1), dilation_h(1), dilation_w(1),
|
||||
mode(mode), split_k_slices(1), groups (1) { }
|
||||
|
||||
/// Constructor
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dProblemSize(
|
||||
int N,
|
||||
int H,
|
||||
int W,
|
||||
int C,
|
||||
int K,
|
||||
int R,
|
||||
int S,
|
||||
int P,
|
||||
int Q,
|
||||
int pad_h,
|
||||
int pad_w,
|
||||
int stride_h,
|
||||
int stride_w,
|
||||
int dilation_h,
|
||||
int dilation_w,
|
||||
Mode mode,
|
||||
int split_k_slices = 1,
|
||||
int groups = 1
|
||||
):
|
||||
N(N), H(H), W(W), C(C), K(K), R(R), S(S), P(P), Q(Q),
|
||||
pad_h(pad_h), pad_w(pad_w), stride_h(stride_h), stride_w(stride_w),
|
||||
dilation_h(dilation_h), dilation_w(dilation_w),
|
||||
mode(mode), split_k_slices(split_k_slices), groups (groups) { }
|
||||
|
||||
/// Constructs convolution problem size from cutlass Tensor4DCoord and MatrixCoord
|
||||
// set user-defined output size and sets P and Q (include all data members in ctor)
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dProblemSize(
|
||||
cutlass::Tensor4DCoord input_size, // NHWC
|
||||
cutlass::Tensor4DCoord filter_size, // KRSC
|
||||
cutlass::Tensor4DCoord padding, // pad_h, _, pad_w, _
|
||||
cutlass::MatrixCoord stride, // stride_h, stride_w
|
||||
cutlass::MatrixCoord dilation, // dilation_h, dilation_w
|
||||
cutlass::Tensor4DCoord output_size, // NPQK
|
||||
cutlass::conv::Mode mode = cutlass::conv::Mode::kCrossCorrelation,
|
||||
int split_k_slices = 1,
|
||||
int groups = 1
|
||||
):
|
||||
N(input_size.n()), H(input_size.h()), W(input_size.w()), C(input_size.c()),
|
||||
K(filter_size.n()), R(filter_size.h()), S(filter_size.w()),
|
||||
pad_h(padding[0]), pad_w(padding[2]),
|
||||
stride_h(stride.row()), stride_w(stride.column()),
|
||||
dilation_h(dilation.row()), dilation_w(dilation.column()),
|
||||
P(output_size.h()), Q(output_size.w()),
|
||||
mode(mode), split_k_slices(split_k_slices), groups(groups) {}
|
||||
|
||||
/// Constructs convolution problem size from cutlass Tensor4DCoord and MatrixCoord
|
||||
// computes output size and sets P and Q (skip output from ctor arguments)
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dProblemSize(
|
||||
cutlass::Tensor4DCoord input_size, // NHWC
|
||||
cutlass::Tensor4DCoord filter_size, // KRSC
|
||||
cutlass::Tensor4DCoord padding, // pad_h, _, pad_w, _
|
||||
cutlass::MatrixCoord stride, // stride_h, stride_w
|
||||
cutlass::MatrixCoord dilation, // dilation_h, dilation_w
|
||||
cutlass::conv::Mode mode = cutlass::conv::Mode::kCrossCorrelation,
|
||||
int split_k_slices = 1,
|
||||
int groups = 1
|
||||
):
|
||||
N(input_size.n()), H(input_size.h()), W(input_size.w()), C(input_size.c()),
|
||||
K(filter_size.n()), R(filter_size.h()), S(filter_size.w()),
|
||||
pad_h(padding[0]), pad_w(padding[2]),
|
||||
stride_h(stride.row()), stride_w(stride.column()),
|
||||
dilation_h(dilation.row()), dilation_w(dilation.column()),
|
||||
mode(mode), split_k_slices(split_k_slices), groups(groups) {
|
||||
// set output P and Q
|
||||
P = ((H + pad_h * 2 - R * dilation_h) / stride_h) + 1;
|
||||
Q = ((W + pad_w * 2 - S * dilation_w) / stride_w) + 1;
|
||||
}
|
||||
|
||||
/// Constructs convolution problem size from cutlass Tensor4DCoord and MatrixCoord
|
||||
// set user-defined output size and sets P and Q (skip padding, striding, and dilation)
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dProblemSize(
|
||||
cutlass::Tensor4DCoord input_size, // NHWC
|
||||
cutlass::Tensor4DCoord filter_size, // KRSC
|
||||
cutlass::Tensor4DCoord output_size, // NPQK
|
||||
cutlass::conv::Mode mode = cutlass::conv::Mode::kCrossCorrelation,
|
||||
int split_k_slices = 1,
|
||||
int groups = 1
|
||||
):
|
||||
N(input_size.n()), H(input_size.h()), W(input_size.w()), C(input_size.c()),
|
||||
K(filter_size.n()), R(filter_size.h()), S(filter_size.w()),
|
||||
P(output_size.h()), Q(output_size.w()),
|
||||
pad_h(R / 2), pad_w(S / 2), stride_h(1), stride_w(1),
|
||||
dilation_h(1), dilation_w(1),
|
||||
mode(mode), split_k_slices(split_k_slices), groups(groups) {}
|
||||
|
||||
// Reset covolution mode in the problem
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dProblemSize reset_mode(cutlass::conv::Mode mode_) {
|
||||
Conv2dProblemSize tmp(*this);
|
||||
tmp.mode = mode_;
|
||||
return tmp;
|
||||
}
|
||||
|
||||
// Reset covolution mode in the problem
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dProblemSize reset_split_k_slices(int split_k_slices_) {
|
||||
Conv2dProblemSize tmp(*this);
|
||||
tmp.split_k_slices = split_k_slices_;
|
||||
return tmp;
|
||||
}
|
||||
|
||||
/// Equality operator (ignores mode and split_k_slice)
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool operator==(Conv2dProblemSize const &conv) const {
|
||||
return (
|
||||
(N == conv.N) && (W == conv.H) && (W == conv.W) && (C == conv.C) &&
|
||||
(K == conv.K) && (R == conv.R) && (S == conv.S) &&
|
||||
(P == conv.P) && (Q == conv.Q) &&
|
||||
(pad_h == conv.pad_h) && (pad_w == conv.pad_w) &&
|
||||
(stride_h == conv.stride_h) && (stride_w == conv.stride_w) &&
|
||||
(dilation_h == conv.dilation_h) && (dilation_h == conv.dilation_h)
|
||||
);
|
||||
}
|
||||
|
||||
/// Inequality operator
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool operator!=(Conv2dProblemSize const &rhs) const {
|
||||
return !(*this == rhs);
|
||||
}
|
||||
|
||||
/// Returns activation extent as Tensor4DCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::Tensor4DCoord activation_extent() const {
|
||||
|
||||
return cutlass::Tensor4DCoord ({N, H, W, C});
|
||||
}
|
||||
|
||||
/// Returns filter extent as Tensor4DCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::Tensor4DCoord filter_extent() const {
|
||||
|
||||
return cutlass::Tensor4DCoord ({K, R, S, C});
|
||||
}
|
||||
|
||||
/// Returns output extent as Tensor4DCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::Tensor4DCoord output_extent() const {
|
||||
|
||||
return cutlass::Tensor4DCoord ({N, P, Q, K});
|
||||
}
|
||||
|
||||
/// Returns activation size in number of elements
|
||||
CUTLASS_HOST_DEVICE
|
||||
int64_t activation_size() const {
|
||||
|
||||
return (N * H * W * C);
|
||||
}
|
||||
|
||||
/// Returns filter size in number of elements
|
||||
CUTLASS_HOST_DEVICE
|
||||
int64_t filter_size() const {
|
||||
|
||||
return (K * R * S * C);
|
||||
}
|
||||
|
||||
/// Returns output size in number of elements
|
||||
CUTLASS_HOST_DEVICE
|
||||
int64_t output_size() const {
|
||||
|
||||
return (N * P * Q * K);
|
||||
}
|
||||
|
||||
/// Returns output extent as Tensor4DCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::Tensor4DCoord padding() const {
|
||||
|
||||
return cutlass::Tensor4DCoord ({pad_h, pad_h, pad_w, pad_w});
|
||||
}
|
||||
|
||||
/// Returns stride as MatrixCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::MatrixCoord stride() const {
|
||||
|
||||
return cutlass::MatrixCoord ({stride_h, stride_w});
|
||||
}
|
||||
|
||||
/// Returns dilation as MatrixCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::MatrixCoord dilation() const {
|
||||
|
||||
return cutlass::MatrixCoord ({dilation_h, dilation_w});
|
||||
}
|
||||
};
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// ImplicitGemm helper functions //
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Determine the problem size of the implicit GEMM operation
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::gemm::GemmCoord implicit_gemm_problem_size(
|
||||
Operator conv_operator,
|
||||
Conv2dProblemSize const &problem_size) {
|
||||
// Compute problem size
|
||||
switch (conv_operator) {
|
||||
case Operator::kFprop:
|
||||
return gemm::GemmCoord(
|
||||
problem_size.N * problem_size.P * problem_size.Q,
|
||||
problem_size.K,
|
||||
problem_size.R * problem_size.S * problem_size.C
|
||||
);
|
||||
case Operator::kDgrad:
|
||||
return gemm::GemmCoord(
|
||||
problem_size.N * problem_size.H * problem_size.W,
|
||||
problem_size.C,
|
||||
problem_size.R * problem_size.S * problem_size.K
|
||||
);
|
||||
case Operator::kWgrad:
|
||||
return gemm::GemmCoord(
|
||||
problem_size.K,
|
||||
problem_size.R * problem_size.S * problem_size.C,
|
||||
problem_size.N * problem_size.P * problem_size.Q
|
||||
);
|
||||
default:
|
||||
break;
|
||||
}
|
||||
return gemm::GemmCoord();
|
||||
}
|
||||
|
||||
// Determine the number of gemm_k iterations for conv2d problem using implicit gemm algorithm
|
||||
CUTLASS_HOST_DEVICE
|
||||
int implicit_gemm_k_iterations(
|
||||
Operator conv_operator,
|
||||
int threadblock_K,
|
||||
Conv2dProblemSize const &problem_size) {
|
||||
|
||||
int iterations = 0;
|
||||
int elements_per_split_k_slice = 0;
|
||||
|
||||
switch (conv_operator) {
|
||||
case Operator::kFprop:
|
||||
elements_per_split_k_slice = (problem_size.C + problem_size.split_k_slices - 1) / problem_size.split_k_slices;
|
||||
iterations = problem_size.R * problem_size.S * ((elements_per_split_k_slice + threadblock_K - 1) / threadblock_K);
|
||||
break;
|
||||
|
||||
case Operator::kDgrad:
|
||||
elements_per_split_k_slice = (problem_size.K + problem_size.split_k_slices - 1) / problem_size.split_k_slices;
|
||||
iterations = problem_size.R * problem_size.S * ((elements_per_split_k_slice + threadblock_K - 1) / threadblock_K);
|
||||
break;
|
||||
|
||||
case Operator::kWgrad:
|
||||
elements_per_split_k_slice = (problem_size.N * problem_size.P * problem_size.Q + problem_size.split_k_slices - 1) / problem_size.split_k_slices;
|
||||
iterations = (elements_per_split_k_slice + threadblock_K - 1) / threadblock_K;
|
||||
break;
|
||||
|
||||
default:
|
||||
break;
|
||||
}
|
||||
|
||||
return iterations;
|
||||
}
|
||||
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
// Mapping function (ImplicitGemm A, B, C -> Conv Activation, Filter, Output)
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
/// Returns ImplicitGemm tensor A extent as Tensor4DCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::Tensor4DCoord implicit_gemm_tensor_a_extent(
|
||||
Operator conv_operator,
|
||||
Conv2dProblemSize const &problem_size) {
|
||||
switch (conv_operator) {
|
||||
case cutlass::conv::Operator::kFprop: return problem_size.activation_extent();
|
||||
case cutlass::conv::Operator::kDgrad: return problem_size.output_extent();
|
||||
case cutlass::conv::Operator::kWgrad: return problem_size.output_extent();
|
||||
default : break;
|
||||
}
|
||||
return cutlass::Tensor4DCoord();
|
||||
}
|
||||
|
||||
/// Returns ImplicitGemm tensor B extent as Tensor4DCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::Tensor4DCoord implicit_gemm_tensor_b_extent(
|
||||
Operator conv_operator,
|
||||
Conv2dProblemSize const &problem_size) {
|
||||
switch (conv_operator) {
|
||||
case cutlass::conv::Operator::kFprop: return problem_size.filter_extent();
|
||||
case cutlass::conv::Operator::kDgrad: return problem_size.filter_extent();
|
||||
case cutlass::conv::Operator::kWgrad: return problem_size.activation_extent();
|
||||
default : break;
|
||||
}
|
||||
return cutlass::Tensor4DCoord();
|
||||
}
|
||||
|
||||
/// Returns ImplicitGemm tensor C extent as Tensor4DCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::Tensor4DCoord implicit_gemm_tensor_c_extent(
|
||||
Operator conv_operator,
|
||||
Conv2dProblemSize const &problem_size) {
|
||||
switch (conv_operator) {
|
||||
case cutlass::conv::Operator::kFprop: return problem_size.output_extent();
|
||||
case cutlass::conv::Operator::kDgrad: return problem_size.activation_extent();
|
||||
case cutlass::conv::Operator::kWgrad: return problem_size.filter_extent();
|
||||
default : break;
|
||||
}
|
||||
return cutlass::Tensor4DCoord();
|
||||
}
|
||||
|
||||
/// Returns ImplicitGemm tensor A size in number of elements
|
||||
CUTLASS_HOST_DEVICE
|
||||
int64_t implicit_gemm_tensor_a_size(
|
||||
Operator conv_operator,
|
||||
Conv2dProblemSize const &problem_size) {
|
||||
switch (conv_operator) {
|
||||
case cutlass::conv::Operator::kFprop: return problem_size.activation_size();
|
||||
case cutlass::conv::Operator::kDgrad: return problem_size.output_size();
|
||||
case cutlass::conv::Operator::kWgrad: return problem_size.output_size();
|
||||
default : break;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
/// Returns ImplicitGemm tensor B size in number of elements
|
||||
CUTLASS_HOST_DEVICE
|
||||
int64_t implicit_gemm_tensor_b_size(
|
||||
Operator conv_operator,
|
||||
Conv2dProblemSize const &problem_size) {
|
||||
switch (conv_operator) {
|
||||
case cutlass::conv::Operator::kFprop: return problem_size.filter_size();
|
||||
case cutlass::conv::Operator::kDgrad: return problem_size.filter_size();
|
||||
case cutlass::conv::Operator::kWgrad: return problem_size.activation_size();
|
||||
default : break;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
/// Returns ImplicitGemm tensor C size in number of elements
|
||||
CUTLASS_HOST_DEVICE
|
||||
int64_t implicit_gemm_tensor_c_size(
|
||||
Operator conv_operator,
|
||||
Conv2dProblemSize const &problem_size) {
|
||||
switch (conv_operator) {
|
||||
case cutlass::conv::Operator::kFprop: return problem_size.output_size();
|
||||
case cutlass::conv::Operator::kDgrad: return problem_size.activation_size();
|
||||
case cutlass::conv::Operator::kWgrad: return problem_size.filter_size();
|
||||
default : break;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
@@ -0,0 +1,453 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 This file contains definitions and utility functions for describing convolution problem sizes.
|
||||
|
||||
Conv3dProblem desciption:
|
||||
activation (NDHWC),
|
||||
filter (KTRSC),
|
||||
output (NZPQK),
|
||||
pading (pad_d, pad_h, pad_w),
|
||||
stride (stride_d, stride_h, stride_w),
|
||||
dilation (dilation_d, dilation_h, dilation_w).
|
||||
|
||||
Free functions to map:
|
||||
Map tensor extents (Conv3d -> ImplicitGemm) : implicit_gemm_tensor_[a|b|c]_extent(ConvolutionOperator)
|
||||
Map tensor sizes (Conv3d -> ImplicitGemm) : implicit_gemm_tensor_[a|b|c]_size(ConvolutionOperator)
|
||||
Map tensor problem sizes (Conv3d -> ImplicitGemm): implicit_gemm_problem_size(ConvolutionOperator)
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Problem size structure
|
||||
struct Conv3dProblemSize : public Conv2dProblemSize {
|
||||
//
|
||||
// Type definitions
|
||||
//
|
||||
|
||||
// 3D coordinate for padding, stride, and dilation in (d, h, w) dimensions
|
||||
using Coord3D = Coord<3>;
|
||||
|
||||
//
|
||||
// Data members
|
||||
//
|
||||
|
||||
// Conv3d strictly problem size parameters
|
||||
int D, T, Z; // input depth, filter depth, output depth
|
||||
int pad_d; // padding in depth dimension
|
||||
int stride_d; // stride in depth dimension
|
||||
int dilation_d; // dilation in depth dimension
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
public:
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dProblemSize():
|
||||
D(0), T(0), Z(0),
|
||||
pad_d(0),
|
||||
stride_d(1),
|
||||
dilation_d(1),
|
||||
Conv2dProblemSize() { }
|
||||
|
||||
/// Constructor for default padding, stride, dilation, and split-K
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dProblemSize(
|
||||
int N,
|
||||
int D,
|
||||
int H,
|
||||
int W,
|
||||
int C,
|
||||
int Z,
|
||||
int P,
|
||||
int Q,
|
||||
int K,
|
||||
int T,
|
||||
int R,
|
||||
int S,
|
||||
Mode mode
|
||||
):
|
||||
D(D), T(T), Z(Z),
|
||||
pad_d(T / 2), stride_d(1), dilation_d(1),
|
||||
Conv2dProblemSize(N, H, W, C, P, Q, K, R, S, mode) { }
|
||||
|
||||
/// Constructor
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dProblemSize(
|
||||
int N,
|
||||
int D,
|
||||
int H,
|
||||
int W,
|
||||
int C,
|
||||
int K,
|
||||
int T,
|
||||
int R,
|
||||
int S,
|
||||
int Z,
|
||||
int P,
|
||||
int Q,
|
||||
int pad_d,
|
||||
int pad_h,
|
||||
int pad_w,
|
||||
int stride_d,
|
||||
int stride_h,
|
||||
int stride_w,
|
||||
int dilation_d,
|
||||
int dilation_h,
|
||||
int dilation_w,
|
||||
Mode mode,
|
||||
int split_k_slices = 1,
|
||||
int groups = 1
|
||||
):
|
||||
D(D), T(T), Z(Z),
|
||||
pad_d(pad_d), stride_d(stride_d), dilation_d(dilation_d),
|
||||
Conv2dProblemSize(
|
||||
N, H, W, C, K, R, S, P, Q,
|
||||
pad_h, pad_w,
|
||||
stride_h, stride_w,
|
||||
dilation_h, dilation_w,
|
||||
mode, split_k_slices, groups) { }
|
||||
|
||||
/// Constructs convolution problem size from cutlass Tensor5DCoord and Coord3D
|
||||
// set *user-defined* output size and sets Z, P, and Q (include all data members in ctor)
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dProblemSize(
|
||||
cutlass::Tensor5DCoord input_size, // NDHWC
|
||||
cutlass::Tensor5DCoord filter_size, // KTRSC
|
||||
Coord3D padding, // pad_d, pad_h, pad_w
|
||||
Coord3D stride, // stride_d, stride_h, stride_w
|
||||
Coord3D dilation, // dilation_d, dilation_h, dilation_w
|
||||
cutlass::Tensor5DCoord output_size, // NZPQK
|
||||
cutlass::conv::Mode mode = cutlass::conv::Mode::kCrossCorrelation,
|
||||
int split_k_slices = 1,
|
||||
int groups = 1
|
||||
):
|
||||
D(input_size.d()), T(filter_size.d()), Z(output_size.d()),
|
||||
pad_d(padding[0]), stride_d(stride[0]), dilation_d(dilation[0]),
|
||||
Conv2dProblemSize(
|
||||
{input_size.n(), input_size.h(), input_size.w(), input_size.c()},
|
||||
{filter_size.n(), filter_size.h(), filter_size.w(), filter_size.c()},
|
||||
{padding[1], padding[1], padding[2], padding[2]},
|
||||
{stride[1], stride[2]},
|
||||
{dilation[1], dilation[2]},
|
||||
{output_size.n(), output_size.h(), output_size.w(), output_size.c()},
|
||||
mode, split_k_slices, groups
|
||||
) { }
|
||||
|
||||
/// Constructs convolution problem size from cutlass Tensor5DCoord and Coord3D
|
||||
// *computes* output size and sets Z, P and Q (include all data members in ctor)
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dProblemSize(
|
||||
cutlass::Tensor5DCoord input_size, // NDHWC
|
||||
cutlass::Tensor5DCoord filter_size, // KTRSC
|
||||
Coord3D padding, // pad_d, pad_h, pad_w
|
||||
Coord3D stride, // stride_d, stride_h, stride_w
|
||||
Coord3D dilation, // dilation_d, dilation_h, dilation_w
|
||||
cutlass::conv::Mode mode = cutlass::conv::Mode::kCrossCorrelation,
|
||||
int split_k_slices = 1,
|
||||
int groups = 1
|
||||
):
|
||||
D(input_size.d()), T(filter_size.d()),
|
||||
pad_d(padding[0]), stride_d(stride[0]), dilation_d(dilation[0]),
|
||||
Conv2dProblemSize(
|
||||
{input_size.n(), input_size.h(), input_size.w(), input_size.c()},
|
||||
{filter_size.n(), filter_size.h(), filter_size.w(), filter_size.c()},
|
||||
{padding[1], padding[1], padding[2], padding[2]},
|
||||
{stride[1], stride[2]},
|
||||
{dilation[1], dilation[2]},
|
||||
mode, split_k_slices, groups
|
||||
) {
|
||||
// set output Z
|
||||
Z = ((D + pad_d - T * dilation_d) / stride_d) + 1;
|
||||
}
|
||||
|
||||
/// Equality operator (ignores mode and split_k_slice)
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool operator==(Conv3dProblemSize const &conv) const {
|
||||
return (
|
||||
(N == conv.N) && (D == conv.D) && (H == conv.H) && (W == conv.W) && (C == conv.C) &&
|
||||
(K == conv.K) && (T == conv.T) && (R == conv.R) && (S == conv.S) &&
|
||||
(Z == conv.Z) &&(P == conv.P) && (Q == conv.Q) &&
|
||||
(pad_d == conv.pad_d) && (pad_h == conv.pad_h) && (pad_w == conv.pad_w) &&
|
||||
(stride_d == conv.stride_d) && (stride_h == conv.stride_h) && (stride_w == conv.stride_h) &&
|
||||
(dilation_d == conv.dilation_d) && (dilation_h == conv.dilation_h) && (dilation_h == conv.dilation_h)
|
||||
);
|
||||
}
|
||||
|
||||
/// Inequality operator
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool operator!=(Conv3dProblemSize const &rhs) const {
|
||||
return !(*this == rhs);
|
||||
}
|
||||
|
||||
// Reset covolution mode in the problem
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dProblemSize reset_mode(cutlass::conv::Mode mode_) {
|
||||
Conv3dProblemSize tmp(*this);
|
||||
tmp.mode = mode_;
|
||||
return tmp;
|
||||
}
|
||||
|
||||
// Reset covolution mode in the problem
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dProblemSize reset_split_k_slices(int split_k_slices_) {
|
||||
Conv3dProblemSize tmp(*this);
|
||||
tmp.split_k_slices = split_k_slices_;
|
||||
return tmp;
|
||||
}
|
||||
|
||||
/// Returns activation extent as Tensor5DCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::Tensor5DCoord activation_extent() const {
|
||||
|
||||
return cutlass::Tensor5DCoord ({N, D, H, W, C});
|
||||
}
|
||||
|
||||
/// Returns filter extent as Tensor5DCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::Tensor5DCoord filter_extent() const {
|
||||
|
||||
return cutlass::Tensor5DCoord ({K, T, R, S, C});
|
||||
}
|
||||
|
||||
/// Returns output extent as Tensor5DCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::Tensor5DCoord output_extent() const {
|
||||
|
||||
return cutlass::Tensor5DCoord ({N, Z, P, Q, K});
|
||||
}
|
||||
|
||||
/// Returns activation size in number of elements
|
||||
CUTLASS_HOST_DEVICE
|
||||
int64_t activation_size() const {
|
||||
|
||||
return (N * D * H * W * C);
|
||||
}
|
||||
|
||||
/// Returns filter size in number of elements
|
||||
CUTLASS_HOST_DEVICE
|
||||
int64_t filter_size() const {
|
||||
|
||||
return (K * T * R * S * C);
|
||||
}
|
||||
|
||||
/// Returns output size in number of elements
|
||||
CUTLASS_HOST_DEVICE
|
||||
int64_t output_size() const {
|
||||
|
||||
return (N * Z * P * Q * K);
|
||||
}
|
||||
|
||||
/// Returns output extent as Tensor5DCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
Coord3D padding() const {
|
||||
|
||||
return Coord3D ({pad_d, pad_h, pad_w});
|
||||
}
|
||||
|
||||
/// Returns stride as MatrixCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
Coord3D stride() const {
|
||||
|
||||
return Coord3D ({stride_d, stride_h, stride_w});
|
||||
}
|
||||
|
||||
/// Returns dilation as MatrixCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
Coord3D dilation() const {
|
||||
|
||||
return Coord3D ({dilation_d, dilation_h, dilation_w});
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// ImplicitGemm helper functions //
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Determine the problem size of the implicit GEMM operation
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::gemm::GemmCoord implicit_gemm_problem_size(
|
||||
Operator conv_operator,
|
||||
Conv3dProblemSize const &problem_size) {
|
||||
// Compute problem size
|
||||
switch (conv_operator) {
|
||||
case Operator::kFprop:
|
||||
return gemm::GemmCoord(
|
||||
problem_size.N * problem_size.Z * problem_size.P * problem_size.Q,
|
||||
problem_size.K,
|
||||
problem_size.T * problem_size.R * problem_size.S * problem_size.C
|
||||
);
|
||||
case Operator::kDgrad:
|
||||
return gemm::GemmCoord(
|
||||
problem_size.N * problem_size.D * problem_size.H * problem_size.W,
|
||||
problem_size.C,
|
||||
problem_size.T * problem_size.R * problem_size.S * problem_size.K
|
||||
);
|
||||
case Operator::kWgrad:
|
||||
return gemm::GemmCoord(
|
||||
problem_size.K,
|
||||
problem_size.T * problem_size.R * problem_size.S * problem_size.C,
|
||||
problem_size.N * problem_size.Z * problem_size.P * problem_size.Q
|
||||
);
|
||||
default:
|
||||
break;
|
||||
}
|
||||
return gemm::GemmCoord();
|
||||
}
|
||||
|
||||
// Determine the number of gemm_k iterations for conv2d problem using implicit gemm algorithm
|
||||
CUTLASS_HOST_DEVICE
|
||||
int implicit_gemm_k_iterations(
|
||||
Operator conv_operator,
|
||||
int threadblock_K,
|
||||
Conv3dProblemSize const &problem_size) {
|
||||
|
||||
int iterations = 0;
|
||||
int elements_per_split_k_slice = 0;
|
||||
|
||||
switch (conv_operator) {
|
||||
case Operator::kFprop:
|
||||
elements_per_split_k_slice = (problem_size.C + problem_size.split_k_slices - 1) / problem_size.split_k_slices;
|
||||
iterations = problem_size.T * problem_size.R * problem_size.S * ((elements_per_split_k_slice + threadblock_K - 1) / threadblock_K);
|
||||
break;
|
||||
|
||||
case Operator::kDgrad:
|
||||
elements_per_split_k_slice = (problem_size.K + problem_size.split_k_slices - 1) / problem_size.split_k_slices;
|
||||
iterations = problem_size.T * problem_size.R * problem_size.S * ((elements_per_split_k_slice + threadblock_K - 1) / threadblock_K);
|
||||
break;
|
||||
|
||||
case Operator::kWgrad:
|
||||
elements_per_split_k_slice = (problem_size.N * problem_size.Z * problem_size.P * problem_size.Q + problem_size.split_k_slices - 1) / problem_size.split_k_slices;
|
||||
iterations = (elements_per_split_k_slice + threadblock_K - 1) / threadblock_K;
|
||||
break;
|
||||
|
||||
default:
|
||||
break;
|
||||
}
|
||||
|
||||
return iterations;
|
||||
}
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
// Mapping function (ImplicitGemm A, B, C -> Conv Activation, Filter, Output)
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
/// Returns ImplicitGemm tensor A extent as Tensor5DCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::Tensor5DCoord implicit_gemm_tensor_a_extent(
|
||||
Operator conv_operator,
|
||||
Conv3dProblemSize const &problem_size) {
|
||||
switch (conv_operator) {
|
||||
case cutlass::conv::Operator::kFprop: return problem_size.activation_extent();
|
||||
case cutlass::conv::Operator::kDgrad: return problem_size.output_extent();
|
||||
case cutlass::conv::Operator::kWgrad: return problem_size.output_extent();
|
||||
default : break;
|
||||
}
|
||||
return cutlass::Tensor5DCoord();
|
||||
}
|
||||
|
||||
/// Returns ImplicitGemm tensor B extent as Tensor5DCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::Tensor5DCoord implicit_gemm_tensor_b_extent(
|
||||
Operator conv_operator,
|
||||
Conv3dProblemSize const &problem_size) {
|
||||
switch (conv_operator) {
|
||||
case cutlass::conv::Operator::kFprop: return problem_size.filter_extent();
|
||||
case cutlass::conv::Operator::kDgrad: return problem_size.filter_extent();
|
||||
case cutlass::conv::Operator::kWgrad: return problem_size.activation_extent();
|
||||
default : break;
|
||||
}
|
||||
return cutlass::Tensor5DCoord();
|
||||
}
|
||||
|
||||
/// Returns ImplicitGemm tensor C extent as Tensor5DCoord
|
||||
CUTLASS_HOST_DEVICE
|
||||
cutlass::Tensor5DCoord implicit_gemm_tensor_c_extent(
|
||||
Operator conv_operator,
|
||||
Conv3dProblemSize const &problem_size) {
|
||||
switch (conv_operator) {
|
||||
case cutlass::conv::Operator::kFprop: return problem_size.output_extent();
|
||||
case cutlass::conv::Operator::kDgrad: return problem_size.activation_extent();
|
||||
case cutlass::conv::Operator::kWgrad: return problem_size.filter_extent();
|
||||
default : break;
|
||||
}
|
||||
return cutlass::Tensor5DCoord();
|
||||
}
|
||||
|
||||
/// Returns ImplicitGemm tensor A size in number of elements
|
||||
CUTLASS_HOST_DEVICE
|
||||
int64_t implicit_gemm_tensor_a_size(
|
||||
Operator conv_operator,
|
||||
Conv3dProblemSize const &problem_size) {
|
||||
switch (conv_operator) {
|
||||
case cutlass::conv::Operator::kFprop: return problem_size.activation_size();
|
||||
case cutlass::conv::Operator::kDgrad: return problem_size.output_size();
|
||||
case cutlass::conv::Operator::kWgrad: return problem_size.output_size();
|
||||
default : break;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
/// Returns ImplicitGemm tensor B size in number of elements
|
||||
CUTLASS_HOST_DEVICE
|
||||
int64_t implicit_gemm_tensor_b_size(
|
||||
Operator conv_operator,
|
||||
Conv3dProblemSize const &problem_size) {
|
||||
switch (conv_operator) {
|
||||
case cutlass::conv::Operator::kFprop: return problem_size.filter_size();
|
||||
case cutlass::conv::Operator::kDgrad: return problem_size.filter_size();
|
||||
case cutlass::conv::Operator::kWgrad: return problem_size.activation_size();
|
||||
default : break;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
/// Returns ImplicitGemm tensor C size in number of elements
|
||||
CUTLASS_HOST_DEVICE
|
||||
int64_t implicit_gemm_tensor_c_size(
|
||||
Operator conv_operator,
|
||||
Conv3dProblemSize const &problem_size) {
|
||||
switch (conv_operator) {
|
||||
case cutlass::conv::Operator::kFprop: return problem_size.output_size();
|
||||
case cutlass::conv::Operator::kDgrad: return problem_size.activation_size();
|
||||
case cutlass::conv::Operator::kWgrad: return problem_size.filter_size();
|
||||
default : break;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
@@ -0,0 +1,118 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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
|
||||
|
||||
This file contains definitions and utility functions for describing convolution problem sizes in terms of
|
||||
activation (NHWC), filter (KRSC), output (NPQK), pading (pad_h, pad_w), stride (stride_h, stride_w),
|
||||
dilation (dilation_h, dilation_w). Furthermore, it defines helper functions to map cutlass' implicit gemm
|
||||
tensor extents, sizes, data types to that of convolutions extents, sizes, and data types.
