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add license in file (#303)

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[ROCm/composable_kernel commit: d3051d7517]
This commit is contained in:
Chao Liu
2022-06-24 23:32:43 -05:00
committed by GitHub
parent 9df0a11a51
commit 2ef299e0ad
500 changed files with 3919 additions and 2445 deletions
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3
test/batched_gemm/batched_gemm_fp16.cpp
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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "profiler/include/profile_batched_gemm_impl.hpp"

3
test/batched_gemm/batched_gemm_util.hpp
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@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#ifndef BATCHED_GEMM_UTILS_HPP
#define BATCHED_GEMM_UTILS_HPP

3
test/batched_gemm_reduce/batched_gemm_reduce_fp16.cpp
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@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "profiler/include/profile_batched_gemm_reduce_impl.hpp"

3
test/block_to_ctile_map/test_block_to_ctile_map.cpp
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@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <vector>
#include <gtest/gtest.h>

3
test/conv2d_bwd_data/conv2d_bwd_data.cpp
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@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "config.hpp"
#include "device.hpp"
#include "host_tensor.hpp"

3
test/conv2d_bwd_weight/conv2d_bwd_weight.cpp
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@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>

411
test/conv_util/conv_util.cpp
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@@ -1,204 +1,207 @@
#include <iostream>
#include <string>
#include <vector>
#include <gtest/gtest.h>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/conv_util.hpp"
namespace {
class TestConvUtil : public ::testing::Test
{
public:
void SetNDParams(std::size_t ndims)
{
conv_params.num_dim_spatial_ = ndims;
conv_params.filter_spatial_lengths_ = std::vector<ck::index_t>(ndims, 3);
conv_params.input_spatial_lengths_ = std::vector<ck::index_t>(ndims, 71);
conv_params.conv_filter_strides_ = std::vector<ck::index_t>(ndims, 2);
conv_params.conv_filter_dilations_ = std::vector<ck::index_t>(ndims, 1);
conv_params.input_left_pads_ = std::vector<ck::index_t>(ndims, 1);
conv_params.input_right_pads_ = std::vector<ck::index_t>(ndims, 1);
}
protected:
// ------- default 2D -------
// input NCHW {128,192,71,71},
// weights KCYX {256,192,3,3},
// stride {2,2},
// dilations {1,1},
// padding {{1,1}, {1,1}}
ck::utils::conv::ConvParams conv_params;
};
} // namespace
TEST_F(TestConvUtil, ConvParamsGetOutputSpatialLengths2D)
{
ck::utils::conv::ConvParams conv_params;
std::vector<ck::index_t> out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{36, 36},
"Error: ConvParams 2D default constructor."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{71, 71}, "Error: ConvParams 2D stride {1,1}."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{2, 2};
conv_params.input_left_pads_ = std::vector<ck::index_t>{2, 2};
conv_params.input_right_pads_ = std::vector<ck::index_t>{2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{37, 37},
"Error: ConvParams 2D padding left/right {2,2}."));
conv_params.conv_filter_dilations_ = std::vector<ck::index_t>{2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36, 36}, "Error: ConvParams 2D dilation {2,2}."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{3, 3};
conv_params.input_left_pads_ = std::vector<ck::index_t>{1, 1};
conv_params.input_right_pads_ = std::vector<ck::index_t>{1, 1};
conv_params.conv_filter_dilations_ = std::vector<ck::index_t>{2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(
ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{23, 23},
"Error: ConvParams 2D strides{3,3}, padding {1,1}, dilations {2,2}."));
}
TEST_F(TestConvUtil, ConvParamsGetOutputSpatialLengths1D)
{
SetNDParams(1);
std::vector<ck::index_t> out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36}, "Error: ConvParams 1D."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{1};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{71}, "Error: ConvParams 1D stride {1}."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{2};
conv_params.input_left_pads_ = std::vector<ck::index_t>{2};
conv_params.input_right_pads_ = std::vector<ck::index_t>{2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{37},
"Error: ConvParams 1D padding left/right {2}."));
conv_params.conv_filter_dilations_ = std::vector<ck::index_t>{2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36}, "Error: ConvParams 1D dilation {2}."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{3};
conv_params.input_left_pads_ = std::vector<ck::index_t>{1};
conv_params.input_right_pads_ = std::vector<ck::index_t>{1};
conv_params.conv_filter_dilations_ = std::vector<ck::index_t>{2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(
ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{23},
"Error: ConvParams 1D strides{3}, padding {1}, dilations {2}."));
}
TEST_F(TestConvUtil, ConvParamsGetOutputSpatialLengths3D)
{
SetNDParams(3);
std::vector<ck::index_t> out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36, 36, 36}, "Error: ConvParams 3D."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1, 1};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{71, 71, 71},
"Error: ConvParams 3D stride {1, 1, 1}."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{2, 2, 2};
conv_params.input_left_pads_ = std::vector<ck::index_t>{2, 2, 2};
conv_params.input_right_pads_ = std::vector<ck::index_t>{2, 2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{37, 37, 37},
"Error: ConvParams 3D padding left/right {2, 2, 2}."));
conv_params.conv_filter_dilations_ = std::vector<ck::index_t>{2, 2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{36, 36, 36},
"Error: ConvParams 3D dilation {2, 2, 2}."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{3, 3, 3};
conv_params.input_left_pads_ = std::vector<ck::index_t>{1, 1, 1};
conv_params.input_right_pads_ = std::vector<ck::index_t>{1, 1, 1};
conv_params.conv_filter_dilations_ = std::vector<ck::index_t>{2, 2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len,
std::vector<ck::index_t>{23, 23, 23},
"Error: ConvParams 3D strides{3, 3, 3}, padding {1, 1, 1}, dilations {2, 2, 2}."));
}
TEST(ConvUtil, GetHostTensorDescriptor)
{
namespace tl = ck::tensor_layout::convolution;
std::vector<std::size_t> dims{2, 3, 4, 5};
HostTensorDescriptor h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NHWC{});
EXPECT_TRUE(ck::utils::check_err(
h.GetLengths(), {2, 3, 4, 5}, "Error: wrong NHWC dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4 * 5, 1, 3 * 5, 3}, "Error: wrong NHWC dimensions strides!"));
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCHW{});
EXPECT_TRUE(ck::utils::check_err(
h.GetLengths(), {2, 3, 4, 5}, "Error: wrong NCHW dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4 * 5, 4 * 5, 5, 1}, "Error: wrong NCHW dimensions strides!"));
dims = std::vector<std::size_t>{2, 3, 4};
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NWC{});
EXPECT_TRUE(
ck::utils::check_err(h.GetLengths(), {2, 3, 4}, "Error: wrong NWC dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4, 1, 3}, "Error: wrong NWC dimensions strides!"));
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCW{});
EXPECT_TRUE(
ck::utils::check_err(h.GetLengths(), {2, 3, 4}, "Error: wrong NCW dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4, 4, 1}, "Error: wrong NCW dimensions strides!"));
