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Unverified Commit 9b3365e1 authored by Po Yen Chen's avatar Po Yen Chen Committed by GitHub
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Merge branch 'develop' into gridwise_2d

parents 9608beee b79bbbc2
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Showing with 617 additions and 590 deletions
example/41_grouped_conv_conv_fwd/grouped_conv_conv_fwd_xdl_int4.cpp
View file @ 9b3365e1
...@@ -15,6 +15,7 @@ ...@@ -15,6 +15,7 @@
#include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_gemm_xdl_cshuffle.hpp" #include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_gemm_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp" #include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
......
example/41_grouped_conv_conv_fwd/grouped_conv_conv_fwd_xdl_int8.cpp
View file @ 9b3365e1
...@@ -11,6 +11,7 @@ ...@@ -11,6 +11,7 @@
#include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_gemm_xdl_cshuffle.hpp" #include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_gemm_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp" #include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
......
example/41_grouped_conv_conv_fwd/run_grouped_conv_conv_fwd_example.inc
View file @ 9b3365e1
...@@ -97,7 +97,7 @@ bool run_grouped_conv_conv_fwd(bool do_verification, ...@@ -97,7 +97,7 @@ bool run_grouped_conv_conv_fwd(bool do_verification,
std::array<ck::index_t, NDimSpatial> input1_left_pads{}; std::array<ck::index_t, NDimSpatial> input1_left_pads{};
std::array<ck::index_t, NDimSpatial> input1_right_pads{}; std::array<ck::index_t, NDimSpatial> input1_right_pads{};
auto copy = [](auto& x, auto& y) { std::copy(x.begin(), x.end(), y.begin()); }; auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
copy(in0_g_n_c_wis_desc.GetLengths(), a0_g_n_c_wis_lengths); copy(in0_g_n_c_wis_desc.GetLengths(), a0_g_n_c_wis_lengths);
copy(in0_g_n_c_wis_desc.GetStrides(), a0_g_n_c_wis_strides); copy(in0_g_n_c_wis_desc.GetStrides(), a0_g_n_c_wis_strides);
...@@ -261,7 +261,7 @@ bool run_grouped_conv_conv_fwd(bool do_verification, ...@@ -261,7 +261,7 @@ bool run_grouped_conv_conv_fwd(bool do_verification,
#endif #endif
return ck::utils::check_err( return ck::utils::check_err(
out1_device.mData, out1_host.mData, "Error: incorrect results!", 1e-5f, 1e-4f); out1_device, out1_host, "Error: incorrect results!", 1e-5f, 1e-4f);
} }
return true; return true;
......
example/42_groupnorm/groupnorm_sigmoid_fp16.cpp
View file @ 9b3365e1
...@@ -100,9 +100,9 @@ int main(int argc, char* argv[]) ...@@ -100,9 +100,9 @@ int main(int argc, char* argv[])
Tensor<GammaDataType> gamma({G, C}); Tensor<GammaDataType> gamma({G, C});
Tensor<BetaDataType> beta({G, C}); Tensor<BetaDataType> beta({G, C});
ck::utils::FillUniformDistribution<XDataType>{0.f, 1.f}(x.begin(), x.end()); ck::utils::FillUniformDistribution<XDataType>{0.f, 1.f}(x);
ck::utils::FillUniformDistribution<GammaDataType>{0.f, 1.f}(gamma.begin(), gamma.end()); ck::utils::FillUniformDistribution<GammaDataType>{0.f, 1.f}(gamma);
ck::utils::FillUniformDistribution<BetaDataType>{0.f, 1.f}(beta.begin(), beta.end()); ck::utils::FillUniformDistribution<BetaDataType>{0.f, 1.f}(beta);
DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize()); DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize()); DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
...@@ -167,7 +167,7 @@ int main(int argc, char* argv[]) ...@@ -167,7 +167,7 @@ int main(int argc, char* argv[])
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
y_dev.FromDevice(y.mData.data()); y_dev.FromDevice(y.mData.data());
pass &= ck::utils::check_err(y.mData, host_y.mData, "Error: Incorrect results", 1e-3, 1e-3); pass &= ck::utils::check_err(y, host_y, "Error: Incorrect results", 1e-3, 1e-3);
} }
return (pass ? 0 : 1); return (pass ? 0 : 1);
......
example/CMakeLists.txt
View file @ 9b3365e1
...@@ -12,6 +12,7 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME) ...@@ -12,6 +12,7 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
add_test(NAME ${EXAMPLE_NAME} COMMAND $<TARGET_FILE:${EXAMPLE_NAME}> ${ARGN}) add_test(NAME ${EXAMPLE_NAME} COMMAND $<TARGET_FILE:${EXAMPLE_NAME}> ${ARGN})
add_dependencies(examples ${EXAMPLE_NAME}) add_dependencies(examples ${EXAMPLE_NAME})
add_dependencies(check ${EXAMPLE_NAME}) add_dependencies(check ${EXAMPLE_NAME})
rocm_install(TARGETS ${EXAMPLE_NAME} COMPONENT examples)
endfunction(add_example_executable EXAMPLE_NAME) endfunction(add_example_executable EXAMPLE_NAME)
function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME) function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
...@@ -19,6 +20,7 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME) ...@@ -19,6 +20,7 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
add_executable(${EXAMPLE_NAME} ${FILE_NAME}) add_executable(${EXAMPLE_NAME} ${FILE_NAME})
target_link_libraries(${EXAMPLE_NAME} PRIVATE utility) target_link_libraries(${EXAMPLE_NAME} PRIVATE utility)
add_dependencies(examples ${EXAMPLE_NAME}) add_dependencies(examples ${EXAMPLE_NAME})
rocm_install(TARGETS ${EXAMPLE_NAME} COMPONENT examples)
endfunction(add_example_executable_no_testing EXAMPLE_NAME) endfunction(add_example_executable_no_testing EXAMPLE_NAME)
# add all example subdir # add all example subdir
......