|
||||
|
||||
* Mapping convolutions to Gemm computation *
|
||||
|
||||
Cutlass employs ImplicitGemm algorithm to implement convolutions. ImplicitGemm algorithm runs gemm operation
|
||||
on convolution tensors Activation, Filter, and Output . The underlying gemm operation follows the standard
|
||||
gemm definition:
|
||||
|
||||
C = A * B + C
|
||||
|
||||
A and B are input matrices
|
||||
C is source and output matrix
|
||||
|
||||
|
||||
For the three convolutional operators (Fprop, Dgrad, Wgrad), ImplicitGemm matrices A, B, and C are mapped on
|
||||
to convolution tensors Activation, Filter and Output as per the below table:
|
||||
|
||||
___________________________________________________________________________
|
||||
ConvolutionalOperator | A | B | C
|
||||
___________________________________________________________________________
|
||||
| | | | |
|
||||
| Fprop | Activation | Filter | Output |
|
||||
| Dgrad | Output | Filter | Activation |
|
||||
| Wgrad | Output | Activation | Filter |
|
||||
___________________________________________________________________________
|
||||
|
||||
In convolution codebase, DO NOT mix using (A, B, C) with (Acvitation, Filter, Output).
|
||||
|
||||
For example, a convolution class/function with A, B, Output is confusing and error-prone. Instead use below
|
||||
mapping functions and adhere to using either A, B, C or Acvitation, Filter, Output.
|
||||
|
||||
Map elements' data types (ImplicitGemm -> Conv): GemmToConvElementMap
|
||||
Map elements' data types (Conv -> ImplicitGemm): ConvToGemmElementMap
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/tensor_coord.h"
|
||||
#include "cutlass/fast_math.h"
|
||||
#include "cutlass/gemm/gemm.h"
|
||||
#include "cutlass/matrix_coord.h"
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Convolutional operator
|
||||
enum class Operator {
|
||||
kFprop,
|
||||
kDgrad,
|
||||
kWgrad
|
||||
};
|
||||
|
||||
/// Distinguishes convolution from cross correlation
|
||||
enum class Mode {
|
||||
kCrossCorrelation,
|
||||
kConvolution
|
||||
};
|
||||
|
||||
/// Selects among several implementation variants trading off performance with simplicity
|
||||
enum class IteratorAlgorithm {
|
||||
kAnalytic, ///< functionally correct in all cases but lower performance
|
||||
kOptimized ///< optimized for R <= 32, S <= 32 and unity-stride dgrad
|
||||
};
|
||||
|
||||
/// Distinguishes among partial specializations that accelerate certain problems where convolution
|
||||
/// stride is unit.
|
||||
enum class StrideSupport {
|
||||
kStrided, ///< arbitrary convolution stride
|
||||
kUnity ///< unit convolution stride
|
||||
};
|
||||
|
||||
/// Identifies split-K mode
|
||||
enum class SplitKMode {
|
||||
kNone,
|
||||
kSerial,
|
||||
kParallel
|
||||
};
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
@@ -0,0 +1,263 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Template for device-level Implicit GEMM Convolution
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <limits>
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/device_kernel.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace device {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template<typename ImplicitGemmKernel_>
|
||||
class ImplicitGemmConvolution {
|
||||
public:
|
||||
|
||||
using ImplicitGemmKernel = ImplicitGemmKernel_;
|
||||
|
||||
using ElementA = typename ImplicitGemmKernel::ElementA;
|
||||
using LayoutA = typename ImplicitGemmKernel::LayoutA;
|
||||
using ElementB = typename ImplicitGemmKernel::ElementB;
|
||||
using LayoutB = typename ImplicitGemmKernel::LayoutB;
|
||||
using ElementC = typename ImplicitGemmKernel::ElementC;
|
||||
using LayoutC = typename ImplicitGemmKernel::LayoutC;
|
||||
using ElementAccumulator = typename ImplicitGemmKernel::ElementAccumulator;
|
||||
using ElementCompute = typename ImplicitGemmKernel::ElementCompute;
|
||||
using OperatorClass = typename ImplicitGemmKernel::OperatorClass;
|
||||
using ArchTag = typename ImplicitGemmKernel::ArchTag;
|
||||
using ThreadblockShape = typename ImplicitGemmKernel::ThreadblockShape;
|
||||
using WarpShape = typename ImplicitGemmKernel::WarpShape;
|
||||
using InstructionShape = typename ImplicitGemmKernel::InstructionShape;
|
||||
using ThreadblockSwizzle = typename ImplicitGemmKernel::ThreadblockSwizzle;
|
||||
using EpilogueOutputOp = typename ImplicitGemmKernel::EpilogueOutputOp;
|
||||
static int const kStages = ImplicitGemmKernel::kStages;
|
||||
static int const kConvDim = ImplicitGemmKernel::kConvDim;
|
||||
using WarpMmaOperator = typename ImplicitGemmKernel::WarpMmaOperator;
|
||||
using ArchMmaOperator = typename ImplicitGemmKernel::ArchMmaOperator;
|
||||
using MathOperator = typename ImplicitGemmKernel::MathOperator;
|
||||
|
||||
static cutlass::conv::Operator const kConvolutionalOperator = ImplicitGemmKernel::kConvolutionalOperator;
|
||||
static cutlass::conv::IteratorAlgorithm const kIteratorAlgorithm = ImplicitGemmKernel::kIteratorAlgorithm;
|
||||
|
||||
static int const kWarpCount =
|
||||
(ThreadblockShape::kM / WarpShape::kM) *
|
||||
(ThreadblockShape::kN / WarpShape::kN);
|
||||
|
||||
/// Argument structure
|
||||
using Arguments = typename ImplicitGemmKernel::Arguments;
|
||||
|
||||
private:
|
||||
|
||||
/// Kernel parameters object
|
||||
typename ImplicitGemmKernel::Params params_;
|
||||
|
||||
public:
|
||||
|
||||
/// Constructs Implicit GEMM
|
||||
ImplicitGemmConvolution() { }
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
static Status can_implement(Arguments const &args) {
|
||||
|
||||
// dispatch to iterators
|
||||
Status status = ImplicitGemmKernel::Mma::IteratorA::can_implement(args.problem_size);
|
||||
if (Status::kSuccess != status) {
|
||||
return status;
|
||||
}
|
||||
|
||||
status = ImplicitGemmKernel::Mma::IteratorB::can_implement(args.problem_size);
|
||||
if (Status::kSuccess != status) {
|
||||
return status;
|
||||
}
|
||||
|
||||
// Determine grid shape
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
dim3 grid = threadblock_swizzle.get_grid_shape(
|
||||
threadblock_swizzle.get_tiled_shape(
|
||||
cutlass::conv::implicit_gemm_problem_size(kConvolutionalOperator, args.problem_size),
|
||||
{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
|
||||
args.problem_size.split_k_slices));
|
||||
|
||||
if (!(grid.y <= std::numeric_limits<uint16_t>::max() &&
|
||||
grid.z <= std::numeric_limits<uint16_t>::max())) {
|
||||
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
/// Gets the workspace size
|
||||
static size_t get_workspace_size(Arguments const &args) {
|
||||
|
||||
size_t workspace_bytes = 0;
|
||||
|
||||
// Determine grid shape
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
cutlass::gemm::GemmCoord grid_tiled_shape = threadblock_swizzle.get_tiled_shape(
|
||||
cutlass::conv::implicit_gemm_problem_size(kConvolutionalOperator, args.problem_size),
|
||||
{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
|
||||
args.problem_size.split_k_slices);
|
||||
|
||||
if(args.split_k_mode == SplitKMode::kParallel) {
|
||||
|
||||
// Split-K parallel: CTAs in k-dimension write the partial results in a temporary workspace.
|
||||
// The user needs to call a reduction operator to optain the final output tensor
|
||||
workspace_bytes =
|
||||
sizeof(ElementAccumulator) *
|
||||
size_t(cutlass::conv::implicit_gemm_tensor_c_size(kConvolutionalOperator, args.problem_size)) *
|
||||
size_t(grid_tiled_shape.k());
|
||||
}
|
||||
|
||||
else if(args.split_k_mode == SplitKMode::kSerial && args.problem_size.split_k_slices > 1) {
|
||||
|
||||
// Split-K serial: The user workspace is used to store semaphore and serialize writing the
|
||||
// final reduced output to user's output tensor
|
||||
workspace_bytes = sizeof(int) * size_t(grid_tiled_shape.m()) * size_t(grid_tiled_shape.n());
|
||||
}
|
||||
|
||||
return workspace_bytes;
|
||||
}
|
||||
|
||||
/// Initializes GEMM state from arguments.
|
||||
Status initialize(
|
||||
Arguments const &args,
|
||||
void *workspace = nullptr,
|
||||
cudaStream_t stream = nullptr) {
|
||||
|
||||
if (args.problem_size.split_k_slices > 1) {
|
||||
|
||||
if (!workspace) {
|
||||
return Status::kErrorWorkspaceNull;
|
||||
}
|
||||
|
||||
cudaError_t status = cudaMemsetAsync(workspace, 0, get_workspace_size(args), stream);
|
||||
|
||||
if (status != cudaSuccess) {
|
||||
return Status::kErrorInternal;
|
||||
}
|
||||
}
|
||||
|
||||
// initialize the params structure from the arguments
|
||||
params_ = typename ImplicitGemmKernel::Params(
|
||||
args,
|
||||
static_cast<int *>(workspace)
|
||||
);
|
||||
|
||||
int smem_size = int(sizeof(typename ImplicitGemmKernel::SharedStorage));
|
||||
|
||||
if (smem_size >= (48 << 10)) {
|
||||
cudaError_t result = cudaFuncSetAttribute(cutlass::Kernel<ImplicitGemmKernel>,
|
||||
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
||||
smem_size);
|
||||
|
||||
if (result != cudaSuccess) {
|
||||
return Status::kErrorInternal;
|
||||
}
|
||||
|
||||
result = cudaFuncSetAttribute(
|
||||
cutlass::Kernel<ImplicitGemmKernel>,
|
||||
cudaFuncAttributePreferredSharedMemoryCarveout, 100);
|
||||
|
||||
if (result != cudaSuccess) {
|
||||
return Status::kErrorInternal;
|
||||
}
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
/// Initializes GEMM state from arguments.
|
||||
Status update(Arguments const &args, void *workspace = nullptr) {
|
||||
|
||||
// update the params structure from the arguments
|
||||
params_.ptr_A = args.ref_A.data();
|
||||
params_.ptr_B = args.ref_B.data();
|
||||
params_.ptr_C = args.ref_C.data();
|
||||
params_.ptr_D = args.ref_D.data();
|
||||
params_.output_op = args.output_op;
|
||||
params_.semaphore = static_cast<int *>(workspace);
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
/// Runs the kernel using initialized state.
|
||||
Status run(cudaStream_t stream = nullptr) {
|
||||
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
dim3 grid = threadblock_swizzle.get_grid_shape(params_.grid_tiled_shape);
|
||||
dim3 block(32 * kWarpCount, 1, 1);
|
||||
|
||||
int smem_size = int(sizeof(typename ImplicitGemmKernel::SharedStorage));
|
||||
|
||||
cutlass::Kernel<ImplicitGemmKernel><<<grid, block, smem_size, stream>>>(params_);
|
||||
|
||||
cudaError_t result = cudaGetLastError();
|
||||
|
||||
return result == cudaSuccess ? Status::kSuccess : Status::kErrorInternal;
|
||||
}
|
||||
|
||||
/// Runs the kernel using initialized state.
|
||||
Status operator()(cudaStream_t stream = nullptr) {
|
||||
return run(stream);
|
||||
}
|
||||
|
||||
/// Runs the kernel using initialized state.
|
||||
Status operator()(
|
||||
Arguments const &args,
|
||||
void *workspace = nullptr,
|
||||
cudaStream_t stream = nullptr) {
|
||||
|
||||
Status status = initialize(args, workspace);
|
||||
|
||||
if (status == Status::kSuccess) {
|
||||
status = run(stream);
|
||||
}
|
||||
|
||||
return status;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
@@ -0,0 +1,104 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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
|
||||
Default kernel-level implicit GEMM convolution definitions for threadblock-scoped epilogue.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/gemm/threadblock/default_mma.h"
|
||||
#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_simt.h"
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_tensor_op.h"
|
||||
#include "cutlass/epilogue/threadblock/default_epilogue_volta_tensor_op.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/threadblock/conv2d_tile_iterator.h"
|
||||
#include "cutlass/conv/threadblock/implicit_gemm_pipelined.h"
|
||||
#include "cutlass/conv/threadblock/implicit_gemm_multistage.h"
|
||||
#include "cutlass/conv/kernel/implicit_gemm_convolution.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace kernel {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace detail {
|
||||
|
||||
template <
|
||||
typename ArchTag,
|
||||
typename Shape,
|
||||
typename WarpMmaTensorOp,
|
||||
int PartitionsK,
|
||||
typename OutputOp
|
||||
>
|
||||
struct DefaultConvEpilogue {
|
||||
using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
|
||||
Shape,
|
||||
WarpMmaTensorOp,
|
||||
1,
|
||||
OutputOp,
|
||||
OutputOp::kCount
|
||||
>::Epilogue;
|
||||
};
|
||||
|
||||
template <
|
||||
typename Shape,
|
||||
typename WarpMmaTensorOp,
|
||||
int PartitionsK,
|
||||
typename OutputOp
|
||||
>
|
||||
struct DefaultConvEpilogue<
|
||||
arch::Sm70,
|
||||
Shape,
|
||||
WarpMmaTensorOp,
|
||||
PartitionsK,
|
||||
OutputOp
|
||||
> {
|
||||
|
||||
using Epilogue = typename epilogue::threadblock::DefaultEpilogueVoltaTensorOp<
|
||||
Shape,
|
||||
WarpMmaTensorOp,
|
||||
1,
|
||||
OutputOp,
|
||||
OutputOp::kCount
|
||||
>::Epilogue;
|
||||
};
|
||||
|
||||
} // namespace detail
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,928 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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
|
||||
Default kernel-level implicit GEMM convolution definitions combine threadblock-scoped
|
||||
matrix multiply-add with the appropriate threadblock-scoped epilogue.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/conv/kernel/default_conv2d.h"
|
||||
|
||||
#include "cutlass/conv/threadblock/conv2d_wgrad_output_gradient_tile_access_iterator_analytic.h"
|
||||
#include "cutlass/conv/threadblock/conv2d_wgrad_activation_tile_access_iterator_analytic.h"
|
||||
#include "cutlass/conv/threadblock/conv2d_wgrad_output_gradient_tile_access_iterator_optimized.h"
|
||||
#include "cutlass/conv/threadblock/conv2d_wgrad_activation_tile_access_iterator_optimized.h"
|
||||
#include "cutlass/conv/threadblock/conv2d_tile_iterator.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace kernel {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Defines a kernel for Conv2dWgrad
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename OperatorClass,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
int Stages,
|
||||
typename MathOperatorTag,
|
||||
conv::IteratorAlgorithm IteratorAlgorithm = IteratorAlgorithm::kAnalytic,
|
||||
conv::StrideSupport StrideSupport = StrideSupport::kStrided
|
||||
> struct DefaultConv2dWgrad;
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// OpClassTensorOp convolutions
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Defines a kernel for Conv2dWgrad specialzation for Analytic IteratorAlgorithm and multistage
|
||||
// pipeline.
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename OperatorClass,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
int Stages,
|
||||
typename MathOperatorTag
|
||||
>
|
||||
struct DefaultConv2dWgrad <
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
OperatorClass,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
Stages,
|
||||
MathOperatorTag,
|
||||
IteratorAlgorithm::kAnalytic
|
||||
> {
|
||||
|
||||
// Define the core components from GEMM
|
||||
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
|
||||
ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::ColumnMajor,
|
||||
ElementB, layout::RowMajor, ElementAccumulator, layout::RowMajor, OperatorClass,
|
||||
Stages, MathOperatorTag>;
|
||||
|
||||
// Define iterators over tiles from the A operand
|
||||
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
|
||||
using IteratorA =
|
||||
cutlass::conv::threadblock::Conv2dWgradOutputGradientTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
|
||||
ElementA,
|
||||
ThreadMapA
|
||||
>;
|
||||
|
||||
using SmemIteratorA = typename MmaCore::SmemIteratorA;
|
||||
|
||||
// Define iterators over tiles from the B operand
|
||||
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
|
||||
using IteratorB =
|
||||
cutlass::conv::threadblock::Conv2dWgradActivationTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
|
||||
ElementB,
|
||||
ThreadMapB
|
||||
>;
|
||||
|
||||
using SmemIteratorB = typename MmaCore::SmemIteratorB;
|
||||
|
||||
// Warp-level GEMM components
|
||||
using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
|
||||
using MmaPolicy = typename MmaCore::MmaPolicy;
|
||||
|
||||
// Define the Mma
|
||||
using Mma = threadblock::ImplicitGemmMultistage<
|
||||
ThreadblockShape,
|
||||
IteratorA,
|
||||
SmemIteratorA,
|
||||
arch::CacheOperation::Always,
|
||||
IteratorB,
|
||||
SmemIteratorB,
|
||||
arch::CacheOperation::Always,
|
||||
MmaPolicy,
|
||||
Stages
|
||||
>;
|
||||
|
||||
// Define the epilogue
|
||||
using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
|
||||
ThreadblockShape,
|
||||
WarpMmaTensorOp,
|
||||
1,
|
||||
EpilogueOutputOp,
|
||||
EpilogueOutputOp::kCount
|
||||
>::Epilogue;
|
||||
|
||||
// Define the kernel
|
||||
using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
|
||||
Mma,
|
||||
Epilogue,
|
||||
ThreadblockSwizzle,
|
||||
conv::Operator::kWgrad
|
||||
>;
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Defines a kernel for Conv2dWgrad specialzation for Analytic IteratorAlgorithm and two
|
||||
// pipeline.
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename OperatorClass,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
typename MathOperatorTag
|
||||
>
|
||||
struct DefaultConv2dWgrad <
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
OperatorClass,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
2,
|
||||
MathOperatorTag,
|
||||
IteratorAlgorithm::kAnalytic
|
||||
> {
|
||||
|
||||
// Define the core components from GEMM
|
||||
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
|
||||
ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::ColumnMajor,
|
||||
ElementB, layout::RowMajor, ElementAccumulator, layout::RowMajor, OperatorClass,
|
||||
2, MathOperatorTag>;
|
||||
|
||||
// Define iterators over tiles from the A operand
|
||||
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
|
||||
using IteratorA =
|
||||
cutlass::conv::threadblock::TileIterator<
|
||||
cutlass::conv::threadblock::Conv2dWgradOutputGradientTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
|
||||
ElementA,
|
||||
ThreadMapA
|
||||
>
|
||||
>;
|
||||
|
||||
using SmemIteratorA = typename MmaCore::SmemIteratorA;
|
||||
|
||||
// Define iterators over tiles from the B operand
|
||||
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
|
||||
using IteratorB =
|
||||
cutlass::conv::threadblock::TileIterator<
|
||||
cutlass::conv::threadblock::Conv2dWgradActivationTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
|
||||
ElementB,
|
||||
ThreadMapB
|
||||
>
|
||||
>;
|
||||
|
||||
using SmemIteratorB = typename MmaCore::SmemIteratorB;
|
||||
|
||||
// Warp-level GEMM components
|
||||
using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
|
||||
using MmaPolicy = typename MmaCore::MmaPolicy;
|
||||
|
||||
// Define the Mma
|
||||
using Mma = threadblock::ImplicitGemmPipelined<
|
||||
ThreadblockShape,
|
||||
IteratorA,
|
||||
SmemIteratorA,
|
||||
IteratorB,
|
||||
SmemIteratorB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
MmaPolicy
|
||||
>;
|
||||
|
||||
// Define the epilogue
|
||||
using Epilogue = typename detail::DefaultConvEpilogue<
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpMmaTensorOp,
|
||||
1,
|
||||
EpilogueOutputOp
|
||||
>::Epilogue;
|
||||
|
||||
// Define the kernel
|
||||
using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
|
||||
Mma,
|
||||
Epilogue,
|
||||
ThreadblockSwizzle,
|
||||
conv::Operator::kWgrad
|
||||
>;
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Defines a kernel for Conv2dWgrad specialzation for Optimized IteratorAlgorithm and multistage
|
||||
// pipeline.
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename OperatorClass,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
int Stages,
|
||||
typename MathOperatorTag
|
||||
>
|
||||
struct DefaultConv2dWgrad <
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
OperatorClass,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
Stages,
|
||||
MathOperatorTag,
|
||||
IteratorAlgorithm::kOptimized
|
||||
> {
|
||||
|
||||
// Define the core components from GEMM
|
||||
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
|
||||
ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::ColumnMajor,
|
||||
ElementB, layout::RowMajor, ElementAccumulator, layout::RowMajor, OperatorClass,
|
||||
Stages, MathOperatorTag>;
|
||||
|
||||
// Define iterators over tiles from the A operand
|
||||
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
|
||||
using IteratorA =
|
||||
cutlass::conv::threadblock::Conv2dWgradOutputGradientTileAccessIteratorOptimized<
|
||||
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
|
||||
ElementA,
|
||||
ThreadMapA
|
||||
>;
|
||||
|
||||
using SmemIteratorA = typename MmaCore::SmemIteratorA;
|
||||
|
||||
// Define iterators over tiles from the B operand
|
||||
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
|
||||
using IteratorB =
|
||||
cutlass::conv::threadblock::Conv2dWgradActivationTileAccessIteratorOptimized<
|
||||
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
|
||||
ElementB,
|
||||
ThreadMapB
|
||||
>;
|
||||
|
||||
using SmemIteratorB = typename MmaCore::SmemIteratorB;
|
||||
|
||||
// Warp-level GEMM components
|
||||
using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
|
||||
using MmaPolicy = typename MmaCore::MmaPolicy;
|
||||
|
||||
// Define the Mma
|
||||
using Mma = threadblock::ImplicitGemmMultistage<
|
||||
ThreadblockShape,
|
||||
IteratorA,
|
||||
SmemIteratorA,
|
||||
arch::CacheOperation::Always,
|
||||
IteratorB,
|
||||
SmemIteratorB,
|
||||
arch::CacheOperation::Always,
|
||||
MmaPolicy,
|
||||
Stages
|
||||
>;
|
||||
|
||||
// Define the epilogue
|
||||
using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
|
||||
ThreadblockShape,
|
||||
WarpMmaTensorOp,
|
||||
1,
|
||||
EpilogueOutputOp,
|
||||
EpilogueOutputOp::kCount
|
||||
>::Epilogue;
|
||||
|
||||
// Define the kernel
|
||||
using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
|
||||
Mma,
|
||||
Epilogue,
|
||||
ThreadblockSwizzle,
|
||||
conv::Operator::kWgrad
|
||||
>;
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Defines a kernel for Conv2dWgrad specialzation for Optimized IteratorAlgorithm and two
|
||||
// pipeline.
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename OperatorClass,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
typename MathOperatorTag
|
||||
>
|
||||
struct DefaultConv2dWgrad <
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
OperatorClass,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
2,
|
||||
MathOperatorTag,
|
||||
IteratorAlgorithm::kOptimized
|
||||
> {
|
||||
|
||||
// Define the core components from GEMM
|
||||
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
|
||||
ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::ColumnMajor,
|
||||
ElementB, layout::RowMajor, ElementAccumulator, layout::RowMajor, OperatorClass,
|
||||
2, MathOperatorTag>;
|
||||
|
||||
// Define iterators over tiles from the A operand
|
||||
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
|
||||
using IteratorA =
|
||||
cutlass::conv::threadblock::TileIterator<
|
||||
cutlass::conv::threadblock::Conv2dWgradOutputGradientTileAccessIteratorOptimized<
|
||||
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
|
||||
ElementA,
|
||||
ThreadMapA
|
||||
>
|
||||
>;
|
||||
|
||||
using SmemIteratorA = typename MmaCore::SmemIteratorA;
|
||||
|
||||
// Define iterators over tiles from the B operand
|
||||
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
|
||||
using IteratorB =
|
||||
cutlass::conv::threadblock::TileIterator<
|
||||
cutlass::conv::threadblock::Conv2dWgradActivationTileAccessIteratorOptimized<
|
||||
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
|
||||
ElementB,
|
||||
ThreadMapB
|
||||
>
|
||||
>;
|
||||
|
||||
using SmemIteratorB = typename MmaCore::SmemIteratorB;
|
||||
|
||||
// Warp-level GEMM components
|
||||
using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
|
||||
using MmaPolicy = typename MmaCore::MmaPolicy;
|
||||
|
||||
// Define the Mma
|
||||
using Mma = threadblock::ImplicitGemmPipelined<
|
||||
ThreadblockShape,
|
||||
IteratorA,
|
||||
SmemIteratorA,
|
||||
IteratorB,
|
||||
SmemIteratorB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
MmaPolicy
|
||||
>;
|
||||
|
||||
// Define the epilogue
|
||||
using Epilogue = typename detail::DefaultConvEpilogue<
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpMmaTensorOp,
|
||||
1,
|
||||
EpilogueOutputOp
|
||||
>::Epilogue;
|
||||
|
||||
// Define the kernel
|
||||
using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
|
||||
Mma,
|
||||
Epilogue,
|
||||
ThreadblockSwizzle,
|
||||
conv::Operator::kWgrad
|
||||
>;
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// OpClassSimt convolutions
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/// Defines a kernel for Conv2dWgrad specialzation for Analytic IteratorAlgorithm,
|
||||
/// multi-stage pipeline, and FFMA-based mainloop for SM80
|
||||
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
int Stages,
|
||||
typename MathOperatorTag
|
||||
>
|
||||
struct DefaultConv2dWgrad <
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
arch::OpClassSimt,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
Stages,
|
||||
MathOperatorTag,
|
||||
IteratorAlgorithm::kAnalytic
|
||||
> {
|
||||
|
||||
// Define the core components from GEMM
|
||||
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
|
||||
ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::ColumnMajor,
|
||||
ElementB, layout::RowMajor, ElementAccumulator, layout::RowMajor, arch::OpClassSimt,
|
||||
Stages, MathOperatorTag>;
|
||||
|
||||
// Define iterators over tiles from the A operand
|
||||
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
|
||||
using IteratorA =
|
||||
cutlass::conv::threadblock::Conv2dWgradOutputGradientTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
|
||||
ElementA,
|
||||
ThreadMapA
|
||||
>;
|
||||
|
||||
using SmemIteratorA = typename MmaCore::SmemIteratorA;
|
||||
|
||||
// Define iterators over tiles from the B operand
|
||||
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
|
||||
using IteratorB =
|
||||
cutlass::conv::threadblock::Conv2dWgradActivationTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
|
||||
ElementB,
|
||||
ThreadMapB
|
||||
>;
|
||||
|
||||
using SmemIteratorB = typename MmaCore::SmemIteratorB;
|
||||
|
||||
// Warp-level GEMM components
|
||||
using WarpMmaSimtOp = typename MmaCore::MmaWarpSimt;
|
||||
using MmaPolicy = typename MmaCore::MmaPolicy;
|
||||
|
||||
// Define the Mma
|
||||
using Mma = threadblock::ImplicitGemmMultistage<
|
||||
ThreadblockShape,
|
||||
IteratorA,
|
||||
SmemIteratorA,
|
||||
arch::CacheOperation::Always,
|
||||
IteratorB,
|
||||
SmemIteratorB,
|
||||
arch::CacheOperation::Always,
|
||||
MmaPolicy,
|
||||
Stages
|
||||
>;
|
||||
|
||||
// Define the epilogue
|
||||
using Epilogue = typename epilogue::threadblock::DefaultEpilogueSimt<
|
||||
ThreadblockShape,
|
||||
WarpMmaSimtOp,
|
||||
EpilogueOutputOp,
|
||||
EpilogueOutputOp::kCount
|
||||
>::Epilogue;
|
||||
|
||||
// Define the kernel
|
||||
using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
|
||||
Mma,
|
||||
Epilogue,
|
||||
ThreadblockSwizzle,
|
||||
conv::Operator::kWgrad
|
||||
>;
|
||||
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Defines a kernel for Conv2dWgrad specialzation for Optimized IteratorAlgorithm,
|
||||
/// multi-stage pipeline, and FFMA-based mainloop for SM80
|
||||
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
int Stages,
|
||||
typename MathOperatorTag
|
||||
>
|
||||
struct DefaultConv2dWgrad <
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
arch::OpClassSimt,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
Stages,
|
||||
MathOperatorTag,
|
||||
IteratorAlgorithm::kOptimized
|
||||
> {
|
||||
|
||||
// Define the core components from GEMM
|
||||
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
|
||||
ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::ColumnMajor,
|
||||
ElementB, layout::RowMajor, ElementAccumulator, layout::RowMajor, arch::OpClassSimt,
|
||||
Stages, MathOperatorTag>;
|
||||
|
||||
// Define iterators over tiles from the A operand
|
||||
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
|
||||
using IteratorA =
|
||||
cutlass::conv::threadblock::Conv2dWgradOutputGradientTileAccessIteratorOptimized<
|
||||
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
|
||||
ElementA,
|
||||
ThreadMapA
|
||||
>;
|
||||
|
||||
using SmemIteratorA = typename MmaCore::SmemIteratorA;
|
||||
|
||||
// Define iterators over tiles from the B operand
|
||||
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
|
||||
using IteratorB =
|
||||
cutlass::conv::threadblock::Conv2dWgradActivationTileAccessIteratorOptimized<
|
||||
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
|
||||
ElementB,
|
||||
ThreadMapB
|
||||
>;
|
||||
|
||||
using SmemIteratorB = typename MmaCore::SmemIteratorB;
|
||||
|
||||
// Warp-level GEMM components
|
||||
using WarpMmaSimtOp = typename MmaCore::MmaWarpSimt;
|
||||
using MmaPolicy = typename MmaCore::MmaPolicy;
|
||||
|
||||
// Define the Mma
|
||||
using Mma = threadblock::ImplicitGemmMultistage<
|
||||
ThreadblockShape,
|
||||
IteratorA,
|
||||
SmemIteratorA,
|
||||
arch::CacheOperation::Always,
|
||||
IteratorB,
|
||||
SmemIteratorB,
|
||||
arch::CacheOperation::Always,
|
||||
MmaPolicy,
|
||||
Stages
|
||||
>;
|
||||
|
||||
// Define the epilogue
|
||||
using Epilogue = typename epilogue::threadblock::DefaultEpilogueSimt<
|
||||
ThreadblockShape,
|
||||
WarpMmaSimtOp,
|
||||
EpilogueOutputOp,
|
||||
EpilogueOutputOp::kCount
|
||||
>::Epilogue;
|
||||
|
||||
// Define the kernel
|
||||
using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
|
||||
Mma,
|
||||
Epilogue,
|
||||
ThreadblockSwizzle,
|
||||
conv::Operator::kWgrad
|
||||
>;
|
||||
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Defines a kernel for Conv2dWgrad specialzation for Analytic IteratorAlgorithm,
|
||||
/// 2 stage pipeline, and FFMA-based mainloop for SM50
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
typename MathOperatorTag
|
||||
>
|
||||
struct DefaultConv2dWgrad <
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
arch::OpClassSimt,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
2,
|
||||
MathOperatorTag,
|
||||
IteratorAlgorithm::kAnalytic
|
||||
> {
|
||||
|
||||
// Define the core components from GEMM
|
||||
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
|
||||
ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::ColumnMajor,
|
||||
ElementB, layout::RowMajor, ElementAccumulator, layout::RowMajor, arch::OpClassSimt,
|
||||
2, MathOperatorTag>;
|
||||
|
||||
// Define iterators over tiles from the A operand
|
||||
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
|
||||
using IteratorA =
|
||||
cutlass::conv::threadblock::TileIterator<
|
||||
cutlass::conv::threadblock::Conv2dWgradOutputGradientTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
|
||||
ElementA,
|
||||
ThreadMapA
|
||||
>
|
||||
>;
|
||||
|
||||
using SmemIteratorA = typename MmaCore::SmemIteratorA;
|
||||
|
||||
// Define iterators over tiles from the B operand
|
||||
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
|
||||
using IteratorB =
|
||||
cutlass::conv::threadblock::TileIterator<
|
||||
cutlass::conv::threadblock::Conv2dWgradActivationTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
|
||||
ElementB,
|
||||
ThreadMapB
|
||||
>
|
||||
>;
|
||||
|
||||
using SmemIteratorB = typename MmaCore::SmemIteratorB;
|
||||
|
||||
// Warp-level GEMM components
|
||||
using WarpMmaSimtOp = typename MmaCore::MmaWarpSimt;
|
||||
using MmaPolicy = typename MmaCore::MmaPolicy;
|
||||
|
||||
// Define the Mma
|
||||
using Mma = threadblock::ImplicitGemmPipelined<
|
||||
ThreadblockShape,
|
||||
IteratorA,
|
||||
SmemIteratorA,
|
||||
IteratorB,
|
||||
SmemIteratorB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
MmaPolicy
|
||||
>;
|
||||
|
||||
// Define the epilogue
|
||||
using Epilogue = typename epilogue::threadblock::DefaultEpilogueSimt<
|
||||
ThreadblockShape,
|
||||
WarpMmaSimtOp,
|
||||
EpilogueOutputOp,
|
||||
EpilogueOutputOp::kCount
|
||||
>::Epilogue;
|
||||
|
||||
// Define the kernel
|
||||
using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
|
||||
Mma,
|
||||
Epilogue,
|
||||
ThreadblockSwizzle,
|
||||
conv::Operator::kWgrad
|
||||
>;
|
||||
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Defines a kernel for Conv2dWgrad specialzation for Optimized IteratorAlgorithm,
|
||||
/// 2 stage pipeline, and FFMA-based mainloop for SM50
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
typename MathOperatorTag
|
||||
>
|
||||
struct DefaultConv2dWgrad <
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
arch::OpClassSimt,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
2,
|
||||
MathOperatorTag,
|
||||
IteratorAlgorithm::kOptimized
|
||||
> {
|
||||
|
||||
// Define the core components from GEMM
|
||||
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
|
||||
ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::ColumnMajor,
|
||||
ElementB, layout::RowMajor, ElementAccumulator, layout::RowMajor, arch::OpClassSimt,
|
||||
2, MathOperatorTag>;
|
||||
|
||||
// Define iterators over tiles from the A operand
|
||||
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
|
||||
using IteratorA =
|
||||
cutlass::conv::threadblock::TileIterator<
|
||||
cutlass::conv::threadblock::Conv2dWgradOutputGradientTileAccessIteratorOptimized<
|
||||
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
|
||||
ElementA,
|
||||
ThreadMapA
|
||||
>
|
||||
>;
|
||||
|
||||
using SmemIteratorA = typename MmaCore::SmemIteratorA;
|
||||
|
||||
// Define iterators over tiles from the B operand
|
||||
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
|
||||
using IteratorB =
|
||||
cutlass::conv::threadblock::TileIterator<
|
||||
cutlass::conv::threadblock::Conv2dWgradActivationTileAccessIteratorOptimized<
|
||||
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
|
||||
ElementB,
|
||||
ThreadMapB
|
||||
>
|
||||
>;
|
||||
|
||||
using SmemIteratorB = typename MmaCore::SmemIteratorB;
|
||||
|
||||
// Warp-level GEMM components
|
||||
using WarpMmaSimtOp = typename MmaCore::MmaWarpSimt;
|
||||
using MmaPolicy = typename MmaCore::MmaPolicy;
|
||||
|
||||
// Define the Mma
|
||||
using Mma = threadblock::ImplicitGemmPipelined<
|
||||
ThreadblockShape,
|
||||
IteratorA,
|
||||
SmemIteratorA,
|
||||
IteratorB,
|
||||
SmemIteratorB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
MmaPolicy
|
||||
>;
|
||||
|
||||
// Define the epilogue
|
||||
using Epilogue = typename epilogue::threadblock::DefaultEpilogueSimt<
|
||||
ThreadblockShape,
|
||||
WarpMmaSimtOp,
|
||||
EpilogueOutputOp,
|
||||
EpilogueOutputOp::kCount
|
||||
>::Epilogue;
|
||||
|
||||
// Define the kernel
|
||||
using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
|
||||
Mma,
|
||||
Epilogue,
|
||||
ThreadblockSwizzle,
|
||||
conv::Operator::kWgrad
|
||||
>;
|
||||
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
@@ -0,0 +1,184 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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
|
||||
Default kernel-level implicit GEMM convolution definitions combine threadblock-scoped
|
||||
matrix multiply-add with the appropriate threadblock-scoped epilogue.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/conv/kernel/default_conv2d.h"
|
||||
|
||||
#include "cutlass/conv/threadblock/conv3d_dgrad_output_gradient_tile_access_iterator_analytic.h"
|
||||
#include "cutlass/conv/threadblock/conv3d_dgrad_filter_tile_access_iterator_analytic.h"
|
||||
#include "cutlass/conv/threadblock/conv2d_tile_iterator.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace kernel {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/// Defines a kernel for Conv2dDgrad
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename OperatorClass,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
int Stages,
|
||||
typename MathOperatorTag,
|
||||
conv::IteratorAlgorithm IteratorAlgorithm = IteratorAlgorithm::kAnalytic,
|
||||
conv::StrideSupport StrideSupport = StrideSupport::kStrided
|
||||
> struct DefaultConv3dDgrad;
|
||||
|
||||
/// Defines a kernel for Conv2dDgrad specialzation for Analytic IteratorAlgorithm Dgrad Strided
|
||||
// and multistage pipeline.
