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Unverified Commit 8047fa8f authored by Po Yen Chen's avatar Po Yen Chen Committed by GitHub
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Merge branch 'develop' into feature/optimize-buffer-load-reading-pattern

parents f68dd911 50643dd5
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Showing with 562 additions and 360 deletions
CMakeLists.txt
View file @ 8047fa8f
......@@ -296,31 +296,68 @@ file(GLOB_RECURSE INSTANCE_FILES "${PROJECT_SOURCE_DIR}/*/device_*_instance.cpp"
file(GLOB dir_list RELATIVE ${PROJECT_SOURCE_DIR}/library/src/tensor_operation_instance/gpu ${PROJECT_SOURCE_DIR}/library/src/tensor_operation_instance/gpu/*)
set(CK_DEVICE_INSTANCES)
FOREACH(subdir_path ${dir_list})
IF(IS_DIRECTORY "${PROJECT_SOURCE_DIR}/library/src/tensor_operation_instance/gpu/${subdir_path}")
list(APPEND CK_DEVICE_INSTANCES device_${subdir_path}_instance)
ENDIF()
set(target_dir)
IF(IS_DIRECTORY "${PROJECT_SOURCE_DIR}/library/src/tensor_operation_instance/gpu/${subdir_path}")
set(cmake_instance)
file(READ "${PROJECT_SOURCE_DIR}/library/src/tensor_operation_instance/gpu/${subdir_path}/CMakeLists.txt" cmake_instance)
set(add_inst 0)
if("${cmake_instance}" MATCHES "DTYPES MATCHES \"fp8\" " AND DTYPES MATCHES "fp8")
#message("fp8 instance found!")
set(add_inst 1)
endif()
if("${cmake_instance}" MATCHES "DTYPES MATCHES \"fp16\"" AND DTYPES MATCHES "fp16")
#message("fp16 instance found!")
set(add_inst 1)
endif()
if("${cmake_instance}" MATCHES "DTYPES MATCHES \"fp32\"" AND DTYPES MATCHES "fp32")
#message("fp32 instance found!")
set(add_inst 1)
endif()
if("${cmake_instance}" MATCHES "DTYPES MATCHES \"fp64\"" AND DTYPES MATCHES "fp64")
#message("fp64 instance found!")
set(add_inst 1)
endif()
if("${cmake_instance}" MATCHES "DTYPES MATCHES \"bf16\"" AND DTYPES MATCHES "bf16")
#message("bf16 instance found!")
set(add_inst 1)
endif()
if("${cmake_instance}" MATCHES "DTYPES MATCHES \"int8\"" AND DTYPES MATCHES "int8")
#message("int8 instance found!")
set(add_inst 1)
endif()
if(NOT "${cmake_instance}" MATCHES "DTYPES")
#message("instance should be built for all types!")
set(add_inst 1)
endif()
if(add_inst EQUAL 1 OR NOT DEFINED DTYPES)
list(APPEND CK_DEVICE_INSTANCES device_${subdir_path}_instance)
endif()
ENDIF()
ENDFOREACH()
add_custom_target(instances DEPENDS utility;${CK_DEVICE_INSTANCES} SOURCES ${INSTANCE_FILES})
add_subdirectory(library)
rocm_package_setup_component(tests
if(NOT DEFINED INSTANCES_ONLY)
rocm_package_setup_component(tests
LIBRARY_NAME composablekernel
PACKAGE_NAME tests # Prevent -static suffix on package name
)
)
rocm_package_setup_component(examples
rocm_package_setup_component(examples
LIBRARY_NAME composablekernel
PACKAGE_NAME examples
)
)
rocm_package_setup_component(profiler
rocm_package_setup_component(profiler
LIBRARY_NAME composablekernel
PACKAGE_NAME ckProfiler
)
)
add_subdirectory(library)
add_subdirectory(example)
add_subdirectory(test)
add_subdirectory(profiler)
add_subdirectory(example)
add_subdirectory(test)
add_subdirectory(profiler)
endif()
#Create an interface target for the include only files and call it "composablekernels"
include(CMakePackageConfigHelpers)
......
