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Commit cbf281f0 authored by Bartlomiej Wroblewski's avatar Bartlomiej Wroblewski
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Merge remote-tracking branch 'origin/develop' into bwroblew/contrib

parents f3aceeab d52ec016
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CHANGELOG.md
View file @ cbf281f0
...@@ -25,8 +25,8 @@ Full documentation for Composable Kernel is not yet available. ...@@ -25,8 +25,8 @@ Full documentation for Composable Kernel is not yet available.
- Added multi-embeddings support (#542). - Added multi-embeddings support (#542).
- Added Navi3x blockwise GEMM and real GEMM support (#541). - Added Navi3x blockwise GEMM and real GEMM support (#541).
- Added Navi grouped ConvBwdWeight support (#505). - Added Navi grouped ConvBwdWeight support (#505).
- Added pool3d forward (#697). - Added MaxPool, AvgPool forward (#815).
- Added maxpool backward (#750). - Added MaxPool backward (#750).
### Changed ### Changed
- Changed ... - Changed ...
client_example/19_pool_fwd/avg_pool3d_fwd.cpp
View file @ cbf281f0
...@@ -16,6 +16,9 @@ using InDataType = ck::half_t; ...@@ -16,6 +16,9 @@ using InDataType = ck::half_t;
using OutDataType = ck::half_t; using OutDataType = ck::half_t;
using IndexDataType = int32_t; using IndexDataType = int32_t;
using InLayout = ck::tensor_layout::convolution::NDHWC;
using OutLayout = ck::tensor_layout::convolution::NDHWC;
constexpr ck::index_t InOutRank = 5; constexpr ck::index_t InOutRank = 5;
constexpr ck::index_t WindowRank = 3; constexpr ck::index_t WindowRank = 3;
#if 0 #if 0
...@@ -44,33 +47,41 @@ struct SimpleDeviceMem ...@@ -44,33 +47,41 @@ struct SimpleDeviceMem
int main(int argc, char* argv[]) int main(int argc, char* argv[])
{ {
ck::index_t N = 2; ck::index_t N = 2;
ck::index_t C = 32; ck::index_t C = 32;
ck::index_t Z = 2; ck::index_t Z = 2;
ck::index_t Y = 2; ck::index_t Y = 2;
ck::index_t X = 2; ck::index_t X = 2;
ck::index_t Di = 30; ck::index_t Di = 30;
ck::index_t Hi = 30; ck::index_t Hi = 30;
ck::index_t Wi = 30; ck::index_t Wi = 30;
ck::index_t window_stride_d = 2; ck::index_t window_stride_d = 2;
ck::index_t window_stride_h = 2; ck::index_t window_stride_h = 2;
ck::index_t window_stride_w = 2; ck::index_t window_stride_w = 2;
ck::index_t in_left_pad_d = 1; ck::index_t window_dilation_d = 1;
ck::index_t in_left_pad_h = 1; ck::index_t window_dilation_h = 1;
ck::index_t in_left_pad_w = 1; ck::index_t window_dilation_w = 1;
ck::index_t in_right_pad_d = 1; ck::index_t in_left_pad_d = 1;
ck::index_t in_right_pad_h = 1; ck::index_t in_left_pad_h = 1;
ck::index_t in_right_pad_w = 1; ck::index_t in_left_pad_w = 1;
ck::index_t in_right_pad_d = 1;
ck::index_t Do = (Di + in_left_pad_d + in_right_pad_d - Z) / window_stride_d + 1; ck::index_t in_right_pad_h = 1;
ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Y) / window_stride_h + 1; ck::index_t in_right_pad_w = 1;
ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - X) / window_stride_w + 1;
const ck::index_t Zs = (Z - 1) * window_dilation_d + 1;
const ck::index_t Ys = (Y - 1) * window_dilation_h + 1;
const ck::index_t Xs = (X - 1) * window_dilation_w + 1;
ck::index_t Do = (Di + in_left_pad_d + in_right_pad_d - Zs) / window_stride_d + 1;
ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Ys) / window_stride_h + 1;
ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - Xs) / window_stride_w + 1;
// Pool API only support the order of NCDHW // Pool API only support the order of NCDHW
std::vector<ck::index_t> in_length = {N, C, Di, Hi, Wi}; std::vector<ck::index_t> in_length = {N, C, Di, Hi, Wi};
std::vector<ck::index_t> out_length = {N, C, Do, Ho, Wo}; std::vector<ck::index_t> out_length = {N, C, Do, Ho, Wo};
std::vector<ck::index_t> window_spatial_lengths = {Z, Y, X}; std::vector<ck::index_t> window_spatial_lengths = {Z, Y, X};
std::vector<ck::index_t> window_strides = {window_stride_d, window_stride_h, window_stride_w}; std::vector<ck::index_t> window_strides = {window_stride_d, window_stride_h, window_stride_w};
std::vector<ck::index_t> window_dilations{
window_dilation_d, window_dilation_h, window_dilation_w};
std::vector<ck::index_t> input_left_pads = {in_left_pad_d, in_left_pad_h, in_left_pad_w}; std::vector<ck::index_t> input_left_pads = {in_left_pad_d, in_left_pad_h, in_left_pad_w};
std::vector<ck::index_t> input_right_pads = {in_right_pad_d, in_right_pad_h, in_right_pad_w}; std::vector<ck::index_t> input_right_pads = {in_right_pad_d, in_right_pad_h, in_right_pad_w};
...@@ -90,6 +101,8 @@ int main(int argc, char* argv[]) ...@@ -90,6 +101,8 @@ int main(int argc, char* argv[])
InDataType, InDataType,
OutDataType, OutDataType,
IndexDataType, IndexDataType,
InLayout,
OutLayout,
ReduceOpId, ReduceOpId,
OutputIndex>; OutputIndex>;
...@@ -122,6 +135,7 @@ int main(int argc, char* argv[]) ...@@ -122,6 +135,7 @@ int main(int argc, char* argv[])
out_tensor_stride, out_tensor_stride,
out_tensor_stride, out_tensor_stride,
window_strides, window_strides,
window_dilations,
input_left_pads, input_left_pads,
input_right_pads, input_right_pads,
{2, 3, 4}); {2, 3, 4});
...@@ -181,6 +195,7 @@ int main(int argc, char* argv[]) ...@@ -181,6 +195,7 @@ int main(int argc, char* argv[])
out_tensor_stride, out_tensor_stride,
out_tensor_stride, out_tensor_stride,
window_strides, window_strides,
window_dilations,
input_left_pads, input_left_pads,
input_right_pads, input_right_pads,
{2, 3, 4}); {2, 3, 4});
......
client_example/19_pool_fwd/max_pool2d_fwd.cpp
View file @ cbf281f0
...@@ -10,14 +10,18 @@ ...@@ -10,14 +10,18 @@
#include "ck/tensor_operation/gpu/device/device_pool_fwd.hpp" #include "ck/tensor_operation/gpu/device/device_pool_fwd.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/pool2d_fwd.hpp" #include "ck/library/tensor_operation_instance/gpu/pool3d_fwd.hpp"
using InDataType = ck::half_t; using InDataType = ck::half_t;
using OutDataType = ck::half_t; using OutDataType = ck::half_t;
using IndexDataType = int32_t; using IndexDataType = int32_t;
constexpr ck::index_t InOutRank = 4; // We use pool3d to implement pool2d in this example
constexpr ck::index_t WindowRank = 2; using InLayout = ck::tensor_layout::convolution::NDHWC;
using OutLayout = ck::tensor_layout::convolution::NDHWC;
constexpr ck::index_t InOutRank = 5;
constexpr ck::index_t WindowRank = 3;
#if 1 #if 1
constexpr auto ReduceOpId = ck::ReduceTensorOp::MAX; constexpr auto ReduceOpId = ck::ReduceTensorOp::MAX;
constexpr bool OutputIndex = true; constexpr bool OutputIndex = true;
...@@ -42,31 +46,66 @@ struct SimpleDeviceMem ...@@ -42,31 +46,66 @@ struct SimpleDeviceMem
void* p_mem_; void* p_mem_;
}; };
void TransformPool2dparamToPool3d(std::vector<ck::index_t>& input_lengths,
std::vector<ck::index_t>& window_lengths,
std::vector<ck::index_t>& output_lengths,
std::vector<ck::index_t>& input_stride,
std::vector<ck::index_t>& output_stride,
std::vector<ck::index_t>& indices_stride,
std::vector<ck::index_t>& window_strides,
std::vector<ck::index_t>& window_dilations,
std::vector<ck::index_t>& input_left_pads,
std::vector<ck::index_t>& input_right_pads,
std::vector<ck::index_t>& pooling_dims)
{
// NCHW to NCDHW
input_lengths.insert(input_lengths.begin() + 2, 1);
output_lengths.insert(output_lengths.begin() + 2, 1);
input_stride.insert(input_stride.begin() + 2, 0);
output_stride.insert(output_stride.begin() + 2, 0);
indices_stride.insert(indices_stride.begin() + 2, 0);
// YX to ZYX
window_lengths.insert(window_lengths.begin(), 1);
window_strides.insert(window_strides.begin(), 0);
window_dilations.insert(window_dilations.begin(), 0);
input_left_pads.insert(input_left_pads.begin(), 0);
input_right_pads.insert(input_right_pads.begin(), 0);
pooling_dims = {2, 3, 4};
}
int main(int argc, char* argv[]) int main(int argc, char* argv[])
{ {