dims = std::vector<std::size_t>{2, 3, 4, 5, 6};
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NDHWC{});
EXPECT_TRUE(
ck::utils::check_err(h.GetLengths(), dims, "Error: wrong NDHWC dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(h.GetStrides(),
{3 * 4 * 5 * 6, // N
1, // C
3 * 5 * 6, // D
3 * 6, // H
3}, // W
"Error: wrong NDHWC dimensions strides!"));
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCDHW{});
EXPECT_TRUE(
ck::utils::check_err(h.GetLengths(), dims, "Error: wrong NCDHW dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(h.GetStrides(),
{3 * 4 * 5 * 6, // N
4 * 5 * 6, // C
5 * 6, // D
6, // H
1}, // W
"Error: wrong NCDHW dimensions strides!"));
}
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <string>
#include <vector>
#include <gtest/gtest.h>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/conv_util.hpp"
namespace {
class TestConvUtil : public ::testing::Test
{
public:
void SetNDParams(std::size_t ndims)
{
conv_params.num_dim_spatial_ = ndims;
conv_params.filter_spatial_lengths_ = std::vector<ck::index_t>(ndims, 3);
conv_params.input_spatial_lengths_ = std::vector<ck::index_t>(ndims, 71);
conv_params.conv_filter_strides_ = std::vector<ck::index_t>(ndims, 2);
conv_params.conv_filter_dilations_ = std::vector<ck::index_t>(ndims, 1);
conv_params.input_left_pads_ = std::vector<ck::index_t>(ndims, 1);
conv_params.input_right_pads_ = std::vector<ck::index_t>(ndims, 1);
}
protected:
// ------- default 2D -------
// input NCHW {128,192,71,71},
// weights KCYX {256,192,3,3},
// stride {2,2},
// dilations {1,1},
// padding {{1,1}, {1,1}}
ck::utils::conv::ConvParams conv_params;
};
} // namespace
TEST_F(TestConvUtil, ConvParamsGetOutputSpatialLengths2D)
{
ck::utils::conv::ConvParams conv_params;
std::vector<ck::index_t> out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{36, 36},
"Error: ConvParams 2D default constructor."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{71, 71}, "Error: ConvParams 2D stride {1,1}."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{2, 2};
conv_params.input_left_pads_ = std::vector<ck::index_t>{2, 2};
conv_params.input_right_pads_ = std::vector<ck::index_t>{2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{37, 37},
"Error: ConvParams 2D padding left/right {2,2}."));
conv_params.conv_filter_dilations_ = std::vector<ck::index_t>{2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36, 36}, "Error: ConvParams 2D dilation {2,2}."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{3, 3};
conv_params.input_left_pads_ = std::vector<ck::index_t>{1, 1};
conv_params.input_right_pads_ = std::vector<ck::index_t>{1, 1};
conv_params.conv_filter_dilations_ = std::vector<ck::index_t>{2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(
ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{23, 23},
"Error: ConvParams 2D strides{3,3}, padding {1,1}, dilations {2,2}."));
}
TEST_F(TestConvUtil, ConvParamsGetOutputSpatialLengths1D)
{
SetNDParams(1);
std::vector<ck::index_t> out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36}, "Error: ConvParams 1D."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{1};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{71}, "Error: ConvParams 1D stride {1}."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{2};
conv_params.input_left_pads_ = std::vector<ck::index_t>{2};
conv_params.input_right_pads_ = std::vector<ck::index_t>{2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{37},
"Error: ConvParams 1D padding left/right {2}."));
conv_params.conv_filter_dilations_ = std::vector<ck::index_t>{2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36}, "Error: ConvParams 1D dilation {2}."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{3};
conv_params.input_left_pads_ = std::vector<ck::index_t>{1};
conv_params.input_right_pads_ = std::vector<ck::index_t>{1};
conv_params.conv_filter_dilations_ = std::vector<ck::index_t>{2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(
ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{23},
"Error: ConvParams 1D strides{3}, padding {1}, dilations {2}."));
}
TEST_F(TestConvUtil, ConvParamsGetOutputSpatialLengths3D)
{
SetNDParams(3);
std::vector<ck::index_t> out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len, std::vector<ck::index_t>{36, 36, 36}, "Error: ConvParams 3D."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1, 1};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{71, 71, 71},
"Error: ConvParams 3D stride {1, 1, 1}."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{2, 2, 2};
conv_params.input_left_pads_ = std::vector<ck::index_t>{2, 2, 2};
conv_params.input_right_pads_ = std::vector<ck::index_t>{2, 2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{37, 37, 37},
"Error: ConvParams 3D padding left/right {2, 2, 2}."));
conv_params.conv_filter_dilations_ = std::vector<ck::index_t>{2, 2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(out_spatial_len,
std::vector<ck::index_t>{36, 36, 36},
"Error: ConvParams 3D dilation {2, 2, 2}."));
conv_params.conv_filter_strides_ = std::vector<ck::index_t>{3, 3, 3};
conv_params.input_left_pads_ = std::vector<ck::index_t>{1, 1, 1};
conv_params.input_right_pads_ = std::vector<ck::index_t>{1, 1, 1};
conv_params.conv_filter_dilations_ = std::vector<ck::index_t>{2, 2, 2};
out_spatial_len = conv_params.GetOutputSpatialLengths();
EXPECT_TRUE(ck::utils::check_err(
out_spatial_len,
std::vector<ck::index_t>{23, 23, 23},
"Error: ConvParams 3D strides{3, 3, 3}, padding {1, 1, 1}, dilations {2, 2, 2}."));
}
TEST(ConvUtil, GetHostTensorDescriptor)
{
namespace tl = ck::tensor_layout::convolution;
std::vector<std::size_t> dims{2, 3, 4, 5};
HostTensorDescriptor h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NHWC{});
EXPECT_TRUE(ck::utils::check_err(
h.GetLengths(), {2, 3, 4, 5}, "Error: wrong NHWC dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4 * 5, 1, 3 * 5, 3}, "Error: wrong NHWC dimensions strides!"));
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCHW{});
EXPECT_TRUE(ck::utils::check_err(
h.GetLengths(), {2, 3, 4, 5}, "Error: wrong NCHW dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4 * 5, 4 * 5, 5, 1}, "Error: wrong NCHW dimensions strides!"));
dims = std::vector<std::size_t>{2, 3, 4};
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NWC{});
EXPECT_TRUE(
ck::utils::check_err(h.GetLengths(), {2, 3, 4}, "Error: wrong NWC dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4, 1, 3}, "Error: wrong NWC dimensions strides!"));
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCW{});
EXPECT_TRUE(
ck::utils::check_err(h.GetLengths(), {2, 3, 4}, "Error: wrong NCW dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(
h.GetStrides(), {3 * 4, 4, 1}, "Error: wrong NCW dimensions strides!"));
dims = std::vector<std::size_t>{2, 3, 4, 5, 6};
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NDHWC{});
EXPECT_TRUE(
ck::utils::check_err(h.GetLengths(), dims, "Error: wrong NDHWC dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(h.GetStrides(),
{3 * 4 * 5 * 6, // N
1, // C
3 * 5 * 6, // D
3 * 6, // H
3}, // W
"Error: wrong NDHWC dimensions strides!"));
h = ck::utils::conv::get_host_tensor_descriptor(dims, tl::NCDHW{});
EXPECT_TRUE(
ck::utils::check_err(h.GetLengths(), dims, "Error: wrong NCDHW dimensions lengths!"));
EXPECT_TRUE(ck::utils::check_err(h.GetStrides(),
{3 * 4 * 5 * 6, // N
4 * 5 * 6, // C
5 * 6, // D
6, // H
1}, // W
"Error: wrong NCDHW dimensions strides!"));
}

