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Merge commit '2b8302eb6d2217c0f537c28538265f4003ec416e' into develop

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assistant-librarian[bot]
2025-12-30 16:14:01 +00:00
parent 6850e5e7bb
commit ee8ec5af8d
44 changed files with 4923 additions and 477 deletions
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21
example/38_grouped_conv_bwd_data_multiple_d/CMakeLists.txt
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@@ -9,8 +9,29 @@ add_example_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_
add_example_executable(example_grouped_conv_bwd_data_xdl_fp16_comp_bf8_fp8 grouped_conv_bwd_data_xdl_fp16_comp_bf8_fp8.cpp)
add_example_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_xdl_fp16_comp_bf8_fp8)
add_example_executable(example_grouped_conv_bwd_data_wmma_v3_fp16_comp_bf8_fp8 grouped_conv_bwd_data_wmma_v3_fp16_comp_bf8_fp8.cpp)
add_example_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_wmma_v3_fp16_comp_bf8_fp8)
add_example_executable(example_grouped_conv_bwd_data_bias_relu_xdl_fp16 grouped_conv_bwd_data_bias_relu_xdl_fp16.cpp)
add_example_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_bias_relu_xdl_fp16)
add_example_executable(example_grouped_conv_bwd_data_bias_relu_wmma_v3_fp16 grouped_conv_bwd_data_bias_relu_wmma_v3_fp16.cpp)
add_example_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_bias_relu_wmma_v3_fp16)
add_example_executable(example_grouped_conv_bwd_data_wmma_fp16 grouped_conv_bwd_data_wmma_fp16.cpp)
add_example_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_wmma_fp16)
add_example_executable(example_grouped_conv_bwd_data_wmma_v3_bf16 grouped_conv_bwd_data_wmma_v3_bf16.cpp)
add_example_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_wmma_v3_bf16)
add_example_executable(example_grouped_conv3d_bwd_data_wmma_v3_bf16 grouped_conv3d_bwd_data_wmma_v3_bf16.cpp)
add_example_dependencies(example_grouped_conv_bwd_data example_grouped_conv3d_bwd_data_wmma_v3_bf16)
add_example_executable(example_grouped_conv3d_bwd_data_wmma_v3_fp16 grouped_conv3d_bwd_data_wmma_v3_fp16.cpp)
add_example_dependencies(example_grouped_conv_bwd_data example_grouped_conv3d_bwd_data_wmma_v3_fp16)
add_example_executable(example_grouped_conv_bwd_data_wmma_v3_fp16 grouped_conv_bwd_data_wmma_v3_fp16.cpp)
add_example_dependencies(example_grouped_conv_bwd_data example_grouped_conv_bwd_data_wmma_v3_fp16)

4
example/38_grouped_conv_bwd_data_multiple_d/common.hpp
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@@ -37,7 +37,11 @@ static inline constexpr ck::index_t NDimSpatial = 2;
static constexpr auto ConvBwdDataDefault =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::Default;
static constexpr auto ConvBwdDataFilter1x1Stride1Pad0 =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0;
using FP16 = ck::half_t;
using BF16 = ck::bhalf_t;
using FP32 = float;
using FP8 = ck::f8_t;
using BF8 = ck::bf8_t;