ck::index_t N = 2; ck::index_t N = 2;
ck::index_t C = 32; ck::index_t C = 32;
ck::index_t Y = 2; ck::index_t Y = 2;
ck::index_t X = 2; ck::index_t X = 2;
ck::index_t Hi = 30; ck::index_t Hi = 30;
ck::index_t Wi = 30; ck::index_t Wi = 30;
ck::index_t window_stride_h = 2; ck::index_t window_stride_h = 2;
ck::index_t window_stride_w = 2; ck::index_t window_stride_w = 2;
ck::index_t in_left_pad_h = 1; ck::index_t window_dilation_h = 1;
ck::index_t in_left_pad_w = 1; ck::index_t window_dilation_w = 1;
ck::index_t in_right_pad_h = 1; ck::index_t in_left_pad_h = 1;
ck::index_t in_right_pad_w = 1; ck::index_t in_left_pad_w = 1;
ck::index_t in_right_pad_h = 1;
ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Y) / window_stride_h + 1; ck::index_t in_right_pad_w = 1;
ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - X) / window_stride_w + 1;
const ck::index_t Ys = (Y - 1) * window_dilation_h + 1;
const ck::index_t Xs = (X - 1) * window_dilation_w + 1;
ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Ys) / window_stride_h + 1;
ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - Xs) / window_stride_w + 1;
// Pool API only support the order of NCHW // Pool API only support the order of NCHW
std::vector<ck::index_t> in_length = {N, C, Hi, Wi}; std::vector<ck::index_t> in_length = {N, C, Hi, Wi};
std::vector<ck::index_t> out_length = {N, C, Ho, Wo}; std::vector<ck::index_t> out_length = {N, C, Ho, Wo};
std::vector<ck::index_t> window_spatial_lengths = {Y, X}; std::vector<ck::index_t> window_spatial_lengths = {Y, X};
std::vector<ck::index_t> window_strides = {window_stride_h, window_stride_w}; std::vector<ck::index_t> window_strides = {window_stride_h, window_stride_w};
std::vector<ck::index_t> window_dilations = {window_dilation_h, window_dilation_w};
std::vector<ck::index_t> input_left_pads = {in_left_pad_h, in_left_pad_w}; std::vector<ck::index_t> input_left_pads = {in_left_pad_h, in_left_pad_w};
std::vector<ck::index_t> input_right_pads = {in_right_pad_h, in_right_pad_w}; std::vector<ck::index_t> input_right_pads = {in_right_pad_h, in_right_pad_w};
std::vector<ck::index_t> pooling_dims = {2, 3};
std::size_t in_tensor_size = N * C * Hi * Wi; std::size_t in_tensor_size = N * C * Hi * Wi;
std::size_t out_tensor_size = N * C * Ho * Wo; std::size_t out_tensor_size = N * C * Ho * Wo;
...@@ -75,6 +114,18 @@ int main(int argc, char* argv[]) ...@@ -75,6 +114,18 @@ int main(int argc, char* argv[])
std::vector<ck::index_t> in_tensor_stride = {C * Hi * Wi, 1, Wi * C, C}; std::vector<ck::index_t> in_tensor_stride = {C * Hi * Wi, 1, Wi * C, C};
std::vector<ck::index_t> out_tensor_stride = {C * Ho * Wo, 1, Wo * C, C}; std::vector<ck::index_t> out_tensor_stride = {C * Ho * Wo, 1, Wo * C, C};
TransformPool2dparamToPool3d(in_length,
window_spatial_lengths,
out_length,
in_tensor_stride,
out_tensor_stride,
out_tensor_stride,
window_strides,
window_dilations,
input_left_pads,
input_right_pads,
pooling_dims);
SimpleDeviceMem in_device_buf(sizeof(InDataType) * in_tensor_size); SimpleDeviceMem in_device_buf(sizeof(InDataType) * in_tensor_size);
SimpleDeviceMem out_device_buf(sizeof(OutDataType) * out_tensor_size); SimpleDeviceMem out_device_buf(sizeof(OutDataType) * out_tensor_size);
SimpleDeviceMem out_indices_device_buf(sizeof(IndexDataType) * out_tensor_size); SimpleDeviceMem out_indices_device_buf(sizeof(IndexDataType) * out_tensor_size);
...@@ -84,6 +135,8 @@ int main(int argc, char* argv[]) ...@@ -84,6 +135,8 @@ int main(int argc, char* argv[])
InDataType, InDataType,
OutDataType, OutDataType,
IndexDataType, IndexDataType,
InLayout,
OutLayout,
ReduceOpId, ReduceOpId,
OutputIndex>; OutputIndex>;
...@@ -116,9 +169,10 @@ int main(int argc, char* argv[]) ...@@ -116,9 +169,10 @@ int main(int argc, char* argv[])
out_tensor_stride, out_tensor_stride,
out_tensor_stride, out_tensor_stride,
window_strides, window_strides,
window_dilations,
input_left_pads, input_left_pads,
input_right_pads, input_right_pads,
{2, 3}); pooling_dims);
auto invoker_ptr = op_ptr->MakeInvokerPointer(); auto invoker_ptr = op_ptr->MakeInvokerPointer();
...@@ -175,9 +229,10 @@ int main(int argc, char* argv[]) ...@@ -175,9 +229,10 @@ int main(int argc, char* argv[])
out_tensor_stride, out_tensor_stride,
out_tensor_stride, out_tensor_stride,
window_strides, window_strides,
window_dilations,
input_left_pads, input_left_pads,
input_right_pads, input_right_pads,
{2, 3}); pooling_dims);
auto invoker_ptr = op_ptr->MakeInvokerPointer(); auto invoker_ptr = op_ptr->MakeInvokerPointer();
......
docs/sphinx/requirements.in
View file @ cbf281f0
rocm-docs-core==0.10.3 rocm-docs-core>=0.20.0
sphinxcontrib-bibtex==2.5.0 sphinxcontrib-bibtex==2.5.0
docs/sphinx/requirements.txt
View file @ cbf281f0
...@@ -38,6 +38,8 @@ docutils==0.16 ...@@ -38,6 +38,8 @@ docutils==0.16
# pydata-sphinx-theme # pydata-sphinx-theme
# sphinx # sphinx
# sphinxcontrib-bibtex # sphinxcontrib-bibtex
fastjsonschema==2.18.0
# via rocm-docs-core
gitdb==4.0.10 gitdb==4.0.10
# via gitpython # via gitpython
gitpython==3.1.31 gitpython==3.1.31
...@@ -46,20 +48,12 @@ idna==3.4 ...@@ -46,20 +48,12 @@ idna==3.4
# via requests # via requests
imagesize==1.4.1 imagesize==1.4.1
# via sphinx # via sphinx
importlib-metadata==6.0.0
# via
# sphinx
# sphinxcontrib-bibtex
importlib-resources==5.12.0
# via rocm-docs-core
jinja2==3.1.2 jinja2==3.1.2
# via # via
# myst-parser # myst-parser
# sphinx # sphinx
latexcodec==2.0.1 latexcodec==2.0.1
# via pybtex # via pybtex
linkify-it-py==1.0.3
# via myst-parser
markdown-it-py==2.2.0 markdown-it-py==2.2.0
# via # via
# mdit-py-plugins # mdit-py-plugins
...@@ -70,7 +64,7 @@ mdit-py-plugins==0.3.5 ...@@ -70,7 +64,7 @@ mdit-py-plugins==0.3.5
# via myst-parser # via myst-parser
mdurl==0.1.2 mdurl==0.1.2
# via markdown-it-py # via markdown-it-py
myst-parser[linkify]==1.0.0 myst-parser==1.0.0
# via rocm-docs-core # via rocm-docs-core
packaging==23.0 packaging==23.0
# via # via
...@@ -99,18 +93,17 @@ pyjwt[crypto]==2.6.0 ...@@ -99,18 +93,17 @@ pyjwt[crypto]==2.6.0
# via pygithub # via pygithub
pynacl==1.5.0 pynacl==1.5.0
# via pygithub # via pygithub
pytz==2023.3
# via babel
pyyaml==6.0 pyyaml==6.0
# via # via
# myst-parser # myst-parser
# pybtex # pybtex
# rocm-docs-core
# sphinx-external-toc # sphinx-external-toc
requests==2.28.2 requests==2.28.2
# via # via
# pygithub # pygithub
# sphinx # sphinx
rocm-docs-core==0.10.3 rocm-docs-core>=0.20.0
# via -r requirements.in # via -r requirements.in
six==1.16.0 six==1.16.0
# via # via
...@@ -160,13 +153,7 @@ sphinxcontrib-serializinghtml==1.1.5 ...@@ -160,13 +153,7 @@ sphinxcontrib-serializinghtml==1.1.5
# via sphinx # via sphinx
typing-extensions==4.5.0 typing-extensions==4.5.0
# via pydata-sphinx-theme # via pydata-sphinx-theme
uc-micro-py==1.0.1
# via linkify-it-py
urllib3==1.26.15 urllib3==1.26.15
# via requests # via requests
wrapt==1.15.0 wrapt==1.15.0
# via deprecated # via deprecated
zipp==3.15.0
# via
# importlib-metadata
# importlib-resources
example/01_gemm/CMakeLists.txt
View file @ cbf281f0
...@@ -6,6 +6,8 @@ if(DL_KERNELS) ...@@ -6,6 +6,8 @@ if(DL_KERNELS)
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES) if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
add_example_executable(example_gemm_dl_fp16 gemm_dl_fp16.cpp) add_example_executable(example_gemm_dl_fp16 gemm_dl_fp16.cpp)
add_dependencies(example_gemm_dl example_gemm_dl_fp16) add_dependencies(example_gemm_dl example_gemm_dl_fp16)
add_example_executable(example_gemm_dl_dpp8_fp16 gemm_dl_dpp8_fp16.cpp)
add_dependencies(example_gemm_dl example_gemm_dl_dpp8_fp16)
endif() endif()
if(DTYPES MATCHES "int8" OR NOT DEFINED DTYPES) if(DTYPES MATCHES "int8" OR NOT DEFINED DTYPES)
add_example_executable(example_gemm_dl_int8 gemm_dl_int8.cpp) add_example_executable(example_gemm_dl_int8 gemm_dl_int8.cpp)
......