Dockerfile
View file @ 8047fa8f
......@@ -48,6 +48,7 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
libpthread-stubs0-dev \
llvm-amdgpu \
pkg-config \
python \
python3 \
python3-dev \
python3-pip \
......@@ -63,6 +64,10 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
rm -rf /var/lib/apt/lists/*
#Install latest version of cmake
RUN wget -qO /usr/local/bin/ninja.gz https://github.com/ninja-build/ninja/releases/latest/download/ninja-linux.zip
RUN gunzip /usr/local/bin/ninja.gz
RUN chmod a+x /usr/local/bin/ninja
RUN git clone https://github.com/nico/ninjatracing.git
RUN apt purge --auto-remove -y cmake
RUN apt update
RUN apt install -y software-properties-common lsb-release
......
example/37_batched_gemm_add_add_relu_gemm_add/batched_gemm_add_add_relu_gemm_add_xdl_fp16.cpp
View file @ 8047fa8f
......@@ -173,6 +173,8 @@ using DeviceGemmInstance =
8,
8,
true,
9, // D0sTransferSrcVectorDim
4, // D0sTransferSrcScalaerPerVector
S<8, 32, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
......@@ -189,7 +191,7 @@ int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
bool time_kernel = true;
// GEMM shape
ck::index_t M = 1024;
......
include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_multiple_d_gemm_multiple_d_xdl_cshuffle.hpp
View file @ 8047fa8f
......@@ -196,6 +196,8 @@ template <typename A0Layout,
index_t B0BlockTransferSrcScalarPerVector,
index_t B0BlockTransferDstScalarPerVector_BK1,
bool B0BlockLdsExtraN,
index_t CDE0BlockTransferSrcVectorDim,
index_t CDE0BlockTransferSrcScalaerPerVector,
typename B1BlockTransferThreadClusterLengths_BK0_N_BK1,
typename B1BlockTransferThreadClusterArrangeOrder,
typename B1BlockTransferSrcAccessOrder,
......@@ -492,6 +494,8 @@ struct DeviceBatchedGemmMultipleDGemmMultipleD_Xdl_CShuffle
B0BlockTransferDstScalarPerVector_BK1,
true,
B0BlockLdsExtraN,
CDE0BlockTransferSrcVectorDim,
CDE0BlockTransferSrcScalaerPerVector,
B1BlockTransferThreadClusterLengths_BK0_N_BK1,
B1BlockTransferThreadClusterArrangeOrder,
B1BlockTransferSrcAccessOrder,
......
include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_multiple_d_gemm_multiple_d_xdl_cshuffle_v1.hpp
View file @ 8047fa8f
......@@ -67,6 +67,8 @@ template <typename A0B0B1DataType, // FIXME: don't assume A0/B0/B1 have same dat
index_t B0BlockTransferDstScalarPerVector_BK1,
bool B0ThreadTransferSrcResetCoordinateAfterRun, // ignored
index_t B0BlockLdsExtraN,
index_t CDE0BlockTransferSrcVectorDim,
index_t CDE0BlockTransferSrcScalarPerVector,
typename B1BlockTransferThreadClusterLengths_BK0_N_BK1,
typename B1BlockTransferThreadClusterArrangeOrder,
typename B1BlockTransferSrcAccessOrder,
......@@ -710,13 +712,13 @@ struct GridwiseBatchedGemmMultipleDGemmMultipleD_Xdl_CShuffle
constexpr auto d0_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5 =
make_naive_tensor_descriptor_packed(make_tuple(I1, // MBlockId
I1, // NBlockID
I1, // MRepeat
I1, // NRepeat
I1, // MWaveId
I1, // NWaveId
I1, // MPerXdl