3
test/convnd_bwd_data/convnd_bwd_data.cpp
View File

@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>

381
test/convnd_fwd/conv1d_fwd.cpp
View File

@@ -1,189 +1,192 @@
#include <iostream>
#include <tuple>
#include <vector>
#include <gtest/gtest.h>
#include "ck/utility/data_type.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/conv_util.hpp"
#include "test/convnd_fwd/conv_util.hpp"
namespace {
class Conv1dFwdNWCInstances : public ::testing::Test
{
public:
template <typename T>
bool test_conv1d_nwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPtr>& conv_ptrs,
const ck::utils::conv::ConvParams& params)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
conv::ConvFwdOpInstance<T,
T,
T,
ctl::NWC,
ctl::KXC,
ctl::NWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistributionIntegerValue<T>,
FillUniformDistributionIntegerValue<T>>
conv_instance(params,
true,
FillUniformDistributionIntegerValue<T>{},
FillUniformDistributionIntegerValue<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<1, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(atol_);
run_engine.SetRtol(rtol_);
return run_engine.Test(conv_ptrs);
}
template <typename T>
bool test_default()
{
return test_conv1d_nwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<1>(), params_default_);
}
template <typename T>
bool test_filter1x1_stride1_pad0()
{
return test_conv1d_nwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<1>(),
params_filter1x1_stride1_pad0_);
}
template <typename T>
bool test_filter1x1_pad0()
{
return test_conv1d_nwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<1>(),
params_filter1x1_pad0_);
}
static inline ck::utils::conv::ConvParams params_default_{
1, 4, 256, 64, {3}, {71}, {2}, {2}, {2}, {2}};
static inline ck::utils::conv::ConvParams params_filter1x1_stride1_pad0_{
1, 4, 256, 64, {1}, {28}, {1}, {1}, {0}, {0}};
static inline ck::utils::conv::ConvParams params_filter1x1_pad0_{
1, 4, 256, 64, {1}, {28}, {2}, {1}, {0}, {0}};
private:
double atol_{1e-5};
double rtol_{1e-4};
};
} // anonymous namespace
TEST(Conv1DFwdNWC, IntegerValues)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
using T = float;
ck::utils::conv::ConvParams params{1, 4, 256, 64, {3}, {36}, {1}, {2}, {2}, {2}};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<1, T, T, T, T>(conv_ptrs);
conv::ConvFwdOpInstance<T,
T,
T,
ctl::NWC,
ctl::KXC,
ctl::NWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistributionIntegerValue<T>,
FillUniformDistributionIntegerValue<T>>
conv_instance(params,
true,
FillUniformDistributionIntegerValue<T>{},
FillUniformDistributionIntegerValue<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<1, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(1e-5);
run_engine.SetRtol(1e-4);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
}
TEST(Conv1DFwdNWC, FloatingPointValues)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
using T = ck::half_t;
ck::utils::conv::ConvParams params{1, 4, 256, 64, {3}, {36}, {1}, {2}, {2}, {2}};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<1, T, T, T, float>(conv_ptrs);
conv::ConvFwdOpInstance<T,
T,
T,
ctl::NWC,
ctl::KXC,
ctl::NWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistribution<T>,
FillUniformDistribution<T>>
conv_instance(params, true, FillUniformDistribution<T>{}, FillUniformDistribution<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<1, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(0.1);
run_engine.SetRtol(1e-2);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
}
TEST_F(Conv1dFwdNWCInstances, BF16_default) { EXPECT_TRUE(this->test_default<ck::bhalf_t>()); }
TEST_F(Conv1dFwdNWCInstances, BF16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::bhalf_t>());
}
TEST_F(Conv1dFwdNWCInstances, BF16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::bhalf_t>());
}
TEST_F(Conv1dFwdNWCInstances, F16_default) { EXPECT_TRUE(this->test_default<ck::half_t>()); }
TEST_F(Conv1dFwdNWCInstances, F16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::half_t>());
}
TEST_F(Conv1dFwdNWCInstances, F16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::half_t>());
}
TEST_F(Conv1dFwdNWCInstances, F32_default) { EXPECT_TRUE(this->test_default<float>()); }
TEST_F(Conv1dFwdNWCInstances, F32_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<float>());
}
TEST_F(Conv1dFwdNWCInstances, F32_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<float>());
}
TEST_F(Conv1dFwdNWCInstances, I8_default) { EXPECT_TRUE(this->test_default<int8_t>()); }
TEST_F(Conv1dFwdNWCInstances, I8_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<int8_t>());
}
TEST_F(Conv1dFwdNWCInstances, I8_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<int8_t>());
}
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <tuple>
#include <vector>
#include <gtest/gtest.h>
#include "ck/utility/data_type.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/conv_util.hpp"
#include "test/convnd_fwd/conv_util.hpp"
namespace {
class Conv1dFwdNWCInstances : public ::testing::Test
{
public:
template <typename T>
bool test_conv1d_nwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPtr>& conv_ptrs,
const ck::utils::conv::ConvParams& params)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
conv::ConvFwdOpInstance<T,
T,
T,
ctl::NWC,
ctl::KXC,
ctl::NWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistributionIntegerValue<T>,
FillUniformDistributionIntegerValue<T>>
conv_instance(params,
true,
FillUniformDistributionIntegerValue<T>{},
FillUniformDistributionIntegerValue<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<1, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(atol_);
run_engine.SetRtol(rtol_);
return run_engine.Test(conv_ptrs);
}
template <typename T>
bool test_default()
{
return test_conv1d_nwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<1>(), params_default_);
}
template <typename T>
bool test_filter1x1_stride1_pad0()
{
return test_conv1d_nwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<1>(),
params_filter1x1_stride1_pad0_);
}
template <typename T>
bool test_filter1x1_pad0()
{
return test_conv1d_nwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<1>(),
params_filter1x1_pad0_);
}
static inline ck::utils::conv::ConvParams params_default_{
1, 4, 256, 64, {3}, {71}, {2}, {2}, {2}, {2}};
static inline ck::utils::conv::ConvParams params_filter1x1_stride1_pad0_{
1, 4, 256, 64, {1}, {28}, {1}, {1}, {0}, {0}};
static inline ck::utils::conv::ConvParams params_filter1x1_pad0_{
1, 4, 256, 64, {1}, {28}, {2}, {1}, {0}, {0}};
private:
double atol_{1e-5};
double rtol_{1e-4};
};
} // anonymous namespace
TEST(Conv1DFwdNWC, IntegerValues)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
using T = float;
ck::utils::conv::ConvParams params{1, 4, 256, 64, {3}, {36}, {1}, {2}, {2}, {2}};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<1, T, T, T, T>(conv_ptrs);
conv::ConvFwdOpInstance<T,
T,
T,
ctl::NWC,
ctl::KXC,
ctl::NWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistributionIntegerValue<T>,
FillUniformDistributionIntegerValue<T>>
conv_instance(params,
true,
FillUniformDistributionIntegerValue<T>{},
FillUniformDistributionIntegerValue<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<1, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(1e-5);
run_engine.SetRtol(1e-4);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
}
TEST(Conv1DFwdNWC, FloatingPointValues)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
using T = ck::half_t;
ck::utils::conv::ConvParams params{1, 4, 256, 64, {3}, {36}, {1}, {2}, {2}, {2}};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<1, T, T, T, float>(conv_ptrs);
conv::ConvFwdOpInstance<T,
T,
T,
ctl::NWC,
ctl::KXC,
ctl::NWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistribution<T>,
FillUniformDistribution<T>>
conv_instance(params, true, FillUniformDistribution<T>{}, FillUniformDistribution<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<1, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(0.1);
run_engine.SetRtol(1e-2);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
}
TEST_F(Conv1dFwdNWCInstances, BF16_default) { EXPECT_TRUE(this->test_default<ck::bhalf_t>()); }
TEST_F(Conv1dFwdNWCInstances, BF16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::bhalf_t>());
}
TEST_F(Conv1dFwdNWCInstances, BF16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::bhalf_t>());
}
TEST_F(Conv1dFwdNWCInstances, F16_default) { EXPECT_TRUE(this->test_default<ck::half_t>()); }
TEST_F(Conv1dFwdNWCInstances, F16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::half_t>());
}
TEST_F(Conv1dFwdNWCInstances, F16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::half_t>());
}
TEST_F(Conv1dFwdNWCInstances, F32_default) { EXPECT_TRUE(this->test_default<float>()); }
TEST_F(Conv1dFwdNWCInstances, F32_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<float>());
}
TEST_F(Conv1dFwdNWCInstances, F32_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<float>());
}
TEST_F(Conv1dFwdNWCInstances, I8_default) { EXPECT_TRUE(this->test_default<int8_t>()); }
TEST_F(Conv1dFwdNWCInstances, I8_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<int8_t>());
}
TEST_F(Conv1dFwdNWCInstances, I8_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<int8_t>());
}

