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Commit 612539c7 authored by Bartlomiej Kocot's avatar Bartlomiej Kocot
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Support NHWGC conv2d_bwd_weight

parent 3b18f1e3
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library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_instance.cpp 0 → 100644
View file @ 612539c7
// 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 "device_grouped_conv2d_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_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_f32_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC,
GKYXC,
NHWGK,
BF16,
F32,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_weight_xdl_c_shuffle_bf16_instances<NHWGC,
GKYXC,
NHWGK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(instances,
device_grouped_conv2d_bwd_weight_xdl_c_shuffle_bf16_instances<
NHWGC,
GKYXC,
NHWGK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_instance.cpp 0 → 100644
View file @ 612539c7
// 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 "device_grouped_conv2d_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_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC,
GKYXC,
NHWGK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_weight_xdl_c_shuffle_f16_default_instances<
NHWGC,
GKYXC,
NHWGK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_weight_xdl_c_shuffle_f16_default_instances<
NHWGC,
GKYXC,
NHWGK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_instance.cpp 0 → 100644
View file @ 612539c7
// 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 "device_grouped_conv2d_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_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC,
GKYXC,
NHWGK,
F32,
F32,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_weight_xdl_c_shuffle_f32_default_instances<
NHWGC,
GKYXC,
NHWGK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv2d_bwd_weight_xdl_c_shuffle_f32_default_instances<
NHWGC,
GKYXC,
NHWGK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
profiler/README.md
View file @ 612539c7
......@@ -141,3 +141,46 @@ avg_time: 0.768321
tflops: 86.6679
GB/s: 127.947
```
## Profile grouped convolution backward weight kernels
```bash
# arg1: tensor operation (grouped_conv_bwd_data: Grouped Convolution Backward Data)
# arg2: data type (0: Input fp32, Weight fp32, Output fp32
# 1: Input fp16, Weight fp16, Output fp16
# 2: Input bf16, Weight fp32, Output bf16)
# arg3: tensor layout (0: Input[G, N, C, Hi, Wi], Weight[G, K, C, Y, X], Output[G, N, K, Ho, Wo]
# 1: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, N, Ho, Wo, K]
# 2: Input[N, Hi, Wi, G, C], Weight[G, K, Y, X, C], Output[N, Ho, Wo, G, K]
# arg4: verification (0: no, 1: yes)
# arg5: initialization (0: no init, 1: integer value, 2: decimal value)
# arg6: print tensor value (0: no; 1: yes)
# arg7: time kernel (0: no, 1: yes)
# Following arguments (depending on number of spatial dims):
# Number of spatial dimensions (1=Conv1d, 2=Conv2d, 3=Conv3d)
# G, N, K, C,
# <filter spatial dimensions>, (ie Y, X for 2D)
# <input image spatial dimensions>, (ie Hi, Wi for 2D)
# <strides>, (ie Sy, Sx for 2D)
# <dilations>, (ie Dy, Dx for 2D)
# <left padding>, (ie LeftPy, LeftPx for 2D)
# <right padding>, (ie RightPy, RightPx for 2D)
# 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
```
Result (MI100, FP16, GNHWC_GKYXC_GNHWK)
```
input: dim 5, lengths {32, 512, 1024, 28, 28}, strides {411041792, 802816, 1, 28672, 1024}
weight: dim 5, lengths {32, 512, 1024, 3, 3}, strides {4718592, 9216, 1, 3072, 1024}
output: dim 5, lengths {32, 512, 512, 26, 26}, strides {177209344, 346112, 1, 13312, 512}
....