include/ck/tensor_operation/gpu/device/device_grouped_conv_bwd_data.hpp deleted 100644 → 0
View file @ 9608beee
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <array>
#include "ck/tensor_operation/gpu/device/device_base.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
template <ck::index_t NDimSpatial,
typename InputLayout,
typename WeightLayout,
typename OutputLayout,
typename InputDataType,
typename WeightDataType,
typename OutputDataType,
typename InputElementwiseOperation,
typename WeightElementwiseOperation,
typename OutputElementwiseOperation>
struct DeviceGroupedConvBwdData : public BaseOperator
{
virtual std::unique_ptr<BaseArgument>
MakeArgumentPointer(void* p_input,
const void* p_weight,
const void* p_output,
const std::array<index_t, NDimSpatial + 3>& input_g_n_c_wis_lengths,
const std::array<index_t, NDimSpatial + 3>& input_g_n_c_wis_strides,
const std::array<index_t, NDimSpatial + 3>& weight_g_k_c_xs_lengths,
const std::array<index_t, NDimSpatial + 3>& weight_g_k_c_xs_strides,
const std::array<index_t, NDimSpatial + 3>& output_g_n_k_wos_lengths,
const std::array<index_t, NDimSpatial + 3>& output_g_n_k_wos_strides,
const std::array<index_t, NDimSpatial>& conv_filter_strides,
const std::array<index_t, NDimSpatial>& conv_filter_dilations,
const std::array<index_t, NDimSpatial>& input_left_pads,
const std::array<index_t, NDimSpatial>& input_right_pads,
const InputElementwiseOperation& input_element_op,
const WeightElementwiseOperation& weight_element_op,
const OutputElementwiseOperation& output_element_op) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
};
} // namespace device
} // namespace tensor_operation
} // namespace ck
include/ck/tensor_operation/gpu/device/device_grouped_conv_bwd_data_multiple_d.hpp
View file @ 9b3365e1
...@@ -3,10 +3,9 @@ ...@@ -3,10 +3,9 @@
#pragma once #pragma once
#include <vector> #include <array>
#include "ck/tensor_operation/gpu/device/device_base.hpp" #include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_bwd_data.hpp"
namespace ck { namespace ck {
namespace tensor_operation { namespace tensor_operation {
...@@ -63,100 +62,6 @@ struct DeviceGroupedConvBwdDataMultipleD : public BaseOperator ...@@ -63,100 +62,6 @@ struct DeviceGroupedConvBwdDataMultipleD : public BaseOperator
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0; virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
}; };
template <ck::index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename ELayout,
typename ADataType,
typename BDataType,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation>
struct DeviceGroupedConvBwdDataMultipleD<NDimSpatial,
ALayout,
BLayout,
Tuple<>,
ELayout,
ADataType,
BDataType,
Tuple<>,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation>
: public DeviceGroupedConvBwdData<NDimSpatial,
ELayout,
BLayout,
ALayout,
EDataType,
BDataType,
ADataType,
CDEElementwiseOperation,
BElementwiseOperation,
AElementwiseOperation>
{
virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(
const void* p_a, // output image
const void* p_b, // weight
const std::array<const void*, 0>&, // bias
void* p_e, // input image
const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_lengths, // output image
const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_strides, // output image
const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths, // weight
const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides, // weight
const std::array<std::array<index_t, NDimSpatial + 3>, 0>&, // bias
const std::array<std::array<index_t, NDimSpatial + 3>, 0>&, // bias
const std::array<index_t, NDimSpatial + 3>& e_g_n_c_wis_lengths, // input image
const std::array<index_t, NDimSpatial + 3>& e_g_n_c_wis_strides, // input image
const std::array<index_t, NDimSpatial>& conv_filter_strides,
const std::array<index_t, NDimSpatial>& conv_filter_dilations,
const std::array<index_t, NDimSpatial>& input_left_pads,
const std::array<index_t, NDimSpatial>& input_right_pads,
const AElementwiseOperation& a_element_op,
const BElementwiseOperation& b_element_op,
const CDEElementwiseOperation& cde_element_op) = 0;
std::unique_ptr<BaseArgument>
MakeArgumentPointer(void* p_input,
const void* p_weight,
const void* p_output,
const std::array<index_t, NDimSpatial + 3>& input_g_n_c_wis_lengths,
const std::array<index_t, NDimSpatial + 3>& input_g_n_c_wis_strides,
const std::array<index_t, NDimSpatial + 3>& weight_g_k_c_xs_lengths,
const std::array<index_t, NDimSpatial + 3>& weight_g_k_c_xs_strides,
const std::array<index_t, NDimSpatial + 3>& output_g_n_k_wos_lengths,
const std::array<index_t, NDimSpatial + 3>& output_g_n_k_wos_strides,
const std::array<index_t, NDimSpatial>& conv_filter_strides,
const std::array<index_t, NDimSpatial>& conv_filter_dilations,
const std::array<index_t, NDimSpatial>& input_left_pads,
const std::array<index_t, NDimSpatial>& input_right_pads,
const CDEElementwiseOperation& input_element_op,
const BElementwiseOperation& weight_element_op,
const AElementwiseOperation& output_element_op) override final
{
return MakeArgumentPointer(p_output,
p_weight,
std::array<const void*, 0>{},
p_input,
output_g_n_k_wos_lengths,
output_g_n_k_wos_strides,
weight_g_k_c_xs_lengths,
weight_g_k_c_xs_strides,
std::array<std::array<index_t, NDimSpatial + 3>, 0>{},
std::array<std::array<index_t, NDimSpatial + 3>, 0>{},
input_g_n_c_wis_lengths,
input_g_n_c_wis_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
output_element_op,
weight_element_op,
input_element_op);
}
};
} // namespace device } // namespace device
} // namespace tensor_operation } // namespace tensor_operation
} // namespace ck } // namespace ck
include/ck/tensor_operation/gpu/device/device_conv_bwd_weight.hpp → include/ck/tensor_operation/gpu/device/device_grouped_conv_bwd_weight.hpp
View file @ 9b3365e1
...@@ -3,7 +3,7 @@ ...@@ -3,7 +3,7 @@
#pragma once #pragma once
#include <vector> #include <array>
#include "ck/tensor_operation/gpu/device/device_base.hpp" #include "ck/tensor_operation/gpu/device/device_base.hpp"
...@@ -11,7 +11,7 @@ namespace ck { ...@@ -11,7 +11,7 @@ namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
template <ck::index_t NumDimSpatial, template <ck::index_t NDimSpatial,
typename InLayout, typename InLayout,
typename WeiLayout, typename WeiLayout,
typename OutLayout, typename OutLayout,
...@@ -21,22 +21,23 @@ template <ck::index_t NumDimSpatial, ...@@ -21,22 +21,23 @@ template <ck::index_t NumDimSpatial,
typename InElementwiseOperation, typename InElementwiseOperation,
typename WeiElementwiseOperation, typename WeiElementwiseOperation,
typename OutElementwiseOperation> typename OutElementwiseOperation>
struct DeviceConvBwdWeight : public BaseOperator struct DeviceGroupedConvBwdWeight : public BaseOperator
{ {
virtual std::unique_ptr<BaseArgument> virtual std::unique_ptr<BaseArgument>
MakeArgumentPointer(const void* p_in, MakeArgumentPointer(const void* p_in,
void* p_wei, void* p_wei,
const void* p_out, const void* p_out,
ck::index_t G,
ck::index_t N, ck::index_t N,
ck::index_t K, ck::index_t K,
ck::index_t C, ck::index_t C,
std::vector<ck::index_t> input_spatial_lengths, std::array<ck::index_t, NDimSpatial> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths, std::array<ck::index_t, NDimSpatial> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths, std::array<ck::index_t, NDimSpatial> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides, std::array<ck::index_t, NDimSpatial> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations, std::array<ck::index_t, NDimSpatial> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads, std::array<ck::index_t, NDimSpatial> input_left_pads,
std::vector<ck::index_t> input_right_pads, std::array<ck::index_t, NDimSpatial> input_right_pads,
InElementwiseOperation in_element_op, InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op, WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op, OutElementwiseOperation out_element_op,
......
include/ck/tensor_operation/gpu/device/device_grouped_conv_fwd.hpp
View file @ 9b3365e1
...@@ -14,39 +14,38 @@ namespace device { ...@@ -14,39 +14,38 @@ namespace device {
// Convolution Forward: // Convolution Forward:
// input : input image A[G, N, C, Hi, Wi], // input : input image A[G, N, C, Hi, Wi],
// input : weight B[G, K, C, Y, X], // input : weight B[G, K, C, Y, X],
// input : D0[G, N, K, Ho, Wo], D1[G, N, K, Ho, Wo], ...