example/01_gemm/gemm_dl_dpp8_fp16.cpp 0 → 100644
View file @ cbf281f0
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_dl_dpp8.hpp"
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
using ALayout = Col;
using BLayout = Row;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// clang-format off
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmDlDpp8
// ######| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| M1Per| N1Per| KPer| M11N11Thread| M11N11Thread| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| CThreadTransfer| CThreadTransfer| CThreadTransfer|
// ######| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Spacialization| Size| Block| Block| Block| | ThreadM111| ThreadN111| Thread| ClusterM110Xs| ClusterN110Xs| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccess| SrcVectorTensor| SrcVectorTensor| DstVectorTensor| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccess| SrcVectorTensor| SrcVectorTensor| DstVectorTensor| SrcDstAccess| SrcDstVectorDim| DstScalarPerVector|
// ######| | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | K0_M0_M1_K1| K0_M0_M1_K1| ArrangeOrder| Order| Lengths_K0_M0_M1_K1| ContiguousDimOrder| Lengths_K0_M0_M1_K1| K0_N0_N1_K1| K0_N0_N1_K1| ArrangeOrder| Order| Lengths_K0_N0_N1_K1| ContiguousDimOrder| Lengths_K0_N0_N1_K1| Order| | |
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout, AElementOp, BElementOp, CElementOp, GemmDefault, 256, 128, 128, 16, 2, 1, 8, 8, S<8, 8>, S<4, 1>, S<2, 1, 4, 2>, S<8, 1, 32, 1>, S<0, 3, 1, 2>, S<0, 3, 1, 2>, S<1, 1, 4, 1>, S<0, 3, 1, 2>, S<1, 1, 4, 2>, S<2, 1, 4, 2>, S<8, 1, 32, 1>, S<0, 3, 1, 2>, S<0, 3, 1, 2>, S<1, 1, 4, 1>, S<0, 3, 1, 2>, S<1, 1, 4, 2>, S<0, 1, 2, 3, 4, 5>, 5, 4>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
#include "run_gemm_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); }
example/13_pool2d_fwd/pool2d_fwd_common.hpp
View file @ cbf281f0
...@@ -39,31 +39,35 @@ bool pool_test(bool do_verification, ...@@ -39,31 +39,35 @@ bool pool_test(bool do_verification,
ck::index_t Wi, ck::index_t Wi,
ck::index_t window_stride_h, ck::index_t window_stride_h,
ck::index_t window_stride_w, ck::index_t window_stride_w,
ck::index_t window_dilation_h,
ck::index_t window_dilation_w,
ck::index_t in_left_pad_h, ck::index_t in_left_pad_h,
ck::index_t in_left_pad_w, ck::index_t in_left_pad_w,
ck::index_t in_right_pad_h, ck::index_t in_right_pad_h,
ck::index_t in_right_pad_w) ck::index_t in_right_pad_w)
{ {
using DevicePoolFwdInstance = using DevicePoolFwdInstance =
ck::tensor_operation::device::DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C< ck::tensor_operation::device::DevicePool2dFwd_NHWC_NHWC<InDataType,
InDataType, // InDataType OutDataType,
OutDataType, // OutDataType IndexDataType,
IndexDataType, // IndexDataType ComputeDataType,
ComputeDataType, // ComputeDataType ReduceOpId,
ReduceOpId, OutputIndex,
OutputIndex, 64, // BlockSize
64, // BlockSize 64, // ReduceMThreadClusterSize
64, // ReduceMThreadClusterSize 1, // ReduceKThreadClusterSize
1, // ReduceKThreadClusterSize 4, // ReduceMThreadSliceSize
4, // ReduceMThreadSliceSize 1, // ReduceKThreadSliceSize
1, // ReduceKThreadSliceSize 1>; // InSrcOutDstVectorSize
4>; // InSrcOutDstVectorSize
const ck::index_t Ys = (Y - 1) * window_dilation_h + 1;
const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Y) / window_stride_h + 1; const ck::index_t Xs = (X - 1) * window_dilation_w + 1;
const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - X) / window_stride_w + 1; const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Ys) / window_stride_h + 1;
const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - Xs) / window_stride_w + 1;
const std::vector<ck::index_t> window_spatial_lengths{Y, X}; const std::vector<ck::index_t> window_spatial_lengths{Y, X};
const std::vector<ck::index_t> window_strides{window_stride_h, window_stride_w}; const std::vector<ck::index_t> window_strides{window_stride_h, window_stride_w};
const std::vector<ck::index_t> window_dilations{window_dilation_h, window_dilation_w};
const std::vector<ck::index_t> input_left_pads{in_left_pad_h, in_left_pad_w}; const std::vector<ck::index_t> input_left_pads{in_left_pad_h, in_left_pad_w};
const std::vector<ck::index_t> input_right_pads{in_right_pad_h, in_right_pad_w}; const std::vector<ck::index_t> input_right_pads{in_right_pad_h, in_right_pad_w};
...@@ -123,6 +127,7 @@ bool pool_test(bool do_verification, ...@@ -123,6 +127,7 @@ bool pool_test(bool do_verification,
{C * Ho * Wo, 1, Wo * C, C}, {C * Ho * Wo, 1, Wo * C, C},
{C * Ho * Wo, 1, Wo * C, C}, {C * Ho * Wo, 1, Wo * C, C},
window_strides, window_strides,
window_dilations,
input_left_pads, input_left_pads,
input_right_pads, input_right_pads,
{2, 3}); {2, 3});
...@@ -144,8 +149,8 @@ bool pool_test(bool do_verification, ...@@ -144,8 +149,8 @@ bool pool_test(bool do_verification,
float gb_per_sec = num_btype / 1.E6 / ave_time; float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s" std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
<< std::endl; << " GB / s " << std::endl;
bool pass = true; bool pass = true;
...@@ -169,6 +174,7 @@ bool pool_test(bool do_verification, ...@@ -169,6 +174,7 @@ bool pool_test(bool do_verification,
out_indices_n_c_ho_wo_host, out_indices_n_c_ho_wo_host,
window_spatial_lengths, window_spatial_lengths,
window_strides, window_strides,
window_dilations,
input_left_pads, input_left_pads,
input_right_pads); input_right_pads);
......
example/13_pool2d_fwd/pool2d_fwd_fp16.cpp
View file @ cbf281f0
...@@ -34,18 +34,20 @@ int main(int argc, char* argv[]) ...@@ -34,18 +34,20 @@ int main(int argc, char* argv[])
bool time_kernel; bool time_kernel;
// Pool shape // Pool shape
ck::index_t N = 128; ck::index_t N = 128;
ck::index_t C = 192; ck::index_t C = 192;
ck::index_t Y = 3; ck::index_t Y = 3;
ck::index_t X = 3; ck::index_t X = 3;
ck::index_t Hi = 71; ck::index_t Hi = 71;
ck::index_t Wi = 71; ck::index_t Wi = 71;
ck::index_t window_stride_h = 2; ck::index_t window_stride_h = 2;
ck::index_t window_stride_w = 2; ck::index_t window_stride_w = 2;
ck::index_t in_left_pad_h = 1; ck::index_t window_dilation_h = 1;
ck::index_t in_left_pad_w = 1; ck::index_t window_dilation_w = 1;
ck::index_t in_right_pad_h = 1; ck::index_t in_left_pad_h = 1;
ck::index_t in_right_pad_w = 1; ck::index_t in_left_pad_w = 1;
ck::index_t in_right_pad_h = 1;
ck::index_t in_right_pad_w = 1;
if(argc == 1) if(argc == 1)
{ {
...@@ -59,31 +61,33 @@ int main(int argc, char* argv[]) ...@@ -59,31 +61,33 @@ int main(int argc, char* argv[])
init_method = std::stoi(argv[2]); init_method = std::stoi(argv[2]);
time_kernel = static_cast<bool>(std::stoi(argv[3])); time_kernel = static_cast<bool>(std::stoi(argv[3]));
} }
else if(argc == 16) else if(argc == 18)
{ {
do_verification = std::stoi(argv[1]); do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]); init_method = std::stoi(argv[2]);
time_kernel = static_cast<bool>(std::stoi(argv[3])); time_kernel = static_cast<bool>(std::stoi(argv[3]));
N = std::stoi(argv[4]); N = std::stoi(argv[4]);
C = std::stoi(argv[5]); C = std::stoi(argv[5]);
Y = std::stoi(argv[6]); Y = std::stoi(argv[6]);
X = std::stoi(argv[7]); X = std::stoi(argv[7]);
Hi = std::stoi(argv[8]); Hi = std::stoi(argv[8]);
Wi = std::stoi(argv[9]); Wi = std::stoi(argv[9]);
window_stride_h = std::stoi(argv[10]); window_stride_h = std::stoi(argv[10]);
window_stride_w = std::stoi(argv[11]); window_stride_w = std::stoi(argv[11]);
in_left_pad_h = std::stoi(argv[12]); window_dilation_h = std::stoi(argv[12]);
in_left_pad_w = std::stoi(argv[13]); window_dilation_w = std::stoi(argv[13]);
in_right_pad_h = std::stoi(argv[14]); in_left_pad_h = std::stoi(argv[14]);
in_right_pad_w = std::stoi(argv[15]); in_left_pad_w = std::stoi(argv[15]);
in_right_pad_h = std::stoi(argv[16]);
in_right_pad_w = std::stoi(argv[17]);
} }
else else
{ {
printf("arg1: verification (0=no, 1=yes)\n"); printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"); printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n"); printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 15: N, C, Y, X, Hi, Wi, Sy, Sx, LeftPy, LeftPx, RightPy, " printf("arg4 to 15: N, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
"RightPx\n"); "RightPx\n");
exit(0); exit(0);
} }
...@@ -107,6 +111,8 @@ int main(int argc, char* argv[]) ...@@ -107,6 +111,8 @@ int main(int argc, char* argv[])
Wi, Wi,
window_stride_h, window_stride_h,
window_stride_w, window_stride_w,
window_dilation_h,
window_dilation_w,
in_left_pad_h, in_left_pad_h,
in_left_pad_w, in_left_pad_w,
in_right_pad_h, in_right_pad_h,
......