Best configuration parameters:
name: DeviceGroupedConvBwdWeight_Xdl_CShuffle<256, 256, 128, 4, Default, 8, 4, 2, 8, 4, 8, 2, 1, 1, 8>
avg_time: 68.5216
tflops: 95.337
GB/s: 69.2301
```
Note: This kernel use atomic add, this will cause output buffer to be accumulated multiple times, causing verification failure. To work around it, do not use CK's own timer and do verification at the same time.
profiler/include/profiler/profile_grouped_conv_bwd_weight_impl.hpp
View file @ 612539c7
......@@ -139,6 +139,8 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification,
std::array<ck::index_t, NDimSpatial> input_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> output_spatial_lengths{};
std::array<ck::index_t, NDimSpatial + 3> input_strides{};
std::array<ck::index_t, NDimSpatial + 3> output_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
std::array<ck::index_t, NDimSpatial> input_left_pads{};
......@@ -149,6 +151,8 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification,
range_copy(conv_param.input_spatial_lengths_, begin(input_spatial_lengths));
range_copy(conv_param.filter_spatial_lengths_, begin(filter_spatial_lengths));
range_copy(conv_param.output_spatial_lengths_, begin(output_spatial_lengths));
range_copy(in_g_n_c_wis_desc.GetStrides(), begin(input_strides));
range_copy(out_g_n_k_wos_desc.GetStrides(), begin(output_strides));
range_copy(conv_param.conv_filter_strides_, begin(conv_filter_strides));
range_copy(conv_param.conv_filter_dilations_, begin(conv_filter_dilations));
range_copy(conv_param.input_left_pads_, begin(input_left_pads));
......@@ -167,6 +171,8 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification,
input_spatial_lengths,
filter_spatial_lengths,
output_spatial_lengths,
input_strides,
output_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
......
profiler/src/profile_grouped_conv_bwd_weight.cpp
View file @ 612539c7
......@@ -15,6 +15,7 @@ enum struct ConvLayout
{
GNCHW_GKCYX_GNKHW, // 0
GNHWC_GKYXC_GNHWK, // 1
NHWGC_GKYXC_NHWGK, // 2
};
enum struct ConvDataType
......@@ -37,6 +38,8 @@ static void print_helper_msg()
"N, K, Ho, Wo]\n"
<< " 1: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, "
"N, Ho, Wo, K]\n"
<< " 2: Input[N, Hi, Wi, G, C], Weight[G, K, Y, X, C], Output[N, "
"Ho, Wo, G, K]\n"
<< "arg4: verification (0: no, 1: yes)\n"
<< "arg5: initialization (0: no init, 1: integer value, 2: decimal value)\n"
<< "arg6: print tensor value (0: no; 1: yes)\n"
......@@ -82,6 +85,7 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
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;
......@@ -90,6 +94,7 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
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;
constexpr auto I1 = ck::Number<1>{};
......@@ -157,6 +162,22 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
return profile(I2, GNHWC{}, GKYXC{}, GNHWK{}, BF16{}, F32{}, BF16{});
}
}
else if(num_dim_spatial == 2 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_F32_BF16)
{
// fp32 atomic add is used for weight tensor in bf16 kernel
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, BF16{}, F32{}, BF16{});
}
}
else if(num_dim_spatial == 3 && layout == ConvLayout::GNHWC_GKYXC_GNHWK)
{
if(data_type == ConvDataType::F32_F32_F32)
......