116
example/38_grouped_conv_bwd_data_multiple_d/common_conv3d.hpp Normal file
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@@ -0,0 +1,116 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
#include <cstdlib>
#include <initializer_list>
#include <iostream>
#include <numeric>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/convolution_backward_data_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_conv_bwd_data.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
#include "ck/library/utility/convolution_parameter.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
using ::ck::DeviceMem;
using ::ck::hip_check_error;
using ::ck::HostTensorDescriptor;
using ::ck::Tensor;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static inline constexpr ck::index_t NDimSpatial = 3;
static constexpr auto ConvBwdDataDefault =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::Default;
static constexpr auto ConvBwdDataFilter1x1Stride1Pad0 =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0;
using FP16 = ck::half_t;
using BF16 = ck::bhalf_t;
using FP32 = float;
using FP8 = ck::f8_t;
using BF8 = ck::bf8_t;
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
};
#define DefaultConvParams \
ck::utils::conv::ConvParam \
{ \
NDimSpatial, 32, 4, 192, 192, {3, 3, 3}, {28, 28, 28}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, \
{ \
1, 1, 1 \
} \
}
inline void print_help_msg()
{
std::cerr << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: time kernel (0=no, 1=yes)\n"
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
}
inline bool parse_cmd_args(int argc,
char* argv[],
ExecutionConfig& config,
ck::utils::conv::ConvParam& conv_params)
{
constexpr int num_execution_config_args =
3; // arguments for do_verification, init_method, time_kernel
constexpr int num_conv_param_leading_args = 5; // arguments for num_dim_spatial_, G_, N_, K_, C_
constexpr int threshold_to_catch_partial_args = 1 + num_execution_config_args;
constexpr int threshold_to_catch_all_args =
threshold_to_catch_partial_args + num_conv_param_leading_args;
if(argc == 1)
{
// use default
config = ExecutionConfig{};
}
// catch only ExecutionConfig arguments
else if(argc == threshold_to_catch_partial_args)
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
}
// catch both ExecutionConfig & ConvParam arguments
else if(threshold_to_catch_all_args < argc && ((argc - threshold_to_catch_all_args) % 3 == 0))
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
const ck::index_t num_dim_spatial = std::stoi(argv[4]);
conv_params = ck::utils::conv::parse_conv_param(
num_dim_spatial, threshold_to_catch_partial_args + 1, argv);
}
else
{
print_help_msg();
return false;
}
return true;
}

31
example/38_grouped_conv_bwd_data_multiple_d/grouped_conv3d_bwd_data_wmma_v3_bf16.cpp Normal file
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@@ -0,0 +1,31 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_wmma_cshuffle_v3.hpp"
#include "common_conv3d.hpp"
using OutDataType = BF16;
using WeiDataType = BF16;
using AccDataType = FP32;
using CShuffleDataType = BF16;
using DsDataType = ck::Tuple<>;
using InDataType = BF16;
using InLayout = ck::tensor_layout::convolution::NDHWGC;
using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
using DsLayout = ck::Tuple<>;
using OutLayout = ck::tensor_layout::convolution::NDHWGK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
// clang-format off
using DeviceConvInstance = ck::tensor_operation::device::DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffleV3
// ######| NDimSpatial| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle | CShuffle | CDEBlockTransfer| CDEBlockTransfer|
// ######| | | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| Size| Block| Block| Block| | | Wmma| Wmma| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| MRepeat | NRepeat | _MBlock_MPerBlock| ScalarPerVector|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< NDimSpatial, OutLayout, WeiLayout, DsLayout, InLayout, OutDataType, WeiDataType, AccDataType, CShuffleDataType, DsDataType, InDataType, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 128, 64, 64, 32, 8, 8, 16, 16, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, S<8,8,8>>;
// clang-format on
#include "run_grouped_conv3d_bwd_data_example.inc"
int main(int argc, char* argv[]) { return run_grouped_conv_bwd_data_example(argc, argv); }

30
example/38_grouped_conv_bwd_data_multiple_d/grouped_conv3d_bwd_data_wmma_v3_fp16.cpp Normal file
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@@ -0,0 +1,30 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_wmma_cshuffle_v3.hpp"
#include "common_conv3d.hpp"
using OutDataType = FP16;
using WeiDataType = FP16;
using AccDataType = FP32;
using CShuffleDataType = FP16;
using DsDataType = ck::Tuple<>;
using InDataType = FP16;
using InLayout = ck::tensor_layout::convolution::NDHWGC;
using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
using DsLayout = ck::Tuple<>;
using OutLayout = ck::tensor_layout::convolution::NDHWGK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
// clang-format off
using DeviceConvInstance = ck::tensor_operation::device::DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffleV3
// ######| NDimSpatial| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle | CShuffle | CDEBlockTransfer| CDEBlockTransfer|
// ######| | | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| Size| Block| Block| Block| | | Wmma| Wmma| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| MRepeat | NRepeat | _MBlock_MPerBlock| ScalarPerVector|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< NDimSpatial, OutLayout, WeiLayout, DsLayout, InLayout, OutDataType, WeiDataType, AccDataType, CShuffleDataType, DsDataType, InDataType, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, true, true, 128, 64, 64, 32, 8, 8, 16, 16, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, S<8,8,8>>;
// clang-format on
#include "run_grouped_conv3d_bwd_data_example.inc"
int main(int argc, char* argv[]) { return run_grouped_conv_bwd_data_example(argc, argv); }