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename OperatorClass,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
int Stages,
|
||||
typename MathOperatorTag
|
||||
>
|
||||
struct DefaultConv3dDgrad <
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
OperatorClass,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
Stages,
|
||||
MathOperatorTag,
|
||||
IteratorAlgorithm::kAnalytic,
|
||||
StrideSupport::kStrided
|
||||
> {
|
||||
|
||||
// Define the core components from GEMM
|
||||
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
|
||||
ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::RowMajor,
|
||||
ElementB, layout::RowMajor, ElementAccumulator, layout::RowMajor, OperatorClass,
|
||||
Stages, MathOperatorTag>;
|
||||
|
||||
// Define iterators over tiles from the A operand
|
||||
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
|
||||
using IteratorA =
|
||||
cutlass::conv::threadblock::Conv3dDgradOutputGradientTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
|
||||
ElementA,
|
||||
ThreadMapA,
|
||||
StrideSupport::kStrided
|
||||
>;
|
||||
|
||||
using SmemIteratorA = typename MmaCore::SmemIteratorA;
|
||||
|
||||
// Define iterators over tiles from the B operand
|
||||
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
|
||||
using IteratorB =
|
||||
cutlass::conv::threadblock::Conv3dDgradFilterTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
|
||||
ElementB,
|
||||
ThreadMapB
|
||||
>;
|
||||
|
||||
using SmemIteratorB = typename MmaCore::SmemIteratorB;
|
||||
|
||||
// Warp-level GEMM components
|
||||
using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
|
||||
using MmaPolicy = typename MmaCore::MmaPolicy;
|
||||
|
||||
// Define the Mma
|
||||
using Mma = threadblock::ImplicitGemmMultistage<
|
||||
ThreadblockShape,
|
||||
IteratorA,
|
||||
SmemIteratorA,
|
||||
arch::CacheOperation::Always,
|
||||
IteratorB,
|
||||
SmemIteratorB,
|
||||
arch::CacheOperation::Global,
|
||||
MmaPolicy,
|
||||
Stages
|
||||
>;
|
||||
|
||||
// Define the epilogue
|
||||
using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
|
||||
ThreadblockShape,
|
||||
WarpMmaTensorOp,
|
||||
1,
|
||||
EpilogueOutputOp,
|
||||
EpilogueOutputOp::kCount
|
||||
>::Epilogue;
|
||||
|
||||
// Define the kernel
|
||||
using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
|
||||
Mma,
|
||||
Epilogue,
|
||||
ThreadblockSwizzle,
|
||||
conv::Operator::kDgrad,
|
||||
Conv3dProblemSize
|
||||
>;
|
||||
};
|
||||
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
@@ -0,0 +1,181 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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
|
||||
Default kernel-level implicit GEMM convolution definitions combine threadblock-scoped
|
||||
matrix multiply-add with the appropriate threadblock-scoped epilogue.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/conv/kernel/default_conv2d.h"
|
||||
|
||||
#include "cutlass/conv/threadblock/conv3d_fprop_activation_tile_access_iterator_analytic.h"
|
||||
#include "cutlass/conv/threadblock/conv3d_fprop_filter_tile_access_iterator_analytic.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace kernel {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/// Defines a kernel for Conv2dFprop
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename OperatorClass,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
int Stages,
|
||||
typename MathOperatorTag,
|
||||
conv::IteratorAlgorithm IteratorAlgorithm = IteratorAlgorithm::kAnalytic,
|
||||
conv::StrideSupport StrideSupport = StrideSupport::kStrided
|
||||
> struct DefaultConv3dFprop;
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Defines a kernel for Conv2dFprop specialzation for Analytic IteratorAlgorithm and multistage
|
||||
// pipeline.
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
int Stages,
|
||||
typename MathOperatorTag
|
||||
>
|
||||
struct DefaultConv3dFprop <
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
arch::OpClassTensorOp,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
Stages,
|
||||
MathOperatorTag,
|
||||
IteratorAlgorithm::kAnalytic
|
||||
> {
|
||||
|
||||
// Define the core components from GEMM
|
||||
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
|
||||
ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::RowMajor,
|
||||
ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
|
||||
Stages, MathOperatorTag>;
|
||||
|
||||
// Define iterators over tiles from the A operand
|
||||
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
|
||||
using IteratorA =
|
||||
cutlass::conv::threadblock::Conv3dFpropActivationTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
|
||||
ElementA,
|
||||
ThreadMapA
|
||||
>;
|
||||
|
||||
using SmemIteratorA = typename MmaCore::SmemIteratorA;
|
||||
|
||||
// Define iterators over tiles from the B operand
|
||||
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
|
||||
using IteratorB =
|
||||
cutlass::conv::threadblock::Conv3dFpropFilterTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
|
||||
ElementB,
|
||||
ThreadMapB
|
||||
>;
|
||||
|
||||
using SmemIteratorB = typename MmaCore::SmemIteratorB;
|
||||
|
||||
// Warp-level GEMM components
|
||||
using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
|
||||
using MmaPolicy = typename MmaCore::MmaPolicy;
|
||||
|
||||
// Define the Mma
|
||||
using Mma = threadblock::ImplicitGemmMultistage<
|
||||
ThreadblockShape,
|
||||
IteratorA,
|
||||
SmemIteratorA,
|
||||
arch::CacheOperation::Always,
|
||||
IteratorB,
|
||||
SmemIteratorB,
|
||||
arch::CacheOperation::Global,
|
||||
MmaPolicy,
|
||||
Stages
|
||||
>;
|
||||
|
||||
// Define the epilogue
|
||||
using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
|
||||
ThreadblockShape,
|
||||
WarpMmaTensorOp,
|
||||
1,
|
||||
EpilogueOutputOp,
|
||||
EpilogueOutputOp::kCount
|
||||
>::Epilogue;
|
||||
|
||||
// Define the kernel
|
||||
using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
|
||||
Mma,
|
||||
Epilogue,
|
||||
ThreadblockSwizzle,
|
||||
conv::Operator::kFprop,
|
||||
Conv3dProblemSize
|
||||
>;
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
@@ -0,0 +1,504 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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
|
||||
Default kernel-level implicit GEMM convolution definitions combine threadblock-scoped
|
||||
matrix multiply-add with the appropriate threadblock-scoped epilogue.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/conv/kernel/default_conv2d.h"
|
||||
|
||||
#include "cutlass/conv/threadblock/conv3d_wgrad_output_gradient_tile_access_iterator_analytic.h"
|
||||
#include "cutlass/conv/threadblock/conv3d_wgrad_activation_tile_access_iterator_analytic.h"
|
||||
#include "cutlass/conv/threadblock/conv3d_wgrad_output_gradient_tile_access_iterator_optimized.h"
|
||||
#include "cutlass/conv/threadblock/conv3d_wgrad_activation_tile_access_iterator_optimized.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace kernel {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Defines a kernel for Conv2dWgrad
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename OperatorClass,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
int Stages,
|
||||
typename MathOperatorTag,
|
||||
conv::IteratorAlgorithm IteratorAlgorithm = IteratorAlgorithm::kAnalytic,
|
||||
conv::StrideSupport StrideSupport = StrideSupport::kStrided
|
||||
> struct DefaultConv3dWgrad;
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Defines a kernel for Conv3dWgrad specialzation for Analytic IteratorAlgorithm and multistage
|
||||
// pipeline.
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename OperatorClass,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
int Stages,
|
||||
typename MathOperatorTag
|
||||
>
|
||||
struct DefaultConv3dWgrad <
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
OperatorClass,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
Stages,
|
||||
MathOperatorTag,
|
||||
IteratorAlgorithm::kAnalytic
|
||||
> {
|
||||
|
||||
// Define the core components from GEMM
|
||||
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
|
||||
ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::ColumnMajor,
|
||||
ElementB, layout::RowMajor, ElementAccumulator, layout::RowMajor, OperatorClass,
|
||||
Stages, MathOperatorTag>;
|
||||
|
||||
// Define iterators over tiles from the A operand
|
||||
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
|
||||
using IteratorA =
|
||||
cutlass::conv::threadblock::Conv3dWgradOutputGradientTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
|
||||
ElementA,
|
||||
ThreadMapA
|
||||
>;
|
||||
|
||||
using SmemIteratorA = typename MmaCore::SmemIteratorA;
|
||||
|
||||
// Define iterators over tiles from the B operand
|
||||
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
|
||||
using IteratorB =
|
||||
cutlass::conv::threadblock::Conv3dWgradActivationTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
|
||||
ElementB,
|
||||
ThreadMapB
|
||||
>;
|
||||
|
||||
using SmemIteratorB = typename MmaCore::SmemIteratorB;
|
||||
|
||||
// Warp-level GEMM components
|
||||
using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
|
||||
using MmaPolicy = typename MmaCore::MmaPolicy;
|
||||
|
||||
// Define the Mma
|
||||
using Mma = threadblock::ImplicitGemmMultistage<
|
||||
ThreadblockShape,
|
||||
IteratorA,
|
||||
SmemIteratorA,
|
||||
arch::CacheOperation::Always,
|
||||
IteratorB,
|
||||
SmemIteratorB,
|
||||
arch::CacheOperation::Always,
|
||||
MmaPolicy,
|
||||
Stages
|
||||
>;
|
||||
|
||||
// Define the epilogue
|
||||
using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
|
||||
ThreadblockShape,
|
||||
WarpMmaTensorOp,
|
||||
1,
|
||||
EpilogueOutputOp,
|
||||
EpilogueOutputOp::kCount
|
||||
>::Epilogue;
|
||||
|
||||
// Define the kernel
|
||||
using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
|
||||
Mma,
|
||||
Epilogue,
|
||||
ThreadblockSwizzle,
|
||||
conv::Operator::kWgrad,
|
||||
Conv3dProblemSize
|
||||
>;
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/// Defines a kernel for Conv3dWgrad specialzation for Analytic IteratorAlgorithm and two
|
||||
// pipeline.
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename OperatorClass,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
typename MathOperatorTag
|
||||
>
|
||||
struct DefaultConv3dWgrad <
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
OperatorClass,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
2,
|
||||
MathOperatorTag,
|
||||
IteratorAlgorithm::kAnalytic
|
||||
> {
|
||||
|
||||
// Define the core components from GEMM
|
||||
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
|
||||
ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::ColumnMajor,
|
||||
ElementB, layout::RowMajor, ElementAccumulator, layout::RowMajor, OperatorClass,
|
||||
2, MathOperatorTag>;
|
||||
|
||||
// Define iterators over tiles from the A operand
|
||||
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
|
||||
using IteratorA =
|
||||
cutlass::conv::threadblock::TileIterator<
|
||||
cutlass::conv::threadblock::Conv3dWgradOutputGradientTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
|
||||
ElementA,
|
||||
ThreadMapA
|
||||
>
|
||||
>;
|
||||
|
||||
using SmemIteratorA = typename MmaCore::SmemIteratorA;
|
||||
|
||||
// Define iterators over tiles from the B operand
|
||||
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
|
||||
using IteratorB =
|
||||
cutlass::conv::threadblock::TileIterator<
|
||||
cutlass::conv::threadblock::Conv3dWgradActivationTileAccessIteratorAnalytic<
|
||||
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
|
||||
ElementB,
|
||||
ThreadMapB
|
||||
>
|
||||
>;
|
||||
|
||||
using SmemIteratorB = typename MmaCore::SmemIteratorB;
|
||||
|
||||
// Warp-level GEMM components
|
||||
using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
|
||||
using MmaPolicy = typename MmaCore::MmaPolicy;
|
||||
|
||||
// Define the Mma
|
||||
using Mma = threadblock::ImplicitGemmPipelined<
|
||||
ThreadblockShape,
|
||||
IteratorA,
|
||||
SmemIteratorA,
|
||||
IteratorB,
|
||||
SmemIteratorB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
MmaPolicy
|
||||
>;
|
||||
|
||||
// Define the epilogue
|
||||
using Epilogue = typename detail::DefaultConvEpilogue<
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpMmaTensorOp,
|
||||
1,
|
||||
EpilogueOutputOp
|
||||
>::Epilogue;
|
||||
|
||||
// Define the kernel
|
||||
using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
|
||||
Mma,
|
||||
Epilogue,
|
||||
ThreadblockSwizzle,
|
||||
conv::Operator::kWgrad,
|
||||
Conv3dProblemSize
|
||||
>;
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Defines a kernel for Conv3dWgrad specialzation for Optimized IteratorAlgorithm and multistage
|
||||
// pipeline.
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename OperatorClass,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
int Stages,
|
||||
typename MathOperatorTag
|
||||
>
|
||||
struct DefaultConv3dWgrad <
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
OperatorClass,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
Stages,
|
||||
MathOperatorTag,
|
||||
IteratorAlgorithm::kOptimized
|
||||
> {
|
||||
|
||||
// Define the core components from GEMM
|
||||
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
|
||||
ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::ColumnMajor,
|
||||
ElementB, layout::RowMajor, ElementAccumulator, layout::RowMajor, OperatorClass,
|
||||
Stages, MathOperatorTag>;
|
||||
|
||||
// Define iterators over tiles from the A operand
|
||||
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
|
||||
using IteratorA =
|
||||
cutlass::conv::threadblock::Conv3dWgradOutputGradientTileAccessIteratorOptimized<
|
||||
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
|
||||
ElementA,
|
||||
ThreadMapA
|
||||
>;
|
||||
|
||||
using SmemIteratorA = typename MmaCore::SmemIteratorA;
|
||||
|
||||
// Define iterators over tiles from the B operand
|
||||
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
|
||||
using IteratorB =
|
||||
cutlass::conv::threadblock::Conv3dWgradActivationTileAccessIteratorOptimized<
|
||||
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
|
||||
ElementB,
|
||||
ThreadMapB
|
||||
>;
|
||||
|
||||
using SmemIteratorB = typename MmaCore::SmemIteratorB;
|
||||
|
||||
// Warp-level GEMM components
|
||||
using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
|
||||
using MmaPolicy = typename MmaCore::MmaPolicy;
|
||||
|
||||
// Define the Mma
|
||||
using Mma = threadblock::ImplicitGemmMultistage<
|
||||
ThreadblockShape,
|
||||
IteratorA,
|
||||
SmemIteratorA,
|
||||
arch::CacheOperation::Always,
|
||||
IteratorB,
|
||||
SmemIteratorB,
|
||||
arch::CacheOperation::Always,
|
||||
MmaPolicy,
|
||||
Stages
|
||||
>;
|
||||
|
||||
// Define the epilogue
|
||||
using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
|
||||
ThreadblockShape,
|
||||
WarpMmaTensorOp,
|
||||
1,
|
||||
EpilogueOutputOp,
|
||||
EpilogueOutputOp::kCount
|
||||
>::Epilogue;
|
||||
|
||||
// Define the kernel
|
||||
using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
|
||||
Mma,
|
||||
Epilogue,
|
||||
ThreadblockSwizzle,
|
||||
conv::Operator::kWgrad,
|
||||
Conv3dProblemSize
|
||||
>;
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/// Defines a kernel for Conv3dWgrad specialzation for Optimized IteratorAlgorithm and two
|
||||
// pipeline.
|
||||
template <
|
||||
typename ElementA,
|
||||
typename LayoutA,
|
||||
typename ElementB,
|
||||
typename LayoutB,
|
||||
typename ElementC,
|
||||
typename LayoutC,
|
||||
typename ElementAccumulator,
|
||||
typename OperatorClass,
|
||||
typename ArchTag,
|
||||
typename ThreadblockShape,
|
||||
typename WarpShape,
|
||||
typename InstructionShape,
|
||||
typename EpilogueOutputOp,
|
||||
typename ThreadblockSwizzle,
|
||||
typename MathOperatorTag
|
||||
>
|
||||
struct DefaultConv3dWgrad <
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
ElementAccumulator,
|
||||
OperatorClass,
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpShape,
|
||||
InstructionShape,
|
||||
EpilogueOutputOp,
|
||||
ThreadblockSwizzle,
|
||||
2,
|
||||
MathOperatorTag,
|
||||
IteratorAlgorithm::kOptimized
|
||||
> {
|
||||
|
||||
// Define the core components from GEMM
|
||||
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
|
||||
ThreadblockShape, WarpShape, InstructionShape, ElementA, layout::ColumnMajor,
|
||||
ElementB, layout::RowMajor, ElementAccumulator, layout::RowMajor, OperatorClass,
|
||||
2, MathOperatorTag>;
|
||||
|
||||
// Define iterators over tiles from the A operand
|
||||
using ThreadMapA = typename MmaCore::IteratorThreadMapA;
|
||||
using IteratorA =
|
||||
cutlass::conv::threadblock::TileIterator<
|
||||
cutlass::conv::threadblock::Conv3dWgradOutputGradientTileAccessIteratorOptimized<
|
||||
cutlass::MatrixShape<ThreadblockShape::kM, ThreadblockShape::kK>,
|
||||
ElementA,
|
||||
ThreadMapA
|
||||
>
|
||||
>;
|
||||
|
||||
using SmemIteratorA = typename MmaCore::SmemIteratorA;
|
||||
|
||||
// Define iterators over tiles from the B operand
|
||||
using ThreadMapB = typename MmaCore::IteratorThreadMapB;
|
||||
using IteratorB =
|
||||
cutlass::conv::threadblock::TileIterator<
|
||||
cutlass::conv::threadblock::Conv3dWgradActivationTileAccessIteratorOptimized<
|
||||
cutlass::MatrixShape<ThreadblockShape::kK, ThreadblockShape::kN>,
|
||||
ElementB,
|
||||
ThreadMapB
|
||||
>
|
||||
>;
|
||||
|
||||
using SmemIteratorB = typename MmaCore::SmemIteratorB;
|
||||
|
||||
// Warp-level GEMM components
|
||||
using WarpMmaTensorOp = typename MmaCore::MmaTensorOp;
|
||||
using MmaPolicy = typename MmaCore::MmaPolicy;
|
||||
|
||||
// Define the Mma
|
||||
using Mma = threadblock::ImplicitGemmPipelined<
|
||||
ThreadblockShape,
|
||||
IteratorA,
|
||||
SmemIteratorA,
|
||||
IteratorB,
|
||||
SmemIteratorB,
|
||||
ElementC,
|
||||
LayoutC,
|
||||
MmaPolicy
|
||||
>;
|
||||
|
||||
// Define the epilogue
|
||||
using Epilogue = typename detail::DefaultConvEpilogue<
|
||||
ArchTag,
|
||||
ThreadblockShape,
|
||||
WarpMmaTensorOp,
|
||||
1,
|
||||
EpilogueOutputOp
|
||||
>::Epilogue;
|
||||
|
||||
// Define the kernel
|
||||
using Kernel = cutlass::conv::kernel::ImplicitGemmConvolution<
|
||||
Mma,
|
||||
Epilogue,
|
||||
ThreadblockSwizzle,
|
||||
conv::Operator::kWgrad,
|
||||
Conv3dProblemSize
|
||||
>;
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
@@ -0,0 +1,424 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Template for a pipelined Implicit GEMM kernel.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
#include "cutlass/aligned_buffer.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/matrix_shape.h"
|
||||
#include "cutlass/semaphore.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/gemm/gemm.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
#include "cutlass/conv/conv3d_problem_size.h"
|
||||
#include "cutlass/epilogue/threadblock/output_iterator_parameter.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace kernel {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Mma_, ///! Threadblock-scoped matrix multiply-accumulate
|
||||
typename Epilogue_, ///! Epilogue
|
||||
typename ThreadblockSwizzle_, ///! Threadblock swizzling function
|
||||
conv::Operator ConvOperator, ///! Convolutional operator (Fprop, Dgrad, Wgrad)
|
||||
typename ConvProblemSize_ = Conv2dProblemSize ///! Convolutional operator on 2D or 3D problem
|
||||
>
|
||||
struct ImplicitGemmConvolution {
|
||||
|
||||
using Mma = Mma_;
|
||||
using Epilogue = Epilogue_;
|
||||
using EpilogueOutputOp = typename Epilogue::OutputOp;
|
||||
using ThreadblockSwizzle = ThreadblockSwizzle_;
|
||||
static Operator const kConvolutionalOperator = ConvOperator;
|
||||
|
||||
using ElementA = typename Mma::IteratorA::Element;
|
||||
using LayoutA = typename Mma::IteratorA::Layout;
|
||||
using ElementB = typename Mma::IteratorB::Element;
|
||||
using LayoutB = typename Mma::IteratorB::Layout;
|
||||
using ElementC = typename EpilogueOutputOp::ElementOutput;
|
||||
|
||||
/// Set output tensor C layout
|
||||
using LayoutC = LayoutA;
|
||||
|
||||
using ElementAccumulator = typename EpilogueOutputOp::ElementAccumulator;
|
||||
using ElementCompute = typename EpilogueOutputOp::ElementCompute;
|
||||
|
||||
using WarpMmaOperator = typename Mma::Policy::Operator;
|
||||
|
||||
using ArchMmaOperator = typename WarpMmaOperator::ArchMmaOperator;
|
||||
using MathOperator = typename ArchMmaOperator::Operator;
|
||||
|
||||
using OperatorClass = typename WarpMmaOperator::OperatorClass;
|
||||
using ArchTag = typename WarpMmaOperator::ArchTag;
|
||||
|
||||
using ThreadblockShape = typename Mma::Shape;
|
||||
using WarpShape = typename WarpMmaOperator::Shape;
|
||||
using InstructionShape = typename ArchMmaOperator::Shape;
|
||||
|
||||
static int const kStages = Mma::kStages;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = Mma::IteratorA::kIteratorAlgorithm;
|
||||
|
||||
/// Warp count (concept: GemmShape)
|
||||
using WarpCount = typename Mma::WarpCount;
|
||||
static int const kThreadCount = 32 * WarpCount::kCount;
|
||||
|
||||
using TensorRefA = typename Mma::IteratorA::TensorRef;
|
||||
using TensorRefB = typename Mma::IteratorB::TensorRef;
|
||||
using TensorRefC = cutlass::TensorRef<ElementC, LayoutC>;
|
||||
|
||||
/// Check iterator A and B convolution dimension are the same and
|
||||
// set device::ImplicitGemmConvolution::kConvDim
|
||||
static_assert(Mma::IteratorA::kConvDim == Mma::IteratorB::kConvDim,
|
||||
"Convolution on different different dimensions is not supported");
|
||||
static int const kConvDim = Mma::IteratorA::kConvDim;
|
||||
|
||||
/// Conv dimension and problem size structure (Conv2d or Conv3d)
|
||||
using ConvProblemSize = ConvProblemSize_;
|
||||
|
||||
/// Wgrad C stride idx for implicit gemm algorithm
|
||||
// Conv2d row-major matrix C (KxRSC)
|
||||
// Conv3d row-major matrix C (KxTRSC)
|
||||
static int const kWgradCStrideIdx =
|
||||
cutlass::platform::is_same<LayoutC, cutlass::layout::TensorNHWC>::value ? 2 : 3;
|
||||
|
||||
/// This chooses the appropriate stride element of the C tensor.
|
||||
static int const kTensorCStrideIdx =
|
||||
(kConvolutionalOperator == conv::Operator::kWgrad ? kWgradCStrideIdx : 0);
|
||||
|
||||
//
|
||||
//
|
||||
//
|
||||
using ConvOutputIteratorParameter = epilogue::threadblock::ConvOutputIteratorParameter<
|
||||
LayoutC,
|
||||
typename Epilogue::OutputTileIterator::Layout,
|
||||
TensorRefC,
|
||||
ConvOperator,
|
||||
ConvProblemSize
|
||||
>;
|
||||
|
||||
/// Argument structure
|
||||
struct Arguments {
|
||||
|
||||
//
|
||||
// Data members
|
||||
//
|
||||
|
||||
ConvProblemSize problem_size;
|
||||
TensorRefA ref_A;
|
||||
TensorRefB ref_B;
|
||||
TensorRefC ref_C;
|
||||
TensorRefC ref_D;
|
||||
typename EpilogueOutputOp::Params output_op;
|
||||
SplitKMode split_k_mode;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
/// Default ctor
|
||||
CUTLASS_HOST_DEVICE
|
||||
Arguments() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Arguments(
|
||||
ConvProblemSize const & problem_size
|
||||
):
|
||||
problem_size(problem_size) { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Arguments(
|
||||
ConvProblemSize const & problem_size,
|
||||
TensorRefA const & ref_A,
|
||||
TensorRefB const & ref_B,
|
||||
TensorRefC const & ref_C,
|
||||
TensorRefC const & ref_D,
|
||||
typename EpilogueOutputOp::Params const & output_op,
|
||||
SplitKMode const & split_k_mode = SplitKMode::kSerial
|
||||
):
|
||||
problem_size(problem_size),
|
||||
ref_A(ref_A),
|
||||
ref_B(ref_B),
|
||||
ref_C(ref_C),
|
||||
ref_D(ref_D),
|
||||
output_op(output_op),
|
||||
split_k_mode(split_k_mode)
|
||||
{
|
||||
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
/// Parameters structure
|
||||
struct Params {
|
||||
ConvProblemSize problem_size;
|
||||
cutlass::gemm::GemmCoord grid_tiled_shape;
|
||||
gemm::GemmCoord implicit_gemm_problem_size;
|
||||
int gemm_k_iterations;
|
||||
typename Mma::IteratorA::Params iterator_A;
|
||||
typename Mma::IteratorA::Element const *ptr_A;
|
||||
typename Mma::IteratorB::Params iterator_B;
|
||||
typename Mma::IteratorB::Element const *ptr_B;
|
||||
typename Epilogue::OutputTileIterator::Params iterator_C;
|
||||
typename Epilogue::OutputTileIterator::Element *ptr_C;
|
||||
typename Epilogue::OutputTileIterator::Params iterator_D;
|
||||
typename Epilogue::OutputTileIterator::Element *ptr_D;
|
||||
typename EpilogueOutputOp::Params output_op;
|
||||
int *semaphore;
|
||||
SplitKMode split_k_mode;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(): gemm_k_iterations(0) { }
|
||||
|
||||
///
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
Arguments const &args,
|
||||
int *semaphore = nullptr
|
||||
):
|
||||
problem_size(args.problem_size),
|
||||
implicit_gemm_problem_size(cutlass::conv::implicit_gemm_problem_size(kConvolutionalOperator, args.problem_size)),
|
||||
grid_tiled_shape(grid_tiled_shape),
|
||||
iterator_A(args.problem_size, args.ref_A.layout()),
|
||||
ptr_A(args.ref_A.data()),
|
||||
iterator_B(args.problem_size, args.ref_B.layout()),
|
||||
ptr_B(args.ref_B.data()),
|
||||
iterator_C(ConvOutputIteratorParameter::layout(args.ref_C)),
|
||||
ptr_C(args.ref_C.data()),
|
||||
iterator_D(ConvOutputIteratorParameter::layout(args.ref_D)),
|
||||
ptr_D(args.ref_D.data()),
|
||||
output_op(args.output_op),
|
||||
semaphore(semaphore),
|
||||
split_k_mode(args.split_k_mode)
|
||||
{
|
||||
gemm_k_iterations = implicit_gemm_k_iterations(kConvolutionalOperator, ThreadblockShape::kK, args.problem_size);
|
||||
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
grid_tiled_shape = threadblock_swizzle.get_tiled_shape(
|
||||
implicit_gemm_problem_size,
|
||||
{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
|
||||
args.problem_size.split_k_slices);
|
||||
}
|
||||
};
|
||||
|
||||
/// Shared memory storage structure
|
||||
union SharedStorage {
|
||||
typename Mma::SharedStorage main_loop;
|
||||
typename Epilogue::SharedStorage epilogue;
|
||||
};
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
ImplicitGemmConvolution() { }
|
||||
|
||||
/// Executes one ImplicitGEMM
|
||||
CUTLASS_DEVICE
|
||||
void operator()(Params const ¶ms, SharedStorage &shared_storage) {
|
||||
|
||||
// Compute threadblock location
|
||||
ThreadblockSwizzle threadblock_swizzle;
|
||||
|
||||
cutlass::gemm::GemmCoord threadblock_tile_idx =
|
||||
threadblock_swizzle.get_tile_offset(params.grid_tiled_shape);
|
||||
|
||||
// Early exit if CTA is out of range
|
||||
if (params.grid_tiled_shape.m() <= threadblock_tile_idx.m() ||
|
||||
params.grid_tiled_shape.n() <= threadblock_tile_idx.n()) {
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
// Compute position within threadblock
|
||||
int thread_idx = threadIdx.x;
|
||||
|
||||
// Construct iterators to A and B operands
|
||||
typename Mma::IteratorA iterator_A(
|
||||
params.iterator_A,
|
||||
params.problem_size,
|
||||
params.ptr_A,
|
||||
thread_idx,
|
||||
MatrixCoord(
|
||||
threadblock_tile_idx.m() * Mma::Shape::kM,
|
||||
threadblock_tile_idx.k() * Mma::Shape::kK
|
||||
)
|
||||
);
|
||||
|
||||
typename Mma::IteratorB iterator_B(
|
||||
params.iterator_B,
|
||||
params.problem_size,
|
||||
params.ptr_B,
|
||||
thread_idx,
|
||||
MatrixCoord(
|
||||
threadblock_tile_idx.k() * Mma::Shape::kK,
|
||||
threadblock_tile_idx.n() * Mma::Shape::kN
|
||||
)
|
||||
);
|
||||
|
||||
// Broadcast the warp_id computed by lane 0 to ensure dependent code
|
||||
// is compiled as warp-uniform.