test/grouped_convnd_bwd_weight/CMakeLists.txt
View file @ 612539c7
......@@ -2,8 +2,10 @@ list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0)
add_gtest_executable(test_grouped_convnd_bwd_weight grouped_convnd_bwd_weight.cpp)
add_gtest_executable(test_grouped_convnd_bwd_weight test_grouped_convnd_bwd_weight.cpp)
target_link_libraries(test_grouped_convnd_bwd_weight PRIVATE utility device_grouped_conv1d_bwd_weight_instance device_grouped_conv2d_bwd_weight_instance device_grouped_conv3d_bwd_weight_instance)
add_gtest_executable(test_grouped_convnd_bwd_weight_interface test_grouped_convnd_bwd_weight_interface.cpp)
target_link_libraries(test_grouped_convnd_bwd_weight_interface PRIVATE utility device_grouped_conv1d_bwd_weight_instance device_grouped_conv2d_bwd_weight_instance device_grouped_conv3d_bwd_weight_instance)
set(target 1)
endif()
endforeach()
\ No newline at end of file
test/grouped_convnd_bwd_weight/grouped_convnd_bwd_weight.cpp → test/grouped_convnd_bwd_weight/test_grouped_convnd_bwd_weight.cpp
View file @ 612539c7
......@@ -9,55 +9,88 @@
#include <gtest/gtest.h>
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "profiler/profile_grouped_conv_bwd_weight_impl.hpp"
template <typename Tuple>
class TestGroupedConvndBwdWeight : public ::testing::Test
{
protected:
using DataType = std::tuple_element_t<0, Tuple>;
using InDataType = std::tuple_element_t<0, Tuple>;
using WeiDataType = std::tuple_element_t<1, Tuple>;
using OutDataType = std::tuple_element_t<2, Tuple>;
using InLayout = std::tuple_element_t<3, Tuple>;
using WeiLayout = std::tuple_element_t<4, Tuple>;
using OutLayout = std::tuple_element_t<5, Tuple>;
std::vector<ck::utils::conv::ConvParam> conv_params;
ck::index_t split_k{2};
template <ck::index_t NDimSpatial>
void Run()
{
EXPECT_FALSE(conv_params.empty());
bool pass = true;
for(auto& param : conv_params)
{
bool pass;
EXPECT_FALSE(conv_params.empty());
pass = ck::profiler::profile_grouped_conv_bwd_weight_impl<
NDimSpatial,
ck::tuple_element_t<NDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::GNWC,
ck::tensor_layout::convolution::GNHWC,
ck::tensor_layout::convolution::GNDHWC>>,
ck::tuple_element_t<NDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::GKXC,
ck::tensor_layout::convolution::GKYXC,
ck::tensor_layout::convolution::GKZYXC>>,
ck::tuple_element_t<NDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::GNWK,
ck::tensor_layout::convolution::GNHWK,
ck::tensor_layout::convolution::GNDHWK>>,
DataType,
DataType,
DataType>(true, // do_verification
1, // init_method: integer value
false, // do_log
false, // time_kernel
param,
split_k);
EXPECT_TRUE(pass);
pass = pass && ck::profiler::profile_grouped_conv_bwd_weight_impl<NDimSpatial,
InLayout,
WeiLayout,
OutLayout,
InDataType,
WeiDataType,
OutDataType>(
true, // do_verification
1, // init_method: integer value
false, // do_log
false, // time_kernel
param,
split_k);
}
EXPECT_TRUE(pass);
}
};
using KernelTypes =
::testing::Types<std::tuple<float>, std::tuple<ck::half_t>, std::tuple<ck::bhalf_t>>;
TYPED_TEST_SUITE(TestGroupedConvndBwdWeight, KernelTypes);
template <typename Tuple>
class TestGroupedConvndBwdWeight1d : public TestGroupedConvndBwdWeight<Tuple>
{
};
template <typename Tuple>
class TestGroupedConvndBwdWeight2d : public TestGroupedConvndBwdWeight<Tuple>
{
};
template <typename Tuple>
class TestGroupedConvndBwdWeight3d : public TestGroupedConvndBwdWeight<Tuple>
{
};
using namespace ck::tensor_layout::convolution;
using KernelTypes1d =
::testing::Types<std::tuple<float, float, float, GNWC, GKXC, GNWK>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, GNWC, GKXC, GNWK>,
std::tuple<ck::bhalf_t, float, ck::bhalf_t, GNWC, GKXC, GNWK>>;