34
example/38_grouped_conv_bwd_data_multiple_d/grouped_conv_bwd_data_bias_relu_wmma_v3_fp16.cpp Normal file
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@@ -0,0 +1,34 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_wmma_cshuffle_v3.hpp"
#include "common.hpp"
using OutDataType = FP16;
using WeiDataType = FP16;
using AccDataType = FP32;
using CShuffleDataType = FP16;
using BiasDataType = FP16; // bias
using InDataType = FP16;
using OutLayout = ck::tensor_layout::convolution::GNHWK;
using WeiLayout = ck::tensor_layout::convolution::GKYXC;
using BiasLayout = ck::Tuple<ck::tensor_layout::convolution::G_C>;
using InLayout = ck::tensor_layout::convolution::GNHWC;
using OutElementOp = PassThrough;
using WeiElementOp = PassThrough;
using InElementOp = ck::tensor_operation::element_wise::AddRelu;
// clang-format off
using DeviceConvInstance = ck::tensor_operation::device::DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffleV3
// ######| NDimSpatial| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ######| | | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< NDimSpatial, OutLayout, WeiLayout, BiasLayout, InLayout, OutDataType, WeiDataType, AccDataType, CShuffleDataType, ck::Tuple<BiasDataType>, InDataType, OutElementOp, WeiElementOp, InElementOp, ConvBwdDataDefault, true, true, 64, 64, 64, 32, 8, 8, 16, 16, 4, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, S<8,8,8>>;
// clang-format on
#include "run_grouped_conv_bwd_data_bias_relu_example.inc"
int main(int argc, char* argv[]) { return run_grouped_conv_bwd_data_bias_relu_example(argc, argv); }

34
example/38_grouped_conv_bwd_data_multiple_d/grouped_conv_bwd_data_wmma_v3_bf16.cpp Normal file
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@@ -0,0 +1,34 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_wmma_cshuffle_v3.hpp"
#include "common.hpp"
using OutDataType = BF16;
using WeiDataType = BF16;
using AccDataType = FP32;
using CShuffleDataType = BF16;
using DsDataType = ck::Tuple<>;
using InDataType = BF16;
using OutLayout = ck::tensor_layout::convolution::GNHWK;
using WeiLayout = ck::tensor_layout::convolution::GKYXC;
using DsLayout = ck::Tuple<>;
using InLayout = ck::tensor_layout::convolution::GNHWC;
using OutElementOp = PassThrough;
using WeiElementOp = PassThrough;
using InElementOp = PassThrough;
// clang-format off
using DeviceConvInstance = ck::tensor_operation::device::DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffleV3
// ######| NDimSpatial| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle | CShuffle | CDEBlockTransfer| CDEBlockTransfer|
// ######| | | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| Size| Block| Block| Block| | | Wmma| Wmma| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| MRepeat | NRepeat | _MBlock_MPerBlock| ScalarPerVector|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< NDimSpatial, OutLayout, WeiLayout, DsLayout, InLayout, OutDataType, WeiDataType, AccDataType, CShuffleDataType, DsDataType, InDataType, OutElementOp, WeiElementOp, InElementOp, ConvBwdDataDefault, true, true, 128, 64, 64, 32, 8, 8, 16, 16, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, S<8,8,8>>;
// clang-format on
#include "run_grouped_conv_bwd_data_example.inc"
int main(int argc, char* argv[]) { return run_grouped_conv_bwd_data_example(argc, argv); }

35
example/38_grouped_conv_bwd_data_multiple_d/grouped_conv_bwd_data_wmma_v3_fp16.cpp Normal file
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@@ -0,0 +1,35 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_wmma_cshuffle_v3.hpp"
#include "common.hpp"
using OutDataType = FP16;
using WeiDataType = FP16;
using AccDataType = FP32;
using CShuffleDataType = FP16;
using DsDataType = ck::Tuple<>;
using InDataType = FP16;
using OutLayout = ck::tensor_layout::convolution::GNHWK;
using WeiLayout = ck::tensor_layout::convolution::GKYXC;
using DsLayout = ck::Tuple<>;
using InLayout = ck::tensor_layout::convolution::GNHWC;
using OutElementOp = PassThrough;
using WeiElementOp = PassThrough;
using InElementOp = PassThrough;
// clang-format off
using DeviceConvInstance = ck::tensor_operation::device::DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffleV3
// ######| NDimSpatial| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle | CShuffle | CDEBlockTransfer| CDEBlockTransfer|
// ######| | | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| Size| Block| Block| Block| | | Wmma| Wmma| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| MRepeat | NRepeat | _MBlock_MPerBlock| ScalarPerVector|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< NDimSpatial, OutLayout, WeiLayout, DsLayout, InLayout, OutDataType, WeiDataType, AccDataType, CShuffleDataType, DsDataType, InDataType, OutElementOp, WeiElementOp, InElementOp, ConvBwdDataDefault, true, true, 64, 64, 64, 32, 8, 8, 16, 16, 4, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, S<8,8,8>>;
// clang-format on
#include "run_grouped_conv_bwd_data_example.inc"
int main(int argc, char* argv[]) { return run_grouped_conv_bwd_data_example(argc, argv); }