// output : output image E[G, N, K, Ho, Wo] // output : output image E[G, N, K, Ho, Wo]
// C = a_op(A) * b_op(B) // C = a_op(A) * b_op(B)
// E = cde_op(C, D0, D1, ...) // E = cde_op(C, D0, D1, ...)
template <index_t NDimSpatial, template <index_t NDimSpatial,
typename ALayout, typename InLayout,
typename BLayout, typename WeiLayout,
typename CLayout, typename OutLayout,
typename ADataType, typename InDataType,
typename BDataType, typename WeiDataType,
typename CDataType, typename OutDataType,
typename AElementwiseOperation, typename InElementwiseOperation,
typename BElementwiseOperation, typename WeiElementwiseOperation,
typename CElementwiseOperation> typename OutElementwiseOperation>
struct DeviceGroupedConvFwd : public BaseOperator struct DeviceGroupedConvFwd : public BaseOperator
{ {
virtual std::unique_ptr<BaseArgument> virtual std::unique_ptr<BaseArgument>
MakeArgumentPointer(const void* p_a, // input image MakeArgumentPointer(const void* p_in, // input image
const void* p_b, // weight const void* p_wei, // weight
void* p_c, // output image void* p_out, // output image
const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_lengths, const std::array<index_t, NDimSpatial + 3>& in_g_n_c_wis_lengths,
const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_strides, const std::array<index_t, NDimSpatial + 3>& in_g_n_c_wis_strides,
const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths, const std::array<index_t, NDimSpatial + 3>& wei_g_k_c_xs_lengths,
const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides, const std::array<index_t, NDimSpatial + 3>& wei_g_k_c_xs_strides,
const std::array<index_t, NDimSpatial + 3>& c_g_n_k_wos_lengths, const std::array<index_t, NDimSpatial + 3>& out_g_n_k_wos_lengths,
const std::array<index_t, NDimSpatial + 3>& c_g_n_k_wos_strides, const std::array<index_t, NDimSpatial + 3>& out_g_n_k_wos_strides,
const std::array<index_t, NDimSpatial>& conv_filter_strides, const std::array<index_t, NDimSpatial>& conv_filter_strides,
const std::array<index_t, NDimSpatial>& conv_filter_dilations, const std::array<index_t, NDimSpatial>& conv_filter_dilations,
const std::array<index_t, NDimSpatial>& input_left_pads, const std::array<index_t, NDimSpatial>& input_left_pads,
const std::array<index_t, NDimSpatial>& input_right_pads, const std::array<index_t, NDimSpatial>& input_right_pads,
const AElementwiseOperation& a_element_op, const InElementwiseOperation& in_element_op,
const BElementwiseOperation& b_element_op, const WeiElementwiseOperation& wei_element_op,
const CElementwiseOperation& c_element_op) = 0; const OutElementwiseOperation& out_element_op) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0; virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
}; };
......
include/ck/tensor_operation/gpu/device/impl/device_conv2d_backward_weight_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp
View file @ 9b3365e1
...@@ -67,6 +67,8 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_ ...@@ -67,6 +67,8 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
WeiElementwiseOperation, WeiElementwiseOperation,
OutElementwiseOperation> OutElementwiseOperation>
{ {
static constexpr ck::index_t NDimSpatial = 2;
using DeviceOp = using DeviceOp =
DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K; DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K;
...@@ -107,18 +109,18 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_ ...@@ -107,18 +109,18 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
static constexpr auto BBlockLdsN0PerBlock = NPerBlock / BBlockLdsN1PerBlock; static constexpr auto BBlockLdsN0PerBlock = NPerBlock / BBlockLdsN1PerBlock;
static constexpr auto BBlockLdsN1Padding = 4; static constexpr auto BBlockLdsN1Padding = 4;
static auto static auto MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N, ck::index_t N,
ck::index_t K, ck::index_t K,
ck::index_t C, ck::index_t C,
std::vector<ck::index_t> input_spatial_lengths, std::array<ck::index_t, NDimSpatial> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths, std::array<ck::index_t, NDimSpatial> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths, std::array<ck::index_t, NDimSpatial> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides, std::array<ck::index_t, NDimSpatial> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations, std::array<ck::index_t, NDimSpatial> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads, std::array<ck::index_t, NDimSpatial> input_left_pads,
std::vector<ck::index_t> input_right_pads, std::array<ck::index_t, NDimSpatial> input_right_pads,
ck::index_t batch_k) ck::index_t batch_k)
{ {
using namespace ck; using namespace ck;
...@@ -390,13 +392,13 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_ ...@@ -390,13 +392,13 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
ck::index_t N, ck::index_t N,
ck::index_t K, ck::index_t K,
ck::index_t C, ck::index_t C,
std::vector<ck::index_t> input_spatial_lengths, std::array<ck::index_t, NDimSpatial> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths, std::array<ck::index_t, NDimSpatial> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths, std::array<ck::index_t, NDimSpatial> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides, std::array<ck::index_t, NDimSpatial> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations, std::array<ck::index_t, NDimSpatial> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads, std::array<ck::index_t, NDimSpatial> input_left_pads,
std::vector<ck::index_t> input_right_pads, std::array<ck::index_t, NDimSpatial> input_right_pads,
ck::index_t M01, ck::index_t M01,
ck::index_t N01, ck::index_t N01,
InElementwiseOperation in_element_op, InElementwiseOperation in_element_op,
...@@ -473,11 +475,11 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_ ...@@ -473,11 +475,11 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
index_t Conv_N_; index_t Conv_N_;
index_t Conv_K_; index_t Conv_K_;
index_t Conv_C_; index_t Conv_C_;
std::vector<index_t> output_spatial_lengths_; std::array<index_t, NDimSpatial> output_spatial_lengths_;
std::vector<index_t> filter_spatial_lengths_; std::array<index_t, NDimSpatial> filter_spatial_lengths_;
std::vector<index_t> conv_filter_strides_; std::array<index_t, NDimSpatial> conv_filter_strides_;
std::vector<index_t> input_left_pads_; std::array<index_t, NDimSpatial> input_left_pads_;
std::vector<index_t> input_right_pads_; std::array<index_t, NDimSpatial> input_right_pads_;
index_t k_batch_; index_t k_batch_;
}; };
...@@ -682,13 +684,13 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_ ...@@ -682,13 +684,13 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
ck::index_t N, ck::index_t N,
ck::index_t K, ck::index_t K,
ck::index_t C, ck::index_t C,
std::vector<ck::index_t> input_spatial_lengths, std::array<ck::index_t, NDimSpatial> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths, std::array<ck::index_t, NDimSpatial> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths, std::array<ck::index_t, NDimSpatial> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides, std::array<ck::index_t, NDimSpatial> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations, std::array<ck::index_t, NDimSpatial> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads, std::array<ck::index_t, NDimSpatial> input_left_pads,
std::vector<ck::index_t> input_right_pads, std::array<ck::index_t, NDimSpatial> input_right_pads,
InElementwiseOperation in_element_op, InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op, WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op, OutElementwiseOperation out_element_op,
...@@ -724,13 +726,13 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_ ...@@ -724,13 +726,13 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
ck::index_t N, ck::index_t N,
ck::index_t K, ck::index_t K,
ck::index_t C, ck::index_t C,
std::vector<ck::index_t> input_spatial_lengths, std::array<ck::index_t, NDimSpatial> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths, std::array<ck::index_t, NDimSpatial> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths, std::array<ck::index_t, NDimSpatial> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides, std::array<ck::index_t, NDimSpatial> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations, std::array<ck::index_t, NDimSpatial> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads, std::array<ck::index_t, NDimSpatial> input_left_pads,
std::vector<ck::index_t> input_right_pads, std::array<ck::index_t, NDimSpatial> input_right_pads,
InElementwiseOperation in_element_op, InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op, WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op, OutElementwiseOperation out_element_op,
......