I1, // NGroupNum
I1, // NInputNum
m0, // MRepeat
n0, // NRepeat
m1, // MWaveId
n1, // NWaveId
m2, // MPerXdl
n2, // NGroupNum
n3, // NInputNum
n4)); // registerNum
auto d0s_thread_buf = generate_tuple(
......@@ -732,8 +734,9 @@ struct GridwiseBatchedGemmMultipleDGemmMultipleD_Xdl_CShuffle
const auto wave_id = GetGemm0WaveIdx();
const auto wave_m_n_id = GetGemm0WaveMNIdx(wave_id[I2]); // I2: 0~63
constexpr auto acc0_thread_desc = make_naive_tensor_descriptor_packed(
make_tuple(Number<Gemm0MXdlPerWave>{}, Number<Gemm0NXdlPerWave>{}, n2, n4));
static_assert(CDE0BlockTransferSrcScalarPerVector <= n4,
"vector load must be not greater than n4");
static_assert(n4 % CDE0BlockTransferSrcScalarPerVector == 0);
auto d0s_threadwise_copy = generate_tuple(
[&](auto i) {
......@@ -742,10 +745,19 @@ struct GridwiseBatchedGemmMultipleDGemmMultipleD_Xdl_CShuffle
A0B0B1DataType,
decltype(d0s_griddesc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5[i]),
decltype(d0_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5),
Sequence<I1, I1, I1, I1, I1, I1, I1, I1, I1, n4>,
Sequence<I1, // MBlockId
I1, // NBlockID
m0, // MRepeat
n0, // NRepeat
m1, // MWaveId
n1, // NWaveId
m2, // MPerXdl
n2, // NGroupNum
n3, // NInputNum
n4>,
Sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9>,
9,
n4,
9, // CDE0BlockTransferSrcVectorDim
CDE0BlockTransferSrcScalarPerVector,
1,
false>(d0s_griddesc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5[i],
make_multi_index(block_work_idx[I0], // MBlockId
......@@ -898,66 +910,42 @@ struct GridwiseBatchedGemmMultipleDGemmMultipleD_Xdl_CShuffle
blockwise_gemm0,
acc0_thread_buf,
num_k_block_main_loop);
// bias+gelu
// multiple d
if constexpr(NumD0Tensor)
{
static_for<0, Gemm0MXdlPerWave, 1>{}([&](auto mr) {
static_for<0, Gemm0NXdlPerWave, 1>{}([&](auto nr) {
static_for<0, n2, 1>{}([&](auto groupid) {
static_for<0, NumD0Tensor, 1>{}([&](auto i) {
d0s_threadwise_copy(i).Run(
d0s_griddesc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5[i],
d0s_grid_buf[i],
d0_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0, I0, I0),
d0s_thread_buf(i));
});
static_for<0, n4, 1>{}([&](auto i) {
constexpr index_t c_offset = acc0_thread_desc.CalculateOffset(
make_tuple(mr, nr, groupid, i));
// get reference to src data
const auto src_data_refs = generate_tie(
// return type should be lvalue
[&](auto iSrc) -> const auto& {
return d0s_thread_buf[iSrc][i];
},
Number<NumD0Tensor>{});
// get reference to dst data
auto dst_data_refs = generate_tie(
// return type should be lvalue
[&](auto) -> auto& {
return acc0_thread_buf(Number<c_offset>{});
},
Number<2>{});
unpack2(cde0_element_op, dst_data_refs, src_data_refs);
});
static_for<0, NumD0Tensor, 1>{}([&](auto i) {
d0s_threadwise_copy(i).MoveSrcSliceWindow(
d0s_griddesc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5[i],
make_multi_index(0, 0, 0, 0, 0, 0, 0, 1, 0, 0));
});
});
static_for<0, NumD0Tensor, 1>{}([&](auto i) {