47
example/38_grouped_conv_bwd_data_multiple_d/grouped_conv_bwd_data_wmma_v3_fp16_comp_bf8_fp8.cpp Normal file
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@@ -0,0 +1,47 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_wmma_cshuffle_v3.hpp"
#include "common.hpp"
using OutDataType = FP16;
using WeiDataType = FP16;
using AccDataType = FP32;
using CShuffleDataType = FP16;
using DsDataType = ck::Tuple<>;
using InDataType = FP16;
using AComputeType = BF8;
using BComputeType = FP8;
using OutLayout = ck::tensor_layout::convolution::GNHWK;
using WeiLayout = ck::tensor_layout::convolution::GKYXC;
using DsLayout = ck::Tuple<>;
using InLayout = ck::tensor_layout::convolution::GNHWC;
using OutElementOp = PassThrough;
using WeiElementOp = PassThrough;
using InElementOp = PassThrough;
static constexpr auto BlkGemmPipeSched = ck::BlockGemmPipelineScheduler::Intrawave;
static constexpr auto BlkGemmPipelineVer = ck::BlockGemmPipelineVersion::v1;
// clang-format off
using DeviceConvInstance = ck::tensor_operation::device::DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffleV3
// ######| NDimSpatial| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer| Loop| ACompute| BCompute|
// ######| | | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector| Scheduler| Type| Type|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock| | | |
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< NDimSpatial, OutLayout, WeiLayout, DsLayout, InLayout, OutDataType, WeiDataType, AccDataType, CShuffleDataType, DsDataType, InDataType, OutElementOp, WeiElementOp, InElementOp, ConvBwdDataDefault, true, true, 64, 64, 64, 32, 8, 8, 16, 16, 4, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, S<8,8,8>, BlkGemmPipeSched,BlkGemmPipelineVer, AComputeType, BComputeType , false , false>;
// clang-format on
#include "run_grouped_conv_bwd_data_example.inc"
int main(int argc, char* argv[])
{
// temp disable on gfx11
if(ck::is_gfx11_supported())
{
return 0;
}
return run_grouped_conv_bwd_data_example(argc, argv);
}