include/ck/tensor_operation/gpu/device/impl/device_convnd_bwd_weight_nwc_kxc_nwk_xdl_cshuffle.hpp → include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_gnwc_gkxc_gnwk_xdl_cshuffle.hpp
View file @ 9b3365e1
...@@ -4,13 +4,14 @@ ...@@ -4,13 +4,14 @@
#pragma once #pragma once
#include <iostream> #include <iostream>
#include <numeric>
#include <sstream> #include <sstream>
#include "ck/utility/common_header.hpp" #include "ck/utility/common_header.hpp"
#include "ck/tensor_description/tensor_descriptor.hpp" #include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp" #include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_conv_bwd_weight.hpp" #include "ck/tensor_operation/gpu/device/device_grouped_conv_bwd_weight.hpp"
#include "ck/tensor_operation/gpu/device/convolution_backward_weight_specialization.hpp" #include "ck/tensor_operation/gpu/device/convolution_backward_weight_specialization.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_bwd_weight.hpp" #include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_bwd_weight.hpp"
#include "ck/host_utility/device_prop.hpp" #include "ck/host_utility/device_prop.hpp"
...@@ -20,6 +21,104 @@ namespace ck { ...@@ -20,6 +21,104 @@ namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
namespace {
struct ComputePtrOffsetOfStridedBatch
{
__host__ __device__ constexpr long_index_t GetAPtrOffset(index_t g_idx) const
{
return g_idx * static_cast<long_index_t>(BatchStrideA_);
}
__host__ __device__ constexpr long_index_t GetBPtrOffset(index_t g_idx) const
{
return g_idx * static_cast<long_index_t>(BatchStrideB_);
}
__host__ __device__ constexpr long_index_t GetCPtrOffset(index_t g_idx) const
{
return g_idx * static_cast<long_index_t>(BatchStrideC_);
}
index_t BatchStrideA_;
index_t BatchStrideB_;
index_t BatchStrideC_;
};
} // namespace
template <typename GridwiseGemm,
typename FloatAB,
typename FloatC,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation,
typename AGridDesc_B_K0_M_K1,
typename BGridDesc_B_K0_N_K1,
typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
typename Block2CTileMap,
typename ComputePtrOffsetOfBatch,
bool HasMainKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_batched_gemm_xdlops_bwd_weight(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const AElementwiseOperation a_element_op,
const BElementwiseOperation b_element_op,
const CElementwiseOperation c_element_op,
const index_t batch_count,
const AGridDesc_B_K0_M_K1 a_b_k0_m_k1_grid_desc,
const BGridDesc_B_K0_N_K1 b_b_k0_n_k1_grid_desc,
const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
c_grid_desc_mblock_mperblock_nblock_nperblock,
const Block2CTileMap block_2_ctile_map,
const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
const index_t num_blocks_per_batch =
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetCPtrOffset(g_idx)));
__shared__ FloatAB p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB)];
GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
p_b_grid + b_batch_offset,
p_c_grid + c_batch_offset,
p_shared,
a_b_k0_m_k1_grid_desc,
b_b_k0_n_k1_grid_desc,
c_grid_desc_mblock_mperblock_nblock_nperblock,
a_element_op,
b_element_op,
c_element_op,
block_2_ctile_map);
#else
ignore = p_a_grid;
ignore = p_b_grid;
ignore = p_c_grid;
ignore = a_b_k0_m_k1_grid_desc;
ignore = b_b_k0_n_k1_grid_desc;
ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
ignore = a_element_op;
ignore = b_element_op;
ignore = c_element_op;
ignore = batch_count;
ignore = block_2_ctile_map;
ignore = compute_ptr_offset_of_batch;
#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
}
// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C] // out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
template <ck::index_t NDimSpatial, template <ck::index_t NDimSpatial,
typename InDataType, typename InDataType,
...@@ -57,21 +156,21 @@ template <ck::index_t NDimSpatial, ...@@ -57,21 +156,21 @@ template <ck::index_t NDimSpatial,
index_t CShuffleNXdlPerWavePerShuffle, index_t CShuffleNXdlPerWavePerShuffle,
typename CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock, typename CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
index_t CBlockTransferScalarPerVector_NWaveNPerXdl> index_t CBlockTransferScalarPerVector_NWaveNPerXdl>
struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle
: public DeviceConvBwdWeight< : public DeviceGroupedConvBwdWeight<
NDimSpatial, NDimSpatial,
ck::tuple_element_t<NDimSpatial - 1, ck::tuple_element_t<NDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::NWC, ck::Tuple<ck::tensor_layout::convolution::GNWC,
ck::tensor_layout::convolution::NHWC, ck::tensor_layout::convolution::GNHWC,
ck::tensor_layout::convolution::NDHWC>>, ck::tensor_layout::convolution::GNDHWC>>,
ck::tuple_element_t<NDimSpatial - 1, ck::tuple_element_t<NDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::KXC, ck::Tuple<ck::tensor_layout::convolution::GKXC,
ck::tensor_layout::convolution::KYXC, ck::tensor_layout::convolution::GKYXC,
ck::tensor_layout::convolution::KZYXC>>, ck::tensor_layout::convolution::GKZYXC>>,
ck::tuple_element_t<NDimSpatial - 1, ck::tuple_element_t<NDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::NWK, ck::Tuple<ck::tensor_layout::convolution::GNWK,
ck::tensor_layout::convolution::NHWK, ck::tensor_layout::convolution::GNHWK,
ck::tensor_layout::convolution::NDHWK>>, ck::tensor_layout::convolution::GNDHWK>>,
InDataType, InDataType,
WeiDataType, WeiDataType,
OutDataType, OutDataType,
...@@ -79,7 +178,7 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -79,7 +178,7 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
WeiElementwiseOperation, WeiElementwiseOperation,
OutElementwiseOperation> OutElementwiseOperation>
{ {
using DeviceOp = DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle; using DeviceOp = DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle;
using ADataType = OutDataType; using ADataType = OutDataType;
using BDataType = InDataType; using BDataType = InDataType;
...@@ -117,18 +216,18 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -117,18 +216,18 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
static constexpr auto BBlockLdsN1Padding = 4; static constexpr auto BBlockLdsN1Padding = 4;
template <ck::index_t NDim, typename ck::enable_if<NDim == 1, bool>::type = false> template <ck::index_t NDim, typename ck::enable_if<NDim == 1, bool>::type = false>
static auto static auto MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N, ck::index_t N,
ck::index_t K, ck::index_t K,
ck::index_t C, ck::index_t C,
std::vector<ck::index_t> input_spatial_lengths, std::array<ck::index_t, NDimSpatial> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths, std::array<ck::index_t, NDimSpatial> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths, std::array<ck::index_t, NDimSpatial> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides, std::array<ck::index_t, NDimSpatial> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations, std::array<ck::index_t, NDimSpatial> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads, std::array<ck::index_t, NDimSpatial> input_left_pads,
std::vector<ck::index_t> input_right_pads, std::array<ck::index_t, NDimSpatial> input_right_pads,
ck::index_t batch_k) ck::index_t batch_k)
{ {
using namespace ck; using namespace ck;
...@@ -269,18 +368,18 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -269,18 +368,18 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
} }
template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false> template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
static auto static auto MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N, ck::index_t N,
ck::index_t K, ck::index_t K,
ck::index_t C, ck::index_t C,
std::vector<ck::index_t> input_spatial_lengths, std::array<ck::index_t, NDimSpatial> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths, std::array<ck::index_t, NDimSpatial> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths, std::array<ck::index_t, NDimSpatial> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides, std::array<ck::index_t, NDimSpatial> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations, std::array<ck::index_t, NDimSpatial> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads, std::array<ck::index_t, NDimSpatial> input_left_pads,
std::vector<ck::index_t> input_right_pads, std::array<ck::index_t, NDimSpatial> input_right_pads,
ck::index_t batch_k) ck::index_t batch_k)
{ {
using namespace ck; using namespace ck;