|
||||
int warp_idx = __shfl_sync(0xffffffff, threadIdx.x / 32, 0);
|
||||
int lane_idx = threadIdx.x % 32;
|
||||
|
||||
//
|
||||
// Main loop
|
||||
//
|
||||
|
||||
// Construct thread-scoped matrix multiply
|
||||
Mma mma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);
|
||||
|
||||
typename Mma::FragmentC accumulators;
|
||||
|
||||
accumulators.clear();
|
||||
|
||||
// Compute threadblock-scoped matrix multiply-add
|
||||
mma(params.gemm_k_iterations, accumulators, iterator_A, iterator_B, accumulators);
|
||||
|
||||
//
|
||||
// Epilogue
|
||||
//
|
||||
|
||||
EpilogueOutputOp output_op(params.output_op);
|
||||
|
||||
// Construct the semaphore.
|
||||
int block_idx = threadblock_tile_idx.m() + threadblock_tile_idx.n() * params.grid_tiled_shape.m();
|
||||
|
||||
Semaphore semaphore(params.semaphore + block_idx, thread_idx);
|
||||
|
||||
// Compute logical position within grid
|
||||
threadblock_tile_idx =
|
||||
threadblock_swizzle.get_tile_offset(params.grid_tiled_shape);
|
||||
|
||||
// If performing a reduction via split-K, fetch the initial synchronization
|
||||
if (params.split_k_mode == SplitKMode::kSerial && params.grid_tiled_shape.k() > 1) {
|
||||
|
||||
// Fetch the synchronization lock initially but do not block.
|
||||
semaphore.fetch();
|
||||
|
||||
// Indicate which position in a serial reduction the output operator is currently updating
|
||||
output_op.set_k_partition(threadblock_tile_idx.k(), params.grid_tiled_shape.k());
|
||||
}
|
||||
|
||||
MatrixCoord threadblock_offset(
|
||||
threadblock_tile_idx.m() * Mma::Shape::kM,
|
||||
threadblock_tile_idx.n() * Mma::Shape::kN
|
||||
);
|
||||
|
||||
// Tile iterator writing to destination tensor
|
||||
typename Epilogue::OutputTileIterator iterator_D(
|
||||
params.iterator_D,
|
||||
params.ptr_D,
|
||||
ConvOutputIteratorParameter::extent(params.problem_size),
|
||||
thread_idx,
|
||||
threadblock_offset
|
||||
);
|
||||
|
||||
// Tile iterator reading from source accumulator tensor
|
||||
typename Epilogue::OutputTileIterator iterator_C(
|
||||
params.iterator_C,
|
||||
params.ptr_C,
|
||||
ConvOutputIteratorParameter::extent(params.problem_size),
|
||||
thread_idx,
|
||||
threadblock_offset
|
||||
);
|
||||
|
||||
|
||||
// Construct the epilogue
|
||||
Epilogue epilogue(
|
||||
shared_storage.epilogue,
|
||||
thread_idx,
|
||||
warp_idx,
|
||||
lane_idx);
|
||||
|
||||
// Wait on the semaphore - this latency may have been covered by iterator construction
|
||||
if (params.split_k_mode == SplitKMode::kSerial && params.grid_tiled_shape.k() > 1) {
|
||||
|
||||
// For subsequent threadblocks, the source matrix is held in the 'D' tensor.
|
||||
if (threadblock_tile_idx.k()) {
|
||||
iterator_C = iterator_D;
|
||||
}
|
||||
|
||||
semaphore.wait(threadblock_tile_idx.k());
|
||||
|
||||
__threadfence();
|
||||
}
|
||||
// Each split-k-slice writes to a unique tensor location
|
||||
else if (params.split_k_mode == SplitKMode::kParallel) {
|
||||
iterator_D.add_pointer_offset(threadblock_tile_idx.k() *
|
||||
cutlass::conv::implicit_gemm_tensor_c_size(ConvOperator, params.problem_size));
|
||||
}
|
||||
|
||||
// Run efficient epilogue
|
||||
epilogue(output_op, iterator_D, accumulators, iterator_C);
|
||||
|
||||
//
|
||||
// Release the semaphore
|
||||
//
|
||||
|
||||
if (params.split_k_mode == SplitKMode::kSerial && params.grid_tiled_shape.k() > 1) {
|
||||
|
||||
int lock = 0;
|
||||
if (params.grid_tiled_shape.k() == threadblock_tile_idx.k() + 1) {
|
||||
|
||||
// The final threadblock resets the semaphore for subsequent grids.
|
||||
lock = 0;
|
||||
}
|
||||
else {
|
||||
// Otherwise, the semaphore is incremented
|
||||
lock = threadblock_tile_idx.k() + 1;
|
||||
}
|
||||
|
||||
semaphore.release(lock);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace kernel
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
@@ -0,0 +1,240 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM B (filter tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
#include "cutlass/conv/threadblock/conv2d_params.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv2dDgradFilterTileAccessIteratorAnalytic {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kAnalytic;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 2;
|
||||
using ConvProblemSize = typename conv::Conv2dProblemSize;
|
||||
|
||||
static_assert(sizeof_bits<Element>::value >= 8,
|
||||
"DGRAD requires elements of size 8b or larger.");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
using Params = Conv2dAnalyticParams<Layout>;
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
Conv2dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
// For a fixed filter position (r,s) find and fill offset_k_, offset_c_ in strided and contiguous dimension
|
||||
int filter_r_;
|
||||
int filter_s_;
|
||||
int offset_k_[ThreadMap::Iterations::kStrided];
|
||||
int offset_c_[ThreadMap::Iterations::kContiguous];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dDgradFilterTileAccessIteratorAnalytic(
|
||||
Params const ¶ms,
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)),
|
||||
filter_r_(0),
|
||||
filter_s_(0) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
|
||||
offset_c_[c] = threadblock_offset.column() + thread_coord.contiguous()
|
||||
+ c * ThreadMap::Delta::kContiguous;
|
||||
}
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
offset_k_[s] =
|
||||
threadblock_offset.row() + thread_coord.strided() + s * ThreadMap::Delta::kStrided;
|
||||
}
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
// moves to the next tile
|
||||
++filter_s_;
|
||||
if (filter_s_ < problem_size_.S) {
|
||||
return;
|
||||
}
|
||||
filter_s_ = 0;
|
||||
++filter_r_;
|
||||
if (filter_r_ < problem_size_.R) {
|
||||
return;
|
||||
}
|
||||
filter_r_ = 0;
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
offset_k_[s] += Shape::kRow * problem_size_.split_k_slices;
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the filter tensor w that is currently pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
|
||||
int c = offset_c_[iteration_contiguous_];
|
||||
int k = offset_k_[iteration_strided_];
|
||||
|
||||
return TensorCoord(k, filter_r_, filter_s_, c);
|
||||
}
|
||||
|
||||
/// Returns true if the current coordinate is within the filter tensor w
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
|
||||
return coord.n() < problem_size_.K && coord.c() < problem_size_.C;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dDgradFilterTileAccessIteratorAnalytic &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv2dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
+283
@@ -0,0 +1,283 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM B (filter tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
|
||||
#include "cutlass/conv/threadblock/conv2d_params.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_,
|
||||
conv::StrideSupport StrideSupport_ = conv::StrideSupport::kUnity
|
||||
>
|
||||
class Conv2dDgradFilterTileAccessIteratorOptimized {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kOptimized;
|
||||
static StrideSupport const kStrideSupport = StrideSupport_;
|
||||
static int const kConvDim = 2;
|
||||
using ConvProblemSize = typename conv::Conv2dProblemSize;
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
struct Params : Conv2dDgradFilterIteratorOptimizedParams {
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(Conv2dDgradFilterIteratorOptimizedParams const &base):
|
||||
Conv2dDgradFilterIteratorOptimizedParams(base) { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
):
|
||||
Conv2dDgradFilterIteratorOptimizedParams(
|
||||
problem_size,
|
||||
layout,
|
||||
sizeof_bits<Element>::value,
|
||||
{Shape::kRow, Shape::kColumn},
|
||||
ThreadMap::kThreads,
|
||||
ThreadMap::kElementsPerAccess,
|
||||
{ThreadMap::Iterations::kContiguous, ThreadMap::Iterations::kStrided},
|
||||
{ThreadMap::Delta::kContiguous, ThreadMap::Delta::kStrided}
|
||||
) { }
|
||||
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
Conv2dDgradFilterIteratorOptimizedParams const ¶ms_;
|
||||
Conv2dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
uint32_t predicates_;
|
||||
int filter_rs_;
|
||||
int filter_k_;
|
||||
|
||||
//
|
||||
// Assertions
|
||||
//
|
||||
|
||||
// We map predicates into bits packed in this uint32_t container
|
||||
static_assert(ThreadMap::Iterations::kStrided *
|
||||
ThreadMap::Iterations::kContiguous < sizeof(predicates_) * 8,
|
||||
"Currently, the number of loads per iteration is limited by the size of the predicates container.");
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dDgradFilterTileAccessIteratorOptimized(
|
||||
Conv2dDgradFilterIteratorOptimizedParams const ¶ms,
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)),
|
||||
predicates_(0),
|
||||
filter_rs_(0),
|
||||
filter_k_(0) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
filter_k_ = threadblock_offset.row() + thread_coord.strided();
|
||||
Index column = threadblock_offset.column() + thread_coord.contiguous();
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
|
||||
|
||||
int filter_k = filter_k_ + s * ThreadMap::Delta::kStrided;
|
||||
int filter_c = column + c * ThreadMap::Delta::kContiguous;
|
||||
|
||||
uint32_t pred = ((filter_k < problem_size_.K && filter_c < problem_size_.C) ? 1u : 0);
|
||||
|
||||
int pred_idx = c + s * ThreadMap::Iterations::kContiguous;
|
||||
|
||||
predicates_ |= (pred << pred_idx);
|
||||
}
|
||||
}
|
||||
|
||||
pointer_ += (
|
||||
filter_k_ * params.layout.stride()[2] + column
|
||||
) * sizeof_bits<Element>::value / 8;
|
||||
|
||||
set_iteration_index(0);
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
|
||||
LongIndex next = params_.inc_next_rs;
|
||||
|
||||
// moves to the next tile
|
||||
++filter_rs_;
|
||||
if (filter_rs_ == params_.RS) {
|
||||
|
||||
filter_rs_ = 0;
|
||||
next = params_.inc_next_k;
|
||||
filter_k_ += params_.filter_k_delta;
|
||||
}
|
||||
|
||||
// Clear predicates if needed
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
if (filter_k_ + s * ThreadMap::Delta::kStrided >= problem_size_.K) {
|
||||
uint32_t kClearMask = ((1u << ThreadMap::Iterations::kContiguous) - 1) << (s * ThreadMap::Iterations::kContiguous);
|
||||
predicates_ = (predicates_ & (~kClearMask));
|
||||
}
|
||||
}
|
||||
|
||||
pointer_ += next;
|
||||
}
|
||||
|
||||
/// Returns true if the current coordinate is within the filter tensor W
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() {
|
||||
LongIndex pred_idx = iteration_contiguous_ + iteration_strided_ * ThreadMap::Iterations::kContiguous;
|
||||
return (predicates_ & (1u << pred_idx));
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
return reinterpret_cast<AccessType const *>(pointer_ +
|
||||
iteration_contiguous_ * ThreadMap::Delta::kContiguous * sizeof_bits<Element>::value / 8);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dDgradFilterTileAccessIteratorOptimized &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
|
||||
// Move to the next K coordinate within the tile
|
||||
pointer_ += params_.inc_next_strided;
|
||||
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv2dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
+525
@@ -0,0 +1,525 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM A (output gradient tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
#include "cutlass/conv/threadblock/conv2d_params.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_,
|
||||
conv::StrideSupport StrideSupport_ = conv::StrideSupport::kStrided
|
||||
>
|
||||
class Conv2dDgradOutputGradientTileAccessIteratorAnalytic;
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
// Conv2dDgradOutputGradientTileAccessIteratorAnalytic strided dgrad needs special handling using
|
||||
// unscaled coordinations
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv2dDgradOutputGradientTileAccessIteratorAnalytic <
|
||||
Shape_,
|
||||
Element_,
|
||||
ThreadMap_,
|
||||
conv::StrideSupport::kStrided
|
||||
> {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kAnalytic;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 2;
|
||||
using ConvProblemSize = typename conv::Conv2dProblemSize;
|
||||
|
||||
static_assert(sizeof_bits<Element>::value >= 8,
|
||||
"DGRAD requires elements of size 8b or greater.");
|
||||
|
||||
//
|
||||
// Simpligying assertions
|
||||
//
|
||||
|
||||
static_assert(ThreadMap::Iterations::kContiguous == 1,
|
||||
"Require Iterations::kContiguous == 1");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
using Params = Conv2dAnalyticParams<Layout>;
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
Conv2dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
int filter_k_;
|
||||
int filter_r_;
|
||||
int filter_s_;
|
||||
|
||||
int offset_n_[ThreadMap::Iterations::kStrided];
|
||||
int offset_w_[ThreadMap::Iterations::kStrided];
|
||||
int offset_h_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
private:
|
||||
|
||||
/// Returns the coordinate in the output tensor Dy that is currently pointed to
|
||||
/// by the iterator but DOES NOT scale by the convolution stride. This is needed
|
||||
/// to compute predicates in the valid() method. The return value of the public at()
|
||||
/// method is correctly scaled.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord unscaled_at_() const {
|
||||
int n = offset_n_[iteration_strided_];
|
||||
int h = offset_h_[iteration_strided_];
|
||||
int w = offset_w_[iteration_strided_];
|
||||
|
||||
int r = filter_r_;
|
||||
int s = filter_s_;
|
||||
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
r = (problem_size_.R - 1 - r);
|
||||
s = (problem_size_.S - 1 - s);
|
||||
}
|
||||
|
||||
int p = (h + problem_size_.pad_h - r * problem_size_.dilation_h);
|
||||
int q = (w + problem_size_.pad_w - s * problem_size_.dilation_w);
|
||||
|
||||
return TensorCoord(n, p, q, filter_k_);
|
||||
}
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dDgradOutputGradientTileAccessIteratorAnalytic(
|
||||
Params const ¶ms,
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord() // threadblock offset - units are whole CTA tiles
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)),
|
||||
filter_k_(0),
|
||||
filter_r_(0),
|
||||
filter_s_(0) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
filter_k_ = threadblock_offset.column() + thread_coord.contiguous();
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
int offset_nhw = threadblock_offset.row() + thread_coord.strided() + s * ThreadMap::Delta::kStrided;
|
||||
|
||||
offset_n_[s] = offset_nhw / (problem_size_.H * problem_size_.W);
|
||||
int residual = offset_nhw % (problem_size_.H * problem_size_.W);
|
||||
|
||||
offset_h_[s] = residual / problem_size_.W;
|
||||
offset_w_[s] = residual % problem_size_.W;
|
||||
}
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
// move to the next tile
|
||||
++filter_s_;
|
||||
if (filter_s_ < problem_size_.S) {
|
||||
return;
|
||||
}
|
||||
filter_s_ = 0;
|
||||
++filter_r_;
|
||||
if (filter_r_ < problem_size_.R) {
|
||||
return;
|
||||
}
|
||||
filter_r_ = 0;
|
||||
|
||||
filter_k_ += Shape_::kColumn * problem_size_.split_k_slices;
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the output tensor Dy that is currently pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
|
||||
TensorCoord coord = unscaled_at_();
|
||||
|
||||
return TensorCoord(
|
||||
coord.n(),
|
||||
coord.h() / problem_size_.stride_h,
|
||||
coord.w() / problem_size_.stride_w,
|
||||
coord.c());
|
||||
}
|
||||
|
||||
|
||||
/// Returns true if the current coordinate is within the output tensor Dy
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
|
||||
TensorCoord unscaled_coord = unscaled_at_();
|
||||
TensorCoord coord = at();
|
||||
|
||||
return
|
||||
!(unscaled_coord.h() % problem_size_.stride_h) && !(unscaled_coord.w() % problem_size_.stride_w) &&
|
||||
coord.n() < problem_size_.N &&
|
||||
coord.h() >= 0 && coord.h() < problem_size_.P &&
|
||||
coord.w() >= 0 && coord.w() < problem_size_.Q &&
|
||||
coord.c() < problem_size_.K;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dDgradOutputGradientTileAccessIteratorAnalytic &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv2dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
// Conv2dDgradOutputGradientTileAccessIteratorAnalytic for unity strides can be optimized by
|
||||
// eliminating modulo arithmetic to compute unscaled coordinates
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv2dDgradOutputGradientTileAccessIteratorAnalytic <
|
||||
Shape_,
|
||||
Element_,
|
||||
ThreadMap_,
|
||||
conv::StrideSupport::kUnity
|
||||
> {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kAnalytic;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kUnity;
|
||||
static int const kConvDim = 2;
|
||||
using ConvProblemSize = typename conv::Conv2dProblemSize;
|
||||
|
||||
static_assert(sizeof_bits<Element>::value >= 8,
|
||||
"DGRAD requires elements of size 8b or greater.");
|
||||
|
||||
//
|
||||
// Simpligying assertions
|
||||
//
|
||||
|
||||
static_assert(ThreadMap::Iterations::kContiguous == 1,
|
||||
"Require Iterations::kContiguous == 1");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
struct Params {
|
||||
|
||||
Layout layout;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
): layout(layout) {
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
Conv2dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
int filter_k_;
|
||||
int filter_r_;
|
||||
int filter_s_;
|
||||
|
||||
int offset_n_[ThreadMap::Iterations::kStrided];
|
||||
int offset_w_[ThreadMap::Iterations::kStrided];
|
||||
int offset_h_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dDgradOutputGradientTileAccessIteratorAnalytic(
|
||||
Params const ¶ms,
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord() // threadblock offset - units are whole CTA tiles
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)),
|
||||
filter_k_(0),
|
||||
filter_r_(0),
|
||||
filter_s_(0) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
filter_k_ = threadblock_offset.column() + thread_coord.contiguous();
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
int offset_nhw = threadblock_offset.row() + thread_coord.strided() + s * ThreadMap::Delta::kStrided;
|
||||
|
||||
offset_n_[s] = offset_nhw / (problem_size_.H * problem_size_.W);
|
||||
int residual = offset_nhw % (problem_size_.H * problem_size_.W);
|
||||
|
||||
offset_h_[s] = residual / problem_size_.W;
|
||||
offset_w_[s] = residual % problem_size_.W;
|
||||
}
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
// move to the next tile
|
||||
++filter_s_;
|
||||
if (filter_s_ < problem_size_.S) {
|
||||
return;
|
||||
}
|
||||
filter_s_ = 0;
|
||||
++filter_r_;
|
||||
if (filter_r_ < problem_size_.R) {
|
||||
return;
|
||||
}
|
||||
filter_r_ = 0;
|
||||
|
||||
filter_k_ += Shape_::kColumn * problem_size_.split_k_slices;
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the output tensor Dy that is currently pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
|
||||
int n = offset_n_[iteration_strided_];
|
||||
int h = offset_h_[iteration_strided_];
|
||||
int w = offset_w_[iteration_strided_];
|
||||
|
||||
int r = filter_r_;
|
||||
int s = filter_s_;
|
||||
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
r = (problem_size_.R - 1 - r);
|
||||
s = (problem_size_.S - 1 - s);
|
||||
}
|
||||
|
||||
int p = (h + problem_size_.pad_h - r * problem_size_.dilation_h) / problem_size_.stride_h;
|
||||
int q = (w + problem_size_.pad_w - s * problem_size_.dilation_w) / problem_size_.stride_w;
|
||||
|
||||
return TensorCoord(n, p, q, filter_k_);
|
||||
|
||||
}
|
||||
|
||||
|
||||
/// Returns true if the current coordinate is within the output tensor Dy
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
|
||||
return coord.n() < problem_size_.N &&
|
||||
coord.h() >= 0 && coord.h() < problem_size_.P &&
|
||||
coord.w() >= 0 && coord.w() < problem_size_.Q &&
|
||||
coord.c() < problem_size_.K;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dDgradOutputGradientTileAccessIteratorAnalytic &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv2dProblemSize const &problem_size) {
|
||||
|
||||
// Conv2dDgradFilterTileAccessIteratorAnalytic unity stride specialization
|
||||
// only supports (stride_h, stride_w) = (1, 1)
|
||||
if (problem_size.stride() != MatrixCoord({1, 1})) {
|
||||
return Status::kErrorNotSupported;
|
||||
}
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
+437
@@ -0,0 +1,437 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM A (output gradient tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/matrix_shape.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
#include "cutlass/conv/threadblock/conv2d_params.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_,
|
||||
conv::StrideSupport StrideSupport_ = conv::StrideSupport::kUnity
|
||||
>
|
||||
class Conv2dDgradOutputGradientTileAccessIteratorOptimized {
|
||||
public:
|
||||
|
||||
static_assert(StrideSupport_ == conv::StrideSupport::kUnity,
|
||||
"Only unit-stride dgrad is supported at this time.");
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNHWC;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kOptimized;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kUnity;
|
||||
static int const kConvDim = 2;
|
||||
using ConvProblemSize = typename conv::Conv2dProblemSize;
|
||||
|
||||
using Mask = uint64_t;
|
||||
|
||||
//
|
||||
// Simplifying assertions
|
||||
//
|
||||
static_assert(ThreadMap::Iterations::kContiguous == 1,
|
||||
"Require Iterations::kContiguous == 1");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
struct Params : Conv2dDgradOutputGradientIteratorOptimizedParams {
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(Conv2dDgradOutputGradientIteratorOptimizedParams const &base):
|
||||
Conv2dDgradOutputGradientIteratorOptimizedParams(base) { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
):
|
||||
Conv2dDgradOutputGradientIteratorOptimizedParams(
|
||||
problem_size,
|
||||
layout,
|
||||
sizeof_bits<Element>::value,
|
||||
{Shape::kRow, Shape::kColumn},
|
||||
ThreadMap::kThreads,
|
||||
ThreadMap::kElementsPerAccess,
|
||||
{ThreadMap::Iterations::kContiguous, ThreadMap::Iterations::kStrided},
|
||||
{ThreadMap::Delta::kContiguous, ThreadMap::Delta::kStrided}
|
||||
) { }
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
Conv2dDgradOutputGradientIteratorOptimizedParams const ¶ms_;
|
||||
Conv2dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
|
||||
// One pointer per access
|
||||
char const *pointer_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
// current filter position (r, s)
|
||||
int filter_r_;
|
||||
int filter_s_;
|
||||
int filter_k_;
|
||||
|
||||
Index masks_[ThreadMap::Iterations::kStrided][2];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dDgradOutputGradientTileAccessIteratorOptimized(
|
||||
Conv2dDgradOutputGradientIteratorOptimizedParams const ¶ms,
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord() // tile index - units are threadblock-scoped tiles
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
filter_k_(0),
|
||||
filter_r_(0),
|
||||
filter_s_(0) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
filter_k_ = threadblock_offset.column() + thread_coord.contiguous();
|
||||
|
||||
int offset_n[ThreadMap::Iterations::kStrided];
|
||||
int offset_h[ThreadMap::Iterations::kStrided];
|
||||
int offset_w[ThreadMap::Iterations::kStrided];
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
|
||||
pointer_[s] = reinterpret_cast<char const *>(ptr);
|
||||
|
||||
int offset_nhw = threadblock_offset.row() + thread_coord.strided() + s * ThreadMap::Delta::kStrided;
|
||||
|
||||
// The subseqnet fast_divmod() operations are equivalent to the following logical computation:
|
||||
//
|
||||
//
|
||||
// offset_n[s] = offset_nhw / (problem_size_.H * problem_size_.W);
|
||||
// int residual = offset_nhw % (problem_size_.H * problem_size_.W);
|
||||
//
|
||||
// offset_h[s] = residual / problem_size_.W;
|
||||
// offset_w[s] = residual % problem_size_.W;
|
||||
//
|
||||
|
||||
int residual;
|
||||
|
||||
params_.hw_divmod(offset_n[s], residual, offset_nhw);
|
||||
params_.w_divmod(offset_h[s], offset_w[s], residual);
|
||||
|
||||
TensorCoord coord = at_(offset_n[s], offset_h[s], offset_w[s], 0, 0);
|
||||
|
||||
pointer_[s] += params_.layout(coord) * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
clear_mask();
|
||||
|
||||
CUTLASS_PRAGMA_NO_UNROLL
|
||||
for (int r = 0; r < problem_size_.R; ++r) {
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s_idx = 0; s_idx < ThreadMap::Iterations::kStrided; ++s_idx) {
|
||||
|
||||
int r_ = r;
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
r_ = problem_size_.R - 1 - r;
|
||||
}
|
||||
|
||||
int p = offset_h[s_idx] + problem_size_.pad_h - r_ * problem_size_.dilation_h;
|
||||
|
||||
bool pred = (offset_n[s_idx] < problem_size_.N && p >= 0 && p < problem_size_.P);
|
||||
masks_[s_idx][0] |= (pred << r);
|
||||
}
|
||||
}
|
||||
|
||||
CUTLASS_PRAGMA_NO_UNROLL
|
||||
for (int s = 0; s < problem_size_.S; ++s) {
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s_idx = 0; s_idx < ThreadMap::Iterations::kStrided; ++s_idx) {
|
||||
|
||||
int s_ = s;
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
s_ = problem_size_.S - 1 - s;
|
||||
}
|
||||
|
||||
int q = offset_w[s_idx] + problem_size_.pad_w - s_ * problem_size_.dilation_w;
|
||||
|
||||
bool pred = (q >= 0 && q < problem_size_.Q);
|
||||
masks_[s_idx][1] |= (pred << s);
|
||||
}
|
||||
}
|
||||
|
||||
if (filter_k_ >= problem_size.K) {
|
||||
clear_mask();
|
||||
}
|
||||
|
||||
set_iteration_index(0);
|
||||
}
|
||||
|
||||
private:
|
||||
|
||||
/// Returns the coordinate in the output gradient tensor dy that is correspoinding to
|
||||
// output nhw and filter position k, r, s
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at_(int n, int h, int w, int r, int s) const {
|
||||
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
r = problem_size_.R - 1 - r;
|
||||
s = problem_size_.S - 1 - s;
|
||||
}
|
||||
|
||||
int p = h + problem_size_.pad_h - r * problem_size_.dilation_h;
|
||||
int q = w + problem_size_.pad_w - s * problem_size_.dilation_w;
|
||||
|
||||
return TensorCoord(n, p, q, filter_k_);
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_byte_offset_(LongIndex byte_offset) {
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
pointer_[s] += byte_offset;
|
||||
}
|
||||
}
|
||||
|
||||
/// Clears the predicates
|
||||
CUTLASS_HOST_DEVICE
|
||||
void clear_mask_(bool clear) {
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
|
||||
// We are using inline PTX assembly here to avoid an CUDA C++ compilation
|
||||
// artifact in which control flow instructions are generated. Instead, our
|
||||
// intent is to predicate the mov instructions.