example/13_pool2d_fwd/pool2d_fwd_fp32.cpp
View file @ cbf281f0
...@@ -34,18 +34,20 @@ int main(int argc, char* argv[]) ...@@ -34,18 +34,20 @@ int main(int argc, char* argv[])
bool time_kernel; bool time_kernel;
// Pool shape // Pool shape
ck::index_t N = 128; ck::index_t N = 128;
ck::index_t C = 192; ck::index_t C = 192;
ck::index_t Y = 3; ck::index_t Y = 3;
ck::index_t X = 3; ck::index_t X = 3;
ck::index_t Hi = 71; ck::index_t Hi = 71;
ck::index_t Wi = 71; ck::index_t Wi = 71;
ck::index_t window_stride_h = 2; ck::index_t window_stride_h = 2;
ck::index_t window_stride_w = 2; ck::index_t window_stride_w = 2;
ck::index_t in_left_pad_h = 1; ck::index_t window_dilation_h = 1;
ck::index_t in_left_pad_w = 1; ck::index_t window_dilation_w = 1;
ck::index_t in_right_pad_h = 1; ck::index_t in_left_pad_h = 1;
ck::index_t in_right_pad_w = 1; ck::index_t in_left_pad_w = 1;
ck::index_t in_right_pad_h = 1;
ck::index_t in_right_pad_w = 1;
if(argc == 1) if(argc == 1)
{ {
...@@ -59,31 +61,33 @@ int main(int argc, char* argv[]) ...@@ -59,31 +61,33 @@ int main(int argc, char* argv[])
init_method = std::stoi(argv[2]); init_method = std::stoi(argv[2]);
time_kernel = static_cast<bool>(std::stoi(argv[3])); time_kernel = static_cast<bool>(std::stoi(argv[3]));
} }
else if(argc == 16) else if(argc == 18)
{ {
do_verification = std::stoi(argv[1]); do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]); init_method = std::stoi(argv[2]);
time_kernel = static_cast<bool>(std::stoi(argv[3])); time_kernel = static_cast<bool>(std::stoi(argv[3]));
N = std::stoi(argv[4]); N = std::stoi(argv[4]);
C = std::stoi(argv[5]); C = std::stoi(argv[5]);
Y = std::stoi(argv[6]); Y = std::stoi(argv[6]);
X = std::stoi(argv[7]); X = std::stoi(argv[7]);
Hi = std::stoi(argv[8]); Hi = std::stoi(argv[8]);
Wi = std::stoi(argv[9]); Wi = std::stoi(argv[9]);
window_stride_h = std::stoi(argv[10]); window_stride_h = std::stoi(argv[10]);
window_stride_w = std::stoi(argv[11]); window_stride_w = std::stoi(argv[11]);
in_left_pad_h = std::stoi(argv[12]); window_dilation_h = std::stoi(argv[12]);
in_left_pad_w = std::stoi(argv[13]); window_dilation_w = std::stoi(argv[13]);
in_right_pad_h = std::stoi(argv[14]); in_left_pad_h = std::stoi(argv[14]);
in_right_pad_w = std::stoi(argv[15]); in_left_pad_w = std::stoi(argv[15]);
in_right_pad_h = std::stoi(argv[16]);
in_right_pad_w = std::stoi(argv[17]);
} }
else else
{ {
printf("arg1: verification (0=no, 1=yes)\n"); printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"); printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n"); printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 15: N, C, Y, X, Hi, Wi, Sy, Sx, LeftPy, LeftPx, RightPy, " printf("arg4 to 15: N, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
"RightPx\n"); "RightPx\n");
exit(0); exit(0);
} }
...@@ -107,6 +111,8 @@ int main(int argc, char* argv[]) ...@@ -107,6 +111,8 @@ int main(int argc, char* argv[])
Wi, Wi,
window_stride_h, window_stride_h,
window_stride_w, window_stride_w,
window_dilation_h,
window_dilation_w,
in_left_pad_h, in_left_pad_h,
in_left_pad_w, in_left_pad_w,
in_right_pad_h, in_right_pad_h,
......
example/48_pool3d_fwd/pool3d_fwd_common.hpp
View file @ cbf281f0
...@@ -18,7 +18,45 @@ ...@@ -18,7 +18,45 @@
#include "ck/library/utility/literals.hpp" #include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_pool_fwd.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_pool_fwd.hpp"
template <typename InDataType, template <typename TensorLayout>
std::vector<ck::index_t> f_tensor_strides_ncdhw(ck::index_t N_,
ck::index_t C_,
ck::index_t D,
ck::index_t H,
ck::index_t W,
TensorLayout layout)
{
using namespace ck::literals;
(void)N_;
if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCDHW>::value)
return {C_ * D * H * W, D * H * W, H * W, W, 1_uz};
else if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NDHWC>::value)
return {D * C_ * H * W, 1_uz, C_ * H * W, W * C_, C_};
};
template <typename TensorLayout>
HostTensorDescriptor f_host_tensor_descriptor(std::size_t N_,
std::size_t C_,
std::size_t D,
std::size_t H,
std::size_t W,
TensorLayout layout)
{
using namespace ck::literals;
if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCDHW>::value)
{
return HostTensorDescriptor({N_, C_, D, H, W}, {C_ * D * H * W, D * H * W, H * W, W, 1_uz});
}
else if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NDHWC>::value)
{
return HostTensorDescriptor({N_, C_, D, H, W},
{D * C_ * H * W, 1_uz, C_ * H * W, W * C_, C_});
}
};
template <typename DevicePoolFwdInstance,
typename InDataType,
typename OutDataType, typename OutDataType,
typename ComputeDataType, typename ComputeDataType,
typename IndexDataType, typename IndexDataType,
...@@ -40,6 +78,9 @@ bool pool3d_test(bool do_verification, ...@@ -40,6 +78,9 @@ bool pool3d_test(bool do_verification,
ck::index_t window_stride_d, ck::index_t window_stride_d,
ck::index_t window_stride_h, ck::index_t window_stride_h,
ck::index_t window_stride_w, ck::index_t window_stride_w,
ck::index_t window_dilation_d,
ck::index_t window_dilation_h,
ck::index_t window_dilation_w,
ck::index_t in_left_pad_d, ck::index_t in_left_pad_d,
ck::index_t in_left_pad_h, ck::index_t in_left_pad_h,
ck::index_t in_left_pad_w, ck::index_t in_left_pad_w,
...@@ -47,53 +88,21 @@ bool pool3d_test(bool do_verification, ...@@ -47,53 +88,21 @@ bool pool3d_test(bool do_verification,
ck::index_t in_right_pad_h, ck::index_t in_right_pad_h,
ck::index_t in_right_pad_w) ck::index_t in_right_pad_w)
{ {
using DevicePoolFwdInstance = const ck::index_t Zs = (Z - 1) * window_dilation_d + 1;
ck::tensor_operation::device::DevicePool3dFwd_Input_N_Di_Hi_Wi_C_Output_N_Do_Ho_Wo_C< const ck::index_t Ys = (Y - 1) * window_dilation_h + 1;
InDataType, // InDataType const ck::index_t Xs = (X - 1) * window_dilation_w + 1;
OutDataType, // OutDataType const ck::index_t Do = (Di + in_left_pad_d + in_right_pad_d - Zs) / window_stride_d + 1;
IndexDataType, // IndexDataType const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Ys) / window_stride_h + 1;
ComputeDataType, // ComputeDataType const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - Xs) / window_stride_w + 1;
ReduceOpId,
OutputIndex,
64, // BlockSize
64, // ReduceMThreadClusterSize
1, // ReduceKThreadClusterSize
4, // ReduceMThreadSliceSize
1, // ReduceKThreadSliceSize
4>; // InSrcOutDstVectorSize
const ck::index_t Do = (Di + in_left_pad_d + in_right_pad_d - Z) / window_stride_d + 1;
const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Y) / window_stride_h + 1;