...@@ -436,18 +535,18 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -436,18 +535,18 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
} }
template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false> template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
static auto static auto MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N, ck::index_t N,
ck::index_t K, ck::index_t K,
ck::index_t C, ck::index_t C,
std::vector<ck::index_t> input_spatial_lengths, std::array<ck::index_t, NDimSpatial> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths, std::array<ck::index_t, NDimSpatial> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths, std::array<ck::index_t, NDimSpatial> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides, std::array<ck::index_t, NDimSpatial> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations, std::array<ck::index_t, NDimSpatial> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads, std::array<ck::index_t, NDimSpatial> input_left_pads,
std::vector<ck::index_t> input_right_pads, std::array<ck::index_t, NDimSpatial> input_right_pads,
ck::index_t batch_k) ck::index_t batch_k)
{ {
using namespace ck; using namespace ck;
...@@ -664,8 +763,8 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -664,8 +763,8 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
} }
template <index_t Dim> template <index_t Dim>
static auto MakeDescriptor_M0(const std::vector<index_t>& shape, static auto MakeDescriptor_M0(const std::array<index_t, Dim>& shape,
const std::vector<index_t>& stride, const std::array<index_t, Dim>& stride,
index_t gridSize, index_t gridSize,
index_t blockSize) index_t blockSize)
{ {
...@@ -759,16 +858,17 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -759,16 +858,17 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
Argument(const InDataType* p_in_grid, Argument(const InDataType* p_in_grid,
WeiDataType* p_wei_grid, WeiDataType* p_wei_grid,
const OutDataType* p_out_grid, const OutDataType* p_out_grid,
ck::index_t G,
ck::index_t N, ck::index_t N,
ck::index_t K, ck::index_t K,
ck::index_t C, ck::index_t C,
std::vector<ck::index_t> input_spatial_lengths, std::array<ck::index_t, NDimSpatial> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths, std::array<ck::index_t, NDimSpatial> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths, std::array<ck::index_t, NDimSpatial> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides, std::array<ck::index_t, NDimSpatial> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations, std::array<ck::index_t, NDimSpatial> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads, std::array<ck::index_t, NDimSpatial> input_left_pads,
std::vector<ck::index_t> input_right_pads, std::array<ck::index_t, NDimSpatial> input_right_pads,
ck::index_t M01, ck::index_t M01,
ck::index_t N01, ck::index_t N01,
InElementwiseOperation in_element_op, InElementwiseOperation in_element_op,
...@@ -783,11 +883,13 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -783,11 +883,13 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
c_grid_desc_m_n_{}, c_grid_desc_m_n_{},
c_grid_desc_mblock_mperblock_nblock_nperblock_{}, c_grid_desc_mblock_mperblock_nblock_nperblock_{},
block_2_ctile_map_{}, block_2_ctile_map_{},
compute_ptr_offset_of_batch_{},
M01_{M01}, M01_{M01},
N01_{N01}, N01_{N01},
a_element_op_{out_element_op}, a_element_op_{out_element_op},
b_element_op_{in_element_op}, b_element_op_{in_element_op},
c_element_op_{wei_element_op}, c_element_op_{wei_element_op},
Conv_G_{G},
Conv_N_{N}, Conv_N_{N},
Conv_K_{K}, Conv_K_{K},
Conv_C_{C}, Conv_C_{C},
...@@ -819,6 +921,26 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -819,6 +921,26 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
block_2_ctile_map_ = block_2_ctile_map_ =
GridwiseGemm::MakeCBlockClusterAdaptor(c_grid_desc_m_n_, M01, N01, k_batch_); GridwiseGemm::MakeCBlockClusterAdaptor(c_grid_desc_m_n_, M01, N01, k_batch_);
// A/B/C Batch Stride
compute_ptr_offset_of_batch_.BatchStrideA_ =
N * K *
std::accumulate(begin(output_spatial_lengths),
end(output_spatial_lengths),
index_t{1},
std::multiplies<>{});
compute_ptr_offset_of_batch_.BatchStrideB_ =
N * C *
std::accumulate(begin(input_spatial_lengths),
end(input_spatial_lengths),
index_t{1},
std::multiplies<>{});
compute_ptr_offset_of_batch_.BatchStrideC_ =
K * C *
std::accumulate(begin(filter_spatial_lengths),
end(filter_spatial_lengths),
index_t{1},
std::multiplies<>{});
if(GridwiseGemm::CheckValidity(a_grid_desc_kbatch_k0_m_k1_, if(GridwiseGemm::CheckValidity(a_grid_desc_kbatch_k0_m_k1_,
b_grid_desc_kbatch_k0_n_k1_, b_grid_desc_kbatch_k0_n_k1_,
c_grid_desc_m_n_, c_grid_desc_m_n_,
...@@ -836,21 +958,29 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -836,21 +958,29 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
BGridDesc_K0_N_K1 b_grid_desc_kbatch_k0_n_k1_; BGridDesc_K0_N_K1 b_grid_desc_kbatch_k0_n_k1_;
CGridDesc_M_N c_grid_desc_m_n_; CGridDesc_M_N c_grid_desc_m_n_;
CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock c_grid_desc_mblock_mperblock_nblock_nperblock_; CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock c_grid_desc_mblock_mperblock_nblock_nperblock_;
Block2CTileMap block_2_ctile_map_; Block2CTileMap block_2_ctile_map_;
// for computing batch offset
ComputePtrOffsetOfStridedBatch compute_ptr_offset_of_batch_;
index_t M01_; index_t M01_;
index_t N01_; index_t N01_;
InElementwiseOperation a_element_op_; InElementwiseOperation a_element_op_;
OutElementwiseOperation b_element_op_; OutElementwiseOperation b_element_op_;
WeiElementwiseOperation c_element_op_; WeiElementwiseOperation c_element_op_;
// for checking IsSupportedArgument() // for checking IsSupportedArgument()
index_t Conv_G_;
index_t Conv_N_; index_t Conv_N_;
index_t Conv_K_; index_t Conv_K_;
index_t Conv_C_; index_t Conv_C_;
std::vector<index_t> output_spatial_lengths_; std::array<ck::index_t, NDimSpatial> output_spatial_lengths_;
std::vector<index_t> filter_spatial_lengths_; std::array<ck::index_t, NDimSpatial> filter_spatial_lengths_;
std::vector<index_t> conv_filter_strides_; std::array<ck::index_t, NDimSpatial> conv_filter_strides_;
std::vector<index_t> input_left_pads_; std::array<ck::index_t, NDimSpatial> input_left_pads_;
std::vector<index_t> input_right_pads_; std::array<ck::index_t, NDimSpatial> input_right_pads_;
index_t k_batch_; index_t k_batch_;
}; };
...@@ -873,14 +1003,12 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -873,14 +1003,12 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
<< arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I2) << ", " << arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I2) << ", "
<< arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I3) << "}" << std::endl; << arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I3) << "}" << std::endl;
std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", " std::cout << "arg.c_grid_desc_m_n_{" << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
<< arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl; << arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
} }
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{}) float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{ {
ShowInfo(arg);
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_, if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_, arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_m_n_, arg.c_grid_desc_m_n_,
...@@ -891,7 +1019,7 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -891,7 +1019,7 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
} }
const index_t grid_size = const index_t grid_size =
arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_); arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_) * arg.Conv_G_;
const auto K0 = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1); const auto K0 = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1);
...@@ -900,17 +1028,18 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -900,17 +1028,18 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
auto launch_kernel = [&](auto has_main_k_block_loop) { auto launch_kernel = [&](auto has_main_k_block_loop) {
constexpr bool has_main_loop = has_main_k_block_loop.value; constexpr bool has_main_loop = has_main_k_block_loop.value;
const auto kernel = kernel_gemm_xdlops_bwd_weight< const auto kernel = kernel_batched_gemm_xdlops_bwd_weight<
GridwiseGemm, GridwiseGemm,