|
||||
#if defined(__CUDA_ARCH__)
|
||||
asm volatile(
|
||||
"{\n"
|
||||
" .reg .pred p;\n"
|
||||
" .reg .u32 m;"
|
||||
" mov.u32 m, %2;"
|
||||
" setp.ne.b32 p, %1, 0;\n"
|
||||
" @p mov.u32 m, 0;\n"
|
||||
" mov.u32 %0, m;\n"
|
||||
"}\n"
|
||||
:
|
||||
"=r"(masks_[s][0])
|
||||
:
|
||||
"r"((int)clear),
|
||||
"r"(masks_[s][0])
|
||||
);
|
||||
asm volatile(
|
||||
"{\n"
|
||||
" .reg .pred p;\n"
|
||||
" .reg .u32 m;"
|
||||
" mov.u32 m, %2;"
|
||||
" setp.ne.b32 p, %1, 0;\n"
|
||||
" @p mov.u32 m, 0;\n"
|
||||
" mov.u32 %0, m;\n"
|
||||
"}\n"
|
||||
:
|
||||
"=r"(masks_[s][1])
|
||||
:
|
||||
"r"((int)clear),
|
||||
"r"(masks_[s][1])
|
||||
);
|
||||
#else
|
||||
if (clear) {
|
||||
masks_[s][0] = 0;
|
||||
masks_[s][1] = 0;
|
||||
}
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
public:
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
add_byte_offset_(pointer_offset * sizeof_bits<Element>::value / 8);
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
|
||||
int next_idx = 0;
|
||||
|
||||
// moves to the next tile
|
||||
++filter_s_;
|
||||
if (filter_s_ == problem_size_.S) {
|
||||
filter_s_ = 0;
|
||||
++filter_r_;
|
||||
|
||||
if (filter_r_ < problem_size_.R) {
|
||||
next_idx = 1;
|
||||
}
|
||||
else {
|
||||
filter_r_ = 0;
|
||||
next_idx = 2;
|
||||
}
|
||||
}
|
||||
|
||||
add_byte_offset_(params_.inc_next[next_idx]);
|
||||
|
||||
if (next_idx == 2) {
|
||||
filter_k_ += params_.filter_k_delta;
|
||||
}
|
||||
|
||||
clear_mask_(filter_k_ >= problem_size_.K);
|
||||
}
|
||||
|
||||
/// Clears the predicates
|
||||
CUTLASS_HOST_DEVICE
|
||||
void clear_mask() {
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
masks_[s][0] = Mask(0);
|
||||
masks_[s][1] = Mask(0);
|
||||
}
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() {
|
||||
|
||||
return
|
||||
(masks_[iteration_strided_][0] & (Index(1) << filter_r_)) &&
|
||||
(masks_[iteration_strided_][1] & (Index(1) << filter_s_));
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_[iteration_strided_]);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dDgradOutputGradientTileAccessIteratorOptimized &operator++() {
|
||||
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv2dProblemSize const &problem_size) {
|
||||
|
||||
// This is specialized for unit stride
|
||||
if (problem_size.stride() != MatrixCoord({1, 1})) {
|
||||
return Status::kErrorNotSupported;
|
||||
}
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorNotSupported;
|
||||
}
|
||||
|
||||
// Limit on filter size
|
||||
if (problem_size.R > 32 || problem_size.S > 32) {
|
||||
return Status::kErrorNotSupported;
|
||||
}
|
||||
return Status::kSuccess;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
+274
@@ -0,0 +1,274 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM A (activation tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNHWC or TensorNCxHWx<Interleave> layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/matrix_shape.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
#include "cutlass/conv/threadblock/conv2d_params.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename Layout_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv2dFpropActivationTileAccessIteratorAnalytic {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = Layout_;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kAnalytic;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 2;
|
||||
using ConvProblemSize = typename conv::Conv2dProblemSize;
|
||||
|
||||
//
|
||||
// Simplifying assertions
|
||||
//
|
||||
static_assert(ThreadMap::Iterations::kContiguous == 1,
|
||||
"Require Iterations::kContiguous == 1");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
using Params = Conv2dAnalyticParams<Layout>;
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
Conv2dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
int filter_c_;
|
||||
int filter_r_;
|
||||
int filter_s_;
|
||||
|
||||
int offset_n_[ThreadMap::Iterations::kStrided];
|
||||
int offset_p_[ThreadMap::Iterations::kStrided];
|
||||
int offset_q_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropActivationTileAccessIteratorAnalytic(
|
||||
Params const ¶ms,
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord() // tile index - units are threadblock-scoped tiles
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)),
|
||||
filter_c_(0),
|
||||
filter_r_(0),
|
||||
filter_s_(0) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
filter_c_ = threadblock_offset.column() + thread_coord.contiguous();
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
int offset_npq = threadblock_offset.row() + thread_coord.strided() + s * ThreadMap::Delta::kStrided;
|
||||
|
||||
offset_n_[s] = offset_npq / (problem_size_.P * problem_size_.Q);
|
||||
int residual = offset_npq % (problem_size_.P * problem_size_.Q);
|
||||
|
||||
offset_p_[s] = residual / problem_size_.Q;
|
||||
offset_q_[s] = residual % problem_size_.Q;
|
||||
}
|
||||
|
||||
set_iteration_index(0);
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
// moves to the next tile
|
||||
++filter_s_;
|
||||
if (filter_s_ < problem_size_.S) {
|
||||
return;
|
||||
}
|
||||
filter_s_ = 0;
|
||||
++filter_r_;
|
||||
if (filter_r_ < problem_size_.R) {
|
||||
return;
|
||||
}
|
||||
filter_r_ = 0;
|
||||
|
||||
filter_c_ += Shape::kColumn * problem_size_.split_k_slices;
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the activations tensor X that is currently pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
int n = offset_n_[iteration_strided_];
|
||||
int p = offset_p_[iteration_strided_];
|
||||
int q = offset_q_[iteration_strided_];
|
||||
|
||||
int r = filter_r_;
|
||||
int s = filter_s_;
|
||||
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
r = (problem_size_.R - 1 - filter_r_);
|
||||
s = (problem_size_.S - 1 - filter_s_);
|
||||
}
|
||||
|
||||
int h = p * problem_size_.stride_h - problem_size_.pad_h + r * problem_size_.dilation_h;
|
||||
int w = q * problem_size_.stride_w - problem_size_.pad_w + s * problem_size_.dilation_w;
|
||||
|
||||
return TensorCoord(n, h, w, filter_c_);
|
||||
}
|
||||
|
||||
/// Returns true if the current coordinate is within the activations tensor X
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
|
||||
return coord.n() < problem_size_.N &&
|
||||
coord.h() >= 0 && coord.h() < problem_size_.H &&
|
||||
coord.w() >= 0 && coord.w() < problem_size_.W &&
|
||||
coord.c() < problem_size_.C;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
AccessType const *ptr = reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
|
||||
return ptr;
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropActivationTileAccessIteratorAnalytic &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv2dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
if (platform::is_same<Layout, layout::TensorNCxHWx<32>>::value) {
|
||||
if (problem_size.C % 32) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
}
|
||||
|
||||
if (platform::is_same<Layout, layout::TensorNCxHWx<64>>::value) {
|
||||
if (problem_size.C % 64) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
+438
@@ -0,0 +1,438 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM A (activation tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNHWC or TensorNCxHWx<Interleave> layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/matrix_shape.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
#include "cutlass/conv/threadblock/conv2d_params.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename Layout_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv2dFpropActivationTileAccessIteratorOptimized {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = Layout_;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kOptimized;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 2;
|
||||
using ConvProblemSize = typename conv::Conv2dProblemSize;
|
||||
|
||||
using Mask = uint64_t;
|
||||
|
||||
//
|
||||
// Simplifying assertions
|
||||
//
|
||||
static_assert(ThreadMap::Iterations::kContiguous == 1,
|
||||
"Require Iterations::kContiguous == 1");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
struct Params : Conv2dFpropActivationIteratorOptimizedParams<Layout> {
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(Conv2dFpropActivationIteratorOptimizedParams<Layout> const &base):
|
||||
Conv2dFpropActivationIteratorOptimizedParams<Layout>(base) { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
):
|
||||
Conv2dFpropActivationIteratorOptimizedParams<Layout>(
|
||||
problem_size,
|
||||
layout,
|
||||
sizeof_bits<Element>::value,
|
||||
{Shape::kRow, Shape::kColumn},
|
||||
ThreadMap::kThreads,
|
||||
ThreadMap::kElementsPerAccess,
|
||||
{ThreadMap::Iterations::kContiguous, ThreadMap::Iterations::kStrided},
|
||||
{ThreadMap::Delta::kContiguous, ThreadMap::Delta::kStrided}
|
||||
) {
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
Conv2dFpropActivationIteratorOptimizedParams<Layout> const ¶ms_;
|
||||
Conv2dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
|
||||
// One pointer per access
|
||||
char const *pointer_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
// current filter position (r, s)
|
||||
int filter_r_;
|
||||
int filter_s_;
|
||||
int filter_c_;
|
||||
|
||||
Index masks_[ThreadMap::Iterations::kStrided][2];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropActivationTileAccessIteratorOptimized(
|
||||
Conv2dFpropActivationIteratorOptimizedParams<Layout> const ¶ms,
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord() // tile index - units are threadblock-scoped tiles
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
filter_c_(0),
|
||||
filter_r_(0),
|
||||
filter_s_(0) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
filter_c_ = threadblock_offset.column() + thread_coord.contiguous();
|
||||
|
||||
int offset_n[ThreadMap::Iterations::kStrided];
|
||||
int offset_p[ThreadMap::Iterations::kStrided];
|
||||
int offset_q[ThreadMap::Iterations::kStrided];
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
|
||||
pointer_[s] = reinterpret_cast<char const *>(ptr);
|
||||
|
||||
int offset_npq = threadblock_offset.row() + thread_coord.strided() + s * ThreadMap::Delta::kStrided;
|
||||
|
||||
// The subseqnet fast_divmod() operations are equivalent to the following logical computation:
|
||||
//
|
||||
//
|
||||
// offset_n[s] = offset_npq / (problem_size_.P * problem_size_.Q);
|
||||
// int residual = offset_npq % (problem_size_.P * problem_size_.Q);
|
||||
//
|
||||
// offset_p[s] = residual / problem_size_.Q;
|
||||
// offset_q[s] = residual % problem_size_.Q;
|
||||
//
|
||||
|
||||
int residual;
|
||||
|
||||
params.pq_divmod(offset_n[s], residual, offset_npq);
|
||||
params.q_divmod(offset_p[s], offset_q[s], residual);
|
||||
|
||||
TensorCoord coord = at_(offset_n[s], offset_p[s], offset_q[s], 0, 0);
|
||||
|
||||
pointer_[s] += params_.layout(coord) * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
clear_mask();
|
||||
|
||||
CUTLASS_PRAGMA_NO_UNROLL
|
||||
for (int r = 0; r < problem_size_.R; ++r) {
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s_idx = 0; s_idx < ThreadMap::Iterations::kStrided; ++s_idx) {
|
||||
|
||||
int r_ = r;
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
r_ = problem_size_.R - 1 - r;
|
||||
}
|
||||
|
||||
int h = offset_p[s_idx] * problem_size_.stride_h - problem_size_.pad_h + r_ * problem_size_.dilation_h;
|
||||
|
||||
bool pred = (offset_n[s_idx] < problem_size_.N && h >= 0 && h < problem_size_.H);
|
||||
masks_[s_idx][0] |= (pred << r);
|
||||
}
|
||||
}
|
||||
|
||||
CUTLASS_PRAGMA_NO_UNROLL
|
||||
for (int s = 0; s < problem_size_.S; ++s) {
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s_idx = 0; s_idx < ThreadMap::Iterations::kStrided; ++s_idx) {
|
||||
|
||||
int s_ = s;
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
s_ = problem_size_.S - 1 - s;
|
||||
}
|
||||
|
||||
int w = offset_q[s_idx] * problem_size_.stride_w - problem_size_.pad_w + s_ * problem_size_.dilation_w;
|
||||
|
||||
bool pred = (w >= 0 && w < problem_size_.W);
|
||||
masks_[s_idx][1] |= (pred << s);
|
||||
}
|
||||
}
|
||||
|
||||
if (filter_c_ >= problem_size.C) {
|
||||
clear_mask();
|
||||
}
|
||||
|
||||
set_iteration_index(0);
|
||||
}
|
||||
|
||||
private:
|
||||
|
||||
/// Returns the coordinate in the activations tensor X that is correspoinding to
|
||||
// output npq and filter position r, s
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at_(int n, int p, int q, int r, int s) const {
|
||||
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
r = problem_size_.R - 1 - r;
|
||||
s = problem_size_.S - 1 - s;
|
||||
}
|
||||
|
||||
int h = p * problem_size_.stride_h - problem_size_.pad_h + r * problem_size_.dilation_h;
|
||||
int w = q * problem_size_.stride_w - problem_size_.pad_w + s * problem_size_.dilation_w;
|
||||
|
||||
return TensorCoord(n, h, w, filter_c_);
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_byte_offset_(LongIndex byte_offset) {
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
pointer_[s] += byte_offset;
|
||||
}
|
||||
}
|
||||
|
||||
/// Clears the predicates
|
||||
CUTLASS_HOST_DEVICE
|
||||
void clear_mask_(bool clear) {
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
|
||||
// We are using inline PTX assembly here to avoid an CUDA C++ compilation
|
||||
// artifact in which control flow instructions are generated. Instead, our
|
||||
// intent is to predicate the mov instructions.
|
||||
#if defined(__CUDA_ARCH__)
|
||||
asm volatile(
|
||||
"{\n"
|
||||
" .reg .pred p;\n"
|
||||
" .reg .u32 m;"
|
||||
" mov.u32 m, %2;"
|
||||
" setp.ne.b32 p, %1, 0;\n"
|
||||
" @p mov.u32 m, 0;\n"
|
||||
" mov.u32 %0, m;\n"
|
||||
"}\n"
|
||||
:
|
||||
"=r"(masks_[s][0])
|
||||
:
|
||||
"r"((int)clear),
|
||||
"r"(masks_[s][0])
|
||||
);
|
||||
asm volatile(
|
||||
"{\n"
|
||||
" .reg .pred p;\n"
|
||||
" .reg .u32 m;"
|
||||
" mov.u32 m, %2;"
|
||||
" setp.ne.b32 p, %1, 0;\n"
|
||||
" @p mov.u32 m, 0;\n"
|
||||
" mov.u32 %0, m;\n"
|
||||
"}\n"
|
||||
:
|
||||
"=r"(masks_[s][1])
|
||||
:
|
||||
"r"((int)clear),
|
||||
"r"(masks_[s][1])
|
||||
);
|
||||
#else
|
||||
if (clear) {
|
||||
masks_[s][0] = 0;
|
||||
masks_[s][1] = 0;
|
||||
}
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
public:
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
add_byte_offset_(pointer_offset * sizeof_bits<Element>::value / 8);
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
|
||||
int next_idx = 0;
|
||||
|
||||
// moves to the next tile
|
||||
++filter_s_;
|
||||
if (filter_s_ == problem_size_.S) {
|
||||
filter_s_ = 0;
|
||||
++filter_r_;
|
||||
|
||||
if (filter_r_ < problem_size_.R) {
|
||||
next_idx = 1;
|
||||
}
|
||||
else {
|
||||
filter_r_ = 0;
|
||||
next_idx = 2;
|
||||
}
|
||||
}
|
||||
|
||||
add_byte_offset_(params_.inc_next[next_idx]);
|
||||
|
||||
if (next_idx == 2) {
|
||||
filter_c_ += params_.filter_c_delta;
|
||||
}
|
||||
|
||||
clear_mask_(filter_c_ >= problem_size_.C);
|
||||
}
|
||||
|
||||
/// Clears the predicates
|
||||
CUTLASS_HOST_DEVICE
|
||||
void clear_mask() {
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
masks_[s][0] = Mask(0);
|
||||
masks_[s][1] = Mask(0);
|
||||
}
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() {
|
||||
|
||||
return
|
||||
(masks_[iteration_strided_][0] & (Index(1) << filter_r_)) &&
|
||||
(masks_[iteration_strided_][1] & (Index(1) << filter_s_));
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_[iteration_strided_]);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropActivationTileAccessIteratorOptimized &operator++() {
|
||||
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv2dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
if (platform::is_same<Layout, layout::TensorNCxHWx<32>>::value) {
|
||||
if (problem_size.C % 32) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
}
|
||||
|
||||
if (platform::is_same<Layout, layout::TensorNCxHWx<64>>::value) {
|
||||
if (problem_size.C % 64) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
}
|
||||
|
||||
// Conv2dFpropActivationTileAccessIteratorOptimized has constraint on filter positions
|
||||
// due to the number of mask bits.
|
||||
if (problem_size.R > 32 || problem_size.S > 32) {
|
||||
return Status::kErrorNotSupported;
|
||||
}
|
||||
return Status::kSuccess;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
@@ -0,0 +1,252 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM B (filter tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNHWC or TensorCxRSKx<Interleave> layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
#include "cutlass/conv/threadblock/conv2d_params.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename Layout_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv2dFpropFilterTileAccessIteratorAnalytic {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = Layout_;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kAnalytic;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 2;
|
||||
using ConvProblemSize = typename conv::Conv2dProblemSize;
|
||||
|
||||
//
|
||||
// Simplifying assertions
|
||||
//
|
||||
static_assert(ThreadMap::Iterations::kContiguous == 1,
|
||||
"Require Iterations::kContiguous == 1");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
using Params = Conv2dAnalyticParams<Layout>;
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
Conv2dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
int filter_r_;
|
||||
int filter_s_;
|
||||
int filter_c_;
|
||||
|
||||
int offset_k_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropFilterTileAccessIteratorAnalytic(
|
||||
Params const ¶ms,
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)),
|
||||
filter_r_(0),
|
||||
filter_s_(0),
|
||||
filter_c_(0) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
filter_c_ = threadblock_offset.row() + thread_coord.contiguous();
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
offset_k_[s] = threadblock_offset.column() + thread_coord.strided() + s * ThreadMap::Delta::kStrided;
|
||||
}
|
||||
|
||||
set_iteration_index(0);
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * 8 / sizeof_bits<Element>::value;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
// moves to the next tile
|
||||
++filter_s_;
|
||||
if (filter_s_ < problem_size_.S) {
|
||||
return;
|
||||
}
|
||||
filter_s_ = 0;
|
||||
|
||||
++filter_r_;
|
||||
if (filter_r_ < problem_size_.R) {
|
||||
return;
|
||||
}
|
||||
filter_r_ = 0;
|
||||
|
||||
filter_c_ += Shape::kRow * problem_size_.split_k_slices;
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the filter tensor W that is currently pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
|
||||
int k = offset_k_[iteration_strided_];
|
||||
|
||||
return TensorCoord(k, filter_r_, filter_s_, filter_c_);
|
||||
}
|
||||
|
||||
/// Returns true if the current coordinate is within the activations tensor W
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
|
||||
return coord.n() < problem_size_.K &&
|
||||
coord.c() < problem_size_.C;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropFilterTileAccessIteratorAnalytic &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv2dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
if (platform::is_same<Layout, layout::TensorCxRSKx<32>>::value) {
|
||||
if (problem_size.K % 32) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
}
|
||||
|
||||
if (platform::is_same<Layout, layout::TensorCxRSKx<64>>::value) {
|
||||
if (problem_size.K % 64) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
+282
@@ -0,0 +1,282 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM B (filter tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNHWC or TensorCxRSKx<Interleave> layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
|
||||
#include "cutlass/conv/threadblock/conv2d_params.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename Layout_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv2dFpropFilterTileAccessIteratorOptimized{
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = Layout_;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kOptimized;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 2;
|
||||
using ConvProblemSize = typename conv::Conv2dProblemSize;
|
||||
|
||||
//
|
||||
// Simplifying assertions
|
||||
//
|
||||
static_assert(ThreadMap::Iterations::kContiguous == 1,
|
||||
"Require Iterations::kContiguous == 1");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
struct Params : Conv2dFpropFilterIteratorOptimizedParams<Layout> {
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(Conv2dFpropFilterIteratorOptimizedParams<Layout> const &base):
|
||||
Conv2dFpropFilterIteratorOptimizedParams<Layout>(base) { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
):
|
||||
Conv2dFpropFilterIteratorOptimizedParams<Layout>(
|
||||
problem_size,
|
||||
layout,
|
||||
sizeof_bits<Element>::value,
|
||||
{Shape::kRow, Shape::kColumn},
|
||||
ThreadMap::kThreads,
|
||||
ThreadMap::kElementsPerAccess,
|
||||
{ThreadMap::Iterations::kContiguous, ThreadMap::Iterations::kStrided},
|
||||
{ThreadMap::Delta::kContiguous, ThreadMap::Delta::kStrided}
|
||||
) {
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
Conv2dFpropFilterIteratorOptimizedParams<Layout> const ¶ms_;
|
||||
Conv2dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
uint32_t predicates_;
|
||||
int filter_rs_;
|
||||
int filter_c_;
|
||||
|
||||
//
|
||||
// Assertions
|
||||
//
|
||||
|
||||
// We map predicates into bits packed in this uint32_t container
|
||||
static_assert(ThreadMap::Iterations::kStrided < sizeof(predicates_) * 8,
|
||||
"Currently, the number of loads per iteration is limited by the size of the predicates container.");
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropFilterTileAccessIteratorOptimized(
|
||||
Conv2dFpropFilterIteratorOptimizedParams<Layout> const ¶ms,
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)),
|
||||
predicates_(0),
|
||||
filter_rs_(0),
|
||||
filter_c_(0) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
filter_c_ = threadblock_offset.row() + thread_coord.contiguous();
|
||||
Index column = threadblock_offset.column() + thread_coord.strided();
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
uint32_t pred = ((column + s * ThreadMap::Delta::kStrided < problem_size_.K) ? 1u : 0);
|
||||
predicates_ |= (pred << s);
|
||||
}
|
||||
|
||||
if (filter_c_ >= problem_size.C) {
|
||||
predicates_ = 0u;
|
||||
}
|
||||
|
||||
pointer_ += (
|
||||
params_.layout({filter_c_, column})
|
||||
) * sizeof_bits<Element>::value / 8;
|
||||
|
||||
set_iteration_index(0);
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
|
||||
LongIndex next = params_.inc_next_rs;
|
||||
|
||||
// moves to the next tile
|
||||
++filter_rs_;
|
||||
if (filter_rs_ == params_.RS) {
|
||||
|
||||
filter_rs_ = 0;
|
||||
next = params_.inc_next_c;
|
||||
filter_c_ += params_.filter_c_delta;
|
||||
}
|
||||
|
||||
if (filter_c_ >= problem_size_.C) {
|
||||
predicates_ = 0;
|
||||
}
|
||||
|
||||
pointer_ += next;
|
||||
}
|
||||
|
||||
/// Returns true if the current coordinate is within the filter tensor W
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() {
|
||||
return (predicates_ & (1u << iteration_strided_));
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
return reinterpret_cast<AccessType const *>(pointer_);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropFilterTileAccessIteratorOptimized &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
|
||||
// Move to the next K coordinate within the tile
|
||||
pointer_ += params_.inc_next_k;
|
||||
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv2dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
if (platform::is_same<Layout, layout::TensorCxRSKx<32>>::value) {
|
||||
if (problem_size.K % 32) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
}
|
||||
|
||||
if (platform::is_same<Layout, layout::TensorCxRSKx<64>>::value) {
|
||||
if (problem_size.K % 64) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
@@ -0,0 +1,609 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Extracts the host-params objects into non-template code.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#define TRACE_CONV_PARAMS_INITIALIZERS_ENABLED 0
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/fast_math.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
|
||||
#if TRACE_CONV_PARAMS_INITIALIZERS_ENABLED
|
||||
#include <fstream>
|
||||
#endif
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Params structure used for all Conv2d analytic tile iterators
|
||||
template< typename Layout_ = layout::TensorNHWC >
|
||||
struct Conv2dAnalyticParams {
|
||||
|
||||
using Layout = Layout_;
|
||||
|
||||
Layout layout;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dAnalyticParams() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dAnalyticParams(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
): layout(layout) {
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
#if TRACE_CONV_PARAMS_INITIALIZERS_ENABLED
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void TraceIteratorParams(
|
||||
char const *conv_operator,
|
||||
char const *operand,
|
||||
int element_size_bits,
|
||||
MatrixCoord threadblock_shape,
|
||||
int thread_count,
|
||||
int access_size,
|
||||
layout::PitchLinearCoord threadmap_iterations,
|
||||
layout::PitchLinearCoord threadmap_delta
|
||||
) {
|
||||
|
||||
#if !defined(__CUDA_ARCH__)
|
||||
|
||||
char const *fname = "conv_iterator_params.csv";
|
||||
|
||||
std::ifstream test(fname);
|
||||
bool file_exists = test.is_open();
|
||||
|
||||
if (file_exists) {
|
||||
test.close();
|
||||
}
|
||||
|
||||
std::ofstream trace("conv_iterator_params.csv", std::ofstream::app);
|
||||
|
||||
if (!file_exists) {
|
||||
trace
|
||||
<< "Operator,Operand,ElementSize,CtaRows,CtaColumns,ThreadCount,AccessSize,"
|
||||
<< "IterationsContiguous,IterationsStrided,DeltaContiguous,DeltaStrided\n";
|
||||
}
|
||||
|
||||
trace << conv_operator << "," << operand << "," << element_size_bits << ","
|
||||
<< threadblock_shape.row() << "," << threadblock_shape.column()
|
||||
<< "," << thread_count << "," << access_size
|
||||
<< "," << threadmap_iterations.contiguous() << "," << threadmap_iterations.strided()
|
||||
<< "," << threadmap_delta.contiguous() << "," << threadmap_delta.strided() << "\n";
|
||||
#endif
|
||||
}
|
||||
|
||||
#define TRACE_CONV_INITIALIZERS(conv_op, operand, element_size, cta_shape, thread_count, access_size, iterations, delta) \
|
||||
TraceIteratorParams(conv_op, operand, element_size, cta_shape, thread_count, access_size, iterations, delta);
|
||||
|
||||
#else
|
||||
|
||||
#define TRACE_CONV_INITIALIZERS(conv_op, operand, element_size, cta_shape, thread_count, access_size, iterations, delta) {}
|
||||
|
||||
#endif
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Parameters structure used for Conv2dFpropActivationTileIteratorOptimized
|
||||
template< typename Layout_ = layout::TensorNHWC >
|
||||
struct Conv2dFpropActivationIteratorOptimizedParams;
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Parameters structure used for Conv2dFpropActivationTileIteratorOptimized
|
||||
template<>
|
||||
struct Conv2dFpropActivationIteratorOptimizedParams<layout::TensorNHWC> {
|
||||
|
||||
using Layout = layout::TensorNHWC;
|
||||
|
||||
Layout layout;
|
||||
|
||||
int64_t inc_next[3]; // {next S, next R, next C}
|
||||
int filter_c_delta; // number of logical elements to add to filter_c_
|
||||
int PQ; // product of P*Q
|
||||
|
||||
FastDivmod pq_divmod;
|
||||
FastDivmod q_divmod;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropActivationIteratorOptimizedParams() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropActivationIteratorOptimizedParams(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout, ///< layout object
|
||||
int element_size_bits, ///< size of each element in bits
|
||||
MatrixCoord threadblock_shape,
|
||||
int thread_count,
|
||||
int access_size,
|
||||
layout::PitchLinearCoord threadmap_iterations,
|
||||
layout::PitchLinearCoord threadmap_delta
|
||||
):
|
||||
layout(layout), PQ(problem_size.P * problem_size.Q), pq_divmod(PQ), q_divmod(problem_size.Q) {
|
||||
|
||||
TRACE_CONV_INITIALIZERS("conv2d_fprop", "activation",
|
||||
element_size_bits, threadblock_shape, thread_count, access_size, threadmap_iterations, threadmap_delta);
|
||||
|
||||
int conv_sign = (problem_size.mode == Mode::kConvolution ? -1 : 1);
|
||||
|
||||
// next S
|
||||
inc_next[0] = conv_sign * (int64_t(layout.stride()[0]) * problem_size.dilation_w) * element_size_bits / 8;
|
||||
|
||||
// next R
|
||||
inc_next[1] = conv_sign * (
|
||||
int64_t(layout.stride()[1]) * problem_size.dilation_h
|
||||
- (problem_size.S - 1) * layout.stride()[0] * problem_size.dilation_w
|
||||
) * element_size_bits / 8;
|
||||
|
||||
// next C
|
||||
inc_next[2] = (
|
||||
threadblock_shape.column() * problem_size.split_k_slices
|
||||
- conv_sign * int64_t(problem_size.R - 1) * layout.stride()[1] * problem_size.dilation_h
|
||||
- conv_sign * int64_t(problem_size.S - 1) * layout.stride()[0] * problem_size.dilation_w
|
||||
) * element_size_bits / 8;
|
||||
|
||||
// logical offset added to internal channel counter - units are elements, not bytes
|
||||
filter_c_delta = threadblock_shape.column() * problem_size.split_k_slices;
|
||||
}
|
||||
};
|
||||
|
||||
/// Parameters structure used for Conv2dFpropActivationTileIteratorOptimized
|
||||
template <int Interleaved_>
|
||||
struct Conv2dFpropActivationIteratorOptimizedParams<layout::TensorNCxHWx<Interleaved_>> {
|
||||
static int const kInterleaved = Interleaved_;
|
||||
|
||||
using Layout = layout::TensorNCxHWx<kInterleaved>;
|
||||
|
||||
Layout layout;
|
||||
|
||||
int64_t inc_next[3]; // {next S, next R, next C}
|
||||
int filter_c_delta; // number of logical elements to add to filter_c_
|
||||
int PQ; // product of P*Q
|
||||
|
||||
FastDivmod pq_divmod;
|
||||
FastDivmod q_divmod;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropActivationIteratorOptimizedParams() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropActivationIteratorOptimizedParams(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout, ///< layout object
|
||||
int element_size_bits, ///< size of each element in bits
|
||||
MatrixCoord threadblock_shape,
|
||||
int thread_count,
|
||||
int access_size,
|
||||
layout::PitchLinearCoord threadmap_iterations,
|
||||
layout::PitchLinearCoord threadmap_delta
|
||||
):
|
||||
layout(layout), PQ(problem_size.P * problem_size.Q), pq_divmod(PQ), q_divmod(problem_size.Q) {
|
||||
|
||||
TRACE_CONV_INITIALIZERS("conv2d_fprop", "activation",
|
||||
element_size_bits, threadblock_shape, thread_count, access_size, threadmap_iterations, threadmap_delta);
|
||||
|
||||
int conv_sign = (problem_size.mode == Mode::kConvolution ? -1 : 1);
|
||||
|
||||
// next S
|
||||
inc_next[0] = conv_sign * (kInterleaved * problem_size.dilation_w) * element_size_bits / 8;
|
||||
|
||||
// next R
|
||||
inc_next[1] = conv_sign * (
|
||||
int64_t(layout.stride()[0]) * problem_size.dilation_h
|
||||
- (problem_size.S - 1) * kInterleaved * problem_size.dilation_w
|
||||
) * element_size_bits / 8;
|
||||
|
||||
// next C
|
||||
inc_next[2] = (
|
||||
threadblock_shape.column() * problem_size.split_k_slices / kInterleaved * int64_t(layout.stride()[1])
|
||||
- conv_sign * int64_t(problem_size.R - 1) * layout.stride()[0] * problem_size.dilation_h
|
||||
- conv_sign * int64_t(problem_size.S - 1) * kInterleaved * problem_size.dilation_w
|
||||
) * element_size_bits / 8;
|
||||
|
||||
// logical offset added to internal channel counter - units are elements, not bytes
|
||||
filter_c_delta = threadblock_shape.column() * problem_size.split_k_slices;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template< typename Layout_ = layout::TensorNHWC >
|
||||
struct Conv2dFpropFilterIteratorOptimizedParams;
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template<>
|
||||
struct Conv2dFpropFilterIteratorOptimizedParams<layout::TensorNHWC>
|
||||
{
|
||||
|
||||
using Layout = layout::TensorNHWC;
|
||||
|
||||
Layout layout;
|
||||
int RS;
|
||||
int filter_c_delta;
|
||||
|
||||
int64_t inc_next_k; // offset in units of bytes to next K position
|
||||
int64_t inc_next_rs; // offset in units of bytes to next RS position
|
||||
int64_t inc_next_c; // offset in units of bytes to next C position
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropFilterIteratorOptimizedParams() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropFilterIteratorOptimizedParams(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout,
|
||||
int element_size_bits, ///< size of each element in bits
|
||||
MatrixCoord threadblock_shape,
|
||||
int thread_count,
|
||||
int access_size,
|
||||
layout::PitchLinearCoord threadmap_iterations,
|
||||
layout::PitchLinearCoord threadmap_delta
|
||||
):
|
||||
layout(layout) {
|
||||
|
||||
TRACE_CONV_INITIALIZERS("conv2d_fprop", "filter",
|
||||
element_size_bits, threadblock_shape, thread_count, access_size, threadmap_iterations, threadmap_delta);
|
||||
|
||||
RS = problem_size.R * problem_size.S;
|
||||
|
||||
inc_next_k = (int64_t(layout.stride()[2]) * threadmap_delta.strided() * element_size_bits) / 8;
|
||||
|
||||
inc_next_rs =
|
||||
( int64_t(layout.stride()[0])
|
||||
- int64_t(layout.stride()[2]) * (threadmap_iterations.strided() - 1) * threadmap_delta.strided()
|
||||
) * element_size_bits / 8;
|
||||
|
||||
inc_next_c =
|
||||
(
|
||||
threadblock_shape.row() * problem_size.split_k_slices
|
||||
- int64_t(RS - 1) * layout.stride()[0]
|
||||
- int64_t(threadmap_iterations.strided() - 1) * threadmap_delta.strided() * layout.stride()[2]
|
||||
) * element_size_bits / 8;
|
||||
|
||||
filter_c_delta = threadblock_shape.row() * problem_size.split_k_slices;
|
||||
}
|
||||
};
|
||||
|
||||
template<int Interleaved_>
|
||||
struct Conv2dFpropFilterIteratorOptimizedParams<layout::TensorCxRSKx<Interleaved_>>
|
||||
{
|
||||
static int const kInterleaved = Interleaved_;
|
||||
using Layout = layout::TensorCxRSKx<kInterleaved>;
|
||||
|
||||
Layout layout;
|
||||
int RS;
|
||||
int filter_c_delta;
|
||||
|
||||
int64_t inc_next_k; // offset in units of bytes to next K position
|
||||
int64_t inc_next_rs; // offset in units of bytes to next RS position
|
||||
int64_t inc_next_c; // offset in units of bytes to next C position
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropFilterIteratorOptimizedParams() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dFpropFilterIteratorOptimizedParams(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout,
|
||||
int element_size_bits, ///< size of each element in bits
|
||||
MatrixCoord threadblock_shape,
|
||||
int thread_count,
|
||||
int access_size,
|
||||
layout::PitchLinearCoord threadmap_iterations,
|
||||
layout::PitchLinearCoord threadmap_delta
|
||||
):
|
||||
layout(layout) {
|
||||
|
||||
TRACE_CONV_INITIALIZERS("conv2d_fprop", "filter",
|
||||
element_size_bits, threadblock_shape, thread_count, access_size, threadmap_iterations, threadmap_delta);
|
||||
|
||||
RS = problem_size.R * problem_size.S;
|
||||
|
||||
inc_next_k = (kInterleaved * threadmap_delta.strided() * element_size_bits) / 8;
|
||||
|
||||
inc_next_rs =
|
||||
( int64_t(layout.stride()[0])
|
||||
- kInterleaved * (threadmap_iterations.strided() - 1) * threadmap_delta.strided()
|
||||
) * element_size_bits / 8;
|
||||
|
||||
inc_next_c =
|
||||
(
|
||||
threadblock_shape.row() * problem_size.split_k_slices / kInterleaved * int64_t(layout.stride()[2])
|
||||
- int64_t(RS - 1) * layout.stride()[0]
|
||||
- int64_t(threadmap_iterations.strided() - 1) * threadmap_delta.strided() * kInterleaved