529
test/convnd_fwd/conv2d_fwd.cpp
View File

@@ -1,263 +1,266 @@
#include <tuple>
#include <vector>
#include <gtest/gtest.h>
#include "ck/utility/data_type.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/conv_util.hpp"
#include "test/convnd_fwd/conv_util.hpp"
namespace {
class Conv2dFwdNHWCInstances : public ::testing::Test
{
public:
template <typename T>
bool test_conv2d_nhwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPtr>& conv_ptrs,
const ck::utils::conv::ConvParams& params)
{
using namespace std::placeholders;
using namespace ck::utils;
conv::ConvFwdOpInstance<T,
T,
T,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistributionIntegerValue<T>,
FillUniformDistributionIntegerValue<T>>
conv_instance(params,
true,
FillUniformDistributionIntegerValue<T>{},
FillUniformDistributionIntegerValue<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<2, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(atol_);
run_engine.SetRtol(rtol_);
return run_engine.Test(conv_ptrs);
}
template <typename T>
bool test_default(bool use_convnd = false)
{
if(use_convnd)
{
return test_conv2d_nhwc_instances<T>(
test::conv::ConvolutionNDFwdInstances<T, T, T>::Get(2), params_default_);
}
else
{
return test_conv2d_nhwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<2>(),
params_default_);
}
}
template <typename T>
bool test_filter1x1_stride1_pad0(bool use_convnd = false)
{
if(use_convnd)
{
return test_conv2d_nhwc_instances<T>(
test::conv::ConvolutionNDFwdInstances<T, T, T>::Get(2),
params_filter1x1_stride1_pad0_);
}
else
{
return test_conv2d_nhwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<2>(),
params_filter1x1_stride1_pad0_);
}
}
template <typename T>
bool test_filter1x1_pad0(bool use_convnd = false)
{
if(use_convnd)
{
return test_conv2d_nhwc_instances<T>(
test::conv::ConvolutionNDFwdInstances<T, T, T>::Get(2), params_filter1x1_pad0_);
}
else
{
return test_conv2d_nhwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<2>(),
params_filter1x1_pad0_);
}
}
template <typename T>
bool test_oddC()
{
return test_conv2d_nhwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<2>(), params_oddC_);
}
static inline ck::utils::conv::ConvParams params_default_{
2, 4, 256, 64, {3, 3}, {36, 36}, {2, 2}, {2, 2}, {2, 2}, {2, 2}};
static inline ck::utils::conv::ConvParams params_filter1x1_stride1_pad0_{
2, 4, 256, 64, {1, 1}, {28, 28}, {1, 1}, {1, 1}, {0, 0}, {0, 0}};
static inline ck::utils::conv::ConvParams params_filter1x1_pad0_{
2, 4, 256, 64, {1, 1}, {28, 28}, {2, 2}, {1, 1}, {0, 0}, {0, 0}};
static inline ck::utils::conv::ConvParams params_oddC_{
2, 4, 256, 3, {3, 3}, {28, 28}, {1, 1}, {1, 1}, {0, 0}, {0, 0}};
private:
double atol_{1e-5};
double rtol_{1e-4};
};
} // anonymous namespace
TEST(Conv2DFwdNHWC, IntegerValues)
{
using namespace std::placeholders;
using namespace ck::utils;
using T = float;
ck::utils::conv::ConvParams params{
2, 4, 256, 64, {3, 3}, {36, 36}, {1, 1}, {2, 2}, {2, 2}, {2, 2}};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<2, T, T, T, T>(conv_ptrs);
conv::ConvFwdOpInstance<T,
T,
T,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistributionIntegerValue<T>,
FillUniformDistributionIntegerValue<T>>
conv_instance(params,
true,
FillUniformDistributionIntegerValue<T>{},
FillUniformDistributionIntegerValue<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<2, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(1e-5);
run_engine.SetRtol(1e-4);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
}
TEST(Conv2DFwdNHWC, FloatingPointValues)
{
using namespace std::placeholders;
using namespace ck::utils;
using T = ck::half_t;
ck::utils::conv::ConvParams params{
2, 4, 256, 64, {3, 3}, {36, 36}, {2, 2}, {2, 2}, {2, 2}, {2, 2}};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<2, T, T, T, float>(conv_ptrs);
conv::ConvFwdOpInstance<T,
T,
T,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistribution<T>,
FillUniformDistribution<T>>
conv_instance(params, true, FillUniformDistribution<T>{}, FillUniformDistribution<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<2, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(2e-4);
run_engine.SetRtol(1e-3);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
}
TEST_F(Conv2dFwdNHWCInstances, BF16_default) { EXPECT_TRUE(this->test_default<ck::bhalf_t>()); }
TEST_F(Conv2dFwdNHWCInstances, BF16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::bhalf_t>());
}
TEST_F(Conv2dFwdNHWCInstances, BF16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::bhalf_t>());
}
TEST_F(Conv2dFwdNHWCInstances, F16_default) { EXPECT_TRUE(this->test_default<ck::half_t>()); }
TEST_F(Conv2dFwdNHWCInstances, F16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::half_t>());
}
TEST_F(Conv2dFwdNHWCInstances, F16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::half_t>());
}
TEST_F(Conv2dFwdNHWCInstances, F16_oddC) { EXPECT_TRUE(this->test_oddC<ck::half_t>()); }
TEST_F(Conv2dFwdNHWCInstances, F32_default) { EXPECT_TRUE(this->test_default<float>()); }
TEST_F(Conv2dFwdNHWCInstances, F32_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<float>());
}
TEST_F(Conv2dFwdNHWCInstances, F32_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<float>());
}
TEST_F(Conv2dFwdNHWCInstances, I8_default) { EXPECT_TRUE(this->test_default<int8_t>()); }
TEST_F(Conv2dFwdNHWCInstances, I8_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<int8_t>());
}
TEST_F(Conv2dFwdNHWCInstances, I8_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<int8_t>());
}
TEST_F(Conv2dFwdNHWCInstances, ND_BF16_default)
{
EXPECT_TRUE(this->test_default<ck::bhalf_t>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_BF16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::bhalf_t>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_BF16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::bhalf_t>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_F16_default)
{
EXPECT_TRUE(this->test_default<ck::half_t>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_F16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::half_t>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_F16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::half_t>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_F32_default) { EXPECT_TRUE(this->test_default<float>(true)); }
TEST_F(Conv2dFwdNHWCInstances, ND_F32_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<float>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_F32_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<float>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_I8_default) { EXPECT_TRUE(this->test_default<int8_t>(true)); }
TEST_F(Conv2dFwdNHWCInstances, ND_I8_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<int8_t>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_I8_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<int8_t>(true));
}
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <tuple>
#include <vector>
#include <gtest/gtest.h>
#include "ck/utility/data_type.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/conv_util.hpp"
#include "test/convnd_fwd/conv_util.hpp"
namespace {
class Conv2dFwdNHWCInstances : public ::testing::Test
{
public:
template <typename T>
bool test_conv2d_nhwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPtr>& conv_ptrs,
const ck::utils::conv::ConvParams& params)
{
using namespace std::placeholders;
using namespace ck::utils;
conv::ConvFwdOpInstance<T,
T,
T,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistributionIntegerValue<T>,
FillUniformDistributionIntegerValue<T>>
conv_instance(params,
true,
FillUniformDistributionIntegerValue<T>{},
FillUniformDistributionIntegerValue<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<2, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(atol_);
run_engine.SetRtol(rtol_);
return run_engine.Test(conv_ptrs);
}
template <typename T>
bool test_default(bool use_convnd = false)
{
if(use_convnd)
{
return test_conv2d_nhwc_instances<T>(
test::conv::ConvolutionNDFwdInstances<T, T, T>::Get(2), params_default_);
}
else
{
return test_conv2d_nhwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<2>(),
params_default_);
}
}
template <typename T>
bool test_filter1x1_stride1_pad0(bool use_convnd = false)
{
if(use_convnd)
{
return test_conv2d_nhwc_instances<T>(
test::conv::ConvolutionNDFwdInstances<T, T, T>::Get(2),
params_filter1x1_stride1_pad0_);
}
else
{
return test_conv2d_nhwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<2>(),
params_filter1x1_stride1_pad0_);
}
}
template <typename T>
bool test_filter1x1_pad0(bool use_convnd = false)
{
if(use_convnd)
{
return test_conv2d_nhwc_instances<T>(
test::conv::ConvolutionNDFwdInstances<T, T, T>::Get(2), params_filter1x1_pad0_);
}
else
{
return test_conv2d_nhwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<2>(),
params_filter1x1_pad0_);
}
}
template <typename T>
bool test_oddC()
{
return test_conv2d_nhwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<2>(), params_oddC_);
}
static inline ck::utils::conv::ConvParams params_default_{
2, 4, 256, 64, {3, 3}, {36, 36}, {2, 2}, {2, 2}, {2, 2}, {2, 2}};
static inline ck::utils::conv::ConvParams params_filter1x1_stride1_pad0_{
2, 4, 256, 64, {1, 1}, {28, 28}, {1, 1}, {1, 1}, {0, 0}, {0, 0}};
static inline ck::utils::conv::ConvParams params_filter1x1_pad0_{
2, 4, 256, 64, {1, 1}, {28, 28}, {2, 2}, {1, 1}, {0, 0}, {0, 0}};
static inline ck::utils::conv::ConvParams params_oddC_{
2, 4, 256, 3, {3, 3}, {28, 28}, {1, 1}, {1, 1}, {0, 0}, {0, 0}};
private:
double atol_{1e-5};
double rtol_{1e-4};
};
} // anonymous namespace
TEST(Conv2DFwdNHWC, IntegerValues)
{
using namespace std::placeholders;
using namespace ck::utils;
using T = float;
ck::utils::conv::ConvParams params{
2, 4, 256, 64, {3, 3}, {36, 36}, {1, 1}, {2, 2}, {2, 2}, {2, 2}};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<2, T, T, T, T>(conv_ptrs);
conv::ConvFwdOpInstance<T,
T,
T,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistributionIntegerValue<T>,
FillUniformDistributionIntegerValue<T>>
conv_instance(params,
true,
FillUniformDistributionIntegerValue<T>{},
FillUniformDistributionIntegerValue<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<2, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(1e-5);
run_engine.SetRtol(1e-4);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
}
TEST(Conv2DFwdNHWC, FloatingPointValues)
{
using namespace std::placeholders;
using namespace ck::utils;
using T = ck::half_t;
ck::utils::conv::ConvParams params{
2, 4, 256, 64, {3, 3}, {36, 36}, {2, 2}, {2, 2}, {2, 2}, {2, 2}};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<2, T, T, T, float>(conv_ptrs);
conv::ConvFwdOpInstance<T,
T,
T,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistribution<T>,
FillUniformDistribution<T>>
conv_instance(params, true, FillUniformDistribution<T>{}, FillUniformDistribution<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<2, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(2e-4);
run_engine.SetRtol(1e-3);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
}
TEST_F(Conv2dFwdNHWCInstances, BF16_default) { EXPECT_TRUE(this->test_default<ck::bhalf_t>()); }
TEST_F(Conv2dFwdNHWCInstances, BF16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::bhalf_t>());
}
TEST_F(Conv2dFwdNHWCInstances, BF16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::bhalf_t>());
}
TEST_F(Conv2dFwdNHWCInstances, F16_default) { EXPECT_TRUE(this->test_default<ck::half_t>()); }
TEST_F(Conv2dFwdNHWCInstances, F16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::half_t>());
}
TEST_F(Conv2dFwdNHWCInstances, F16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::half_t>());
}
TEST_F(Conv2dFwdNHWCInstances, F16_oddC) { EXPECT_TRUE(this->test_oddC<ck::half_t>()); }
TEST_F(Conv2dFwdNHWCInstances, F32_default) { EXPECT_TRUE(this->test_default<float>()); }
TEST_F(Conv2dFwdNHWCInstances, F32_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<float>());
}
TEST_F(Conv2dFwdNHWCInstances, F32_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<float>());
}
TEST_F(Conv2dFwdNHWCInstances, I8_default) { EXPECT_TRUE(this->test_default<int8_t>()); }
TEST_F(Conv2dFwdNHWCInstances, I8_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<int8_t>());
}
TEST_F(Conv2dFwdNHWCInstances, I8_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<int8_t>());
}
TEST_F(Conv2dFwdNHWCInstances, ND_BF16_default)
{
EXPECT_TRUE(this->test_default<ck::bhalf_t>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_BF16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::bhalf_t>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_BF16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::bhalf_t>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_F16_default)
{
EXPECT_TRUE(this->test_default<ck::half_t>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_F16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::half_t>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_F16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::half_t>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_F32_default) { EXPECT_TRUE(this->test_default<float>(true)); }
TEST_F(Conv2dFwdNHWCInstances, ND_F32_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<float>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_F32_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<float>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_I8_default) { EXPECT_TRUE(this->test_default<int8_t>(true)); }
TEST_F(Conv2dFwdNHWCInstances, ND_I8_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<int8_t>(true));
}
TEST_F(Conv2dFwdNHWCInstances, ND_I8_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<int8_t>(true));
}