d0s_threadwise_copy(i).MoveSrcSliceWindow(
d0s_griddesc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5[i],
make_multi_index(0, 0, 0, 1, 0, 0, 0, -n2.value, 0, 0));
});
});
static_for<0, NumD0Tensor, 1>{}([&](auto i) {
d0s_threadwise_copy(i).MoveSrcSliceWindow(
d0s_griddesc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5[i],
make_multi_index(0, 0, 1, -Gemm0NXdlPerWave, 0, 0, 0, 0, 0, 0));
});
static_for<0, NumD0Tensor, 1>{}([&](auto i) {
d0s_threadwise_copy(i).Run(d0s_griddesc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5[i],
d0s_grid_buf[i],
d0_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0, I0, I0),
d0s_thread_buf(i));
});
static_for<0, m0 * n0 * n2 * n4, 1>{}([&](auto i) {
// get reference to src data
const auto src_data_refs = generate_tie(
// return type should be lvalue
[&](auto iSrc) -> const auto& { return d0s_thread_buf[iSrc][i]; },
Number<NumD0Tensor>{});
// get reference to dst data
auto dst_data_refs = generate_tie(
// return type should be lvalue
[&](auto) -> auto& { return acc0_thread_buf(i); },
Number<2>{});
unpack2(cde0_element_op, dst_data_refs, src_data_refs);
});
static_for<0, NumD0Tensor, 1>{}([&](auto i) {
d0s_threadwise_copy(i).MoveSrcSliceWindow(
d0s_griddesc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5[i],
make_multi_index(0, 1, -Gemm0MXdlPerWave, 0, 0, 0, 0, 0, 0, 0));
make_multi_index(0, 1, 0, 0, 0, 0, 0, 0, 0, 0));
});
}
else
{
static_for<0, acc0_thread_buf.Size(), 1>{}(
[&](auto i) { cde0_element_op(acc_thread_buf(i), acc0_thread_buf[i]); });
}
// gemm1
{
// TODO: explore using dynamic buffer for a1 thread buffer
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight.hpp
View file @ 8047fa8f
......@@ -164,6 +164,42 @@ void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f32_instances
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_f32_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC,
GKZYXC,
NDHWGK,
BF16,
F32,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC,
GKZYXC,
NDHWGK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC,
GKZYXC,
NDHWGK,
F32,
F32,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances);
template <ck::index_t NumDimSpatial,
typename InLayout,
typename WeiLayout,
......@@ -273,8 +309,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
}
else if constexpr(NumDimSpatial == 3)
{
if(is_same_v<InLayout, GNDHWC> && is_same_v<WeiLayout, GKZYXC> &&
is_same_v<OutLayout, GNDHWK>)
if constexpr(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>)
......@@ -296,6 +332,29 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
op_ptrs);
}
}
else if constexpr(is_same_v<InLayout, NDHWGC> && is_same_v<WeiLayout, GKZYXC> &&
is_same_v<OutLayout, NDHWGK>)
{
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_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_ndhwgc_gkzyxc_ndhwgk_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_ndhwgc_gkzyxc_ndhwgk_bf16_f32_bf16_instances(
op_ptrs);
}
}
}
return op_ptrs;
......