const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - X) / window_stride_w + 1;
const std::vector<ck::index_t> window_spatial_lengths{Z, Y, X}; const std::vector<ck::index_t> window_spatial_lengths{Z, Y, X};
const std::vector<ck::index_t> window_strides{ const std::vector<ck::index_t> window_strides{
window_stride_d, window_stride_h, window_stride_w}; window_stride_d, window_stride_h, window_stride_w};
const std::vector<ck::index_t> window_dilations{
window_dilation_d, window_dilation_h, window_dilation_w};
const std::vector<ck::index_t> input_left_pads{in_left_pad_d, in_left_pad_h, in_left_pad_w}; const std::vector<ck::index_t> input_left_pads{in_left_pad_d, in_left_pad_h, in_left_pad_w};
const std::vector<ck::index_t> input_right_pads{in_right_pad_d, in_right_pad_h, in_right_pad_w}; const std::vector<ck::index_t> input_right_pads{in_right_pad_d, in_right_pad_h, in_right_pad_w};
// tensor layout
auto f_host_tensor_descriptor = [](std::size_t N_,
std::size_t C_,
std::size_t D,
std::size_t H,
std::size_t W,
auto layout) {
using namespace ck::literals;
if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCDHW>::value)
{
return HostTensorDescriptor({N_, C_, D, H, W},
{C_ * D * H * W, D * H * W, H * W, W, 1_uz});
}
else if constexpr(ck::is_same<decltype(layout),
ck::tensor_layout::convolution::NDHWC>::value)
{
return HostTensorDescriptor({N_, C_, D, H, W},
{D * C_ * H * W, 1_uz, C_ * H * W, W * C_, C_});
}
};
Tensor<InDataType> in_n_c_di_hi_wi(f_host_tensor_descriptor(N, C, Di, Hi, Wi, InLayout{})); Tensor<InDataType> in_n_c_di_hi_wi(f_host_tensor_descriptor(N, C, Di, Hi, Wi, InLayout{}));
Tensor<OutDataType> out_n_c_do_ho_wo_host( Tensor<OutDataType> out_n_c_do_ho_wo_host(
f_host_tensor_descriptor(N, C, Do, Ho, Wo, OutLayout{})); f_host_tensor_descriptor(N, C, Do, Ho, Wo, OutLayout{}));
...@@ -126,10 +135,11 @@ bool pool3d_test(bool do_verification, ...@@ -126,10 +135,11 @@ bool pool3d_test(bool do_verification,
{N, C, Di, Hi, Wi}, {N, C, Di, Hi, Wi},
{Z, Y, X}, {Z, Y, X},
{N, C, Do, Ho, Wo}, {N, C, Do, Ho, Wo},
{Di * C * Hi * Wi, 1, C * Hi * Wi, Wi * C, C}, f_tensor_strides_ncdhw(N, C, Di, Hi, Wi, InLayout{}),
{Do * C * Ho * Wo, 1, C * Ho * Wo, Wo * C, C}, f_tensor_strides_ncdhw(N, C, Do, Ho, Wo, OutLayout{}),
{Do * C * Ho * Wo, 1, C * Ho * Wo, Wo * C, C}, f_tensor_strides_ncdhw(N, C, Do, Ho, Wo, OutLayout{}),
window_strides, window_strides,
window_dilations,
input_left_pads, input_left_pads,
input_right_pads, input_right_pads,
{2, 3, 4}); {2, 3, 4});
...@@ -165,6 +175,7 @@ bool pool3d_test(bool do_verification, ...@@ -165,6 +175,7 @@ bool pool3d_test(bool do_verification,
out_indices_n_c_do_ho_wo_host, out_indices_n_c_do_ho_wo_host,
window_spatial_lengths, window_spatial_lengths,
window_strides, window_strides,
window_dilations,
input_left_pads, input_left_pads,
input_right_pads); input_right_pads);
......
example/48_pool3d_fwd/pool3d_fwd_fp16.cpp
View file @ cbf281f0
...@@ -27,31 +27,49 @@ static constexpr auto ReduceOpId = ck::ReduceTensorOp::AVG; ...@@ -27,31 +27,49 @@ static constexpr auto ReduceOpId = ck::ReduceTensorOp::AVG;
static constexpr bool OutputIndex = false; static constexpr bool OutputIndex = false;
static constexpr bool PropagateNan = false; static constexpr bool PropagateNan = false;
using DevicePoolFwdInstance =
ck::tensor_operation::device::DevicePool3dFwd_NDHWC_NDHWC<InDataType,
OutDataType,
IndexDataType,
ComputeDataType,
ReduceOpId,
OutputIndex,
64, // BlockSize
64, // ReduceMThreadClusterSize
1, // ReduceKThreadClusterSize
1, // ReduceMThreadSliceSize
1, // ReduceKThreadSliceSize
1>; // InSrcOutDstVectorSize
int main() int main()
{ {
bool do_verification = true; bool do_verification = true;
bool time_kernel = false; bool time_kernel = false;
// Pool shape // Pool shape
ck::index_t N = 2; ck::index_t N = 2;
ck::index_t C = 32; ck::index_t C = 32;
ck::index_t Z = 2; ck::index_t Z = 2;
ck::index_t Y = 2; ck::index_t Y = 2;
ck::index_t X = 2; ck::index_t X = 2;
ck::index_t Di = 30; ck::index_t Di = 30;
ck::index_t Hi = 30; ck::index_t Hi = 30;
ck::index_t Wi = 30; ck::index_t Wi = 30;
ck::index_t window_stride_d = 2; ck::index_t window_stride_d = 2;
ck::index_t window_stride_h = 2; ck::index_t window_stride_h = 2;
ck::index_t window_stride_w = 2; ck::index_t window_stride_w = 2;
ck::index_t in_left_pad_d = 1; ck::index_t window_dilation_d = 1;
ck::index_t in_left_pad_h = 1; ck::index_t window_dilation_h = 1;
ck::index_t in_left_pad_w = 1; ck::index_t window_dilation_w = 1;
ck::index_t in_right_pad_d = 1; ck::index_t in_left_pad_d = 1;
ck::index_t in_right_pad_h = 1; ck::index_t in_left_pad_h = 1;
ck::index_t in_right_pad_w = 1; ck::index_t in_left_pad_w = 1;
ck::index_t in_right_pad_d = 1;
ck::index_t in_right_pad_h = 1;
ck::index_t in_right_pad_w = 1;
bool pass = pool3d_test<InDataType, bool pass = pool3d_test<DevicePoolFwdInstance,
InDataType,
OutDataType, OutDataType,
ComputeDataType, ComputeDataType,
IndexDataType, IndexDataType,
...@@ -72,6 +90,9 @@ int main() ...@@ -72,6 +90,9 @@ int main()
window_stride_d, window_stride_d,
window_stride_h, window_stride_h,
window_stride_w, window_stride_w,
window_dilation_d,
window_dilation_h,
window_dilation_w,
in_left_pad_d, in_left_pad_d,
in_left_pad_h, in_left_pad_h,
in_left_pad_w, in_left_pad_w,
......
example/49_maxpool2d_bwd/maxpool2d_bwd_bf16.cpp
View file @ cbf281f0
...@@ -24,18 +24,20 @@ int main() ...@@ -24,18 +24,20 @@ int main()
bool time_kernel = false; bool time_kernel = false;
// Pool shape // Pool shape
ck::index_t N = 1; ck::index_t N = 1;
ck::index_t C = 1; ck::index_t C = 1;
ck::index_t Y = 3; ck::index_t Y = 3;
ck::index_t X = 3; ck::index_t X = 3;
ck::index_t Hi = 32; ck::index_t Hi = 32;
ck::index_t Wi = 32; ck::index_t Wi = 32;
ck::index_t window_stride_h = 1; ck::index_t window_stride_h = 1;
ck::index_t window_stride_w = 1; ck::index_t window_stride_w = 1;
ck::index_t in_left_pad_h = 0; ck::index_t window_dilation_h = 1;
ck::index_t in_left_pad_w = 0; ck::index_t window_dilation_w = 1;
ck::index_t in_right_pad_h = 0; ck::index_t in_left_pad_h = 0;
ck::index_t in_right_pad_w = 0; ck::index_t in_left_pad_w = 0;
ck::index_t in_right_pad_h = 0;
ck::index_t in_right_pad_w = 0;
bool pass = maxpool_bwd_test<InDataType, bool pass = maxpool_bwd_test<InDataType,
OutDataType, OutDataType,
...@@ -53,6 +55,8 @@ int main() ...@@ -53,6 +55,8 @@ int main()
Wi, Wi,
window_stride_h, window_stride_h,
window_stride_w, window_stride_w,
window_dilation_h,
window_dilation_w,
in_left_pad_h, in_left_pad_h,
in_left_pad_w, in_left_pad_w,
in_right_pad_h, in_right_pad_h,
......