631
test/convnd_fwd/conv3d_fwd.cpp
View File

@@ -1,314 +1,317 @@
#include <iostream>
#include <stdexcept>
#include <tuple>
#include <vector>
#include <gtest/gtest.h>
#include "ck/utility/data_type.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/conv_util.hpp"
#include "test/convnd_fwd/conv_util.hpp"
namespace {
class Conv3dFwdNDHWCInstances : public ::testing::Test
{
public:
template <typename T>
bool test_conv3d_nwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPtr>& conv_ptrs,
const ck::utils::conv::ConvParams& params)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
conv::ConvFwdOpInstance<T,
T,
T,
ctl::NDHWC,
ctl::KZYXC,
ctl::NDHWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistributionIntegerValue<T>,
FillUniformDistributionIntegerValue<T>>
conv_instance(params,
true,
FillUniformDistributionIntegerValue<T>{},
FillUniformDistributionIntegerValue<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<3, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(atol_);
run_engine.SetRtol(rtol_);
return run_engine.Test(conv_ptrs);
}
template <typename T>
bool test_default()
{
return test_conv3d_nwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<3>(), params_default_);
}
template <typename T>
bool test_filter1x1_stride1_pad0()
{
return test_conv3d_nwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<3>(),
params_filter1x1_stride1_pad0_);
}
template <typename T>
bool test_filter1x1_pad0()
{
return test_conv3d_nwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<3>(),
params_filter1x1_pad0_);
}
static inline ck::utils::conv::ConvParams params_default_{
3, 4, 256, 64, {3, 3, 3}, {28, 28, 28}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2}};
static inline ck::utils::conv::ConvParams params_filter1x1_stride1_pad0_{
3, 4, 256, 64, {1, 1, 1}, {28, 28, 28}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}};
static inline ck::utils::conv::ConvParams params_filter1x1_pad0_{
3, 4, 256, 64, {1, 1, 1}, {28, 28, 28}, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}};
private:
double atol_{1e-5};
double rtol_{1e-4};
};
} // anonymous namespace
TEST(Conv3DFwdNDHWC, IntegerValues)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
using T = float;
ck::utils::conv::ConvParams params{
3, 4, 256, 64, {3, 3, 3}, {18, 18, 18}, {1, 1, 1}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2}};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<3, T, T, T, T>(conv_ptrs);
conv::ConvFwdOpInstance<T,
T,
T,
ctl::NDHWC,
ctl::KZYXC,
ctl::NDHWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistributionIntegerValue<T>,
FillUniformDistributionIntegerValue<T>>
conv_instance(params,
true,
FillUniformDistributionIntegerValue<T>{},
FillUniformDistributionIntegerValue<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<3, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(1e-5);
run_engine.SetRtol(1e-3);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
}
TEST(Conv3DFwdNDHWC, FloatingPointValues)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
using T = ck::half_t;
ck::utils::conv::ConvParams params{
3, 4, 256, 64, {3, 3, 3}, {18, 18, 18}, {1, 1, 1}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2}};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<3, T, T, T, float>(conv_ptrs);
conv::ConvFwdOpInstance<T,
T,
T,
ctl::NDHWC,
ctl::KZYXC,
ctl::NDHWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistribution<T>,
FillUniformDistribution<T>>
conv_instance(params, true, FillUniformDistribution<T>{}, FillUniformDistribution<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<3, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(1e-3);
run_engine.SetRtol(1e-3);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
}
TEST(Conv3DFwdNDHWC, InputOver2GB)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using namespace ck::utils;
using T = float;
// >2GB Input
conv::ConvParams params;
params.num_dim_spatial_ = 3;
params.N_ = 2;
params.K_ = 16;
params.C_ = 32;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3, 3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{32, 1000, 1000};
params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1, 1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1};
params.input_left_pads_ = std::vector<ck::index_t>{1, 1, 1};
params.input_right_pads_ = std::vector<ck::index_t>{1, 1, 1};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<3, T, T, T, T>(conv_ptrs);
auto arg = conv_ptrs.back()->MakeArgumentPointer(nullptr,
nullptr,
nullptr,
params.N_,
params.K_,
params.C_,
params.input_spatial_lengths_,
params.filter_spatial_lengths_,
params.GetOutputSpatialLengths(),
params.conv_filter_strides_,
params.conv_filter_dilations_,
params.input_left_pads_,
params.input_right_pads_,
PassThrough{},
PassThrough{},
PassThrough{});
EXPECT_FALSE(conv_ptrs.back()->IsSupportedArgument(arg.get()));
}
TEST(Conv3DFwdNDHWC, FiltersOver2GB)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using namespace ck::utils;
using T = float;
// >2GB Filters
conv::ConvParams params;
params.num_dim_spatial_ = 3;
params.N_ = 2;
params.K_ = 16;
params.C_ = 32;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{4, 1000, 1000};
params.input_spatial_lengths_ = std::vector<ck::index_t>{16, 16, 16};
params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1, 1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1};
params.input_left_pads_ = std::vector<ck::index_t>{1, 1, 1};
params.input_right_pads_ = std::vector<ck::index_t>{1, 1, 1};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<3, T, T, T, T>(conv_ptrs);
auto arg = conv_ptrs.back()->MakeArgumentPointer(nullptr,
nullptr,
nullptr,
params.N_,
params.K_,
params.C_,
params.input_spatial_lengths_,
params.filter_spatial_lengths_,
params.GetOutputSpatialLengths(),
params.conv_filter_strides_,
params.conv_filter_dilations_,
params.input_left_pads_,
params.input_right_pads_,
PassThrough{},
PassThrough{},
PassThrough{});
EXPECT_FALSE(conv_ptrs.back()->IsSupportedArgument(arg.get()));
}
TEST(Conv3DFwdNDHWC, OutputOver2GB)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using namespace ck::utils;
using T = float;
// >2GB Output
conv::ConvParams params;
params.num_dim_spatial_ = 3;
params.N_ = 2;
params.K_ = 16;
params.C_ = 2;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{1, 1, 1};
params.input_spatial_lengths_ = std::vector<ck::index_t>{1000, 1000, 30};
params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1, 1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1};
params.input_left_pads_ = std::vector<ck::index_t>{2, 2, 2};
params.input_right_pads_ = std::vector<ck::index_t>{2, 2, 2};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<3, T, T, T, T>(conv_ptrs);
auto arg = conv_ptrs.back()->MakeArgumentPointer(nullptr,
nullptr,
nullptr,
params.N_,
params.K_,
params.C_,
params.input_spatial_lengths_,
params.filter_spatial_lengths_,
params.GetOutputSpatialLengths(),
params.conv_filter_strides_,
params.conv_filter_dilations_,
params.input_left_pads_,
params.input_right_pads_,
PassThrough{},
PassThrough{},
PassThrough{});
EXPECT_FALSE(conv_ptrs.back()->IsSupportedArgument(arg.get()));
}
TEST_F(Conv3dFwdNDHWCInstances, BF16_default) { EXPECT_TRUE(this->test_default<ck::bhalf_t>()); }
TEST_F(Conv3dFwdNDHWCInstances, BF16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::bhalf_t>());
}
TEST_F(Conv3dFwdNDHWCInstances, BF16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::bhalf_t>());
}
TEST_F(Conv3dFwdNDHWCInstances, F16_default) { EXPECT_TRUE(this->test_default<ck::half_t>()); }
TEST_F(Conv3dFwdNDHWCInstances, F16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::half_t>());
}
TEST_F(Conv3dFwdNDHWCInstances, F16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::half_t>());
}
TEST_F(Conv3dFwdNDHWCInstances, F32_default) { EXPECT_TRUE(this->test_default<float>()); }
TEST_F(Conv3dFwdNDHWCInstances, F32_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<float>());
}
TEST_F(Conv3dFwdNDHWCInstances, F32_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<float>());
}
TEST_F(Conv3dFwdNDHWCInstances, I8_default) { EXPECT_TRUE(this->test_default<int8_t>()); }
TEST_F(Conv3dFwdNDHWCInstances, I8_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<int8_t>());
}
TEST_F(Conv3dFwdNDHWCInstances, I8_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<int8_t>());
}
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <stdexcept>
#include <tuple>
#include <vector>
#include <gtest/gtest.h>