|
||||
) * element_size_bits / 8;
|
||||
|
||||
filter_c_delta = threadblock_shape.row() * problem_size.split_k_slices;
|
||||
}
|
||||
};
|
||||
|
||||
/// Parameters object for Conv2d DGRAD OutputGradient (dy) iterator
|
||||
struct Conv2dDgradOutputGradientIteratorOptimizedParams {
|
||||
|
||||
using Layout = layout::TensorNHWC;
|
||||
|
||||
Layout layout;
|
||||
|
||||
int64_t inc_next[3]; // {next S, next R, next K}
|
||||
|
||||
int filter_k_delta; // number of logical elements to add to filter_k_
|
||||
|
||||
int HW; // product of H*W
|
||||
|
||||
FastDivmod hw_divmod;
|
||||
FastDivmod w_divmod;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dDgradOutputGradientIteratorOptimizedParams() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dDgradOutputGradientIteratorOptimizedParams(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout,
|
||||
int element_size_bits, ///< size of each element in bits
|
||||
MatrixCoord threadblock_shape,
|
||||
int thread_count,
|
||||
int access_size,
|
||||
layout::PitchLinearCoord threadmap_iterations,
|
||||
layout::PitchLinearCoord threadmap_delta
|
||||
):
|
||||
layout(layout), HW(problem_size.H *problem_size.W), hw_divmod(HW), w_divmod(problem_size.W) {
|
||||
|
||||
TRACE_CONV_INITIALIZERS("conv2d_dgrad", "output_gradient",
|
||||
element_size_bits, threadblock_shape, thread_count, access_size, threadmap_iterations, threadmap_delta);
|
||||
|
||||
int conv_sign = (problem_size.mode == Mode::kConvolution ? 1 : -1);
|
||||
|
||||
// next S
|
||||
inc_next[0] = conv_sign * (layout.stride()[0] * problem_size.dilation_w) * element_size_bits / 8;
|
||||
|
||||
// next R
|
||||
inc_next[1] = conv_sign * (
|
||||
layout.stride()[1] * problem_size.dilation_h
|
||||
- (problem_size.S - 1) * layout.stride()[0] * problem_size.dilation_w
|
||||
) * element_size_bits / 8;
|
||||
|
||||
// next K
|
||||
inc_next[2] = (
|
||||
threadblock_shape.column() * problem_size.split_k_slices
|
||||
- conv_sign * (problem_size.R - 1) * layout.stride()[1] * problem_size.dilation_h
|
||||
- conv_sign * (problem_size.S - 1) * layout.stride()[0] * problem_size.dilation_w
|
||||
) * element_size_bits / 8;
|
||||
|
||||
// logical offset added to internal channel counter - units are elements, not bytes
|
||||
filter_k_delta = threadblock_shape.column() * problem_size.split_k_slices;
|
||||
}
|
||||
};
|
||||
|
||||
/// Parameters object for Conv2d DGRAD Filter (w) iterator
|
||||
struct Conv2dDgradFilterIteratorOptimizedParams {
|
||||
|
||||
using Layout = layout::TensorNHWC;
|
||||
|
||||
Layout layout;
|
||||
int RS;
|
||||
int filter_k_delta;
|
||||
|
||||
int64_t inc_next_strided; // offset in units of bytes to next K coordinate within tile
|
||||
int64_t inc_next_rs; // offset in units of bytes to next RS position
|
||||
int64_t inc_next_k; // offset in units of bytes to next K position in subsequent tile
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dDgradFilterIteratorOptimizedParams() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dDgradFilterIteratorOptimizedParams(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout,
|
||||
int element_size_bits, ///< size of each element in bits
|
||||
MatrixCoord threadblock_shape,
|
||||
int thread_count,
|
||||
int access_size,
|
||||
layout::PitchLinearCoord threadmap_iterations,
|
||||
layout::PitchLinearCoord threadmap_delta
|
||||
):
|
||||
layout(layout), RS(problem_size.R * problem_size.S) {
|
||||
|
||||
TRACE_CONV_INITIALIZERS("conv2d_dgrad", "filter",
|
||||
element_size_bits, threadblock_shape, thread_count, access_size, threadmap_iterations, threadmap_delta);
|
||||
|
||||
inc_next_strided = (layout.stride()[2] * threadmap_delta.strided() * element_size_bits) / 8;
|
||||
|
||||
inc_next_rs =
|
||||
( layout.stride()[0]
|
||||
- (threadmap_iterations.strided() - 1) * threadmap_delta.strided() * layout.stride()[2]
|
||||
) * element_size_bits / 8;
|
||||
|
||||
inc_next_k =
|
||||
(
|
||||
threadblock_shape.row() * problem_size.split_k_slices * layout.stride()[2]
|
||||
- (problem_size.R * problem_size.S - 1) * layout.stride()[0]
|
||||
- (threadmap_iterations.strided() - 1) * threadmap_delta.strided() * layout.stride()[2]
|
||||
) * element_size_bits / 8;
|
||||
|
||||
filter_k_delta = threadblock_shape.row() * problem_size.split_k_slices;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Parameters object for Conv2d WGRAD Output Gradient (dy) iterator
|
||||
struct Conv2dWgradOutputGradientIteratorOptimizedParams {
|
||||
|
||||
using Layout = layout::TensorNHWC;
|
||||
|
||||
Layout layout;
|
||||
|
||||
int NPQ; // precomputd product of N*P*Q for clearing predicates
|
||||
|
||||
FastDivmod pq_divmod;
|
||||
FastDivmod q_divmod;
|
||||
|
||||
int64_t offset_next_strided; // offset in units of bytes to next npq coordinate within tile
|
||||
int64_t offset_next_contiguous; // offset in units of bytes to next k coordinate within tile
|
||||
int64_t inc_next_npq; // offset in units of bytes to next npq position in subsequent tile
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dWgradOutputGradientIteratorOptimizedParams() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dWgradOutputGradientIteratorOptimizedParams(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout,
|
||||
int element_size_bits, ///< size of each element in bits
|
||||
MatrixCoord threadblock_shape,
|
||||
int thread_count,
|
||||
int access_size,
|
||||
layout::PitchLinearCoord threadmap_iterations,
|
||||
layout::PitchLinearCoord threadmap_delta
|
||||
):
|
||||
layout(layout),
|
||||
NPQ(problem_size.N * problem_size.P * problem_size.Q),
|
||||
pq_divmod(problem_size.P * problem_size.Q),
|
||||
q_divmod(problem_size.Q) {
|
||||
|
||||
TRACE_CONV_INITIALIZERS("conv2d_wgrad", "output_gradient",
|
||||
element_size_bits, threadblock_shape, thread_count, access_size, threadmap_iterations, threadmap_delta);
|
||||
|
||||
// Incremental offsets in unites of bytes (number of elements) * sizeof_bits<Element>::value / 8
|
||||
offset_next_strided = (threadmap_delta.strided() * layout.stride()[0])
|
||||
* element_size_bits / 8;
|
||||
|
||||
offset_next_contiguous = (threadmap_delta.contiguous())
|
||||
* element_size_bits / 8;
|
||||
|
||||
inc_next_npq = (threadblock_shape.column() * problem_size.split_k_slices * layout.stride()[0])
|
||||
* element_size_bits / 8;
|
||||
}
|
||||
};
|
||||
|
||||
struct Conv2dWgradActivationIteratorOptimizedParams {
|
||||
|
||||
using Layout = layout::TensorNHWC;
|
||||
|
||||
Layout layout;
|
||||
|
||||
FastDivmod sc_divmod;
|
||||
FastDivmod pq_divmod;
|
||||
FastDivmod q_divmod;
|
||||
FastDivmod c_divmod;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dWgradActivationIteratorOptimizedParams() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dWgradActivationIteratorOptimizedParams(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
):
|
||||
layout(layout),
|
||||
sc_divmod(problem_size.S * problem_size.C),
|
||||
pq_divmod(problem_size.P * problem_size.Q),
|
||||
q_divmod(problem_size.Q),
|
||||
c_divmod(problem_size.C) {
|
||||
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dWgradActivationIteratorOptimizedParams(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout,
|
||||
int element_size_bits, ///< size of each element in bits
|
||||
MatrixCoord threadblock_shape,
|
||||
int thread_count,
|
||||
int access_size,
|
||||
layout::PitchLinearCoord threadmap_iterations,
|
||||
layout::PitchLinearCoord threadmap_delta
|
||||
):
|
||||
Conv2dWgradActivationIteratorOptimizedParams(
|
||||
problem_size,
|
||||
layout
|
||||
) {
|
||||
|
||||
TRACE_CONV_INITIALIZERS("conv2d_wgrad", "activation",
|
||||
element_size_bits, threadblock_shape, thread_count, access_size, threadmap_iterations, threadmap_delta);
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
@@ -0,0 +1,170 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Template wraps the tile access iterator concept to load whole tiles from tensors in
|
||||
memory used for implicit GEMM convolution.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/matrix_shape.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <typename TileAccessIterator_>
|
||||
class TileIterator {
|
||||
public:
|
||||
using TileAccessIterator = TileAccessIterator_;
|
||||
|
||||
using Shape = typename TileAccessIterator::Shape;
|
||||
using Element = typename TileAccessIterator::Element;
|
||||
using Layout = typename TileAccessIterator::Layout;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using ThreadMap = typename TileAccessIterator::ThreadMap;
|
||||
using AccessType = typename TileAccessIterator::AccessType;
|
||||
using TensorRef = typename TileAccessIterator::TensorRef;
|
||||
using Index = typename TileAccessIterator::Index;
|
||||
using LongIndex = typename TileAccessIterator::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = TileAccessIterator::kIteratorAlgorithm;
|
||||
static StrideSupport const kStrideSupport = TileAccessIterator::kStrideSupport;
|
||||
using Params = typename TileAccessIterator::Params;
|
||||
static int const kConvDim = TileAccessIterator::kConvDim;
|
||||
using ConvProblemSize = typename TileAccessIterator::ConvProblemSize;
|
||||
|
||||
/// Fragment object to be loaded or stored
|
||||
using Fragment = cutlass::Array<
|
||||
Element,
|
||||
ThreadMap::Iterations::kCount * ThreadMap::kElementsPerAccess>;
|
||||
|
||||
private:
|
||||
|
||||
/// Internal state
|
||||
TileAccessIterator tile_access_iterator_;
|
||||
|
||||
public:
|
||||
|
||||
/// Constructor
|
||||
CUTLASS_HOST_DEVICE
|
||||
TileIterator(
|
||||
Params const ¶ms,
|
||||
ConvProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
tile_access_iterator_(params, problem_size, ptr, thread_idx, threadblock_offset) { }
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
tile_access_iterator_.add_pointer_offset(pointer_offset);
|
||||
}
|
||||
|
||||
/// Advances to the next tile in memory.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TileIterator &operator++() {
|
||||
tile_access_iterator_.advance();
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Advances to the next tile in memory.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TileIterator operator++(int) {
|
||||
TileIterator self(*this);
|
||||
operator++();
|
||||
return self;
|
||||
}
|
||||
|
||||
/// Loads a fragment from memory
|
||||
CUTLASS_DEVICE
|
||||
void load_with_pointer_offset(Fragment &frag, Index pointer_offset) {
|
||||
|
||||
frag.clear();
|
||||
AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
|
||||
|
||||
cutlass::arch::global_load<
|
||||
AccessType,
|
||||
sizeof(AccessType)
|
||||
>(
|
||||
frag_ptr[c + s * ThreadMap::Iterations::kContiguous],
|
||||
tile_access_iterator_.get() + pointer_offset,
|
||||
tile_access_iterator_.valid()
|
||||
);
|
||||
|
||||
++tile_access_iterator_;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Loads a fragment from memory
|
||||
CUTLASS_DEVICE
|
||||
void load(Fragment &frag) {
|
||||
tile_access_iterator_.set_iteration_index(0);
|
||||
load_with_pointer_offset(frag, 0);
|
||||
}
|
||||
|
||||
CUTLASS_DEVICE
|
||||
void advance() {
|
||||
tile_access_iterator_.advance();
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(ConvProblemSize const &problem_size) {
|
||||
|
||||
// dispatch to iterator implementation
|
||||
return TileAccessIterator::can_implement(problem_size);
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
+254
@@ -0,0 +1,254 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM B (activation tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
#include "cutlass/conv/threadblock/conv2d_params.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv2dWgradActivationTileAccessIteratorAnalytic {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kAnalytic;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 2;
|
||||
using ConvProblemSize = typename conv::Conv2dProblemSize;
|
||||
|
||||
static_assert(sizeof_bits<Element>::value >= 8,
|
||||
"WGRAD requires elements of size 8b or greater.");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
using Params = Conv2dAnalyticParams<Layout>;
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
Conv2dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
// Filter postion (r,s,c) in contiguous dimension stays constant for each gemm_iteration_k
|
||||
int filter_r_[ThreadMap::Iterations::kContiguous];
|
||||
int filter_s_[ThreadMap::Iterations::kContiguous];
|
||||
int filter_c_[ThreadMap::Iterations::kContiguous];
|
||||
|
||||
int offset_npq_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dWgradActivationTileAccessIteratorAnalytic(
|
||||
Params const ¶ms,
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr))
|
||||
{
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
// initialize r,s,c filter position for every contiguous iteration
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for(int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
|
||||
|
||||
int rsc_offset = threadblock_offset.column() + thread_coord.contiguous()
|
||||
+ c * ThreadMap::Delta::kContiguous;
|
||||
|
||||
filter_r_[c] = rsc_offset / (problem_size_.S * problem_size_.C);
|
||||
int residual = rsc_offset % (problem_size_.S * problem_size_.C);
|
||||
|
||||
filter_s_[c] = residual / problem_size_.C;
|
||||
filter_c_[c] = residual % problem_size_.C;
|
||||
}
|
||||
|
||||
// initialize n, p, q offset for every strided iteration
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
|
||||
offset_npq_[s] = threadblock_offset.row() + thread_coord.strided()
|
||||
+ s * ThreadMap::Delta::kStrided;
|
||||
}
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
|
||||
// moves to the next GEMM-K offset (offset_npq_) in GEMM-B by a CTA-K tile
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
offset_npq_[s] += Shape::kRow * problem_size_.split_k_slices;
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the activation tensor x that is currently pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
|
||||
int r = filter_r_[iteration_contiguous_];
|
||||
int s = filter_s_[iteration_contiguous_];
|
||||
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
r = (problem_size_.R - 1 - r);
|
||||
s = (problem_size_.S - 1 - s);
|
||||
}
|
||||
|
||||
int n = offset_npq_[iteration_strided_] / (problem_size_.P * problem_size_.Q);
|
||||
int residual = offset_npq_[iteration_strided_] % (problem_size_.P * problem_size_.Q);
|
||||
|
||||
int p = residual / problem_size_.Q;
|
||||
int q = residual % problem_size_.Q;
|
||||
|
||||
int h = p * problem_size_.stride_h - problem_size_.pad_h + r * problem_size_.dilation_h;
|
||||
int w = q * problem_size_.stride_w - problem_size_.pad_w + s * problem_size_.dilation_w;
|
||||
|
||||
return TensorCoord(n, h, w, filter_c_[iteration_contiguous_]);
|
||||
}
|
||||
|
||||
/// Returns true if the current coordinate is within the activation tensor x
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
TensorCoord coord = at();
|
||||
|
||||
return coord.n() < problem_size_.N &&
|
||||
coord.h() >= 0 && coord.h() < problem_size_.H &&
|
||||
coord.w() >= 0 && coord.w() < problem_size_.W &&
|
||||
coord.c() < problem_size_.C;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dWgradActivationTileAccessIteratorAnalytic &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv2dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
+273
@@ -0,0 +1,273 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM B (activation tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv2dWgradActivationTileAccessIteratorOptimized {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kAnalytic;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 2;
|
||||
using ConvProblemSize = typename conv::Conv2dProblemSize;
|
||||
|
||||
static_assert(sizeof_bits<Element>::value >= 8,
|
||||
"WGRAD requires elements of size 8b or greater.");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
using Params = Conv2dWgradActivationIteratorOptimizedParams;
|
||||
|
||||
private:
|
||||
|
||||
Conv2dWgradActivationIteratorOptimizedParams const ¶ms_;
|
||||
Conv2dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
// Precomputed effective filter postion (r,s) in contiguous dimension stays constant for each gemm_iteration_k
|
||||
// required for npq -> nhw translation
|
||||
int precomputed_filter_r_[ThreadMap::Iterations::kContiguous];
|
||||
int precomputed_filter_s_[ThreadMap::Iterations::kContiguous];
|
||||
|
||||
// Channel dimension in contiguous dimension stays constant for each gemm_iteration_k
|
||||
int filter_c_[ThreadMap::Iterations::kContiguous];
|
||||
|
||||
int offset_npq_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dWgradActivationTileAccessIteratorOptimized(
|
||||
Conv2dWgradActivationIteratorOptimizedParams const ¶ms,
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr))
|
||||
{
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
// initialize r,s,c filter position for every contiguous iteration
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for(int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
|
||||
|
||||
int rsc_offset = threadblock_offset.column() + thread_coord.contiguous()
|
||||
+ c * ThreadMap::Delta::kContiguous;
|
||||
|
||||
// The subseqnet fast_divmod() operations are equivalent to the following logical computation:
|
||||
//
|
||||
//
|
||||
// filter_r_[c] = rsc_offset / (problem_size_.S * problem_size_.C);
|
||||
// int residual = rsc_offset % (problem_size_.S * problem_size_.C);
|
||||
//
|
||||
// filter_s_[c] = residual / problem_size_.C;
|
||||
// filter_c_[c] = residual % problem_size_.C;
|
||||
|
||||
int residual;
|
||||
params_.sc_divmod(precomputed_filter_r_[c], residual, rsc_offset);
|
||||
params_.c_divmod(precomputed_filter_s_[c], filter_c_[c], residual);
|
||||
|
||||
int r = precomputed_filter_r_[c];
|
||||
int s = precomputed_filter_s_[c];
|
||||
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
r = (problem_size_.R - 1 - r);
|
||||
s = (problem_size_.S - 1 - s);
|
||||
}
|
||||
|
||||
precomputed_filter_r_[c] = - problem_size_.pad_h + r * problem_size_.dilation_h;
|
||||
precomputed_filter_s_[c] = - problem_size_.pad_w + s * problem_size_.dilation_w;
|
||||
|
||||
}
|
||||
|
||||
// initialize n, p, q offset for every strided iteration
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
|
||||
offset_npq_[s] = threadblock_offset.row() + thread_coord.strided()
|
||||
+ s * ThreadMap::Delta::kStrided;
|
||||
}
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
|
||||
// moves to the next GEMM-K offset (offset_npq_) in GEMM-B by a CTA-K tile
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
offset_npq_[s] += Shape::kRow * problem_size_.split_k_slices;
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the activation tensor x that is currently pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
|
||||
// The subseqnet fast_divmod() operations are equivalent to the following logical computation:
|
||||
//
|
||||
//
|
||||
// int n = offset_npq_[iteration_strided_] / (problem_size_.P * problem_size_.Q);
|
||||
// int residual = offset_npq_[iteration_strided_] % (problem_size_.P * problem_size_.Q);
|
||||
//
|
||||
// int p = residual / problem_size_.Q;
|
||||
// int q = residual % problem_size_.Q;
|
||||
|
||||
int residual, n, p, q;
|
||||
|
||||
params_.pq_divmod(n, residual, offset_npq_[iteration_strided_]);
|
||||
params_.q_divmod(p, q, residual);
|
||||
|
||||
int h = p * problem_size_.stride_h + precomputed_filter_r_[iteration_contiguous_];
|
||||
int w = q * problem_size_.stride_w + precomputed_filter_s_[iteration_contiguous_];
|
||||
|
||||
return TensorCoord(n, h, w, filter_c_[iteration_contiguous_]);
|
||||
}
|
||||
|
||||
/// Returns true if the current coordinate is within the activation tensor x
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
TensorCoord coord = at();
|
||||
|
||||
return coord.n() < problem_size_.N &&
|
||||
coord.h() >= 0 && coord.h() < problem_size_.H &&
|
||||
coord.w() >= 0 && coord.w() < problem_size_.W &&
|
||||
coord.c() < problem_size_.C;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dWgradActivationTileAccessIteratorOptimized &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv2dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
+234
@@ -0,0 +1,234 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM A (output gradient tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv3d_problem_size.h"
|
||||
#include "cutlass/conv/threadblock/conv2d_params.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv2dWgradOutputGradientTileAccessIteratorAnalytic {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kAnalytic;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 2;
|
||||
using ConvProblemSize = typename conv::Conv2dProblemSize;
|
||||
|
||||
static_assert(sizeof_bits<Element>::value >= 8,
|
||||
"WGRAD requires elements of size 8b or greater.");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
using Params = Conv2dAnalyticParams<Layout>;
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
Conv2dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
int filter_k_[ThreadMap::Iterations::kContiguous];
|
||||
|
||||
int offset_npq_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dWgradOutputGradientTileAccessIteratorAnalytic(
|
||||
Params const ¶ms,
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
// initialize filter_k for every contiguous iteration
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
|
||||
filter_k_[c] = threadblock_offset.row() + thread_coord.contiguous()
|
||||
+ c * ThreadMap::Delta::kContiguous;
|
||||
}
|
||||
|
||||
// initialize n, p, q offset for every strided iteration
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
offset_npq_[s] = threadblock_offset.column() + thread_coord.strided()
|
||||
+ s * ThreadMap::Delta::kStrided;
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
// moves to the next GEMM-K offset (offset_npq_) in GEMM-A by a CTA-K tile
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
offset_npq_[s] += Shape::kColumn * problem_size_.split_k_slices;
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the output gradient tensor Dy that is currently pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
|
||||
int npq = offset_npq_[iteration_strided_];
|
||||
|
||||
int n = npq / (problem_size_.P * problem_size_.Q);
|
||||
int residual = npq % (problem_size_.P * problem_size_.Q);
|
||||
|
||||
int p = residual / problem_size_.Q;
|
||||
int q = residual % problem_size_.Q;
|
||||
|
||||
return TensorCoord(n, p, q, filter_k_[iteration_contiguous_]);
|
||||
}
|
||||
|
||||
|
||||
/// Returns true if the current coordinate is within the output gradient tensor Dy
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
TensorCoord coord = at();
|
||||
|
||||
return coord.n() < problem_size_.N &&
|
||||
coord.h() < problem_size_.P &&
|
||||
coord.w() < problem_size_.Q &&
|
||||
coord.c() < problem_size_.K;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dWgradOutputGradientTileAccessIteratorAnalytic &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv2dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
+300
@@ -0,0 +1,300 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM A (output gradient tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv2d_problem_size.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv2dWgradOutputGradientTileAccessIteratorOptimized {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kOptimized;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 2;
|
||||
using ConvProblemSize = typename conv::Conv2dProblemSize;
|
||||
|
||||
static_assert(sizeof_bits<Element>::value >= 8,
|
||||
"WGRAD requires elements of size 8b or greater.");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
struct Params : Conv2dWgradOutputGradientIteratorOptimizedParams {
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(Conv2dWgradOutputGradientIteratorOptimizedParams const &base):
|
||||
Conv2dWgradOutputGradientIteratorOptimizedParams(base) { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
):
|
||||
Conv2dWgradOutputGradientIteratorOptimizedParams(
|
||||
problem_size,
|
||||
layout,
|
||||
sizeof_bits<Element>::value,
|
||||
{Shape::kRow, Shape::kColumn},
|
||||
ThreadMap::kThreads,
|
||||
ThreadMap::kElementsPerAccess,
|
||||
{ThreadMap::Iterations::kContiguous, ThreadMap::Iterations::kStrided},
|
||||
{ThreadMap::Delta::kContiguous, ThreadMap::Delta::kStrided}
|
||||
) { }
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
Conv2dWgradOutputGradientIteratorOptimizedParams const ¶ms_;
|
||||
Conv2dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
uint32_t predicates_;
|
||||
int filter_k_;
|
||||
int offset_npq_;
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dWgradOutputGradientTileAccessIteratorOptimized(
|
||||
Conv2dWgradOutputGradientIteratorOptimizedParams const ¶ms,
|
||||
Conv2dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)),
|
||||
predicates_(0),
|
||||
filter_k_(0),
|
||||
offset_npq_(0) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
filter_k_ = threadblock_offset.row() + thread_coord.contiguous();
|
||||
offset_npq_ = threadblock_offset.column() + thread_coord.strided();
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
|
||||
|
||||
int filter_k = filter_k_ + c * ThreadMap::Delta::kContiguous;
|
||||
int offset_npq = offset_npq_ + s * ThreadMap::Delta::kStrided;
|
||||
|
||||
bool predicate = valid_(at_(offset_npq, filter_k));
|
||||
|
||||
uint32_t pred = (predicate ? 1u : 0);
|
||||
|
||||
int pred_idx = c + s * ThreadMap::Iterations::kContiguous;
|
||||
|
||||
predicates_ |= (pred << pred_idx);
|
||||
}
|
||||
}
|
||||
|
||||
// Offset pointer to (iteration_strided_, iteration_contiguous_) = (0, 0)
|
||||
pointer_ += (
|
||||
offset_npq_ * params.layout.stride()[0] + filter_k_
|
||||
) * sizeof_bits<Element>::value / 8;
|
||||
|
||||
set_iteration_index(0);
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
// moves to the next GEMM-K offset (offset_npq_) in GEMM-A by a CTA-K tile
|
||||
offset_npq_ += Shape::kColumn * problem_size_.split_k_slices;
|
||||
|
||||
// Clear predicates if needed
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
if (offset_npq_ + s * ThreadMap::Delta::kStrided >= params_.NPQ) {
|
||||
uint32_t kClearMask = ((1u << ThreadMap::Iterations::kContiguous) - 1) << (s * ThreadMap::Iterations::kContiguous);
|
||||
predicates_ = (predicates_ & (~kClearMask));
|
||||
}
|
||||
}
|
||||
|
||||
pointer_ += params_.inc_next_npq;
|
||||
}
|
||||
|
||||
private:
|
||||
/// Returns the coordinate in the output gradient tensor Dy that is pointed to
|
||||
/// by offset_npq and k.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at_(int offset_npq, int k) const {
|
||||
|
||||
// The subseqnet fast_divmod() operations are equivalent to the following logical computation:
|
||||
//
|
||||
//
|
||||
// int npq = offset_npq;
|
||||
// int n = npq / (problem_size_.P * problem_size_.Q);
|
||||
// int residual = npq % (problem_size_.P * problem_size_.Q);
|
||||
//
|
||||
// int p = residual / problem_size_.Q;
|
||||
// int q = residual % problem_size_.Q;
|
||||
|
||||
int residual, n, p, q;
|
||||
|
||||
params_.pq_divmod(n, residual, offset_npq);
|
||||
params_.q_divmod(p, q, residual);
|
||||
|
||||
return TensorCoord(n, p, q, k);
|
||||
}
|
||||
|
||||
/// Returns true if the coord is within the output gradient tensor Dy
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid_(TensorCoord coord) const {
|
||||
|
||||
return coord.n() < problem_size_.N &&
|
||||
coord.c() < problem_size_.K;
|
||||
}
|
||||
|
||||
public:
|
||||
|
||||
/// Returns true if the current coordinate is within the output gradient tensor Dy
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
|
||||
LongIndex pred_idx = iteration_contiguous_ + iteration_strided_ * ThreadMap::Iterations::kContiguous;
|
||||
return (predicates_ & (1u << pred_idx));
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
return reinterpret_cast<AccessType const *>(
|
||||
pointer_ +
|
||||
iteration_strided_ * params_.offset_next_strided +
|
||||
iteration_contiguous_ * params_.offset_next_contiguous
|
||||
);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv2dWgradOutputGradientTileAccessIteratorOptimized &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv2dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
@@ -0,0 +1,263 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM B (filter tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNDHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv3d_problem_size.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv3dDgradFilterTileAccessIteratorAnalytic {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNDHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kAnalytic;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 3;
|
||||
using ConvProblemSize = typename conv::Conv3dProblemSize;
|
||||
|
||||
static_assert(sizeof_bits<Element>::value >= 8,
|
||||
"DGRAD requires elements of size 8b or larger.");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
struct Params {
|
||||
|
||||
Layout layout;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
Conv3dProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
): layout(layout) {
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
Conv3dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
// For a fixed filter position (t,r,s) find and fill offset_k_, offset_c_ in strided and contiguous dimension
|
||||
int filter_t_;
|
||||
int filter_r_;
|
||||
int filter_s_;
|
||||
int offset_k_[ThreadMap::Iterations::kStrided];
|
||||
int offset_c_[ThreadMap::Iterations::kContiguous];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dDgradFilterTileAccessIteratorAnalytic(
|
||||
Params const ¶ms,
|
||||
Conv3dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)),
|
||||
filter_t_(0),
|
||||
filter_r_(0),
|
||||
filter_s_(0) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
|
||||
offset_c_[c] = threadblock_offset.column() + thread_coord.contiguous()
|
||||
+ c * ThreadMap::Delta::kContiguous;
|
||||
}
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
offset_k_[s] =
|
||||
threadblock_offset.row() + thread_coord.strided() + s * ThreadMap::Delta::kStrided;
|
||||
}
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
// moves to the next tile
|
||||
++filter_s_;
|
||||
if (filter_s_ < problem_size_.S) {
|
||||
return;
|
||||
}
|
||||
filter_s_ = 0;
|
||||
++filter_r_;
|
||||
if (filter_r_ < problem_size_.R) {
|
||||
return;
|
||||
}
|
||||
filter_r_ = 0;
|
||||
++filter_t_;
|
||||
if (filter_t_ < problem_size_.T) {
|
||||
return;
|
||||
}
|
||||
filter_t_ = 0;
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
offset_k_[s] += Shape::kRow * problem_size_.split_k_slices;
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the filter tensor w that is currently pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
|
||||
int c = offset_c_[iteration_contiguous_];
|
||||
int k = offset_k_[iteration_strided_];
|
||||
|
||||
return TensorCoord(k, filter_t_, filter_r_, filter_s_, c);
|
||||
}
|
||||
|
||||
/// Returns true if the current coordinate is within the filter tensor w
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
|
||||
return coord.n() < problem_size_.K && coord.c() < problem_size_.C;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dDgradFilterTileAccessIteratorAnalytic &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv3dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
+331
@@ -0,0 +1,331 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM A (output gradient tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNDHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv3d_problem_size.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_,
|
||||
conv::StrideSupport StrideSupport_ = conv::StrideSupport::kStrided
|
||||
>
|
||||
class Conv3dDgradOutputGradientTileAccessIteratorAnalytic;
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
// Conv3dDgradOutputGradientTileAccessIteratorAnalytic strided dgrad needs special handling using
|
||||
// unscaled coordinations
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv3dDgradOutputGradientTileAccessIteratorAnalytic <
|
||||
Shape_,
|
||||
Element_,
|
||||
ThreadMap_,
|
||||
conv::StrideSupport::kStrided
|
||||
> {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNDHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kAnalytic;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 3;
|
||||
using ConvProblemSize = typename conv::Conv3dProblemSize;
|
||||
|
||||
static_assert(sizeof_bits<Element>::value >= 8,
|
||||
"DGRAD requires elements of size 8b or greater.");
|
||||
|
||||
//
|
||||
// Simpligying assertions
|
||||
//
|
||||
|
||||
static_assert(ThreadMap::Iterations::kContiguous == 1,
|
||||
"Require Iterations::kContiguous == 1");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
struct Params {
|
||||
|
||||
Layout layout;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
ConvProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
): layout(layout) {
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
ConvProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
int filter_k_;
|
||||
int filter_t_;
|
||||
int filter_r_;
|
||||
int filter_s_;
|
||||
|
||||
int offset_n_[ThreadMap::Iterations::kStrided];
|
||||
int offset_d_[ThreadMap::Iterations::kStrided];
|
||||
int offset_w_[ThreadMap::Iterations::kStrided];
|
||||
int offset_h_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
private:
|
||||
|
||||
/// Returns the coordinate in the output tensor Dy that is currently pointed to
|
||||
/// by the iterator but DOES NOT scale by the convolution stride. This is needed
|
||||
/// to compute predicates in the valid() method. The return value of the public at()
|
||||
/// method is correctly scaled.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord unscaled_at_() const {
|
||||
int n = offset_n_[iteration_strided_];
|
||||
int d = offset_d_[iteration_strided_];
|
||||
int h = offset_h_[iteration_strided_];
|
||||
int w = offset_w_[iteration_strided_];
|
||||
|
||||
int t = filter_t_;
|
||||
int r = filter_r_;
|
||||
int s = filter_s_;
|
||||
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
t = (problem_size_.T - 1 - t);
|
||||
r = (problem_size_.R - 1 - r);
|
||||
s = (problem_size_.S - 1 - s);
|
||||
}
|
||||
|
||||
int z = (d + problem_size_.pad_d - t * problem_size_.dilation_d);
|
||||
int p = (h + problem_size_.pad_h - r * problem_size_.dilation_h);
|
||||
int q = (w + problem_size_.pad_w - s * problem_size_.dilation_w);
|
||||
|
||||
return TensorCoord(n, z, p, q, filter_k_);
|
||||
}
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dDgradOutputGradientTileAccessIteratorAnalytic(
|
||||
Params const ¶ms,
|
||||
ConvProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord() // threadblock offset - units are whole CTA tiles
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)),
|
||||
filter_k_(0),
|
||||
filter_t_(0),
|
||||
filter_r_(0),
|
||||
filter_s_(0) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
filter_k_ = threadblock_offset.column() + thread_coord.contiguous();
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
int offset_ndhw = threadblock_offset.row() + thread_coord.strided() + s * ThreadMap::Delta::kStrided;
|
||||
|
||||
offset_n_[s] = offset_ndhw / (problem_size_.D * problem_size_.H * problem_size_.W);
|
||||
int residual = offset_ndhw % (problem_size_.D * problem_size_.H * problem_size_.W);
|
||||
|
||||
offset_d_[s] = residual / (problem_size_.H * problem_size_.W);
|
||||
residual = residual % (problem_size_.H * problem_size_.W);