example/49_maxpool2d_bwd/maxpool2d_bwd_common.hpp
View file @ cbf281f0
...@@ -36,6 +36,8 @@ bool maxpool_bwd_test(bool do_verification, ...@@ -36,6 +36,8 @@ bool maxpool_bwd_test(bool do_verification,
ck::index_t Wi, ck::index_t Wi,
ck::index_t window_stride_h, ck::index_t window_stride_h,
ck::index_t window_stride_w, ck::index_t window_stride_w,
ck::index_t window_dilation_h,
ck::index_t window_dilation_w,
ck::index_t in_left_pad_h, ck::index_t in_left_pad_h,
ck::index_t in_left_pad_w, ck::index_t in_left_pad_w,
ck::index_t in_right_pad_h, ck::index_t in_right_pad_h,
...@@ -44,28 +46,30 @@ bool maxpool_bwd_test(bool do_verification, ...@@ -44,28 +46,30 @@ bool maxpool_bwd_test(bool do_verification,
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DevicePoolFwdInstance = using DevicePoolFwdInstance =
ck::tensor_operation::device::DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C< ck::tensor_operation::device::DevicePool2dFwd_NHWC_NHWC<InDataType, // InDataType
InDataType, // InDataType OutDataType, // OutDataType
OutDataType, // OutDataType IndexDataType, // IndexDataType
IndexDataType, // IndexDataType ComputeDataType, // ComputeDataType
ComputeDataType, // ComputeDataType ck::ReduceTensorOp::MAX,
ck::ReduceTensorOp::MAX, true,
true, // OutputIndex 64, // BlockSize
64, // BlockSize 64, // ReduceMThreadClusterSize
64, // ReduceMThreadClusterSize 1, // ReduceKThreadClusterSize
1, // ReduceKThreadClusterSize 4, // ReduceMThreadSliceSize
4, // ReduceMThreadSliceSize 1, // ReduceKThreadSliceSize
1, // ReduceKThreadSliceSize 1>; // InSrcOutDstVectorSize
1>; // InSrcOutDstVectorSize
using DeviceMaxPoolBwdInstance = ck::tensor_operation::device:: using DeviceMaxPoolBwdInstance = ck::tensor_operation::device::
DeviceIndexPoolBwdImpl<DOutDataType, IndexDataType, DInDataType, 4>; DeviceIndexPoolBwdImpl<DOutDataType, IndexDataType, DInDataType, 4>;
const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Y) / window_stride_h + 1; const ck::index_t Ys = (Y - 1) * window_dilation_h + 1;
const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - X) / window_stride_w + 1; const ck::index_t Xs = (X - 1) * window_dilation_w + 1;
const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Ys) / window_stride_h + 1;
const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - Xs) / window_stride_w + 1;
const std::vector<ck::index_t> window_spatial_lengths{Y, X}; const std::vector<ck::index_t> window_spatial_lengths{Y, X};
const std::vector<ck::index_t> window_strides{window_stride_h, window_stride_w}; const std::vector<ck::index_t> window_strides{window_stride_h, window_stride_w};
const std::vector<ck::index_t> window_dilations{window_dilation_h, window_dilation_w};
const std::vector<ck::index_t> input_left_pads{in_left_pad_h, in_left_pad_w}; const std::vector<ck::index_t> input_left_pads{in_left_pad_h, in_left_pad_w};
const std::vector<ck::index_t> input_right_pads{in_right_pad_h, in_right_pad_w}; const std::vector<ck::index_t> input_right_pads{in_right_pad_h, in_right_pad_w};
...@@ -128,6 +132,7 @@ bool maxpool_bwd_test(bool do_verification, ...@@ -128,6 +132,7 @@ bool maxpool_bwd_test(bool do_verification,
{C * Ho * Wo, 1, Wo * C, C}, {C * Ho * Wo, 1, Wo * C, C},
{C * Ho * Wo, 1, Wo * C, C}, {C * Ho * Wo, 1, Wo * C, C},
window_strides, window_strides,
window_dilations,
input_left_pads, input_left_pads,
input_right_pads, input_right_pads,
{2, 3}); {2, 3});
...@@ -191,6 +196,7 @@ bool maxpool_bwd_test(bool do_verification, ...@@ -191,6 +196,7 @@ bool maxpool_bwd_test(bool do_verification,
indices_n_c_ho_wo_host, indices_n_c_ho_wo_host,
window_spatial_lengths, window_spatial_lengths,
window_strides, window_strides,
window_dilations,
input_left_pads, input_left_pads,
input_right_pads); input_right_pads);
ref_pooling_fwd_invoker.Run(ref_pooling_fwd_argument); ref_pooling_fwd_invoker.Run(ref_pooling_fwd_argument);
......
example/49_maxpool2d_bwd/maxpool2d_bwd_fp16.cpp
View file @ cbf281f0
...@@ -24,18 +24,20 @@ int main() ...@@ -24,18 +24,20 @@ int main()
bool time_kernel = false; bool time_kernel = false;
// Pool shape // Pool shape
ck::index_t N = 1; ck::index_t N = 1;
ck::index_t C = 1; ck::index_t C = 1;
ck::index_t Y = 3; ck::index_t Y = 3;
ck::index_t X = 3; ck::index_t X = 3;
ck::index_t Hi = 32; ck::index_t Hi = 32;
ck::index_t Wi = 32; ck::index_t Wi = 32;
ck::index_t window_stride_h = 1; ck::index_t window_stride_h = 1;
ck::index_t window_stride_w = 1; ck::index_t window_stride_w = 1;
ck::index_t in_left_pad_h = 0; ck::index_t window_dilation_h = 1;
ck::index_t in_left_pad_w = 0; ck::index_t window_dilation_w = 1;
ck::index_t in_right_pad_h = 0; ck::index_t in_left_pad_h = 0;
ck::index_t in_right_pad_w = 0; ck::index_t in_left_pad_w = 0;
ck::index_t in_right_pad_h = 0;
ck::index_t in_right_pad_w = 0;
bool pass = maxpool_bwd_test<InDataType, bool pass = maxpool_bwd_test<InDataType,
OutDataType, OutDataType,
...@@ -53,6 +55,8 @@ int main() ...@@ -53,6 +55,8 @@ int main()
Wi, Wi,
window_stride_h, window_stride_h,
window_stride_w, window_stride_w,
window_dilation_h,
window_dilation_w,
in_left_pad_h, in_left_pad_h,
in_left_pad_w, in_left_pad_w,
in_right_pad_h, in_right_pad_h,
......
example/49_maxpool2d_bwd/maxpool2d_bwd_fp32.cpp
View file @ cbf281f0
...@@ -24,18 +24,20 @@ int main() ...@@ -24,18 +24,20 @@ int main()
bool time_kernel = false; bool time_kernel = false;
// Pool shape // Pool shape
ck::index_t N = 1; ck::index_t N = 1;
ck::index_t C = 1; ck::index_t C = 1;
ck::index_t Y = 2; ck::index_t Y = 2;
ck::index_t X = 2; ck::index_t X = 2;
ck::index_t Hi = 32; ck::index_t Hi = 32;
ck::index_t Wi = 32; ck::index_t Wi = 32;
ck::index_t window_stride_h = 2; ck::index_t window_stride_h = 2;
ck::index_t window_stride_w = 2; ck::index_t window_stride_w = 2;
ck::index_t in_left_pad_h = 0; ck::index_t window_dilation_h = 1;
ck::index_t in_left_pad_w = 0; ck::index_t window_dilation_w = 1;
ck::index_t in_right_pad_h = 0; ck::index_t in_left_pad_h = 0;
ck::index_t in_right_pad_w = 0; ck::index_t in_left_pad_w = 0;
ck::index_t in_right_pad_h = 0;
ck::index_t in_right_pad_w = 0;
bool pass = maxpool_bwd_test<InDataType, bool pass = maxpool_bwd_test<InDataType,
OutDataType, OutDataType,
...@@ -53,6 +55,8 @@ int main() ...@@ -53,6 +55,8 @@ int main()
Wi, Wi,
window_stride_h, window_stride_h,
window_stride_w, window_stride_w,
window_dilation_h,
window_dilation_w,
in_left_pad_h, in_left_pad_h,
in_left_pad_w, in_left_pad_w,
in_right_pad_h, in_right_pad_h,
......
include/ck/ck.hpp
View file @ cbf281f0
...@@ -125,6 +125,9 @@ ...@@ -125,6 +125,9 @@
// `s_nop`s to avoid hazard // `s_nop`s to avoid hazard
#define CK_USE_AMD_V_DOT_INLINE_ASM 0 #define CK_USE_AMD_V_DOT_INLINE_ASM 0
// inner product using V_DOT with DPP8 modifiers
#define CK_USE_AMD_V_DOT_DPP8_INLINE_ASM 1
// block synchronization only s_wait lgkmcnt(0), not vmcnt(0) // block synchronization only s_wait lgkmcnt(0), not vmcnt(0)
#define CK_EXPERIMENTAL_BLOCK_SYNC_LDS_WITHOUT_SYNC_VMEM 1 #define CK_EXPERIMENTAL_BLOCK_SYNC_LDS_WITHOUT_SYNC_VMEM 1
......
include/ck/tensor_operation/gpu/block/blockwise_gemm_dl_dpp8.hpp 0 → 100644
View file @ cbf281f0
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/utility/amd_gemm_dpp.hpp"
#include "ck/utility/common_header.hpp"
#include "ck/tensor_description/tensor_adaptor.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_v4r1.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_contraction_dl_dpp8.hpp"
namespace ck {
/**
* DPP8 version of blockwise GEMM algorithm. It uses DPP8 instruction modifier to limit
* the data loaded from LDS to registers.
*
* The algorithm groups threads into groups of size `dpp8::lane_group_size` and splits the matrix C
* between them in such a way that threads from the same group need the same chunk of either
* matrix A (or B, respectively). Without the usage of DPP8, each thread would need to load the
* whole chunk from LDS to its own register space.
* Usage of DPP8 modifiers allow each thread to load less data, exactly `1 / dpp8::lane_group_size`
* of the chunk, and then share that data with other threads from the same lane group.
*
* Assumptions coming from the usage of DPP8:
* 1. `BM10BN10ThreadClusterBM10Xs[1] == dpp8::lane_group_size` or
* `BM10BN10ThreadClusterBN10Xs[1] == dpp8::lane_group_size` -
* - it makes consecutive `dpp8::lane_group_size` threads use the same chunk of either
* matrix A or B;
* - based on these values we determine which matrix to share.
* 2. `BM1PerThreadBM11 % dpp8::lane_group_size == 0` (if sharing A) or
* `BN1PerThreadBN11 % dpp8::lane_group_size == 0` (if sharing B) -
* - we have to make sure that the data to split is divisible by the number of
* threads in the group.