#include "ck/utility/data_type.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/conv_util.hpp"
#include "test/convnd_fwd/conv_util.hpp"
namespace {
class Conv3dFwdNDHWCInstances : public ::testing::Test
{
public:
template <typename T>
bool test_conv3d_nwc_instances(const std::vector<test::conv::DeviceConvFwdNoOpPtr>& conv_ptrs,
const ck::utils::conv::ConvParams& params)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
conv::ConvFwdOpInstance<T,
T,
T,
ctl::NDHWC,
ctl::KZYXC,
ctl::NDHWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistributionIntegerValue<T>,
FillUniformDistributionIntegerValue<T>>
conv_instance(params,
true,
FillUniformDistributionIntegerValue<T>{},
FillUniformDistributionIntegerValue<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<3, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(atol_);
run_engine.SetRtol(rtol_);
return run_engine.Test(conv_ptrs);
}
template <typename T>
bool test_default()
{
return test_conv3d_nwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<3>(), params_default_);
}
template <typename T>
bool test_filter1x1_stride1_pad0()
{
return test_conv3d_nwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<3>(),
params_filter1x1_stride1_pad0_);
}
template <typename T>
bool test_filter1x1_pad0()
{
return test_conv3d_nwc_instances<T>(
ck::utils::conv::ConvolutionFwdInstances<T, T, T>::template Get<3>(),
params_filter1x1_pad0_);
}
static inline ck::utils::conv::ConvParams params_default_{
3, 4, 256, 64, {3, 3, 3}, {28, 28, 28}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2}};
static inline ck::utils::conv::ConvParams params_filter1x1_stride1_pad0_{
3, 4, 256, 64, {1, 1, 1}, {28, 28, 28}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}};
static inline ck::utils::conv::ConvParams params_filter1x1_pad0_{
3, 4, 256, 64, {1, 1, 1}, {28, 28, 28}, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}};
private:
double atol_{1e-5};
double rtol_{1e-4};
};
} // anonymous namespace
TEST(Conv3DFwdNDHWC, IntegerValues)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
using T = float;
ck::utils::conv::ConvParams params{
3, 4, 256, 64, {3, 3, 3}, {18, 18, 18}, {1, 1, 1}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2}};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<3, T, T, T, T>(conv_ptrs);
conv::ConvFwdOpInstance<T,
T,
T,
ctl::NDHWC,
ctl::KZYXC,
ctl::NDHWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistributionIntegerValue<T>,
FillUniformDistributionIntegerValue<T>>
conv_instance(params,
true,
FillUniformDistributionIntegerValue<T>{},
FillUniformDistributionIntegerValue<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<3, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(1e-5);
run_engine.SetRtol(1e-3);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
}
TEST(Conv3DFwdNDHWC, FloatingPointValues)
{
using namespace std::placeholders;
using namespace ck::utils;
namespace ctl = ck::tensor_layout::convolution;
using T = ck::half_t;
ck::utils::conv::ConvParams params{
3, 4, 256, 64, {3, 3, 3}, {18, 18, 18}, {1, 1, 1}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2}};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<3, T, T, T, float>(conv_ptrs);
conv::ConvFwdOpInstance<T,
T,
T,
ctl::NDHWC,
ctl::KZYXC,
ctl::NDHWK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
FillUniformDistribution<T>,
FillUniformDistribution<T>>
conv_instance(params, true, FillUniformDistribution<T>{}, FillUniformDistribution<T>{});
auto reference_conv_fwd_fun =
std::bind(conv::run_reference_convolution_forward<3, T, T, T>, params, _1, _2, _3);
OpInstanceRunEngine<T, T, T> run_engine(conv_instance, reference_conv_fwd_fun);
run_engine.SetAtol(1e-3);
run_engine.SetRtol(1e-3);
EXPECT_TRUE(run_engine.Test(conv_ptrs));
}
TEST(Conv3DFwdNDHWC, InputOver2GB)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using namespace ck::utils;
using T = float;
// >2GB Input
conv::ConvParams params;
params.num_dim_spatial_ = 3;
params.N_ = 2;
params.K_ = 16;
params.C_ = 32;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3, 3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{32, 1000, 1000};
params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1, 1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1};
params.input_left_pads_ = std::vector<ck::index_t>{1, 1, 1};
params.input_right_pads_ = std::vector<ck::index_t>{1, 1, 1};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<3, T, T, T, T>(conv_ptrs);
auto arg = conv_ptrs.back()->MakeArgumentPointer(nullptr,
nullptr,
nullptr,
params.N_,
params.K_,
params.C_,
params.input_spatial_lengths_,
params.filter_spatial_lengths_,
params.GetOutputSpatialLengths(),
params.conv_filter_strides_,
params.conv_filter_dilations_,
params.input_left_pads_,
params.input_right_pads_,
PassThrough{},
PassThrough{},
PassThrough{});
EXPECT_FALSE(conv_ptrs.back()->IsSupportedArgument(arg.get()));
}
TEST(Conv3DFwdNDHWC, FiltersOver2GB)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using namespace ck::utils;
using T = float;
// >2GB Filters
conv::ConvParams params;
params.num_dim_spatial_ = 3;
params.N_ = 2;
params.K_ = 16;
params.C_ = 32;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{4, 1000, 1000};
params.input_spatial_lengths_ = std::vector<ck::index_t>{16, 16, 16};
params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1, 1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1};
params.input_left_pads_ = std::vector<ck::index_t>{1, 1, 1};
params.input_right_pads_ = std::vector<ck::index_t>{1, 1, 1};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<3, T, T, T, T>(conv_ptrs);
auto arg = conv_ptrs.back()->MakeArgumentPointer(nullptr,
nullptr,
nullptr,
params.N_,
params.K_,
params.C_,
params.input_spatial_lengths_,
params.filter_spatial_lengths_,
params.GetOutputSpatialLengths(),
params.conv_filter_strides_,
params.conv_filter_dilations_,
params.input_left_pads_,
params.input_right_pads_,
PassThrough{},
PassThrough{},
PassThrough{});
EXPECT_FALSE(conv_ptrs.back()->IsSupportedArgument(arg.get()));
}
TEST(Conv3DFwdNDHWC, OutputOver2GB)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using namespace ck::utils;
using T = float;
// >2GB Output
conv::ConvParams params;
params.num_dim_spatial_ = 3;
params.N_ = 2;
params.K_ = 16;
params.C_ = 2;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{1, 1, 1};
params.input_spatial_lengths_ = std::vector<ck::index_t>{1000, 1000, 30};
params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1, 1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1};
params.input_left_pads_ = std::vector<ck::index_t>{2, 2, 2};
params.input_right_pads_ = std::vector<ck::index_t>{2, 2, 2};
std::vector<test::conv::DeviceConvFwdNoOpPtr> conv_ptrs;
test::conv::get_test_convolution_fwd_instance<3, T, T, T, T>(conv_ptrs);
auto arg = conv_ptrs.back()->MakeArgumentPointer(nullptr,
nullptr,
nullptr,
params.N_,
params.K_,
params.C_,
params.input_spatial_lengths_,
params.filter_spatial_lengths_,
params.GetOutputSpatialLengths(),
params.conv_filter_strides_,
params.conv_filter_dilations_,
params.input_left_pads_,
params.input_right_pads_,
PassThrough{},
PassThrough{},
PassThrough{});
EXPECT_FALSE(conv_ptrs.back()->IsSupportedArgument(arg.get()));
}
TEST_F(Conv3dFwdNDHWCInstances, BF16_default) { EXPECT_TRUE(this->test_default<ck::bhalf_t>()); }
TEST_F(Conv3dFwdNDHWCInstances, BF16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::bhalf_t>());
}
TEST_F(Conv3dFwdNDHWCInstances, BF16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::bhalf_t>());
}
TEST_F(Conv3dFwdNDHWCInstances, F16_default) { EXPECT_TRUE(this->test_default<ck::half_t>()); }
TEST_F(Conv3dFwdNDHWCInstances, F16_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<ck::half_t>());
}
TEST_F(Conv3dFwdNDHWCInstances, F16_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<ck::half_t>());
}
TEST_F(Conv3dFwdNDHWCInstances, F32_default) { EXPECT_TRUE(this->test_default<float>()); }
TEST_F(Conv3dFwdNDHWCInstances, F32_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<float>());
}
TEST_F(Conv3dFwdNDHWCInstances, F32_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<float>());
}
TEST_F(Conv3dFwdNDHWCInstances, I8_default) { EXPECT_TRUE(this->test_default<int8_t>()); }
TEST_F(Conv3dFwdNDHWCInstances, I8_filter1x1_stride1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_stride1_pad0<int8_t>());
}
TEST_F(Conv3dFwdNDHWCInstances, I8_filter1x1_pad0)
{
EXPECT_TRUE(this->test_filter1x1_pad0<int8_t>());
}