ADataType, // TODO: distiguish A/B datatype ADataType, // TODO: distiguish A/B datatype
CDataType, CDataType,
remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
OutElementwiseOperation, OutElementwiseOperation,
InElementwiseOperation, InElementwiseOperation,
WeiElementwiseOperation, WeiElementwiseOperation,
remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
remove_reference_t<DeviceOp::Block2CTileMap>, remove_reference_t<DeviceOp::Block2CTileMap>,
ComputePtrOffsetOfStridedBatch,
has_main_loop>; has_main_loop>;
return launch_and_time_kernel(stream_config, return launch_and_time_kernel(stream_config,
...@@ -921,13 +1050,15 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -921,13 +1050,15 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
arg.p_a_grid_, arg.p_a_grid_,
arg.p_b_grid_, arg.p_b_grid_,
arg.p_c_grid_, arg.p_c_grid_,
arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.a_element_op_, arg.a_element_op_,
arg.b_element_op_, arg.b_element_op_,
arg.c_element_op_, arg.c_element_op_,
arg.block_2_ctile_map_); arg.Conv_G_,
arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.block_2_ctile_map_,
arg.compute_ptr_offset_of_batch_);
}; };
if(has_main_k0_block_loop) if(has_main_k0_block_loop)
...@@ -998,16 +1129,17 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -998,16 +1129,17 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
static auto MakeArgument(const InDataType* p_in_grid, static auto MakeArgument(const InDataType* p_in_grid,
WeiDataType* p_wei_grid, WeiDataType* p_wei_grid,
const OutDataType* p_out_grid, const OutDataType* p_out_grid,
ck::index_t G,
ck::index_t N, ck::index_t N,
ck::index_t K, ck::index_t K,
ck::index_t C, ck::index_t C,
std::vector<ck::index_t> input_spatial_lengths, std::array<ck::index_t, NDimSpatial> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths, std::array<ck::index_t, NDimSpatial> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths, std::array<ck::index_t, NDimSpatial> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides, std::array<ck::index_t, NDimSpatial> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations, std::array<ck::index_t, NDimSpatial> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads, std::array<ck::index_t, NDimSpatial> input_left_pads,
std::vector<ck::index_t> input_right_pads, std::array<ck::index_t, NDimSpatial> input_right_pads,
InElementwiseOperation in_element_op, InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op, WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op, OutElementwiseOperation out_element_op,
...@@ -1016,6 +1148,7 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -1016,6 +1148,7 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
return Argument{p_in_grid, return Argument{p_in_grid,
p_wei_grid, p_wei_grid,
p_out_grid, p_out_grid,
G,
N, N,
K, K,
C, C,
...@@ -1040,16 +1173,17 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -1040,16 +1173,17 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
MakeArgumentPointer(const void* p_in_grid, MakeArgumentPointer(const void* p_in_grid,
void* p_wei_grid, void* p_wei_grid,
const void* p_out_grid, const void* p_out_grid,
ck::index_t G,
ck::index_t N, ck::index_t N,
ck::index_t K, ck::index_t K,
ck::index_t C, ck::index_t C,
std::vector<ck::index_t> input_spatial_lengths, std::array<ck::index_t, NDimSpatial> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths, std::array<ck::index_t, NDimSpatial> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths, std::array<ck::index_t, NDimSpatial> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides, std::array<ck::index_t, NDimSpatial> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations, std::array<ck::index_t, NDimSpatial> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads, std::array<ck::index_t, NDimSpatial> input_left_pads,
std::vector<ck::index_t> input_right_pads, std::array<ck::index_t, NDimSpatial> input_right_pads,
InElementwiseOperation in_element_op, InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op, WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op, OutElementwiseOperation out_element_op,
...@@ -1058,6 +1192,7 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -1058,6 +1192,7 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
return std::make_unique<Argument>(static_cast<const InDataType*>(p_in_grid), return std::make_unique<Argument>(static_cast<const InDataType*>(p_in_grid),
static_cast<WeiDataType*>(p_wei_grid), static_cast<WeiDataType*>(p_wei_grid),
static_cast<const OutDataType*>(p_out_grid), static_cast<const OutDataType*>(p_out_grid),
G,
N, N,
K, K,
C, C,
...@@ -1086,7 +1221,7 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle ...@@ -1086,7 +1221,7 @@ struct DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle
auto str = std::stringstream(); auto str = std::stringstream();
// clang-format off // clang-format off
str << "DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle" str << "DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle"
<< "<" << "<"
<< BlockSize << ", " << BlockSize << ", "
<< MPerBlock << ", " << MPerBlock << ", "
......
include/ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp
View file @ 9b3365e1
...@@ -364,14 +364,16 @@ struct BlockToCTileMap_KSplit_M00_N00_M01_N01 ...@@ -364,14 +364,16 @@ struct BlockToCTileMap_KSplit_M00_N00_M01_N01
index_t M01 = 1, index_t M01 = 1,
index_t N01 = 1, index_t N01 = 1,
index_t KSplit = 1) index_t KSplit = 1)
: M01_(M01), : c_grid_desc_m_n_(c_grid_desc_m_n),
M01_(M01),
N01_(N01), N01_(N01),
KSplit_(KSplit), KSplit_(KSplit),
underlying_map_(GetBlockToCTileMap(c_grid_desc_m_n, M01, N01, KSplit)) underlying_map_(GetBlockToCTileMap(c_grid_desc_m_n, M01, N01, KSplit))
{ {
} }
__host__ constexpr index_t CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const __host__ __device__ constexpr index_t
CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const
{ {
const auto M0 = math::integer_divide_ceil(c_grid_desc_m_n.GetLength(I0), MPerBlock); const auto M0 = math::integer_divide_ceil(c_grid_desc_m_n.GetLength(I0), MPerBlock);
const auto N0 = math::integer_divide_ceil(c_grid_desc_m_n.GetLength(I1), NPerBlock); const auto N0 = math::integer_divide_ceil(c_grid_desc_m_n.GetLength(I1), NPerBlock);
...@@ -387,7 +389,10 @@ struct BlockToCTileMap_KSplit_M00_N00_M01_N01 ...@@ -387,7 +389,10 @@ struct BlockToCTileMap_KSplit_M00_N00_M01_N01
template <typename TopIdx> template <typename TopIdx>
__host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const __host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
{ {
return underlying_map_.CalculateBottomIndex(idx_top); static_assert(TopIdx::Size() == 1);
return underlying_map_.CalculateBottomIndex(
make_multi_index(idx_top[I0] % CalculateGridSize()));
} }
template <typename CTileIdx, typename CTileDim> template <typename CTileIdx, typename CTileDim>
...@@ -418,6 +423,11 @@ struct BlockToCTileMap_KSplit_M00_N00_M01_N01 ...@@ -418,6 +423,11 @@ struct BlockToCTileMap_KSplit_M00_N00_M01_N01
} }
private: private:
__device__ constexpr index_t CalculateGridSize() const
{
return CalculateGridSize(c_grid_desc_m_n_);
}
__host__ static constexpr auto GetBlockToCTileMap(const CGridDesc_M_N& c_grid_desc_m_n, __host__ static constexpr auto GetBlockToCTileMap(const CGridDesc_M_N& c_grid_desc_m_n,
index_t M01, index_t M01,
index_t N01, index_t N01,
...@@ -450,6 +460,7 @@ struct BlockToCTileMap_KSplit_M00_N00_M01_N01 ...@@ -450,6 +460,7 @@ struct BlockToCTileMap_KSplit_M00_N00_M01_N01
return c_blockid_to_ksplit_m0_n0_block_cluster_adaptor; return c_blockid_to_ksplit_m0_n0_block_cluster_adaptor;
} }
CGridDesc_M_N c_grid_desc_m_n_;
index_t M01_, N01_, KSplit_; index_t M01_, N01_, KSplit_;
using UnderlyingMap = decltype(GetBlockToCTileMap(CGridDesc_M_N{}, 1, 1, 1)); using UnderlyingMap = decltype(GetBlockToCTileMap(CGridDesc_M_N{}, 1, 1, 1));
UnderlyingMap underlying_map_; UnderlyingMap underlying_map_;
......
include/ck/tensor_operation/gpu/grid/gridwise_elementwise_layernorm_welford_variance.hpp
View file @ 9b3365e1
...@@ -289,7 +289,7 @@ struct GridwiseElementwiseLayernormWelfordVariance_mk_to_mk ...@@ -289,7 +289,7 @@ struct GridwiseElementwiseLayernormWelfordVariance_mk_to_mk
XDataType, XDataType,
decltype(thread_buffer_desc_m_k), decltype(thread_buffer_desc_m_k),
GridDesc_M_K, GridDesc_M_K,
YElementwiseOperation, PassThrough,
ThreadBufferLengths_M_K, ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder, ThreadBufferDimAccessOrder,
XSrcVectorDim, XSrcVectorDim,
......