*
* General algorithm:
* C[BM0, BM1, BN0, BN1] += transpose(A[K, BM0, BM1]) * B[K, BN0, BN1]
* A and B are visible to the whole block, C is distributed among each thread
* Assume:
* 1. A:
* 1. ABlockDesc_BK0_BM_BK1 is known at compile-time
* 2. ABlockBuffer is DynamicBuffer
* 2. B:
* 1. BBlockDesc_BK0_BN_BK1 is known at compile-time
* 2. BBlockBuffer is DynamicBuffer
* 3. C:
* 1. CThreadDesc_BM0_BM11_BN0_BN11 is known at compile-time
* 2. CThreadBuffer is StaticBuffer
* 4. BM10BN10ThreadClusterBM10Xs::Size() = BM10BN10ThreadClusterBN10Xs::Size() == 2
*/
template <index_t BlockSize,
typename FloatA,
typename FloatB,
typename FloatC,
typename ABlockDesc_BK0_BM_BK1,
typename BBlockDesc_BK0_BN_BK1,
index_t BM1PerThreadBM11,
index_t BN1PerThreadBN11,
index_t BK0PerThread,
typename BM10BN10ThreadClusterBM10Xs, // Sequence<BM10BN10ThreadClusterBM100,
// BM10BN10ThreadClusterBM101, ...>
typename BM10BN10ThreadClusterBN10Xs, // Sequence<BM10BN10ThreadClusterBN100,
// BM10BN10ThreadClusterBN101, ...>
index_t AThreadCopyScalarPerVector_BM11,
index_t BThreadCopyScalarPerVector_BN11,
typename enable_if<ABlockDesc_BK0_BM_BK1::IsKnownAtCompileTime() &&
BBlockDesc_BK0_BN_BK1::IsKnownAtCompileTime(),
bool>::type = false>
struct BlockwiseGemmDlDpp8_A_BK0_BM_BK1_B_BK0_BN_BK1_C_BM0_BM1_BN0_BN1_loop_BM0_BN0
{
using AIndex = MultiIndex<4>;
using BIndex = MultiIndex<4>;
using CIndex = MultiIndex<4>;
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr auto I2 = Number<2>{};
static constexpr auto I3 = Number<3>{};
static constexpr index_t BK0 = ABlockDesc_BK0_BM_BK1{}.GetLength(I0);
static constexpr index_t BK1 = ABlockDesc_BK0_BM_BK1{}.GetLength(I2);
static constexpr index_t BM = ABlockDesc_BK0_BM_BK1{}.GetLength(I1);
static constexpr index_t BN = BBlockDesc_BK0_BN_BK1{}.GetLength(I1);
static constexpr index_t BM100 = BM10BN10ThreadClusterBM10Xs{}[I0];
static constexpr index_t BN100 = BM10BN10ThreadClusterBN10Xs{}[I0];
static constexpr index_t BM101 = BM10BN10ThreadClusterBM10Xs{}[I1];
static constexpr index_t BN101 = BM10BN10ThreadClusterBN10Xs{}[I1];
static constexpr index_t BM11 = BM1PerThreadBM11;
static constexpr index_t BN11 = BN1PerThreadBN11;
static constexpr index_t BM1 = BM100 * BM101 * BM11;
static constexpr index_t BN1 = BN100 * BN101 * BN11;
static constexpr index_t BM0 = BM / BM1;
static constexpr index_t BN0 = BN / BN1;
// We assume that either `BM101` or `BN101` is equal to `dpp8::lane_group_size`. It makes all
// threads in a lane group need the same chunk of B or A matrices and we can share them using
// DPP.
static_assert(BM101 == dpp8::lane_group_size || BN101 == dpp8::lane_group_size);
static constexpr bool ShareB = BM101 == dpp8::lane_group_size ? true : false;
static constexpr bool ShareA = !ShareB;
// If DPP shares A (B, respectively), lane group gets `BM1PerThreadBM11` (`BN1PerThreadBN11`,
// respectively) elements, so we split them between threads in lane group so each thread loads
// less data from LDS.
static constexpr index_t BM1PerThread =
ShareA ? BM1PerThreadBM11 / dpp8::lane_group_size : BM1PerThreadBM11;
static constexpr index_t BN1PerThread =
ShareB ? BN1PerThreadBN11 / dpp8::lane_group_size : BN1PerThreadBN11;
__host__ __device__ static constexpr auto
MakeABlockDescriptor_BK0_BM0_BM1_BK1(const ABlockDesc_BK0_BM_BK1& a_block_desc_bk0_bm_bk1)
{
const auto a_block_bk0_bm0_bm1_bk1 = transform_tensor_descriptor(
a_block_desc_bk0_bm_bk1,
make_tuple(make_pass_through_transform(Number<BK0>{}),
make_unmerge_transform(make_tuple(Number<BM0>{}, Number<BM1>{})),
make_pass_through_transform(Number<BK1>{})),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
return a_block_bk0_bm0_bm1_bk1;
}
__host__ __device__ static constexpr auto
MakeBBlockDescriptor_BK0_BN0_BN1_BK1(const BBlockDesc_BK0_BN_BK1& b_block_desc_bk0_bn_bk1)
{
const auto b_block_desc_bk0_bn0_bn1_bk1 = transform_tensor_descriptor(
b_block_desc_bk0_bn_bk1,
make_tuple(make_pass_through_transform(Number<BK0>{}),
make_unmerge_transform(make_tuple(Number<BN0>{}, Number<BN1>{})),
make_pass_through_transform(Number<BK1>{})),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
return b_block_desc_bk0_bn0_bn1_bk1;
}
__host__ __device__ static constexpr auto
MakeCBlockAdaptor_BM0_BM100_BM101_BM11_BN0_BN100_BN101_BN11_To_BM_BN()
{
// upper: [BM0, BM100, BM101, BM11, BN0, BN100, BN101, BN11]
// lower: [BM, BN]
constexpr auto c_block_adaptor_m0_m100_m101_m11_n0_n100_n101_n11_to_m_n =
make_single_stage_tensor_adaptor(
make_tuple(make_unmerge_transform(make_tuple(
Number<BM0>{}, Number<BM100>{}, Number<BM101>{}, Number<BM11>{})),
make_unmerge_transform(make_tuple(
Number<BN0>{}, Number<BN100>{}, Number<BN101>{}, Number<BN11>{}))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 1, 2, 3>{}, Sequence<4, 5, 6, 7>{}));
return c_block_adaptor_m0_m100_m101_m11_n0_n100_n101_n11_to_m_n;
}
__host__ __device__ static constexpr auto
MakeCBlockAdaptor_BM0_BM100_BM101_BM11_BN0_BN100_BN101_BN11_To_BM0_BM1_BN0_BN1()
{
// upper: [BM0, BM100, BM101, BM11, BN0, BN100, BN101, BN11]
// lower: [BM0, BM1, BN0, BN1]
constexpr auto c_block_adaptor_m0_m100_m101_m11_n0_n100_n101_n11_to_m0_m1_n0_n1 =
make_single_stage_tensor_adaptor(
make_tuple(make_pass_through_transform(Number<BM0>{}),
make_unmerge_transform(
make_tuple(Number<BM100>{}, Number<BM101>{}, Number<BM11>{})),
make_pass_through_transform(Number<BN0>{}),
make_unmerge_transform(
make_tuple(Number<BN100>{}, Number<BN101>{}, Number<BN11>{}))),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2, 3>{}, Sequence<4>{}, Sequence<5, 6, 7>{}));
return c_block_adaptor_m0_m100_m101_m11_n0_n100_n101_n11_to_m0_m1_n0_n1;
}
__host__ __device__ static constexpr auto GetCThreadTensorLengths_BM0_BM1_BN0_BN1()
{
return Sequence<BM0, BM11, BN0, BN11>{};
}
static constexpr auto a_block_desc_bk0_bm0_bm1_bk1_ =
MakeABlockDescriptor_BK0_BM0_BM1_BK1(ABlockDesc_BK0_BM_BK1{});
static constexpr auto b_block_desc_bk0_bn0_bn1_bk1_ =
MakeBBlockDescriptor_BK0_BN0_BN1_BK1(BBlockDesc_BK0_BN_BK1{});
public:
__device__ BlockwiseGemmDlDpp8_A_BK0_BM_BK1_B_BK0_BN_BK1_C_BM0_BM1_BN0_BN1_loop_BM0_BN0()
: c_thread_origin_data_idx_{CalculateCThreadOriginOnBlock_BM0_BM1_BN0_BN1(
get_thread_local_1d_id())},
a_thread_copy_{CalculateAThreadOriginOnBlock_BK0_BM0_BM1_BK1()},
b_thread_copy_{CalculateBThreadOriginOnBlock_BK0_BN0_BN1_BK1()}
{
static_assert(ABlockDesc_BK0_BM_BK1::IsKnownAtCompileTime() &&
BBlockDesc_BK0_BN_BK1::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time");
static_assert(BM % BM1 == 0 && BN % BN1 == 0, "wrong!");
static_assert(ABlockDesc_BK0_BM_BK1{}.GetLength(I0) ==
BBlockDesc_BK0_BN_BK1{}.GetLength(I0),
"wrong! K dimension not consistent");
static_assert(BM10BN10ThreadClusterBM10Xs::Size() == 2 &&
BM10BN10ThreadClusterBN10Xs::Size() == 2,
"wrong!");
}
__device__ static CIndex CalculateCThreadOriginOnBlock_BM0_BM1_BN0_BN1(index_t thread_id)
{
// lower: [BM0, BM1, BN0, BN1]
// upper: [BM0, BM100, BM101, BM11, BN0, BN100, BN101, BN11]
constexpr auto adaptor0 =
MakeCBlockAdaptor_BM0_BM100_BM101_BM11_BN0_BN100_BN101_BN11_To_BM0_BM1_BN0_BN1();
// lower: [BM0, BM100, BM101, BM11, BN0, BN100, BN101, BN11]
// upper: [Tid, BM0, BM11, BN0, BN11]
constexpr auto adaptor1 = make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(BM100, BN100, BM101, BN101)),
make_pass_through_transform(BM0),
make_pass_through_transform(BM11),
make_pass_through_transform(BN0),
make_pass_through_transform(BN11)),
make_tuple(
Sequence<1, 5, 2, 6>{}, Sequence<0>{}, Sequence<3>{}, Sequence<4>{}, Sequence<7>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
constexpr auto adaptor = chain_tensor_adaptors(adaptor0, adaptor1);
return adaptor.CalculateBottomIndex(make_multi_index(thread_id, 0, 0, 0, 0));
}
__device__ AIndex CalculateAThreadOriginOnBlock_BK0_BM0_BM1_BK1()
{
const auto offsetBM0 = c_thread_origin_data_idx_[I0];
// If sharing matrix A, we need a separate BM1 offset for each thread in lane group.