|
||||
|
||||
offset_h_[s] = residual / problem_size_.W;
|
||||
offset_w_[s] = residual % problem_size_.W;
|
||||
}
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
// move to the next tile
|
||||
++filter_s_;
|
||||
if (filter_s_ < problem_size_.S) {
|
||||
return;
|
||||
}
|
||||
filter_s_ = 0;
|
||||
++filter_r_;
|
||||
if (filter_r_ < problem_size_.R) {
|
||||
return;
|
||||
}
|
||||
filter_r_ = 0;
|
||||
++filter_t_;
|
||||
if (filter_t_ < problem_size_.T) {
|
||||
return;
|
||||
}
|
||||
filter_t_ = 0;
|
||||
|
||||
filter_k_ += Shape_::kColumn * problem_size_.split_k_slices;
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the output tensor Dy that is currently pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
|
||||
TensorCoord coord = unscaled_at_();
|
||||
|
||||
return TensorCoord(
|
||||
coord.n(),
|
||||
coord.d() / problem_size_.stride_d,
|
||||
coord.h() / problem_size_.stride_h,
|
||||
coord.w() / problem_size_.stride_w,
|
||||
coord.c());
|
||||
}
|
||||
|
||||
|
||||
/// Returns true if the current coordinate is within the output tensor Dy
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
|
||||
TensorCoord unscaled_coord = unscaled_at_();
|
||||
TensorCoord coord = at();
|
||||
|
||||
return
|
||||
!(unscaled_coord.d() % problem_size_.stride_d) &&
|
||||
!(unscaled_coord.h() % problem_size_.stride_h) &&
|
||||
!(unscaled_coord.w() % problem_size_.stride_w) &&
|
||||
coord.n() < problem_size_.N &&
|
||||
coord.d() >= 0 && coord.d() < problem_size_.Z &&
|
||||
coord.h() >= 0 && coord.h() < problem_size_.P &&
|
||||
coord.w() >= 0 && coord.w() < problem_size_.Q &&
|
||||
coord.c() < problem_size_.K;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dDgradOutputGradientTileAccessIteratorAnalytic &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(ConvProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
+296
@@ -0,0 +1,296 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM A (activation tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNDHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/matrix_shape.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv3d_problem_size.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv3dFpropActivationTileAccessIteratorAnalytic {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNDHWC;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kAnalytic;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 3;
|
||||
using ConvProblemSize = typename conv::Conv3dProblemSize;
|
||||
|
||||
//
|
||||
// Simplifying assertions
|
||||
//
|
||||
static_assert(ThreadMap::Iterations::kContiguous == 1,
|
||||
"Require Iterations::kContiguous == 1");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
struct Params {
|
||||
|
||||
Layout layout;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
ConvProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
): layout(layout) {
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
ConvProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
int filter_t_;
|
||||
int filter_r_;
|
||||
int filter_s_;
|
||||
int filter_c_;
|
||||
|
||||
int offset_n_[ThreadMap::Iterations::kStrided];
|
||||
int offset_z_[ThreadMap::Iterations::kStrided];
|
||||
int offset_p_[ThreadMap::Iterations::kStrided];
|
||||
int offset_q_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dFpropActivationTileAccessIteratorAnalytic(
|
||||
Params const ¶ms,
|
||||
ConvProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord() // tile index - units are threadblock-scoped tiles
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)),
|
||||
filter_t_(0),
|
||||
filter_r_(0),
|
||||
filter_s_(0),
|
||||
filter_c_(0) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
filter_c_ = threadblock_offset.column() + thread_coord.contiguous();
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
int offset_nzpq = threadblock_offset.row() + thread_coord.strided() + s * ThreadMap::Delta::kStrided;
|
||||
|
||||
offset_n_[s] = offset_nzpq / (problem_size_.Z * problem_size_.P * problem_size_.Q);
|
||||
int residual = offset_nzpq % (problem_size_.Z * problem_size_.P * problem_size_.Q);
|
||||
|
||||
offset_z_[s] = residual / (problem_size_.P * problem_size_.Q);
|
||||
residual = residual % (problem_size_.P * problem_size_.Q);
|
||||
|
||||
offset_p_[s] = residual / problem_size_.Q;
|
||||
offset_q_[s] = residual % problem_size_.Q;
|
||||
}
|
||||
|
||||
set_iteration_index(0);
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
// moves to the next tile
|
||||
++filter_s_;
|
||||
if (filter_s_ < problem_size_.S) {
|
||||
return;
|
||||
}
|
||||
filter_s_ = 0;
|
||||
++filter_r_;
|
||||
if (filter_r_ < problem_size_.R) {
|
||||
return;
|
||||
}
|
||||
filter_r_ = 0;
|
||||
++filter_t_;
|
||||
if (filter_t_ < problem_size_.T) {
|
||||
return;
|
||||
}
|
||||
filter_t_ = 0;
|
||||
|
||||
filter_c_ += Shape::kColumn * problem_size_.split_k_slices;
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the activations tensor X that is currently pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
int n = offset_n_[iteration_strided_];
|
||||
int z = offset_z_[iteration_strided_];
|
||||
int p = offset_p_[iteration_strided_];
|
||||
int q = offset_q_[iteration_strided_];
|
||||
|
||||
int t = filter_t_;
|
||||
int r = filter_r_;
|
||||
int s = filter_s_;
|
||||
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
t = (problem_size_.T - 1 - filter_t_);
|
||||
r = (problem_size_.R - 1 - filter_r_);
|
||||
s = (problem_size_.S - 1 - filter_s_);
|
||||
}
|
||||
|
||||
int d = z * problem_size_.stride_d - problem_size_.pad_d + t * problem_size_.dilation_d;
|
||||
int h = p * problem_size_.stride_h - problem_size_.pad_h + r * problem_size_.dilation_h;
|
||||
int w = q * problem_size_.stride_w - problem_size_.pad_w + s * problem_size_.dilation_w;
|
||||
|
||||
return TensorCoord(n, d, h, w, filter_c_);
|
||||
}
|
||||
|
||||
/// Returns true if the current coordinate is within the activations tensor X
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
|
||||
return coord.n() < problem_size_.N &&
|
||||
coord.d() >= 0 && coord.d() < problem_size_.D &&
|
||||
coord.h() >= 0 && coord.h() < problem_size_.H &&
|
||||
coord.w() >= 0 && coord.w() < problem_size_.W &&
|
||||
coord.c() < problem_size_.C;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
AccessType const *ptr = reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
|
||||
return ptr;
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dFpropActivationTileAccessIteratorAnalytic &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(ConvProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
@@ -0,0 +1,262 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM B (filter tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNDHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv3d_problem_size.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv3dFpropFilterTileAccessIteratorAnalytic {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNDHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kAnalytic;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 3;
|
||||
using ConvProblemSize = typename conv::Conv3dProblemSize;
|
||||
|
||||
//
|
||||
// Simplifying assertions
|
||||
//
|
||||
static_assert(ThreadMap::Iterations::kContiguous == 1,
|
||||
"Require Iterations::kContiguous == 1");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
struct Params {
|
||||
|
||||
Layout layout;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
ConvProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
): layout(layout) {
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
ConvProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
int filter_t_;
|
||||
int filter_r_;
|
||||
int filter_s_;
|
||||
int filter_c_;
|
||||
|
||||
int offset_k_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dFpropFilterTileAccessIteratorAnalytic(
|
||||
Params const ¶ms,
|
||||
ConvProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)),
|
||||
filter_t_(0),
|
||||
filter_r_(0),
|
||||
filter_s_(0),
|
||||
filter_c_(0) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
filter_c_ = threadblock_offset.row() + thread_coord.contiguous();
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
offset_k_[s] = threadblock_offset.column() + thread_coord.strided() + s * ThreadMap::Delta::kStrided;
|
||||
}
|
||||
|
||||
set_iteration_index(0);
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * 8 / sizeof_bits<Element>::value;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
// moves to the next tile
|
||||
++filter_s_;
|
||||
if (filter_s_ < problem_size_.S) {
|
||||
return;
|
||||
}
|
||||
filter_s_ = 0;
|
||||
|
||||
++filter_r_;
|
||||
if (filter_r_ < problem_size_.R) {
|
||||
return;
|
||||
}
|
||||
filter_r_ = 0;
|
||||
|
||||
++filter_t_;
|
||||
if (filter_t_ < problem_size_.T) {
|
||||
return;
|
||||
}
|
||||
filter_t_ = 0;
|
||||
|
||||
filter_c_ += Shape::kRow * problem_size_.split_k_slices;
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the filter tensor W that is currently pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
|
||||
int k = offset_k_[iteration_strided_];
|
||||
|
||||
return TensorCoord(k, filter_t_, filter_r_, filter_s_, filter_c_);
|
||||
}
|
||||
|
||||
/// Returns true if the current coordinate is within the activations tensor W
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
|
||||
return coord.n() < problem_size_.K &&
|
||||
coord.c() < problem_size_.C;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dFpropFilterTileAccessIteratorAnalytic &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(ConvProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
return Status::kSuccess;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
+281
@@ -0,0 +1,281 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM B (activation tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNDHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv3d_problem_size.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv3dWgradActivationTileAccessIteratorAnalytic {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNDHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kAnalytic;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 3;
|
||||
using ConvProblemSize = typename conv::Conv3dProblemSize;
|
||||
|
||||
static_assert(sizeof_bits<Element>::value >= 8,
|
||||
"WGRAD requires elements of size 8b or greater.");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
struct Params {
|
||||
|
||||
Layout layout;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
Conv3dProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
): layout(layout) {
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
Conv3dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
// Filter postion (t,r,s,c) in contiguous dimension stays constant for each gemm_iteration_k
|
||||
int filter_t_[ThreadMap::Iterations::kContiguous];
|
||||
int filter_r_[ThreadMap::Iterations::kContiguous];
|
||||
int filter_s_[ThreadMap::Iterations::kContiguous];
|
||||
int filter_c_[ThreadMap::Iterations::kContiguous];
|
||||
|
||||
int offset_nzpq_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dWgradActivationTileAccessIteratorAnalytic(
|
||||
Params const ¶ms,
|
||||
Conv3dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
// initialize t,r,s,c filter position for every contiguous iteration
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for(int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
|
||||
|
||||
int trsc_offset = threadblock_offset.column() + thread_coord.contiguous()
|
||||
+ c * ThreadMap::Delta::kContiguous;
|
||||
|
||||
filter_t_[c] = trsc_offset / (problem_size_.R * problem_size_.S * problem_size_.C);
|
||||
int residual = trsc_offset % (problem_size_.R * problem_size_.S * problem_size_.C);
|
||||
|
||||
filter_r_[c] = residual / (problem_size_.S * problem_size_.C);
|
||||
residual = residual % (problem_size_.S * problem_size_.C);
|
||||
|
||||
filter_s_[c] = residual / problem_size_.C;
|
||||
filter_c_[c] = residual % problem_size_.C;
|
||||
|
||||
}
|
||||
|
||||
// initialize n, z, p, q offset for every strided iteration
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
|
||||
offset_nzpq_[s] = threadblock_offset.row() + thread_coord.strided()
|
||||
+ s * ThreadMap::Delta::kStrided;
|
||||
}
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
|
||||
// moves to the next GEMM-K offset (offset_nzpq_) in GEMM-B by a CTA-K tile
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
offset_nzpq_[s] += Shape::kRow * problem_size_.split_k_slices;
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the activation tensor x that is currently pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
|
||||
int t = filter_t_[iteration_contiguous_];
|
||||
int r = filter_r_[iteration_contiguous_];
|
||||
int s = filter_s_[iteration_contiguous_];
|
||||
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
t = (problem_size_.T - 1 - t);
|
||||
r = (problem_size_.R - 1 - r);
|
||||
s = (problem_size_.S - 1 - s);
|
||||
}
|
||||
|
||||
int n = offset_nzpq_[iteration_strided_] / (problem_size_.Z * problem_size_.P * problem_size_.Q);
|
||||
int residual = offset_nzpq_[iteration_strided_] % (problem_size_.Z * problem_size_.P * problem_size_.Q);
|
||||
|
||||
int z = residual / (problem_size_.P * problem_size_.Q);
|
||||
residual = residual % (problem_size_.P * problem_size_.Q);
|
||||
|
||||
int p = residual / problem_size_.Q;
|
||||
int q = residual % problem_size_.Q;
|
||||
|
||||
int d = z * problem_size_.stride_d - problem_size_.pad_d + t * problem_size_.dilation_d;
|
||||
int h = p * problem_size_.stride_h - problem_size_.pad_h + r * problem_size_.dilation_h;
|
||||
int w = q * problem_size_.stride_w - problem_size_.pad_w + s * problem_size_.dilation_w;
|
||||
|
||||
return TensorCoord(n, d, h, w, filter_c_[iteration_contiguous_]);
|
||||
}
|
||||
|
||||
/// Returns true if the current coordinate is within the activation tensor x
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
TensorCoord coord = at();
|
||||
|
||||
return coord.n() < problem_size_.N &&
|
||||
coord.d() >= 0 && coord.d() < problem_size_.D &&
|
||||
coord.h() >= 0 && coord.h() < problem_size_.H &&
|
||||
coord.w() >= 0 && coord.w() < problem_size_.W &&
|
||||
coord.c() < problem_size_.C;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dWgradActivationTileAccessIteratorAnalytic &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv3dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
+346
@@ -0,0 +1,346 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM B (activation tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNDHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv3d_problem_size.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv3dWgradActivationTileAccessIteratorOptimized {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNDHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kOptimized;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 3;
|
||||
using ConvProblemSize = typename conv::Conv3dProblemSize;
|
||||
|
||||
static_assert(sizeof_bits<Element>::value >= 8,
|
||||
"WGRAD requires elements of size 8b or greater.");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
struct Params {
|
||||
|
||||
Layout layout;
|
||||
|
||||
int RSC; // product of R*S*C
|
||||
unsigned rsc_mul; // precomputed quantities for fast computation of div/% by RSC
|
||||
unsigned rsc_shr; // in device code.
|
||||
|
||||
int SC; // product of S*C
|
||||
unsigned sc_mul; // precomputed quantities for fast computation of div/% by SC
|
||||
unsigned sc_shr; // in device code.
|
||||
|
||||
unsigned c_mul; // precomputed quantities for fast computation of div/% by C
|
||||
unsigned c_shr; // in device code.
|
||||
|
||||
int ZPQ; // product of Z*P*Q
|
||||
unsigned zpq_mul; // precomputed quantities for fast computation of div/% by ZPQ
|
||||
unsigned zpq_shr; // in device code.
|
||||
|
||||
int PQ; // product of P*Q
|
||||
unsigned pq_mul; // precomputed quantities for fast computation of div/% by PQ
|
||||
unsigned pq_shr; // in device code.
|
||||
|
||||
unsigned q_mul; // precomputed quantities for fast computation of div/% by Q
|
||||
unsigned q_shr; // in device code.
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
Conv3dProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
): layout(layout) {
|
||||
|
||||
// Precompute several quantities for fast modulo arithmetic.
|
||||
RSC = problem_size.R * problem_size.S * problem_size.C;
|
||||
find_divisor(rsc_mul, rsc_shr, RSC);
|
||||
|
||||
SC = problem_size.S * problem_size.C;
|
||||
find_divisor(sc_mul, sc_shr, SC);
|
||||
|
||||
find_divisor(c_mul, c_shr, problem_size.C);
|
||||
|
||||
ZPQ = problem_size.Z * problem_size.P * problem_size.Q;
|
||||
find_divisor(zpq_mul, zpq_shr, ZPQ);
|
||||
|
||||
PQ = problem_size.P * problem_size.Q;
|
||||
find_divisor(pq_mul, pq_shr, PQ);
|
||||
|
||||
find_divisor(q_mul, q_shr, problem_size.Q);
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
Conv3dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
// Precomputed effective filter postion (t,r,s) in contiguous dimension stays constant for each gemm_iteration_k
|
||||
// required for nzpq -> ndhw translation
|
||||
int precomputed_filter_t_[ThreadMap::Iterations::kContiguous];
|
||||
int precomputed_filter_r_[ThreadMap::Iterations::kContiguous];
|
||||
int precomputed_filter_s_[ThreadMap::Iterations::kContiguous];
|
||||
|
||||
// Channel dimension in contiguous dimension stays constant for each gemm_iteration_k
|
||||
int filter_c_[ThreadMap::Iterations::kContiguous];
|
||||
|
||||
int offset_nzpq_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dWgradActivationTileAccessIteratorOptimized(
|
||||
Params const ¶ms,
|
||||
Conv3dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)) {
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
// initialize t,r,s,c filter position for every contiguous iteration
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for(int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
|
||||
|
||||
int trsc_offset = threadblock_offset.column() + thread_coord.contiguous()
|
||||
+ c * ThreadMap::Delta::kContiguous;
|
||||
|
||||
// The subseqnet fast_divmod() operations are equivalent to the following logical computation:
|
||||
//
|
||||
//
|
||||
// filter_t_[c] = trsc_offset / (problem_size_.R * problem_size_.S * problem_size_.C);
|
||||
// int residual = trsc_offset % (problem_size_.R * problem_size_.S * problem_size_.C);
|
||||
//
|
||||
// filter_r_[c] = residual / (problem_size_.S * problem_size_.C);
|
||||
// residual = residual % (problem_size_.S * problem_size_.C);
|
||||
//
|
||||
// filter_s_[c] = residual / problem_size_.C;
|
||||
// filter_c_[c] = residual % problem_size_.C;
|
||||
|
||||
int residual;
|
||||
fast_divmod(precomputed_filter_t_[c], residual, trsc_offset, params_.RSC, params_.rsc_mul, params_.rsc_shr);
|
||||
fast_divmod(precomputed_filter_r_[c], residual, residual, params_.SC, params_.sc_mul, params_.sc_shr);
|
||||
fast_divmod(precomputed_filter_s_[c], filter_c_[c], residual, problem_size_.C, params_.c_mul, params_.c_shr);
|
||||
|
||||
int t = precomputed_filter_t_[c];
|
||||
int r = precomputed_filter_r_[c];
|
||||
int s = precomputed_filter_s_[c];
|
||||
|
||||
if (problem_size_.mode == Mode::kConvolution) {
|
||||
t = (problem_size_.T - 1 - t);
|
||||
r = (problem_size_.R - 1 - r);
|
||||
s = (problem_size_.S - 1 - s);
|
||||
}
|
||||
|
||||
// efective t,r,s for every contiguous dimension
|
||||
precomputed_filter_t_[c] = - problem_size_.pad_d + t * problem_size_.dilation_d;
|
||||
precomputed_filter_r_[c] = - problem_size_.pad_h + r * problem_size_.dilation_h;
|
||||
precomputed_filter_s_[c] = - problem_size_.pad_w + s * problem_size_.dilation_w;
|
||||
|
||||
|
||||
}
|
||||
|
||||
// initialize n, z, p, q offset for every strided iteration
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
|
||||
offset_nzpq_[s] = threadblock_offset.row() + thread_coord.strided()
|
||||
+ s * ThreadMap::Delta::kStrided;
|
||||
}
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
|
||||
// moves to the next GEMM-K offset (offset_nzpq_) in GEMM-B by a CTA-K tile
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
offset_nzpq_[s] += Shape::kRow * problem_size_.split_k_slices;
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the activation tensor x that is currently pointed to
|
||||
/// by the iterator.
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
|
||||
// The subseqnet fast_divmod() operations are equivalent to the following logical computation:
|
||||
//
|
||||
//
|
||||
// int n = offset_nzpq_[iteration_strided_] / (problem_size_.Z * problem_size_.P * problem_size_.Q);
|
||||
// int residual = offset_nzpq_[iteration_strided_] % (problem_size_.Z * problem_size_.P * problem_size_.Q);
|
||||
//
|
||||
// int z = residual / (problem_size_.P * problem_size_.Q);
|
||||
// residual = residual % (problem_size_.P * problem_size_.Q);
|
||||
//
|
||||
// int p = residual / problem_size_.Q;
|
||||
// int q = residual % problem_size_.Q;
|
||||
|
||||
int residual, n, z, p, q;
|
||||
fast_divmod(n, residual, offset_nzpq_[iteration_strided_], params_.ZPQ, params_.zpq_mul, params_.zpq_shr);
|
||||
fast_divmod(z, residual, residual, params_.PQ, params_.pq_mul, params_.pq_shr);
|
||||
fast_divmod(p, q, residual, problem_size_.Q, params_.q_mul, params_.q_shr);
|
||||
|
||||
int d = z * problem_size_.stride_d + precomputed_filter_t_[iteration_contiguous_];
|
||||
int h = p * problem_size_.stride_h + precomputed_filter_r_[iteration_contiguous_];;
|
||||
int w = q * problem_size_.stride_w + precomputed_filter_s_[iteration_contiguous_];
|
||||
|
||||
return TensorCoord(n, d, h, w, filter_c_[iteration_contiguous_]);
|
||||
}
|
||||
|
||||
/// Returns true if the current coordinate is within the activation tensor x
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
TensorCoord coord = at();
|
||||
|
||||
return coord.n() < problem_size_.N &&
|
||||
coord.d() >= 0 && coord.d() < problem_size_.D &&
|
||||
coord.h() >= 0 && coord.h() < problem_size_.H &&
|
||||
coord.w() >= 0 && coord.w() < problem_size_.W &&
|
||||
coord.c() < problem_size_.C;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dWgradActivationTileAccessIteratorOptimized &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv3dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
+256
@@ -0,0 +1,256 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM A (output gradient tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNDHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv3d_problem_size.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv3dWgradOutputGradientTileAccessIteratorAnalytic {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNDHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kAnalytic;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 3;
|
||||
using ConvProblemSize = typename conv::Conv3dProblemSize;
|
||||
|
||||
static_assert(sizeof_bits<Element>::value >= 8,
|
||||
"WGRAD requires elements of size 8b or greater.");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
struct Params {
|
||||
|
||||
Layout layout;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
Conv3dProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
): layout(layout) {
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
Conv3dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
int filter_k_[ThreadMap::Iterations::kContiguous];
|
||||
|
||||
int offset_nzpq_[ThreadMap::Iterations::kStrided];
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dWgradOutputGradientTileAccessIteratorAnalytic(
|
||||
Params const ¶ms,
|
||||
Conv3dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)) {
|
||||
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
// initialize filter_k for every contiguous iteration
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
|
||||
filter_k_[c] = threadblock_offset.row() + thread_coord.contiguous()
|
||||
+ c * ThreadMap::Delta::kContiguous;
|
||||
}
|
||||
|
||||
// initialize n, p, q offset for every strided iteration
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
offset_nzpq_[s] = threadblock_offset.column() + thread_coord.strided()
|
||||
+ s * ThreadMap::Delta::kStrided;
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
// moves to the next GEMM-K offset (offset_nzpq_) in GEMM-A by a CTA-K tile
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
offset_nzpq_[s] += Shape::kColumn * problem_size_.split_k_slices;
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the coordinate in the output gradient tensor Dy that is currently pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at() const {
|
||||
|
||||
int nzpq = offset_nzpq_[iteration_strided_];
|
||||
|
||||
int n = nzpq / (problem_size_.Z * problem_size_.P * problem_size_.Q);
|
||||
int residual = nzpq % (problem_size_.Z * problem_size_.P * problem_size_.Q);
|
||||
|
||||
int z = residual / (problem_size_.P * problem_size_.Q);
|
||||
residual = residual % (problem_size_.P * problem_size_.Q);
|
||||
|
||||
int p = residual / problem_size_.Q;
|
||||
int q = residual % problem_size_.Q;
|
||||
|
||||
return TensorCoord(n, z, p, q, filter_k_[iteration_contiguous_]);
|
||||
}
|
||||
|
||||
|
||||
/// Returns true if the current coordinate is within the output gradient tensor Dy
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
TensorCoord coord = at();
|
||||
|
||||
return coord.n() < problem_size_.N &&
|
||||
coord.d() < problem_size_.Z &&
|
||||
coord.h() < problem_size_.P &&
|
||||
coord.w() < problem_size_.Q &&
|
||||
coord.c() < problem_size_.K;
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
TensorCoord coord = at();
|
||||
LongIndex offset = params_.layout(coord);
|
||||
|
||||
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dWgradOutputGradientTileAccessIteratorAnalytic &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv3dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
+330
@@ -0,0 +1,330 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Templates implementing loading of convolution tiles mapped to GEMM A (output gradient tile)
|
||||
matrix from memory.
|
||||
|
||||
This iterator assumes TensorNDHWC layout of tensors in Global Memory.
|
||||
|
||||
The iterator is specialized for each of the three convolution operators: forward propagation (Fprop),
|
||||
backward data gradient (Dgrad), and backward weight gradient (Wgrad).
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/coord.h"
|
||||
#include "cutlass/predicate_vector.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/tensor_view.h"
|
||||
#include "cutlass/layout/pitch_linear.h"
|
||||
#include "cutlass/layout/tensor.h"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/conv/convolution.h"
|
||||
#include "cutlass/conv/conv3d_problem_size.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <
|
||||
typename Shape_,
|
||||
typename Element_,
|
||||
typename ThreadMap_
|
||||
>
|
||||
class Conv3dWgradOutputGradientTileAccessIteratorOptimized {
|
||||
public:
|
||||
|
||||
//
|
||||
// Types
|
||||
//
|
||||
using Shape = Shape_;
|
||||
using Element = Element_;
|
||||
using Layout = layout::TensorNDHWC;
|
||||
using ThreadMap = ThreadMap_;
|
||||
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
|
||||
using TensorRef = cutlass::TensorRef<Element, Layout>;
|
||||
using TensorCoord = typename Layout::TensorCoord;
|
||||
using Index = typename Layout::Index;
|
||||
using LongIndex = typename Layout::LongIndex;
|
||||
static IteratorAlgorithm const kIteratorAlgorithm = conv::IteratorAlgorithm::kOptimized;
|
||||
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
|
||||
static int const kConvDim = 3;
|
||||
using ConvProblemSize = typename conv::Conv3dProblemSize;
|
||||
|
||||
static_assert(sizeof_bits<Element>::value >= 8,
|
||||
"WGRAD requires elements of size 8b or greater.");
|
||||
|
||||
//
|
||||
// Parameters structure
|
||||
//
|
||||
|
||||
struct Params {
|
||||
|
||||
Layout layout;
|
||||
|
||||
int NZPQ; // precomputd product of N*Z*P*Q for clearing predicates
|
||||
int ZPQ; // product of Z*P*Q
|
||||
unsigned zpq_mul; // precomputed quantities for fast computation of div/% by ZPQ
|
||||
unsigned zpq_shr; // in device code.
|
||||
|
||||
int PQ; // product of P*Q
|
||||
unsigned pq_mul; // precomputed quantities for fast computation of div/% by PQ
|
||||
unsigned pq_shr; // in device code.
|
||||
|
||||
unsigned q_mul; // precomputed quantities for fast computation of div/% by Q
|
||||
unsigned q_shr; // in device code.
|
||||
|
||||
LongIndex offset_next_strided; // offset in units of bytes to next nzpq coordinate within tile
|
||||
LongIndex offset_next_contiguous; // offset in units of bytes to next k coordinate within tile
|
||||
LongIndex inc_next_nzpq; // offset in units of bytes to next nzpq position in subsequent tile
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params() { }
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Params(
|
||||
Conv3dProblemSize const &problem_size,
|
||||
Layout const &layout
|
||||
): layout(layout) {
|
||||
|
||||
// Incremental offsets in unites of bytes (number of elements) * sizeof_bits<Element>::value / 8
|
||||
offset_next_strided = (ThreadMap::Delta::kStrided * layout.stride()[0])
|
||||
* sizeof_bits<Element>::value / 8;
|
||||
|
||||
offset_next_contiguous = (ThreadMap::Delta::kContiguous)
|
||||
* sizeof_bits<Element>::value / 8;
|
||||
|
||||
inc_next_nzpq = (Shape::kColumn * problem_size.split_k_slices * layout.stride()[0])
|
||||
* sizeof_bits<Element>::value / 8;
|
||||
|
||||
// Precompute several quantities for fast modulo arithmetic.
|
||||
NZPQ = problem_size.N * problem_size.Z * problem_size.P * problem_size.Q;
|
||||
ZPQ = problem_size.Z * problem_size.P * problem_size.Q;
|
||||
find_divisor(zpq_mul, zpq_shr, ZPQ);
|
||||
|
||||
PQ = problem_size.P * problem_size.Q;
|
||||
find_divisor(pq_mul, pq_shr, PQ);
|
||||
|
||||
find_divisor(q_mul, q_shr, problem_size.Q);
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
Params const ¶ms_;
|
||||
Conv3dProblemSize const &problem_size_;
|
||||
LongIndex iteration_contiguous_;
|
||||
LongIndex iteration_strided_;
|
||||
char const *pointer_;
|
||||
|
||||
uint32_t predicates_;
|
||||
int filter_k_;
|
||||
int offset_nzpq_;
|
||||
|
||||
public:
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dWgradOutputGradientTileAccessIteratorOptimized(
|
||||
Params const ¶ms,
|
||||
Conv3dProblemSize const &problem_size,
|
||||
Element const *ptr,
|
||||
int thread_idx,
|
||||
MatrixCoord const &threadblock_offset = MatrixCoord()
|
||||
):
|
||||
params_(params),
|
||||
problem_size_(problem_size),
|
||||
pointer_(reinterpret_cast<char const *>(ptr)),
|
||||
predicates_(0),
|
||||
filter_k_(0),
|
||||
offset_nzpq_(0) {
|
||||
|
||||
|
||||
layout::PitchLinearCoord thread_coord = ThreadMap::initial_offset(thread_idx);
|
||||
|
||||
filter_k_ = threadblock_offset.row() + thread_coord.contiguous();
|
||||
offset_nzpq_ = threadblock_offset.column() + thread_coord.strided();
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
|
||||
|
||||
int filter_k = filter_k_ + c * ThreadMap::Delta::kContiguous;
|
||||
int offset_nzpq = offset_nzpq_ + s * ThreadMap::Delta::kStrided;
|
||||
|
||||
bool predicate = valid_(at_(offset_nzpq, filter_k));
|
||||
|
||||
uint32_t pred = (predicate ? 1u : 0);
|
||||
|
||||
int pred_idx = c + s * ThreadMap::Iterations::kContiguous;
|
||||
|
||||
predicates_ |= (pred << pred_idx);
|
||||
}
|
||||
}
|
||||
|
||||
// Offset pointer to (iteration_strided_, iteration_contiguous_) = (0, 0)
|
||||
pointer_ += (
|
||||
offset_nzpq_ * params.layout.stride()[0] + filter_k_
|
||||
) * sizeof_bits<Element>::value / 8;
|
||||
|
||||
set_iteration_index(0);
|
||||
}
|
||||
|
||||
/// Overrides the internal iteration index
|
||||
CUTLASS_HOST_DEVICE
|
||||
void set_iteration_index(Index index) {
|
||||
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
|
||||
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
|
||||
}
|
||||
|
||||
/// Adds a pointer offset in units of Element
|
||||
CUTLASS_HOST_DEVICE
|
||||
void add_pointer_offset(LongIndex pointer_offset) {
|
||||
pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
|
||||
}
|
||||
|
||||
CUTLASS_HOST_DEVICE
|
||||
void advance() {
|
||||
// moves to the next GEMM-K offset (offset_npq_) in GEMM-A by a CTA-K tile
|
||||
offset_nzpq_ += Shape::kColumn * problem_size_.split_k_slices;
|
||||
|
||||
// Clear predicates if needed
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
|
||||
if (offset_nzpq_ + s * ThreadMap::Delta::kStrided >= params_.NZPQ) {
|
||||
uint32_t kClearMask = ((1u << ThreadMap::Iterations::kContiguous) - 1) << (s * ThreadMap::Iterations::kContiguous);
|
||||
predicates_ = (predicates_ & (~kClearMask));
|
||||
}
|
||||
}
|
||||
pointer_ += params_.inc_next_nzpq;
|
||||
}
|
||||
|
||||
private:
|
||||
/// Returns the coordinate in the output gradient tensor Dy that is (offset_nzpq, k) pointed to
|
||||
/// by the iterator.
|
||||
CUTLASS_HOST_DEVICE
|
||||
TensorCoord at_(int offset_nzpq, int k) const {
|
||||
|
||||
// The subseqnet fast_divmod() operations are equivalent to the following logical computation:
|
||||
//
|
||||
//
|
||||
// int nzpq = offset_nzpq_;
|
||||
// int n = nzpq / (problem_size_.Z * problem_size_.P * problem_size_.Q);
|
||||
// int residual = nzpq % (problem_size_.Z * problem_size_.P * problem_size_.Q);
|
||||
//
|
||||
// int z = residual / (problem_size_.P * problem_size_.Q);
|
||||
// residual = residual % (problem_size_.P * problem_size_.Q);
|
||||
//
|
||||
// int p = residual / problem_size_.Q;
|
||||
// int q = residual % problem_size_.Q;
|
||||
|
||||
int residual, n, z, p, q;
|
||||
fast_divmod(n, residual, offset_nzpq, params_.ZPQ, params_.zpq_mul, params_.zpq_shr);
|
||||
fast_divmod(z, residual, residual, params_.PQ, params_.pq_mul, params_.pq_shr);
|
||||
fast_divmod(p, q, residual, problem_size_.Q, params_.q_mul, params_.q_shr);
|
||||
|
||||
return TensorCoord(n, z, p, q, k);
|
||||
}
|
||||
|
||||
/// Returns true if the coord is within the output gradient tensor Dy
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid_(TensorCoord coord) const {
|
||||
|
||||
return coord.n() < problem_size_.N &&
|
||||
coord.c() < problem_size_.K;
|
||||
}
|
||||
|
||||
public:
|
||||
|
||||
/// Returns true if the current coordinate is within the output gradient tensor Dy
|
||||
CUTLASS_HOST_DEVICE
|
||||
bool valid() const {
|
||||
|
||||
LongIndex pred_idx = iteration_contiguous_ + iteration_strided_ * ThreadMap::Iterations::kContiguous;
|
||||
return (predicates_ & (1u << pred_idx));
|
||||
}
|
||||
|
||||
/// Returns a pointer to the vector starting at the current coordinate
|
||||
CUTLASS_HOST_DEVICE
|
||||
AccessType const *get() const {
|
||||
|
||||
return reinterpret_cast<AccessType const *>(
|
||||
pointer_ +
|
||||
iteration_strided_ * params_.offset_next_strided +
|
||||
iteration_contiguous_ * params_.offset_next_contiguous
|
||||
);
|
||||
|
||||
}
|
||||
|
||||
/// Increments to the next memory access
|
||||
CUTLASS_HOST_DEVICE
|
||||
Conv3dWgradOutputGradientTileAccessIteratorOptimized &operator++() {
|
||||
++iteration_contiguous_;
|
||||
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
|
||||
return *this;
|
||||
}
|
||||
iteration_contiguous_ = 0;
|
||||
++iteration_strided_;
|
||||
if (iteration_strided_ < ThreadMap::Iterations::kStrided) {
|
||||
return *this;
|
||||
}
|
||||
iteration_strided_ = 0;
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
/// Determines whether the Implicit GEMM can execute the given problem.