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_bwd_weight.hpp
View file @ 9b3365e1
...@@ -131,17 +131,22 @@ struct ReferenceConvBwdWeight : public device::BaseOperator ...@@ -131,17 +131,22 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
else if constexpr(NDimSpatial == 2) else if constexpr(NDimSpatial == 2)
{ {
auto f_kcyx = [&](auto g, auto k, auto c, auto y, auto x) { auto f_kcyx = [&](auto g, auto k, auto c, auto y, auto x) {
std::size_t N = arg.output_.GetLengths()[1];
std::size_t Ho = arg.output_.GetLengths()[3];
std::size_t Wo = arg.output_.GetLengths()[4];
float v_acc = 0; float v_acc = 0;
for(std::size_t n = 0; n < arg.output_.GetLengths()[1]; ++n) for(std::size_t n = 0; n < N; ++n)
{ {
for(std::size_t ho = 0; ho < arg.output_.GetLengths()[3]; ++ho) for(std::size_t ho = 0; ho < Ho; ++ho)
{ {
auto hi = static_cast<ck::long_index_t>(ho * arg.conv_strides_[0]) + auto hi = static_cast<ck::long_index_t>(ho * arg.conv_strides_[0]) +
static_cast<ck::long_index_t>(y * arg.conv_dilations_[0]) - static_cast<ck::long_index_t>(y * arg.conv_dilations_[0]) -
static_cast<ck::long_index_t>(arg.in_left_pads_[0]); static_cast<ck::long_index_t>(arg.in_left_pads_[0]);
for(std::size_t wo = 0; wo < arg.output_.GetLengths()[4]; ++wo) for(std::size_t wo = 0; wo < Wo; ++wo)
{ {
auto wi = auto wi =
static_cast<ck::long_index_t>(wo * arg.conv_strides_[1]) + static_cast<ck::long_index_t>(wo * arg.conv_strides_[1]) +
......
library/include/ck/library/reference_tensor_operation/cpu/reference_gemm_layernorm.hpp
View file @ 9b3365e1
...@@ -44,8 +44,8 @@ struct ReferenceGemmLayernorm : public device::BaseOperator ...@@ -44,8 +44,8 @@ struct ReferenceGemmLayernorm : public device::BaseOperator
size_t M = acc.mDesc.GetLengths()[0]; size_t M = acc.mDesc.GetLengths()[0];
size_t N = acc.mDesc.GetLengths()[1]; size_t N = acc.mDesc.GetLengths()[1];
Tensor<ComputeDataType> avg_acc_sq(HostTensorDescriptor(std::vector<size_t>({M}))); Tensor<ComputeDataType> avg_acc_sq({M});
Tensor<ComputeDataType> avg_acc(HostTensorDescriptor(std::vector<size_t>({M}))); Tensor<ComputeDataType> avg_acc({M});
Tensor<ComputeDataType> acc_layernorm(acc); Tensor<ComputeDataType> acc_layernorm(acc);
// reduce N dim // reduce N dim
......
library/include/ck/library/reference_tensor_operation/cpu/reference_layernorm.hpp
View file @ 9b3365e1
...@@ -92,9 +92,10 @@ struct ReferenceLayernorm : public device::BaseOperator ...@@ -92,9 +92,10 @@ struct ReferenceLayernorm : public device::BaseOperator
{ {
for(int n = 0; n < N; ++n) for(int n = 0; n < N; ++n)
{ {
auto x_val = ck::type_convert<AccDataType>(arg.x_m_n_(m, n)); auto x_val = ck::type_convert<AccDataType>(arg.x_m_n_(m, n));
auto y_val = (x_val - mean(m)) / sqrt(var(m) + arg.epsilon_); auto y_val = (x_val - mean(m)) / sqrt(var(m) + arg.epsilon_);
y_val = (y_val * arg.gamma_n_(n)) + arg.beta_n_(n); y_val = (y_val * arg.gamma_n_(n)) + arg.beta_n_(n);
arg.acc_elementwise_op_(y_val, y_val);
arg.y_m_n_(m, n) = ck::type_convert<YDataType>(y_val); arg.y_m_n_(m, n) = ck::type_convert<YDataType>(y_val);
} }
} }
......
library/include/ck/library/tensor_operation_instance/device_operation_instance_factory.hpp
View file @ 9b3365e1
...@@ -95,7 +95,7 @@ template <typename Activation> ...@@ -95,7 +95,7 @@ template <typename Activation>
using Add_Activation_Mul_Clamp = using Add_Activation_Mul_Clamp =
ck::tensor_operation::element_wise::Add_Activation_Mul_Clamp<Activation>; ck::tensor_operation::element_wise::Add_Activation_Mul_Clamp<Activation>;
template <typename DeviceOp> template <typename DeviceOp, typename Tag = void>
struct DeviceOperationInstanceFactory; struct DeviceOperationInstanceFactory;
} // namespace instance } // namespace instance
......
library/include/ck/library/tensor_operation_instance/gpu/convolution_backward_weight.hpp deleted 100644 → 0
View file @ 9608beee
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_conv_bwd_weight.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// conv1d backward weight
void add_device_conv1d_bwd_weight_xdl_nwc_kxc_nwk_bf16_f32_bf16_instances(
std::vector<std::unique_ptr<DeviceConvBwdWeight<1,
NWC,
KXC,
NWK,
BF16,
F32,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_conv1d_bwd_weight_xdl_nwc_kxc_nwk_f16_instances(
std::vector<std::unique_ptr<DeviceConvBwdWeight<1,
NWC,
KXC,
NWK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_conv1d_bwd_weight_xdl_nwc_kxc_nwk_f32_instances(
std::vector<std::unique_ptr<DeviceConvBwdWeight<1,
NWC,
KXC,
NWK,
F32,
F32,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances);
// conv2d backward weight
void add_device_conv2d_bwd_weight_xdl_nhwc_kyxc_nhwk_bf16_f32_bf16_instances(
std::vector<std::unique_ptr<DeviceConvBwdWeight<2,
NHWC,
KYXC,
NHWK,
BF16,
F32,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_conv2d_bwd_weight_xdl_nhwc_kyxc_nhwk_f16_instances(
std::vector<std::unique_ptr<DeviceConvBwdWeight<2,
NHWC,
KYXC,
NHWK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_conv2d_bwd_weight_xdl_nhwc_kyxc_nhwk_f32_instances(
std::vector<std::unique_ptr<DeviceConvBwdWeight<2,
NHWC,
KYXC,
NHWK,
F32,
F32,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances);
// conv3d backward weight
void add_device_conv3d_bwd_weight_xdl_ndhwc_kzyxc_ndhwk_bf16_f32_bf16_instances(
std::vector<std::unique_ptr<DeviceConvBwdWeight<3,
NDHWC,
KZYXC,
NDHWK,
BF16,
F32,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_conv3d_bwd_weight_xdl_ndhwc_kzyxc_ndhwk_f16_instances(
std::vector<std::unique_ptr<DeviceConvBwdWeight<3,
NDHWC,
KZYXC,
NDHWK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_conv3d_bwd_weight_xdl_ndhwc_kzyxc_ndhwk_f32_instances(
std::vector<std::unique_ptr<DeviceConvBwdWeight<3,
NDHWC,
KZYXC,
NDHWK,
F32,
F32,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances);
template <ck::index_t NumDimSpatial,
typename InLayout,
typename WeiLayout,
typename OutLayout,
typename InDataType,
typename WeiDataType,
typename OutDataType>
struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceConvBwdWeight<
NumDimSpatial,
InLayout,
WeiLayout,
OutLayout,
InDataType,
WeiDataType,
OutDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>>
{
using DeviceOp = DeviceConvBwdWeight<NumDimSpatial,
InLayout,
WeiLayout,
OutLayout,
InDataType,
WeiDataType,
OutDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
if constexpr(NumDimSpatial == 1 && is_same_v<InLayout, NWC> && is_same_v<WeiLayout, KXC> &&
is_same_v<OutLayout, NWK>)
{
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_conv1d_bwd_weight_xdl_nwc_kxc_nwk_f32_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t>)
{
add_device_conv1d_bwd_weight_xdl_nwc_kxc_nwk_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, ck::bhalf_t> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_conv1d_bwd_weight_xdl_nwc_kxc_nwk_bf16_f32_bf16_instances(op_ptrs);
}
}
else if constexpr(NumDimSpatial == 2 && is_same_v<InLayout, NHWC> &&
is_same_v<WeiLayout, KYXC> && is_same_v<OutLayout, NHWK>)
{