const auto offsetBM1 = ShareA ? c_thread_origin_data_idx_[I1] +
dpp8::get_thread_idx_in_lane_group() * BM1PerThread
: c_thread_origin_data_idx_[I1];
return make_tuple(0, offsetBM0, offsetBM1, 0);
}
__device__ BIndex CalculateBThreadOriginOnBlock_BK0_BN0_BN1_BK1()
{
const auto offsetBN0 = c_thread_origin_data_idx_[I2];
// If sharing matrix B, we need a separate BN1 offset for each thread in lane group.
const auto offsetBN1 = ShareB ? c_thread_origin_data_idx_[I3] +
dpp8::get_thread_idx_in_lane_group() * BN1PerThread
: c_thread_origin_data_idx_[I3];
return make_tuple(0, offsetBN0, offsetBN1, 0);
}
template <typename CThreadDesc_BM0_BM11_BN0_BN11,
typename ABlockBuffer,
typename BBlockBuffer,
typename CThreadBuffer>
__device__ void Run(const CThreadDesc_BM0_BM11_BN0_BN11&,
const ABlockBuffer& a_block_buf,
const BBlockBuffer& b_block_buf,
CThreadBuffer& c_thread_buf) const
{
static_assert(CThreadDesc_BM0_BM11_BN0_BN11::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time");
auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatA>(
a_thread_desc_bk0_bm0_bm1_bk1_.GetElementSpaceSize());
auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatB>(
b_thread_desc_bk0_bn0_bn1_bk1_.GetElementSpaceSize());
constexpr auto threadwise_contraction =
ThreadwiseContractionDlDpp8_A_TK0_TM0_TM1_TK1_B_TK0_TN0_TN1_TK1_C_TM0_TM1_TN0_TN1<
FloatA,
FloatB,
FloatC,
decltype(a_thread_desc_bk0_bm0_bm1_bk1_),
decltype(b_thread_desc_bk0_bn0_bn1_bk1_),
CThreadDesc_BM0_BM11_BN0_BN11,
Sequence<BK0PerThread, BK1>,
Sequence<1, BM1PerThreadBM11>,
Sequence<1, BN1PerThreadBN11>,
ShareA>{};
static_for<0, BN0, 1>{}([&](auto bn0) {
static_for<0, BM0, 1>{}([&](auto bm0) {
a_thread_copy_.Run(a_block_desc_bk0_bm0_bm1_bk1_,
make_tuple(I0, bm0, I0, I0),
a_block_buf,
a_thread_desc_bk0_bm0_bm1_bk1_,
make_tuple(I0, I0, I0, I0),
a_thread_buf);
b_thread_copy_.Run(b_block_desc_bk0_bn0_bn1_bk1_,
make_tuple(I0, bn0, I0, I0),
b_block_buf,
b_thread_desc_bk0_bn0_bn1_bk1_,
make_tuple(I0, I0, I0, I0),
b_thread_buf);
threadwise_contraction.Run(a_thread_buf,
make_tuple(I0, I0, I0, I0),
b_thread_buf,
make_tuple(I0, I0, I0, I0),
c_thread_buf,
make_tuple(bm0, I0, bn0, I0));
static_for<BK0PerThread, BK0, BK0PerThread>{}([&](auto bk0) {
a_thread_copy_.Run(a_block_desc_bk0_bm0_bm1_bk1_,
make_tuple(bk0, bm0, I0, I0),
a_block_buf,
a_thread_desc_bk0_bm0_bm1_bk1_,
make_tuple(I0, I0, I0, I0),
a_thread_buf);
b_thread_copy_.Run(b_block_desc_bk0_bn0_bn1_bk1_,
make_tuple(bk0, bn0, I0, I0),
b_block_buf,
b_thread_desc_bk0_bn0_bn1_bk1_,
make_tuple(I0, I0, I0, I0),
b_thread_buf);
threadwise_contraction.Run(a_thread_buf,
make_tuple(I0, I0, I0, I0),
b_thread_buf,
make_tuple(I0, I0, I0, I0),
c_thread_buf,
make_tuple(bm0, I0, bn0, I0));
});
});
});
}
private:
// A[BK0, BM0, BM1, BK1]
static constexpr auto a_thread_desc_bk0_bm0_bm1_bk1_ = make_naive_tensor_descriptor_packed(
make_tuple(Number<BK0PerThread>{}, Number<BM0>{}, Number<BM1PerThread>{}, Number<BK1>{}));
// B[BK0, BN0, BN1, BK1]
static constexpr auto b_thread_desc_bk0_bn0_bn1_bk1_ = make_naive_tensor_descriptor_packed(
make_tuple(Number<BK0PerThread>{}, Number<BN0>{}, Number<BN1PerThread>{}, Number<BK1>{}));
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4r1<
FloatA,
FloatA,
decltype(a_block_desc_bk0_bm0_bm1_bk1_),
decltype(a_thread_desc_bk0_bm0_bm1_bk1_),
Sequence<BK0PerThread, 1, BM1PerThread, BK1>, // SliceLengths
Sequence<0, 1, 2, 3>, // DimAccessOrder
Sequence<1, 1, BM1PerThread, BK1>, // SrcVectorTensorLengths
Sequence<0, 1, 2, 3>>; // SrcVectorTensorContiguousDimOrder
using BThreadCopy = ThreadwiseTensorSliceTransfer_v4r1<
FloatB,
FloatB,
decltype(b_block_desc_bk0_bn0_bn1_bk1_),
decltype(b_thread_desc_bk0_bn0_bn1_bk1_),
Sequence<BK0PerThread, 1, BN1PerThread, BK1>, // SliceLengths
Sequence<0, 1, 2, 3>, // DimAccessOrder
Sequence<1, 1, BN1PerThread, BK1>, // SrcVectorTensorLengths
Sequence<0, 1, 2, 3>>; // SrcVectorTensorContiguousDimOrder
CIndex c_thread_origin_data_idx_;
AThreadCopy a_thread_copy_;
BThreadCopy b_thread_copy_;
};
} // namespace ck
include/ck/tensor_operation/gpu/block/blockwise_gemm_dl_v2r3.hpp
View file @ cbf281f0
...@@ -11,7 +11,7 @@ ...@@ -11,7 +11,7 @@
namespace ck { namespace ck {
// C[BM0, BM1, BN0, BN1] += transpose(A[K, BM0, BM1]) * B[K, BN0, BN1] // C[BM0, BM1, BN0, BN1] += transpose(A[K, BM0, BM1]) * B[K, BN0, BN1]
// A and B are visable to the whole block, C is distributed among each thread // A and B are visible to the whole block, C is distributed among each thread
// Assume: // Assume:
// 1. A: // 1. A:
// 1. ABlockDesc_BK0_BM_BK1 is known at compile-time // 1. ABlockDesc_BK0_BM_BK1 is known at compile-time
......
include/ck/tensor_operation/gpu/device/device_pool_fwd.hpp
View file @ cbf281f0
...@@ -17,6 +17,8 @@ template <index_t InOutRank, ...@@ -17,6 +17,8 @@ template <index_t InOutRank,
typename InDataType, typename InDataType,
typename OutDataType, typename OutDataType,
typename IndexDataType, typename IndexDataType,
typename InLayout,
typename OutLayout,
ReduceTensorOp ReduceOpId, ReduceTensorOp ReduceOpId,
bool OutputIndex> bool OutputIndex>
struct DevicePoolFwd : public BaseOperator struct DevicePoolFwd : public BaseOperator
...@@ -25,13 +27,14 @@ struct DevicePoolFwd : public BaseOperator ...@@ -25,13 +27,14 @@ struct DevicePoolFwd : public BaseOperator
MakeArgumentPointer(const void* p_in_dev, MakeArgumentPointer(const void* p_in_dev,
void* p_out_dev, void* p_out_dev,
void* p_out_indices_dev, void* p_out_indices_dev,
std::vector<ck::index_t> input_lengths, std::vector<ck::index_t> input_n_c_wis_lengths,
std::vector<ck::index_t> window_lengths, std::vector<ck::index_t> window_xs_lengths,
std::vector<ck::index_t> output_lengths, std::vector<ck::index_t> output_n_c_wos_lengths,
std::vector<ck::index_t> input_stride, std::vector<ck::index_t> input_n_c_wis_stride,
std::vector<ck::index_t> output_stride, std::vector<ck::index_t> output_n_c_wis_stride,
std::vector<ck::index_t> indices_stride, std::vector<ck::index_t> indices_n_c_wis_stride,
std::vector<ck::index_t> window_strides, std::vector<ck::index_t> window_xs_strides,
std::vector<ck::index_t> window_xs_dilations,
std::vector<ck::index_t> input_left_pads, std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads, std::vector<ck::index_t> input_right_pads,
std::vector<ck::index_t> pooling_dims) = 0; std::vector<ck::index_t> pooling_dims) = 0;
......
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