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight/CMakeLists.txt
View file @ 8047fa8f
......@@ -2,4 +2,7 @@ add_instance_library(device_grouped_conv3d_bwd_weight_instance
device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f16_instance.cpp
device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f32_instance.cpp
device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_bf16_instance.cpp
device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp
device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp
device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp
)
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight/device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp 0 → 100644
View file @ 8047fa8f
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_f32_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC,
GKZYXC,
NDHWGK,
BF16,
F32,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_bf16_instances<3,
NDHWGC,
GKZYXC,
NDHWGK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_bf16_instances<
3,
NDHWGC,
GKZYXC,
NDHWGK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight/device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp 0 → 100644
View file @ 8047fa8f
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC,
GKZYXC,
NDHWGK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_instances<3,
NDHWGC,
GKZYXC,
NDHWGK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_instances<
3,
NDHWGC,
GKZYXC,
NDHWGK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight/device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp 0 → 100644
View file @ 8047fa8f
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC,
GKZYXC,
NDHWGK,
F32,
F32,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_f32_instances<3,
NDHWGC,
GKZYXC,
NDHWGK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_f32_instances<
3,
NDHWGC,
GKZYXC,
NDHWGK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
profiler/README.md
View file @ 8047fa8f
......@@ -144,7 +144,7 @@ GB/s: 127.947
## Profile grouped convolution backward weight kernels
```bash
# arg1: tensor operation (grouped_conv_bwd_data: Grouped Convolution Backward Data)
# arg1: tensor operation (grouped_conv_bwd_weight: Grouped Convolution Backward Weight)
# arg2: data type (0: Input fp32, Weight fp32, Output fp32
# 1: Input fp16, Weight fp16, Output fp16
# 2: Input bf16, Weight fp32, Output bf16)
......@@ -167,7 +167,7 @@ GB/s: 127.947
# SplitK
################ op datatype layout verify init log time Ndims G N K C Y X Hi Wi Sy Sx Dy Dx LeftPy LeftPx RightPy RightPx SplitK
./bin/ckProfiler grouped_conv_bwd_data 1 0 1 1 0 1 2 32 256 256 512 3 3 28 28 1 1 1 1 1 0 0 0 1
./bin/ckProfiler grouped_conv_bwd_weight 1 0 1 1 0 1 2 32 256 256 512 3 3 28 28 1 1 1 1 1 0 0 0 1
```
......
profiler/src/profile_grouped_conv_bwd_weight.cpp
View file @ 8047fa8f
......@@ -83,19 +83,7 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using GNWC = ck::tensor_layout::convolution::GNWC;
using GNHWC = ck::tensor_layout::convolution::GNHWC;
using NHWGC = ck::tensor_layout::convolution::NHWGC;
using GNDHWC = ck::tensor_layout::convolution::GNDHWC;
using GKXC = ck::tensor_layout::convolution::GKXC;
using GKYXC = ck::tensor_layout::convolution::GKYXC;
using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
using GNWK = ck::tensor_layout::convolution::GNWK;
using GNHWK = ck::tensor_layout::convolution::GNHWK;
using NHWGK = ck::tensor_layout::convolution::NHWGK;
using GNDHWK = ck::tensor_layout::convolution::GNDHWK;
using namespace ck::tensor_layout::convolution;
constexpr auto I1 = ck::Number<1>{};
constexpr auto I2 = ck::Number<2>{};
......@@ -194,6 +182,22 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
return profile(I3, GNDHWC{}, GKZYXC{}, GNDHWK{}, BF16{}, F32{}, BF16{});
}
}
else if(num_dim_spatial == 3 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_F32_BF16)
{
// fp32 atomic add is used for weight tensor in bf16 kernel
return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, BF16{}, F32{}, BF16{});
}
}
std::cout << "this data_type & layout is not implemented" << std::endl;
......
test/grouped_convnd_bwd_weight/test_grouped_convnd_bwd_weight.cpp
View file @ 8047fa8f
......@@ -85,7 +85,10 @@ using KernelTypes2d = ::testing::Types<
using KernelTypes3d = ::testing::Types<
std::tuple<float, float, float, GNDHWC, GKZYXC, GNDHWK, ck::Number<3>>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, GNDHWC, GKZYXC, GNDHWK, ck::Number<3>>,
std::tuple<ck::bhalf_t, float, ck::bhalf_t, GNDHWC, GKZYXC, GNDHWK, ck::Number<3>>>;
std::tuple<ck::bhalf_t, float, ck::bhalf_t, GNDHWC, GKZYXC, GNDHWK, ck::Number<3>>,
std::tuple<float, float, float, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>,
std::tuple<ck::bhalf_t, float, ck::bhalf_t, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>>;
TYPED_TEST_SUITE(TestGroupedConvndBwdWeight1d, KernelTypes1d);
TYPED_TEST_SUITE(TestGroupedConvndBwdWeight2d, KernelTypes2d);
......
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