using KernelTypes2d =
::testing::Types<std::tuple<float, float, float, GNHWC, GKYXC, GNHWK>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, GNHWC, GKYXC, GNHWK>,
std::tuple<ck::bhalf_t, float, ck::bhalf_t, GNHWC, GKYXC, GNHWK>,
std::tuple<float, float, float, NHWGC, GKYXC, NHWGK>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, NHWGC, GKYXC, NHWGK>,
std::tuple<ck::bhalf_t, float, ck::bhalf_t, NHWGC, GKYXC, NHWGK>>;
using KernelTypes3d =
::testing::Types<std::tuple<float, float, float, GNDHWC, GKZYXC, GNDHWK>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, GNDHWC, GKZYXC, GNDHWK>,
std::tuple<ck::bhalf_t, float, ck::bhalf_t, GNDHWC, GKZYXC, GNDHWK>>;
TYPED_TEST_SUITE(TestGroupedConvndBwdWeight1d, KernelTypes1d);
TYPED_TEST_SUITE(TestGroupedConvndBwdWeight2d, KernelTypes2d);
TYPED_TEST_SUITE(TestGroupedConvndBwdWeight3d, KernelTypes3d);
TYPED_TEST(TestGroupedConvndBwdWeight, Test1D)
TYPED_TEST(TestGroupedConvndBwdWeight1d, Test1D)
{
this->conv_params.clear();
this->conv_params.push_back({1, 2, 128, 128, 256, {1}, {14}, {2}, {1}, {0}, {0}});
......@@ -66,7 +99,7 @@ TYPED_TEST(TestGroupedConvndBwdWeight, Test1D)
this->template Run<1>();
}
TYPED_TEST(TestGroupedConvndBwdWeight, Test2D)
TYPED_TEST(TestGroupedConvndBwdWeight2d, Test2D)
{
this->conv_params.clear();
this->conv_params.push_back(
......@@ -78,7 +111,7 @@ TYPED_TEST(TestGroupedConvndBwdWeight, Test2D)
this->template Run<2>();
}
TYPED_TEST(TestGroupedConvndBwdWeight, Test3D)
TYPED_TEST(TestGroupedConvndBwdWeight3d, Test3D)
{
this->conv_params.clear();
this->conv_params.push_back(
......
test/grouped_convnd_bwd_weight/test_grouped_convnd_bwd_weight_interface.cpp 0 → 100644
View file @ 612539c7
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <iostream>
#include <initializer_list>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/convolution_backward_weight_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_xdl_cshuffle.hpp"
#include "ck/library/utility/convolution_parameter.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
#include <gtest/gtest.h>
using F16 = ck::half_t;
using F32 = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using ConvolutionBackwardWeightSpecialization =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization;
static constexpr auto ConvBwdWeightDefault = ConvolutionBackwardWeightSpecialization::Default;
static constexpr auto Filter1x1Stride1Pad0 =
ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0;
template <typename Tuple, ConvolutionBackwardWeightSpecialization ConvSpec>
class TestGroupedConvndBwdWeight : public ::testing::Test
{
protected:
static constexpr ck::index_t NDimSpatial = 2;
using InLayout = std::tuple_element_t<2, Tuple>;
using WeiLayout = std::tuple_element_t<1, Tuple>;
using OutLayout = std::tuple_element_t<0, Tuple>;
// clang-format off
using GroupedConvBwdWeightDeviceInstance = ck::tensor_operation::device::DeviceGroupedConvBwdWeight_Xdl_CShuffle
//##########| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//##########| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//##########| Spatial| | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//##########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
< NDimSpatial, InLayout, WeiLayout,OutLayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>;
// clang-format on
ck::utils::conv::ConvParam conv_param;
ck::index_t split_k{2};
template <ck::index_t NDimSpatial>
bool Run()
{
const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
conv_param);
const auto wei_g_k_c_xs_desc =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
conv_param);
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