3
test/convnd_fwd/conv_util.hpp
View File

@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <tuple>

3
test/gemm/gemm_dl_fp16.cpp
View File

@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <algorithm>
#include <cstdlib>
#include <iostream>

267
test/gemm/gemm_dl_fp32.cpp
View File

@@ -1,132 +1,135 @@
#include <algorithm>
#include <cstdlib>
#include <iostream>
#include <numeric>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "test/gemm/gemm_util.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceGemmNoOpPtr =
ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_gemm_instance {
void add_device_gemm_dl_f32_f32_f32_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_f32_f32_f32_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_f32_f32_f32_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_f32_f32_f32_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
} // namespace device_gemm_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
int main()
{
using ADataType = float;
using BDataType = float;
using CDataType = float;
using AccDataType = float;
using RowMajor = ck::tensor_layout::gemm::RowMajor;
using ColumnMajor = ck::tensor_layout::gemm::ColumnMajor;
bool res = true;
std::vector<DeviceGemmNoOpPtr> gemmPtrs;
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f32_f32_f32_km_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f32_f32_f32_km_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f32_f32_f32_mk_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f32_f32_f32_mk_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return res ? 0 : 1;
}
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <algorithm>
#include <cstdlib>
#include <iostream>
#include <numeric>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "test/gemm/gemm_util.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceGemmNoOpPtr =
ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_gemm_instance {
void add_device_gemm_dl_f32_f32_f32_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_f32_f32_f32_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_f32_f32_f32_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_dl_f32_f32_f32_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
} // namespace device_gemm_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
int main()
{
using ADataType = float;
using BDataType = float;
using CDataType = float;
using AccDataType = float;
using RowMajor = ck::tensor_layout::gemm::RowMajor;
using ColumnMajor = ck::tensor_layout::gemm::ColumnMajor;
bool res = true;
std::vector<DeviceGemmNoOpPtr> gemmPtrs;
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f32_f32_f32_km_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f32_f32_f32_km_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f32_f32_f32_mk_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_dl_f32_f32_f32_mk_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return res ? 0 : 1;
}

3
test/gemm/gemm_dl_int8.cpp
View File

@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <algorithm>
#include <cstdlib>
#include <iostream>

3
test/gemm/gemm_util.hpp
View File

@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/ck.hpp"