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example/38_grouped_conv_bwd_data_multiple_d/run_grouped_conv3d_bwd_data_example.inc Normal file
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using OutElementOp = PassThrough;
using WeiElementOp = PassThrough;
using InElementOp = PassThrough;
bool run_conv_bwd_data(const ExecutionConfig& config,
const ck::utils::conv::ConvParam& conv_params,
const HostTensorDescriptor& out_g_n_k_wos_desc,
const HostTensorDescriptor& wei_g_k_c_xs_desc,
const HostTensorDescriptor& in_g_n_c_wis_desc,
const OutElementOp& out_element_op,
const WeiElementOp& wei_element_op,
const InElementOp& in_element_op)
{
Tensor<OutDataType> out(out_g_n_k_wos_desc);
Tensor<WeiDataType> wei(wei_g_k_c_xs_desc);
Tensor<InDataType> in_host(in_g_n_c_wis_desc);
Tensor<InDataType> in_device(in_g_n_c_wis_desc);
std::cout << "out: " << out.mDesc << std::endl;
std::cout << "wei: " << wei.mDesc << std::endl;
std::cout << "in: " << in_host.mDesc << std::endl;
switch(config.init_method)
{
case 0: break;
case 1:
out.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
wei.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
break;
default:
out.GenerateTensorValue(GeneratorTensor_3<OutDataType>{0.0, 1.0});
wei.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
}
DeviceMem out_device_buf(sizeof(OutDataType) * out.mDesc.GetElementSpaceSize());
DeviceMem wei_device_buf(sizeof(WeiDataType) * wei.mDesc.GetElementSpaceSize());
DeviceMem in_device_buf(sizeof(InDataType) * in_device.mDesc.GetElementSpaceSize());
out_device_buf.ToDevice(out.mData.data());
wei_device_buf.ToDevice(wei.mData.data());
// reset input to zero
in_device_buf.SetZero();
std::array<ck::index_t, NDimSpatial + 3> a_g_n_k_wos_lengths{};
std::array<ck::index_t, NDimSpatial + 3> a_g_n_k_wos_strides{};
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_lengths{};
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_strides{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_c_wis_lengths{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_c_wis_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
std::array<ck::index_t, NDimSpatial> input_left_pads{};
std::array<ck::index_t, NDimSpatial> input_right_pads{};
auto copy = [](auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
copy(out_g_n_k_wos_desc.GetLengths(), a_g_n_k_wos_lengths);
copy(out_g_n_k_wos_desc.GetStrides(), a_g_n_k_wos_strides);
copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
copy(in_g_n_c_wis_desc.GetLengths(), e_g_n_c_wis_lengths);
copy(in_g_n_c_wis_desc.GetStrides(), e_g_n_c_wis_strides);
copy(conv_params.conv_filter_strides_, conv_filter_strides);
copy(conv_params.conv_filter_dilations_, conv_filter_dilations);
copy(conv_params.input_left_pads_, input_left_pads);
copy(conv_params.input_right_pads_, input_right_pads);
static_assert(std::is_default_constructible_v<DeviceConvInstance>);
// do conv
auto conv = DeviceConvInstance{};
auto invoker = conv.MakeInvoker();
auto argument = conv.MakeArgument(out_device_buf.GetDeviceBuffer(),
wei_device_buf.GetDeviceBuffer(),
std::array<const void*, 0>{},
in_device_buf.GetDeviceBuffer(),
a_g_n_k_wos_lengths,
a_g_n_k_wos_strides,
b_g_k_c_xs_lengths,
b_g_k_c_xs_strides,
std::array<std::array<ck::index_t, NDimSpatial + 3>, 0>{},
std::array<std::array<ck::index_t, NDimSpatial + 3>, 0>{},
e_g_n_c_wis_lengths,
e_g_n_c_wis_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
out_element_op,
wei_element_op,
in_element_op);
if(!conv.IsSupportedArgument(argument))
{
std::cerr << "wrong! device_conv with the specified compilation parameters does "
"not support this Conv problem"
<< std::endl;
return false;
}
std::string op_name = conv.GetTypeString();
float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = conv_params.GetFlops();
std::size_t num_btype = conv_params.GetByte<InDataType, WeiDataType, OutDataType>();
float tflops = static_cast<float>(flop) / 1.E9 / 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::endl;
if(config.do_verification)
{
auto ref_conv = ck::tensor_operation::host::ReferenceConvBwdData<NDimSpatial,
InDataType,
WeiDataType,
OutDataType,
PassThrough,
WeiElementOp,
OutElementOp>();
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(in_host,
wei,
out,
conv_params.conv_filter_strides_,
conv_params.conv_filter_dilations_,
conv_params.input_left_pads_,
conv_params.input_right_pads_,
PassThrough{},
wei_element_op,
out_element_op);
ref_invoker.Run(ref_argument);
in_device_buf.FromDevice(in_device.mData.data());
return ck::utils::check_err(in_device.mData, in_host.mData);
}
return true;
}
int run_grouped_conv_bwd_data_example(int argc, char* argv[])
{
namespace ctc = ck::tensor_layout::convolution;
ExecutionConfig config;
ck::utils::conv::ConvParam conv_params = DefaultConvParams;
if(!parse_cmd_args(argc, argv, config, conv_params))
{
return EXIT_FAILURE;
}
const auto in_element_op = InElementOp{};
const auto wei_element_op = WeiElementOp{};
const auto out_element_op = OutElementOp{};
if(conv_params.num_dim_spatial_ != NDimSpatial)
{
std::cerr << "unsupported # of spatials dimensions" << std::endl;
return EXIT_FAILURE;
}
// output image: GNHWK
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
conv_params);
// weight: GKYXC
const auto wei_g_k_c_xs_desc =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(conv_params);
// input image: GNHWC
const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_params);
return !run_conv_bwd_data(config,
conv_params,
out_g_n_k_wos_desc,
wei_g_k_c_xs_desc,
in_g_n_c_wis_desc,
wei_element_op,
out_element_op,
in_element_op);
}