conv_param);
std::array<ck::index_t, NDimSpatial> input_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> output_spatial_lengths{};
std::array<ck::index_t, NDimSpatial + 3> input_strides{};
std::array<ck::index_t, NDimSpatial + 3> output_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
std::array<ck::index_t, NDimSpatial> input_left_pads{};
std::array<ck::index_t, NDimSpatial> input_right_pads{};
auto range_copy = [](const auto& from, auto to) { std::copy(begin(from), end(from), to); };
range_copy(conv_param.input_spatial_lengths_, begin(input_spatial_lengths));
range_copy(conv_param.filter_spatial_lengths_, begin(filter_spatial_lengths));
range_copy(conv_param.output_spatial_lengths_, begin(output_spatial_lengths));
range_copy(in_g_n_c_wis_desc.GetStrides(), begin(input_strides));
range_copy(out_g_n_k_wos_desc.GetStrides(), begin(output_strides));
range_copy(conv_param.conv_filter_strides_, begin(conv_filter_strides));
range_copy(conv_param.conv_filter_dilations_, begin(conv_filter_dilations));
range_copy(conv_param.input_left_pads_, begin(input_left_pads));
range_copy(conv_param.input_right_pads_, begin(input_right_pads));
auto conv = GroupedConvBwdWeightDeviceInstance{};
auto argument = conv.MakeArgument(nullptr,
nullptr,
nullptr,
conv_param.G_,
conv_param.N_,
conv_param.K_,
conv_param.C_,
input_spatial_lengths,
filter_spatial_lengths,
output_spatial_lengths,
input_strides,
output_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
PassThrough{},
PassThrough{},
PassThrough{},
split_k);
return conv.IsSupportedArgument(argument);
}
};
using GNHWC = ck::tensor_layout::convolution::GNHWC;
using NHWGC = ck::tensor_layout::convolution::NHWGC;
using GKYXC = ck::tensor_layout::convolution::GKYXC;
using GNHWK = ck::tensor_layout::convolution::GNHWK;
using NHWGK = ck::tensor_layout::convolution::NHWGK;
using KernelTypes =
::testing::Types<std::tuple<GNHWK, GKYXC, GNHWC>, std::tuple<NHWGK, GKYXC, NHWGC>>;
template <typename Tuple>
class TestGroupedConvndBwdWeightDefault
: public TestGroupedConvndBwdWeight<Tuple, ConvBwdWeightDefault>
{
};
template <typename Tuple>
class TestGroupedConvndBwdWeightFilter1x1
: public TestGroupedConvndBwdWeight<Tuple, Filter1x1Stride1Pad0>
{
};
TYPED_TEST_SUITE(TestGroupedConvndBwdWeightDefault, KernelTypes);
TYPED_TEST_SUITE(TestGroupedConvndBwdWeightFilter1x1, KernelTypes);
TYPED_TEST(TestGroupedConvndBwdWeightFilter1x1, SpecializationCheck)
{
// Check filter 3,3 instead of 1,1
this->conv_param = {2, 2, 4, 192, 192, {3, 3}, {28, 28}, {1, 1}, {1, 1}, {0, 0}, {0, 0}};
bool is_supported = this->template Run<2>();
EXPECT_FALSE(is_supported);
// Check strides 2,2 instead of 1,1
this->conv_param = {2, 2, 4, 192, 192, {1, 1}, {28, 28}, {2, 2}, {1, 1}, {0, 0}, {0, 0}};
is_supported = this->template Run<2>();
EXPECT_FALSE(is_supported);
// Check with pad
this->conv_param = {2, 2, 4, 192, 192, {1, 1}, {28, 28}, {1, 1}, {1, 1}, {1, 1}, {1, 1}};
is_supported = this->template Run<2>();
EXPECT_FALSE(is_supported);
// Supported version
this->conv_param = {2, 2, 128, 128, 256, {1, 1}, {3, 3}, {1, 1}, {1, 1}, {0, 0}, {0, 0}};
is_supported = this->template Run<2>();
EXPECT_TRUE(is_supported);
}
TYPED_TEST(TestGroupedConvndBwdWeightDefault, VectorLoadCheck)
{
// vector load for A
this->conv_param = {2, 2, 128, 129, 256, {1, 1}, {7, 7}, {2, 2}, {1, 1}, {0, 0}, {0, 0}};
bool is_supported = this->template Run<2>();
EXPECT_FALSE(is_supported);
// vector load for B, E, Ds
this->conv_param = {2, 2, 128, 128, 257, {1, 1}, {7, 7}, {2, 2}, {1, 1}, {0, 0}, {0, 0}};
is_supported = this->template Run<2>();
EXPECT_FALSE(is_supported);
}
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