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_conv2d_bwd_weight_xdl_nhwc_kyxc_nhwk_f32_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t>)
{
add_device_conv2d_bwd_weight_xdl_nhwc_kyxc_nhwk_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, ck::bhalf_t> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_conv2d_bwd_weight_xdl_nhwc_kyxc_nhwk_bf16_f32_bf16_instances(op_ptrs);
}
}
else if constexpr(NumDimSpatial == 3 && is_same_v<InLayout, NDHWC> &&
is_same_v<WeiLayout, KZYXC> && is_same_v<OutLayout, NDHWK>)
{
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_conv3d_bwd_weight_xdl_ndhwc_kzyxc_ndhwk_f32_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t>)
{
add_device_conv3d_bwd_weight_xdl_ndhwc_kzyxc_ndhwk_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, ck::bhalf_t> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_conv3d_bwd_weight_xdl_ndhwc_kzyxc_ndhwk_bf16_f32_bf16_instances(op_ptrs);
}
}
return op_ptrs;
}
};
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_data.hpp
View file @ 9b3365e1
...@@ -5,7 +5,7 @@ ...@@ -5,7 +5,7 @@
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_bwd_data.hpp" #include "ck/tensor_operation/gpu/device/device_grouped_conv_bwd_data_multiple_d.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/device_operation_instance_factory.hpp" #include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
...@@ -17,46 +17,54 @@ namespace instance { ...@@ -17,46 +17,54 @@ namespace instance {
// conv2d backward data // conv2d backward data
void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instances( void add_device_grouped_conv2d_bwd_data_xdl_gnhwc_gkyxc_gnhwk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdData<2, std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<2,
GNHWC, GNHWK,
GKYXC, GKYXC,
GNHWK, Empty_Tuple,
F16, GNHWC,
F16, F16,
F16, F16,
PassThrough, Empty_Tuple,
PassThrough, F16,
PassThrough>>>& instances); PassThrough,
PassThrough,
PassThrough>>>& instances);
template <ck::index_t NumDimSpatial, template <ck::index_t NumDimSpatial,
typename InLayout,
typename WeiLayout,
typename OutLayout, typename OutLayout,
typename InDataType, typename WeiLayout,
typename InLayout,
typename OutDataType,
typename WeiDataType, typename WeiDataType,
typename OutDataType> typename InDataType>
struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupedConvBwdData< struct DeviceOperationInstanceFactory<
NumDimSpatial, ck::tensor_operation::device::DeviceGroupedConvBwdDataMultipleD<
InLayout, NumDimSpatial,
WeiLayout, OutLayout,
OutLayout, WeiLayout,
InDataType, Empty_Tuple,
WeiDataType, InLayout,
OutDataType, OutDataType,
ck::tensor_operation::element_wise::PassThrough, WeiDataType,
ck::tensor_operation::element_wise::PassThrough, Empty_Tuple,
ck::tensor_operation::element_wise::PassThrough>> InDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>>
{ {
using DeviceOp = DeviceGroupedConvBwdData<NumDimSpatial, using DeviceOp =
InLayout, DeviceGroupedConvBwdDataMultipleD<NumDimSpatial,
WeiLayout, OutLayout,
OutLayout, WeiLayout,
InDataType, Empty_Tuple,
WeiDataType, InLayout,
OutDataType, OutDataType,
ck::tensor_operation::element_wise::PassThrough, WeiDataType,
ck::tensor_operation::element_wise::PassThrough, Empty_Tuple,
ck::tensor_operation::element_wise::PassThrough>; InDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
static auto GetInstances() static auto GetInstances()
{ {
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight.hpp 0 → 100644
View file @ 9b3365e1
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_bwd_weight.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// conv1d backward weight
void add_device_grouped_conv1d_bwd_weight_xdl_gnwc_gkxc_gnwk_bf16_f32_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<1,
GNWC,
GKXC,
GNWK,
BF16,
F32,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv1d_bwd_weight_xdl_gnwc_gkxc_gnwk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<1,
GNWC,
GKXC,
GNWK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv1d_bwd_weight_xdl_gnwc_gkxc_gnwk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<1,
GNWC,
GKXC,
GNWK,
F32,
F32,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances);
// conv2d backward weight
void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_bf16_f32_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
GNHWC,
GKYXC,
GNHWK,
BF16,
F32,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
GNHWC,
GKYXC,
GNHWK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
GNHWC,
GKYXC,
GNHWK,
F32,
F32,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances);
// conv3d backward weight
void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
GNDHWC,
GKZYXC,
GNDHWK,
BF16,
F32,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
GNDHWC,
GKZYXC,
GNDHWK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
GNDHWC,
GKZYXC,
GNDHWK,
F32,
F32,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances);
template <ck::index_t NumDimSpatial,
typename InLayout,
typename WeiLayout,
typename OutLayout,
typename InDataType,
typename WeiDataType,
typename OutDataType>
struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupedConvBwdWeight<
NumDimSpatial,
InLayout,
WeiLayout,
OutLayout,
InDataType,
WeiDataType,
OutDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>>
{
using DeviceOp = DeviceGroupedConvBwdWeight<NumDimSpatial,
InLayout,
WeiLayout,
OutLayout,
InDataType,
WeiDataType,
OutDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
if constexpr(NumDimSpatial == 1 && is_same_v<InLayout, GNWC> &&
is_same_v<WeiLayout, GKXC> && is_same_v<OutLayout, GNWK>)
{
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_grouped_conv1d_bwd_weight_xdl_gnwc_gkxc_gnwk_f32_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t>)
{
add_device_grouped_conv1d_bwd_weight_xdl_gnwc_gkxc_gnwk_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, ck::bhalf_t> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_grouped_conv1d_bwd_weight_xdl_gnwc_gkxc_gnwk_bf16_f32_bf16_instances(
op_ptrs);
}
}
else if constexpr(NumDimSpatial == 2 && is_same_v<InLayout, GNHWC> &&
is_same_v<WeiLayout, GKYXC> && is_same_v<OutLayout, GNHWK>)
{
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t>)
{
add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, ck::bhalf_t> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_bf16_f32_bf16_instances(
op_ptrs);
}
}
else if constexpr(NumDimSpatial == 3 && is_same_v<InLayout, GNDHWC> &&
is_same_v<WeiLayout, GKZYXC> && is_same_v<OutLayout, GNDHWK>)
{
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f32_instances(
op_ptrs);
}
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t>)
{
add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f16_instances(
op_ptrs);
}
else if constexpr(is_same_v<InDataType, ck::bhalf_t> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16_instances(
op_ptrs);
}
}
return op_ptrs;
}
};
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
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