|
||||
CUTLASS_HOST_DEVICE
|
||||
static Status can_implement(Conv3dProblemSize const &problem_size) {
|
||||
|
||||
// check alignment constraint on iterator's contiguous dimension
|
||||
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
|
||||
return Status::kErrorInvalidProblem;
|
||||
}
|
||||
|
||||
return Status::kSuccess;
|
||||
}
|
||||
|
||||
};
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace conv
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
@@ -0,0 +1,480 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Template for a multistage threadblock-scoped Implicit GEMM Convolution kernel.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/aligned_buffer.h"
|
||||
#include "cutlass/arch/memory.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/gemm/gemm.h"
|
||||
#include "cutlass/matrix_shape.h"
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/arch/cache_operation.h"
|
||||
#include "cutlass/gemm/threadblock/mma_base.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Structure to compute the matrix product targeting CUDA cores and SIMT math
|
||||
/// instructions.
|
||||
template <
|
||||
/// Size of the Gemm problem - concept: gemm::GemmShape<>
|
||||
typename Shape_,
|
||||
/// Iterates over tiles of A operand in global memory
|
||||
// (concept: ReadableTileIterator | ForwardTileIterator |
|
||||
// MaskedTileIterator)
|
||||
typename IteratorA_,
|
||||
/// Iterates over tiles of A operand in shared memory
|
||||
/// (concept: WriteableTileIterator | RandomAccessTileIterator)
|
||||
typename SmemIteratorA_,
|
||||
/// Cache operation for operand A
|
||||
cutlass::arch::CacheOperation::Kind CacheOpA,
|
||||
/// Iterates over tiles of B operand in global memory
|
||||
// (concept: ReadableTileIterator | ForwardTileIterator |
|
||||
// MaskedTileIterator)
|
||||
typename IteratorB_,
|
||||
/// Iterates over tiles of B operand in shared memory
|
||||
/// (concept: WriteableTileIterator | RandomAccessTileIterator)
|
||||
typename SmemIteratorB_,
|
||||
/// Cache operation for operand B
|
||||
cutlass::arch::CacheOperation::Kind CacheOpB,
|
||||
/// Policy describing tuning details (concept: MmaPolicy)
|
||||
typename Policy_,
|
||||
/// Number of stages,
|
||||
int Stages,
|
||||
/// Used for partial specialization
|
||||
typename Enable = bool>
|
||||
class ImplicitGemmMultistage :
|
||||
public gemm::threadblock::MmaBase<Shape_, Policy_, Stages> {
|
||||
public:
|
||||
///< Base class
|
||||
using Base = gemm::threadblock::MmaBase<Shape_, Policy_, Stages>;
|
||||
///< Size of the Gemm problem - concept: gemm::GemmShape<>
|
||||
using Shape = Shape_;
|
||||
///< Iterates over tiles of A operand in global memory
|
||||
using IteratorA = IteratorA_;
|
||||
///< Iterates over tiles of B operand in global memory
|
||||
using IteratorB = IteratorB_;
|
||||
///< Policy describing tuning details
|
||||
using Policy = Policy_;
|
||||
|
||||
using SmemIteratorA = SmemIteratorA_;
|
||||
using SmemIteratorB = SmemIteratorB_;
|
||||
|
||||
static cutlass::arch::CacheOperation::Kind const kCacheOpA = CacheOpA;
|
||||
static cutlass::arch::CacheOperation::Kind const kCacheOpB = CacheOpB;
|
||||
|
||||
//
|
||||
// Dependent types
|
||||
//
|
||||
|
||||
/// Fragment of accumulator tile
|
||||
|
||||
using ElementC = typename Policy::Operator::ElementC;
|
||||
using FragmentC = typename Policy::Operator::FragmentC;
|
||||
|
||||
/// Warp-level Mma
|
||||
using Operator = typename Policy::Operator;
|
||||
|
||||
/// Internal structure exposed for introspection.
|
||||
struct Detail {
|
||||
|
||||
static_assert(Base::kWarpGemmIterations > 1,
|
||||
"The pipelined structure requires at least two warp-level "
|
||||
"GEMM operations.");
|
||||
|
||||
/// Number of cp.async instructions to load one stage of operand A
|
||||
static int const AsyncCopyIterationsPerStageA =
|
||||
IteratorA::ThreadMap::Iterations::kCount;
|
||||
|
||||
/// Number of cp.async instructions to load one stage of operand B
|
||||
static int const AsyncCopyIterationsPerStageB =
|
||||
IteratorB::ThreadMap::Iterations::kCount;
|
||||
|
||||
/// Number of stages
|
||||
static int const kStages = Stages;
|
||||
|
||||
/// Number of cp.async instructions to load on group of operand A
|
||||
static int const kAccessesPerGroupA =
|
||||
(AsyncCopyIterationsPerStageA + Base::kWarpGemmIterations - 1) / Base::kWarpGemmIterations;
|
||||
|
||||
/// Number of cp.async instructions to load on group of operand B
|
||||
static int const kAccessesPerGroupB =
|
||||
(AsyncCopyIterationsPerStageB + Base::kWarpGemmIterations - 1) / Base::kWarpGemmIterations;
|
||||
};
|
||||
|
||||
private:
|
||||
|
||||
using WarpLoadedFragmentA = typename Operator::FragmentA;
|
||||
using WarpLoadedFragmentB = typename Operator::FragmentB;
|
||||
using WarpTransformedFragmentA = typename Operator::TransformedFragmentA;
|
||||
using WarpTransformedFragmentB = typename Operator::TransformedFragmentB;
|
||||
|
||||
private:
|
||||
|
||||
//
|
||||
// Data members
|
||||
//
|
||||
|
||||
/// Iterator to write threadblock-scoped tile of A operand to shared memory
|
||||
SmemIteratorA smem_iterator_A_;
|
||||
|
||||
/// Iterator to write threadblock-scoped tile of B operand to shared memory
|
||||
SmemIteratorB smem_iterator_B_;
|
||||
|
||||
public:
|
||||
|
||||
/// Construct from tensor references
|
||||
CUTLASS_DEVICE
|
||||
ImplicitGemmMultistage(
|
||||
///< Shared storage needed for internal use by threadblock-scoped GEMM
|
||||
typename Base::SharedStorage &shared_storage,
|
||||
///< ID within the threadblock
|
||||
int thread_idx,
|
||||
///< ID of warp
|
||||
int warp_idx,
|
||||
///< ID of each thread within a warp
|
||||
int lane_idx
|
||||
):
|
||||
Base(shared_storage, thread_idx, warp_idx, lane_idx),
|
||||
smem_iterator_A_(shared_storage.operand_A_ref(), thread_idx),
|
||||
smem_iterator_B_(shared_storage.operand_B_ref(), thread_idx)
|
||||
{
|
||||
// Compute warp location within threadblock tile by mapping the warp_id to
|
||||
// three coordinates:
|
||||
// _m: the warp's position within the threadblock along the M dimension
|
||||
// _n: the warp's position within the threadblock along the N dimension
|
||||
// _k: the warp's position within the threadblock along the K dimension
|
||||
|
||||
int warp_idx_mn = warp_idx % (Base::WarpCount::kM * Base::WarpCount::kN);
|
||||
int warp_idx_k = warp_idx / (Base::WarpCount::kM * Base::WarpCount::kN);
|
||||
|
||||
int warp_idx_m = warp_idx_mn % Base::WarpCount::kM;
|
||||
int warp_idx_n = warp_idx_mn / Base::WarpCount::kM;
|
||||
|
||||
// Add per-warp offsets in units of warp-level tiles
|
||||
this->warp_tile_iterator_A_.add_tile_offset(
|
||||
{warp_idx_m, Base::kWarpGemmIterations * warp_idx_k});
|
||||
this->warp_tile_iterator_B_.add_tile_offset(
|
||||
{Base::kWarpGemmIterations * warp_idx_k, warp_idx_n});
|
||||
}
|
||||
|
||||
CUTLASS_DEVICE
|
||||
void copy_tiles_and_advance(
|
||||
IteratorA &iterator_A, IteratorB &iterator_B,
|
||||
int group_start_A = 0, int group_start_B = 0) {
|
||||
|
||||
iterator_A.set_iteration_index(group_start_A);
|
||||
this->smem_iterator_A_.set_iteration_index(group_start_A);
|
||||
|
||||
// Async Copy for operand A
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int j = 0; j < Detail::kAccessesPerGroupA; ++j) {
|
||||
|
||||
if (group_start_A + j < Detail::AsyncCopyIterationsPerStageA) {
|
||||
typename IteratorA::AccessType *dst_ptr =
|
||||
reinterpret_cast<typename IteratorA::AccessType *>(
|
||||
this->smem_iterator_A_.get());
|
||||
|
||||
int const kSrcBytes = sizeof_bits<typename IteratorA::Element>::value *
|
||||
IteratorA::ThreadMap::kElementsPerAccess / 8;
|
||||
|
||||
cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpA>(
|
||||
dst_ptr, iterator_A.get(), iterator_A.valid());
|
||||
|
||||
++iterator_A;
|
||||
|
||||
++this->smem_iterator_A_;
|
||||
}
|
||||
}
|
||||
|
||||
iterator_B.set_iteration_index(group_start_B);
|
||||
|
||||
this->smem_iterator_B_.set_iteration_index(group_start_B);
|
||||
|
||||
// Async Copy for operand B
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int j = 0; j < Detail::kAccessesPerGroupB; ++j) {
|
||||
if (group_start_B + j < Detail::AsyncCopyIterationsPerStageB) {
|
||||
typename IteratorB::AccessType *dst_ptr =
|
||||
reinterpret_cast<typename IteratorB::AccessType *>(
|
||||
this->smem_iterator_B_.get());
|
||||
|
||||
int const kSrcBytes = sizeof_bits<typename IteratorB::Element>::value *
|
||||
IteratorB::ThreadMap::kElementsPerAccess / 8;
|
||||
|
||||
cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpB>(
|
||||
dst_ptr, iterator_B.get(), iterator_B.valid());
|
||||
|
||||
++iterator_B;
|
||||
++this->smem_iterator_B_;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Perform a threadblock-scoped matrix multiply-accumulate
|
||||
CUTLASS_DEVICE
|
||||
void operator()(
|
||||
///< problem size of GEMM
|
||||
int gemm_k_iterations,
|
||||
///< destination accumulator tile
|
||||
FragmentC &accum,
|
||||
///< iterator over A operand in global memory
|
||||
IteratorA iterator_A,
|
||||
///< iterator over B operand in global memory
|
||||
IteratorB iterator_B,
|
||||
///< initial value of accumulator
|
||||
FragmentC const &src_accum,
|
||||
///< Imaginary strides used for planar-complex only - ignored here
|
||||
int64_t imag_stride_A = 0,
|
||||
int64_t imag_stride_B = 0) {
|
||||
|
||||
//
|
||||
// Prologue
|
||||
//
|
||||
|
||||
// Issue several complete stages
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int stage = 0; stage < Base::kStages - 1;
|
||||
++stage, --gemm_k_iterations) {
|
||||
|
||||
iterator_A.set_iteration_index(0);
|
||||
this->smem_iterator_A_.set_iteration_index(0);
|
||||
|
||||
// Async Copy for operand A
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int j = 0; j < Detail::AsyncCopyIterationsPerStageA; ++j) {
|
||||
typename IteratorA::AccessType *dst_ptr =
|
||||
reinterpret_cast<typename IteratorA::AccessType *>(
|
||||
this->smem_iterator_A_.get());
|
||||
|
||||
int const kSrcBytes =
|
||||
sizeof_bits<typename IteratorA::Element>::value *
|
||||
IteratorA::ThreadMap::kElementsPerAccess / 8;
|
||||
|
||||
cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpA>(
|
||||
dst_ptr, iterator_A.get(), iterator_A.valid());
|
||||
|
||||
++iterator_A;
|
||||
++this->smem_iterator_A_;
|
||||
}
|
||||
|
||||
iterator_B.set_iteration_index(0);
|
||||
this->smem_iterator_B_.set_iteration_index(0);
|
||||
|
||||
// Async Copy for operand B
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int j = 0; j < Detail::AsyncCopyIterationsPerStageB; ++j) {
|
||||
typename IteratorB::AccessType *dst_ptr =
|
||||
reinterpret_cast<typename IteratorB::AccessType *>(
|
||||
this->smem_iterator_B_.get());
|
||||
|
||||
int const kSrcBytes =
|
||||
sizeof_bits<typename IteratorB::Element>::value *
|
||||
IteratorB::ThreadMap::kElementsPerAccess / 8;
|
||||
|
||||
cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpB>(
|
||||
dst_ptr, iterator_B.get(), iterator_B.valid());
|
||||
|
||||
++iterator_B;
|
||||
++this->smem_iterator_B_;
|
||||
}
|
||||
|
||||
// Move to the next stage
|
||||
iterator_A.advance();
|
||||
iterator_B.advance();
|
||||
|
||||
this->smem_iterator_A_.add_tile_offset({0, 1});
|
||||
this->smem_iterator_B_.add_tile_offset({1, 0});
|
||||
|
||||
// Inserts a fence to group cp.async instructions into stages.
|
||||
cutlass::arch::cp_async_fence();
|
||||
}
|
||||
|
||||
// Perform accumulation in the 'd' output operand
|
||||
accum = src_accum;
|
||||
|
||||
// Waits until kStages-2 stages have committed.
|
||||
cutlass::arch::cp_async_wait<Base::kStages - 2>();
|
||||
__syncthreads();
|
||||
|
||||
// Pair of fragments used to overlap shared memory loads and math
|
||||
// instructions
|
||||
WarpLoadedFragmentA warp_loaded_frag_A[2];
|
||||
WarpLoadedFragmentB warp_loaded_frag_B[2];
|
||||
WarpTransformedFragmentA warp_transformed_frag_A[2];
|
||||
WarpTransformedFragmentB warp_transformed_frag_B[2];
|
||||
|
||||
Operator warp_mma;
|
||||
|
||||
this->warp_tile_iterator_A_.set_kgroup_index(0);
|
||||
this->warp_tile_iterator_B_.set_kgroup_index(0);
|
||||
|
||||
this->warp_tile_iterator_A_.load(warp_loaded_frag_A[0]);
|
||||
this->warp_tile_iterator_B_.load(warp_loaded_frag_B[0]);
|
||||
|
||||
++this->warp_tile_iterator_A_;
|
||||
++this->warp_tile_iterator_B_;
|
||||
|
||||
// Start issuing the first group of the next stage outside of the mainloop
|
||||
copy_tiles_and_advance(iterator_A, iterator_B);
|
||||
|
||||
int smem_write_stage_idx = Base::kStages - 1;
|
||||
int smem_read_stage_idx = 0;
|
||||
|
||||
warp_mma.transform(warp_transformed_frag_A[0], warp_transformed_frag_B[0],
|
||||
warp_loaded_frag_A[0], warp_loaded_frag_B[0]);
|
||||
|
||||
//
|
||||
// Mainloop
|
||||
//
|
||||
|
||||
CUTLASS_GEMM_LOOP
|
||||
for (; gemm_k_iterations > (-Base::kStages + 1);) {
|
||||
//
|
||||
// Loop over GEMM K dimension
|
||||
//
|
||||
|
||||
// Computes a warp-level GEMM on data held in shared memory
|
||||
// Each "warp_mma_k" refers to a warp-level matrix multiply-accumulate
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations;
|
||||
++warp_mma_k) {
|
||||
|
||||
// Load warp-level tiles from shared memory, wrapping to k offset if
|
||||
// this is the last group as the case may be.
|
||||
|
||||
this->warp_tile_iterator_A_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations);
|
||||
this->warp_tile_iterator_B_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations);
|
||||
|
||||
this->warp_tile_iterator_A_.load(warp_loaded_frag_A[(warp_mma_k + 1) % 2]);
|
||||
this->warp_tile_iterator_B_.load(warp_loaded_frag_B[(warp_mma_k + 1) % 2]);
|
||||
|
||||
++this->warp_tile_iterator_A_;
|
||||
++this->warp_tile_iterator_B_;
|
||||
|
||||
if (warp_mma_k > 0)
|
||||
warp_mma.transform(warp_transformed_frag_A[warp_mma_k % 2],
|
||||
warp_transformed_frag_B[warp_mma_k % 2],
|
||||
warp_loaded_frag_A[warp_mma_k % 2],
|
||||
warp_loaded_frag_B[warp_mma_k % 2]);
|
||||
|
||||
// Issue global->shared copies for the next stage
|
||||
int group_start_iteration_A, group_start_iteration_B;
|
||||
|
||||
if (warp_mma_k + 1 == Base::kWarpGemmIterations) {
|
||||
group_start_iteration_A = 0;
|
||||
group_start_iteration_B = 0;
|
||||
} else {
|
||||
group_start_iteration_A =
|
||||
(warp_mma_k + 1) * Detail::kAccessesPerGroupA;
|
||||
group_start_iteration_B =
|
||||
(warp_mma_k + 1) * Detail::kAccessesPerGroupB;
|
||||
}
|
||||
|
||||
copy_tiles_and_advance(iterator_A, iterator_B, group_start_iteration_A,
|
||||
group_start_iteration_B);
|
||||
|
||||
warp_mma(
|
||||
accum,
|
||||
warp_transformed_frag_A[warp_mma_k % 2],
|
||||
warp_transformed_frag_B[warp_mma_k % 2],
|
||||
accum
|
||||
);
|
||||
|
||||
if (warp_mma_k + 1 == Base::kWarpGemmIterations)
|
||||
warp_mma.transform(warp_transformed_frag_A[(warp_mma_k + 1) % 2],
|
||||
warp_transformed_frag_B[(warp_mma_k + 1) % 2],
|
||||
warp_loaded_frag_A[(warp_mma_k + 1) % 2],
|
||||
warp_loaded_frag_B[(warp_mma_k + 1) % 2]);
|
||||
|
||||
if (warp_mma_k + 2 == Base::kWarpGemmIterations) {
|
||||
// Inserts a fence to group cp.async instructions into stages.
|
||||
cutlass::arch::cp_async_fence();
|
||||
|
||||
// Waits until kStages-2 stages of cp.async have committed
|
||||
arch::cp_async_wait<Base::kStages - 2>();
|
||||
__syncthreads();
|
||||
|
||||
// Move to the next stage
|
||||
iterator_A.advance();
|
||||
iterator_B.advance();
|
||||
|
||||
this->smem_iterator_A_.add_tile_offset({0, 1});
|
||||
this->smem_iterator_B_.add_tile_offset({1, 0});
|
||||
|
||||
// Add negative offsets to return iterators to the 'start' of the
|
||||
// circular buffer in shared memory
|
||||
if (smem_write_stage_idx == (Base::kStages - 1)) {
|
||||
this->smem_iterator_A_.add_tile_offset({0, -Base::kStages});
|
||||
this->smem_iterator_B_.add_tile_offset({-Base::kStages, 0});
|
||||
smem_write_stage_idx = 0;
|
||||
} else {
|
||||
++smem_write_stage_idx;
|
||||
}
|
||||
|
||||
if (smem_read_stage_idx == (Base::kStages - 1)) {
|
||||
this->warp_tile_iterator_A_.add_tile_offset(
|
||||
{0, -Base::kStages * Policy::kPartitionsK *
|
||||
Base::kWarpGemmIterations});
|
||||
this->warp_tile_iterator_B_.add_tile_offset(
|
||||
{-Base::kStages * Policy::kPartitionsK *
|
||||
Base::kWarpGemmIterations,
|
||||
0});
|
||||
smem_read_stage_idx = 0;
|
||||
} else {
|
||||
++smem_read_stage_idx;
|
||||
}
|
||||
|
||||
--gemm_k_iterations;
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
// Insert fence and wait for all outstanding cp.async operations to commit.
|
||||
cutlass::arch::cp_async_fence();
|
||||
cutlass::arch::cp_async_wait<0>();
|
||||
__syncthreads();
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
@@ -0,0 +1,313 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2020, 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 TOR (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 Template for a double-buffered threadblock-scoped GEMM kernel.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/array.h"
|
||||
#include "cutlass/aligned_buffer.h"
|
||||
#include "cutlass/numeric_conversion.h"
|
||||
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/matrix_shape.h"
|
||||
|
||||
#include "cutlass/gemm/gemm.h"
|
||||
#include "cutlass/gemm/threadblock/mma_base.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace cutlass {
|
||||
namespace conv {
|
||||
namespace threadblock {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
/// Structure to compute the matrix product targeting CUDA cores and SIMT math instructions.
|
||||
template <
|
||||
/// Size of the Gemm problem - concept: gemm::GemmShape<>
|
||||
typename Shape_,
|
||||
/// Iterates over tiles of A operand in global memory
|
||||
// (concept: ReadableTileIterator | ForwardTileIterator | MaskedTileIterator)
|
||||
typename IteratorA_,
|
||||
/// Iterates over tiles of A operand in shared memory
|
||||
/// (concept: WriteableTileIterator | RandomAccessTileIterator)
|
||||
typename SmemIteratorA_,
|
||||
/// Iterates over tiles of B operand in global memory
|
||||
// (concept: ReadableTileIterator | ForwardTileIterator | MaskedTileIterator)
|
||||
typename IteratorB_,
|
||||
/// Iterates over tiles of B operand in shared memory
|
||||
/// (concept: WriteableTileIterator | RandomAccessTileIterator)
|
||||
typename SmemIteratorB_,
|
||||
/// Data type of accumulator matrix
|
||||
typename ElementC_,
|
||||
/// Data type of accumulator matrix
|
||||
typename LayoutC_,
|
||||
/// Policy describing tuning details (concept: MmaPolicy)
|
||||
typename Policy_,
|
||||
/// Transformation applied to A operand
|
||||
typename TransformA_ = NumericArrayConverter<
|
||||
typename SmemIteratorA_::Element,
|
||||
typename IteratorA_::Element,
|
||||
IteratorA_::Fragment::kElements>,
|
||||
///
|
||||
/// Transformation applied to A operand
|
||||
typename TransformB_ = NumericArrayConverter<
|
||||
typename SmemIteratorB_::Element,
|
||||
typename IteratorB_::Element,
|
||||
IteratorB_::Fragment::kElements>,
|
||||
/// Used for partial specialization
|
||||
typename Enable = bool
|
||||
>
|
||||
class ImplicitGemmPipelined : public gemm::threadblock::MmaBase<Shape_, Policy_, 2> {
|
||||
public:
|
||||
|
||||
///< Base class
|
||||
using Base = gemm::threadblock::MmaBase<Shape_, Policy_, 2>;
|
||||
|
||||
using Shape = Shape_; ///< Size of the Gemm problem - concept: gemm::GemmShape<>
|
||||
using IteratorA = IteratorA_; ///< Iterates over tiles of A operand in global memory
|
||||
using IteratorB = IteratorB_; ///< Iterates over tiles of B operand in global memory
|
||||
using ElementC = ElementC_; ///< Data type of accumulator matrix
|
||||
using LayoutC = LayoutC_; ///< Layout of accumulator matrix
|
||||
using Policy = Policy_; ///< Policy describing tuning details
|
||||
|
||||
using SmemIteratorA = SmemIteratorA_;
|
||||
using SmemIteratorB = SmemIteratorB_;
|
||||
|
||||
using TransformA = TransformA_;
|
||||
using TransformB = TransformB_;
|
||||
|
||||
//
|
||||
// Dependent types
|
||||
//
|
||||
|
||||
/// Fragment of operand A loaded from global memory
|
||||
using FragmentA = typename IteratorA::Fragment;
|
||||
|
||||
/// Fragment of operand B loaded from global memory
|
||||
using FragmentB = typename IteratorB::Fragment;
|
||||
|
||||
/// Fragment of accumulator tile
|
||||
using FragmentC = typename Policy::Operator::FragmentC;
|
||||
|
||||
/// Warp-level Mma
|
||||
using Operator = typename Policy::Operator;
|
||||
|
||||
/// Obtain the arch tag from the warp-level operator
|
||||
using ArchTag = typename Policy::Operator::ArchTag;
|
||||
|
||||
/// Complex transform on A operand
|
||||
static ComplexTransform const kTransformA = Operator::kTransformA;
|
||||
|
||||
/// Complex transform on B operand
|
||||
static ComplexTransform const kTransformB = Operator::kTransformB;
|
||||
|
||||
// staticaly assert kStages for MmaPipelined is two (Double-buffered pipeline)
|
||||
static_assert((Base::kStages==2), "MmaPipelined requires kStages set to value 2");
|
||||
|
||||
private:
|
||||
|
||||
using WarpFragmentA = typename Operator::FragmentA;
|
||||
using WarpFragmentB = typename Operator::FragmentB;
|
||||
|
||||
protected:
|
||||
|
||||
/// Iterator to write threadblock-scoped tile of A operand to shared memory
|
||||
SmemIteratorA smem_iterator_A_;
|
||||
|
||||
/// Iterator to write threadblock-scoped tile of B operand to shared memory
|
||||
SmemIteratorB smem_iterator_B_;
|
||||
|
||||
public:
|
||||
|
||||
/// Construct from tensor references
|
||||
CUTLASS_DEVICE
|
||||
ImplicitGemmPipelined(
|
||||
typename Base::SharedStorage &shared_storage, ///< Shared storage needed for internal use by threadblock-scoped GEMM
|
||||
int thread_idx, ///< ID within the threadblock
|
||||
int warp_idx, ///< ID of warp
|
||||
int lane_idx ///< ID of each thread within a warp
|
||||
):
|
||||
Base(shared_storage, thread_idx, warp_idx, lane_idx),
|
||||
smem_iterator_A_(shared_storage.operand_A_ref(), thread_idx),
|
||||
smem_iterator_B_(shared_storage.operand_B_ref(), thread_idx) {
|
||||
|
||||
// Compute warp location within threadblock tile by mapping the warp_id to
|
||||
// three coordinates:
|
||||
// _m: the warp's position within the threadblock along the M dimension
|
||||
// _n: the warp's position within the threadblock along the N dimension
|
||||
// _k: the warp's position within the threadblock along the K dimension
|
||||
|
||||
int warp_idx_mn = warp_idx % (Base::WarpCount::kM * Base::WarpCount::kN);
|
||||
int warp_idx_k = warp_idx / (Base::WarpCount::kM * Base::WarpCount::kN);
|
||||
|
||||
int warp_idx_m = warp_idx_mn % Base::WarpCount::kM;
|
||||
int warp_idx_n = warp_idx_mn / Base::WarpCount::kM;
|
||||
|
||||
// Add per-warp offsets in units of warp-level tiles
|
||||
this->warp_tile_iterator_A_.add_tile_offset({warp_idx_m, Base::kWarpGemmIterations * warp_idx_k});
|
||||
this->warp_tile_iterator_B_.add_tile_offset({Base::kWarpGemmIterations * warp_idx_k, warp_idx_n});
|
||||
}
|
||||
|
||||
/// Perform a threadblock-scoped matrix multiply-accumulate
|
||||
CUTLASS_DEVICE
|
||||
void operator()(
|
||||
int gemm_k_iterations, ///< number of iterations of the mainloop
|
||||
FragmentC &accum, ///< destination accumulator tile
|
||||
IteratorA iterator_A, ///< iterator over A operand in global memory
|
||||
IteratorB iterator_B, ///< iterator over B operand in global memory
|
||||
FragmentC const &src_accum, ///< source accumulator tile
|
||||
TransformA transform_A = TransformA(), ///< transformation applied to A fragment
|
||||
TransformB transform_B = TransformB()) { ///< transformation applied to B fragment
|
||||
|
||||
//
|
||||
// Prologue
|
||||
//
|
||||
|
||||
// Perform accumulation in the 'd' output operand
|
||||
accum = src_accum;
|
||||
|
||||
FragmentA tb_frag_A;
|
||||
FragmentB tb_frag_B;
|
||||
|
||||
tb_frag_A.clear();
|
||||
tb_frag_B.clear();
|
||||
|
||||
// The last kblock is loaded in the prolog
|
||||
iterator_A.load(tb_frag_A);
|
||||
iterator_B.load(tb_frag_B);
|
||||
|
||||
++iterator_A;
|
||||
++iterator_B;
|
||||
|
||||
this->smem_iterator_A_.store(transform_A(tb_frag_A));
|
||||
this->smem_iterator_B_.store(transform_B(tb_frag_B));
|
||||
|
||||
++this->smem_iterator_A_;
|
||||
++this->smem_iterator_B_;
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Pair of fragments used to overlap shared memory loads and math instructions
|
||||
WarpFragmentA warp_frag_A[2];
|
||||
WarpFragmentB warp_frag_B[2];
|
||||
|
||||
this->warp_tile_iterator_A_.set_kgroup_index(0);
|
||||
this->warp_tile_iterator_B_.set_kgroup_index(0);
|
||||
|
||||
this->warp_tile_iterator_A_.load(warp_frag_A[0]);
|
||||
this->warp_tile_iterator_B_.load(warp_frag_B[0]);
|
||||
|
||||
++this->warp_tile_iterator_A_;
|
||||
++this->warp_tile_iterator_B_;
|
||||
|
||||
Operator warp_mma;
|
||||
|
||||
int smem_write_stage_idx = 1;
|
||||
|
||||
// Issue loads during the first warp-level matrix multiply-add *AFTER* issuing
|
||||
// shared memory loads (which have the tighest latency requirement).
|
||||
|
||||
//
|
||||
// Mainloop
|
||||
//
|
||||
|
||||
// Note: The main loop does not support Base::kWarpGemmIterations == 2.
|
||||
CUTLASS_GEMM_LOOP
|
||||
for (; gemm_k_iterations > 0; --gemm_k_iterations) {
|
||||
//
|
||||
// Loop over GEMM K dimension
|
||||
//
|
||||
|
||||
CUTLASS_PRAGMA_UNROLL
|
||||
for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations; ++warp_mma_k) {
|
||||
|
||||
// Load warp-level tiles from shared memory, wrapping to k offset if this is the last group
|
||||
// as the case may be.
|
||||
|
||||
if (warp_mma_k == Base::kWarpGemmIterations - 1) {
|
||||
|
||||
// Write fragments to shared memory
|
||||
this->smem_iterator_A_.store(transform_A(tb_frag_A));
|
||||
|
||||
this->smem_iterator_B_.store(transform_B(tb_frag_B));
|
||||
|
||||
__syncthreads();
|
||||
|
||||
++this->smem_iterator_A_;
|
||||
++this->smem_iterator_B_;
|
||||
|
||||
// Add negative offsets to return iterators to the 'start' of the circular buffer in shared memory
|
||||
if (smem_write_stage_idx == 1) {
|
||||
this->smem_iterator_A_.add_tile_offset({0, -Base::kStages});
|
||||
this->smem_iterator_B_.add_tile_offset({-Base::kStages, 0});
|
||||
}
|
||||
else {
|
||||
this->warp_tile_iterator_A_.add_tile_offset(
|
||||
{0, -Base::kStages * Policy::kPartitionsK * Base::kWarpGemmIterations});
|
||||
this->warp_tile_iterator_B_.add_tile_offset(
|
||||
{-Base::kStages * Policy::kPartitionsK * Base::kWarpGemmIterations,
|
||||
0});
|
||||
}
|
||||
|
||||
smem_write_stage_idx ^= 1;
|
||||
}
|
||||
|
||||
this->warp_tile_iterator_A_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations);
|
||||
this->warp_tile_iterator_B_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations);
|
||||
|
||||
this->warp_tile_iterator_A_.load(warp_frag_A[(warp_mma_k + 1) % 2]);
|
||||
this->warp_tile_iterator_B_.load(warp_frag_B[(warp_mma_k + 1) % 2]);
|
||||
|
||||
++this->warp_tile_iterator_A_;
|
||||
++this->warp_tile_iterator_B_;
|
||||
|
||||
if (warp_mma_k == 0) {
|
||||
|
||||
iterator_A.load(tb_frag_A);
|
||||
iterator_B.load(tb_frag_B);
|
||||
|
||||
++iterator_A;
|
||||
++iterator_B;
|
||||
}
|
||||
|
||||
warp_mma(accum, warp_frag_A[warp_mma_k % 2],
|
||||
warp_frag_B[warp_mma_k % 2], accum);
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace threadblock
|
||||
} // namespace gemm
|
||||
} // namespace cutlass
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
Reference in New Issue
Block a user