231
test/gemm/gemm_xdl_bf16.cpp
View File

@@ -1,114 +1,117 @@
#include <algorithm>
#include <cstdlib>
#include <iostream>
#include <numeric>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "test/gemm/gemm_util.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceGemmNoOpPtr =
ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_gemm_instance {
void add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_km_kn_mn_instances(
std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_km_nk_mn_instances(
std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_mk_nk_mn_instances(
std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_mk_kn_mn_instances(
std::vector<DeviceGemmNoOpPtr>&);
} // namespace device_gemm_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
int main()
{
using RowMajor = ck::tensor_layout::gemm::RowMajor;
using ColumnMajor = ck::tensor_layout::gemm::ColumnMajor;
bool res = true;
std::vector<DeviceGemmNoOpPtr> gemmPtrs;
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_km_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemmBF16<DeviceGemmNoOpPtr,
ColumnMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_km_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemmBF16<DeviceGemmNoOpPtr,
ColumnMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_mk_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemmBF16<DeviceGemmNoOpPtr,
RowMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_mk_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemmBF16<DeviceGemmNoOpPtr,
RowMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return res ? 0 : 1;
}
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <algorithm>
#include <cstdlib>
#include <iostream>
#include <numeric>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "test/gemm/gemm_util.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceGemmNoOpPtr =
ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_gemm_instance {
void add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_km_kn_mn_instances(
std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_km_nk_mn_instances(
std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_mk_nk_mn_instances(
std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_mk_kn_mn_instances(
std::vector<DeviceGemmNoOpPtr>&);
} // namespace device_gemm_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
int main()
{
using RowMajor = ck::tensor_layout::gemm::RowMajor;
using ColumnMajor = ck::tensor_layout::gemm::ColumnMajor;
bool res = true;
std::vector<DeviceGemmNoOpPtr> gemmPtrs;
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_km_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemmBF16<DeviceGemmNoOpPtr,
ColumnMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_km_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemmBF16<DeviceGemmNoOpPtr,
ColumnMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_mk_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemmBF16<DeviceGemmNoOpPtr,
RowMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_bf16_bf16_bf16_mk_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemmBF16<DeviceGemmNoOpPtr,
RowMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return res ? 0 : 1;
}

327
test/gemm/gemm_xdl_fp16.cpp
View File

@@ -1,162 +1,165 @@
#include <algorithm>
#include <cstdlib>
#include <iostream>
#include <numeric>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "test/gemm/gemm_util.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceGemmNoOpPtr =
ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_gemm_instance {
void add_device_gemm_xdl_f16_f16_f16_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f16_f16_f16_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f16_f16_f16_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f16_f16_f16_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_2_stage_f16_f16_f16_mk_nk_mn_instances(
std::vector<DeviceGemmNoOpPtr>&);
} // namespace device_gemm_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
int main()
{
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
using RowMajor = ck::tensor_layout::gemm::RowMajor;
using ColumnMajor = ck::tensor_layout::gemm::ColumnMajor;
bool res = true;
std::vector<DeviceGemmNoOpPtr> gemmPtrs;
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f16_f16_f16_km_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f16_f16_f16_km_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f16_f16_f16_km_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f16_f16_f16_km_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_2_stage_f16_f16_f16_mk_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return res ? 0 : 1;
}
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <algorithm>
#include <cstdlib>
#include <iostream>
#include <numeric>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "test/gemm/gemm_util.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceGemmNoOpPtr =
ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_gemm_instance {
void add_device_gemm_xdl_f16_f16_f16_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f16_f16_f16_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f16_f16_f16_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f16_f16_f16_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_2_stage_f16_f16_f16_mk_nk_mn_instances(
std::vector<DeviceGemmNoOpPtr>&);
} // namespace device_gemm_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
int main()
{
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
using RowMajor = ck::tensor_layout::gemm::RowMajor;
using ColumnMajor = ck::tensor_layout::gemm::ColumnMajor;
bool res = true;
std::vector<DeviceGemmNoOpPtr> gemmPtrs;
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f16_f16_f16_km_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f16_f16_f16_km_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f16_f16_f16_km_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f16_f16_f16_km_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_2_stage_f16_f16_f16_mk_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return res ? 0 : 1;
}

319
test/gemm/gemm_xdl_fp32.cpp
View File

@@ -1,158 +1,161 @@
#include <algorithm>
#include <cstdlib>
#include <iostream>
#include <numeric>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "test/gemm/gemm_util.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceGemmNoOpPtr =
ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_gemm_instance {
void add_device_gemm_xdl_f32_f32_f32_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f32_f32_f32_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f32_f32_f32_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f32_f32_f32_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f32_f32_f32_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f32_f32_f32_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f32_f32_f32_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
} // namespace device_gemm_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
int main()
{
using ADataType = float;
using BDataType = float;
using CDataType = float;
using AccDataType = float;
using RowMajor = ck::tensor_layout::gemm::RowMajor;
using ColumnMajor = ck::tensor_layout::gemm::ColumnMajor;
bool res = true;
std::vector<DeviceGemmNoOpPtr> gemmPtrs;
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f32_f32_f32_km_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f32_f32_f32_km_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f32_f32_f32_km_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f32_f32_f32_mk_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f32_f32_f32_mk_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f32_f32_f32_mk_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f32_f32_f32_mk_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return res ? 0 : 1;
}
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <algorithm>
#include <cstdlib>
#include <iostream>
#include <numeric>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "test/gemm/gemm_util.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceGemmNoOpPtr =
ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_gemm_instance {
void add_device_gemm_xdl_f32_f32_f32_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f32_f32_f32_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f32_f32_f32_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f32_f32_f32_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f32_f32_f32_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f32_f32_f32_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_splitk_f32_f32_f32_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
} // namespace device_gemm_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
int main()
{
using ADataType = float;
using BDataType = float;
using CDataType = float;
using AccDataType = float;
using RowMajor = ck::tensor_layout::gemm::RowMajor;
using ColumnMajor = ck::tensor_layout::gemm::ColumnMajor;
bool res = true;
std::vector<DeviceGemmNoOpPtr> gemmPtrs;
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f32_f32_f32_km_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f32_f32_f32_km_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f32_f32_f32_km_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f32_f32_f32_mk_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f32_f32_f32_mk_kn_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f32_f32_f32_mk_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_splitk_f32_f32_f32_mk_nk_mn_instances(gemmPtrs);
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return res ? 0 : 1;
}

315
test/gemm/gemm_xdl_fp64.cpp
View File

@@ -1,156 +1,159 @@
#include <algorithm>
#include <cstdlib>
#include <iostream>
#include <numeric>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "test/gemm/gemm_util.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceGemmNoOpPtr =
ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_gemm_instance {
void add_device_gemm_xdl_f64_f64_f64_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f64_f64_f64_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f64_f64_f64_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f64_f64_f64_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
} // namespace device_gemm_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
inline std::string get_device_name()
{
hipDeviceProp_t props{};
int device;
auto status = hipGetDevice(&device);
if(status != hipSuccess)
{
return std::string();
}
status = hipGetDeviceProperties(&props, device);
if(status != hipSuccess)
{
return std::string();
}
const std::string name(props.gcnArchName);
return name;
}
int main()
{
if(get_device_name().find("gfx90a") == std::string::npos)
{
std::cout << "TestGemm ..... SUCCESS" << std::endl;
return 0;
}
using ADataType = double;
using BDataType = double;
using CDataType = double;
using AccDataType = double;
using RowMajor = ck::tensor_layout::gemm::RowMajor;
using ColumnMajor = ck::tensor_layout::gemm::ColumnMajor;
bool res = true;
std::vector<DeviceGemmNoOpPtr> gemmPtrs;
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f64_f64_f64_km_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f64_f64_f64_km_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f64_f64_f64_mk_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f64_f64_f64_mk_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return res ? 0 : 1;
}
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <algorithm>
#include <cstdlib>
#include <iostream>
#include <numeric>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "test/gemm/gemm_util.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceGemmNoOpPtr =
ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_gemm_instance {
void add_device_gemm_xdl_f64_f64_f64_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f64_f64_f64_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f64_f64_f64_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_f64_f64_f64_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
} // namespace device_gemm_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
inline std::string get_device_name()
{
hipDeviceProp_t props{};
int device;
auto status = hipGetDevice(&device);
if(status != hipSuccess)
{
return std::string();
}
status = hipGetDeviceProperties(&props, device);
if(status != hipSuccess)
{
return std::string();
}
const std::string name(props.gcnArchName);
return name;
}
int main()
{
if(get_device_name().find("gfx90a") == std::string::npos)
{
std::cout << "TestGemm ..... SUCCESS" << std::endl;
return 0;
}
using ADataType = double;
using BDataType = double;
using CDataType = double;
using AccDataType = double;
using RowMajor = ck::tensor_layout::gemm::RowMajor;
using ColumnMajor = ck::tensor_layout::gemm::ColumnMajor;
bool res = true;
std::vector<DeviceGemmNoOpPtr> gemmPtrs;
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f64_f64_f64_km_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f64_f64_f64_km_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f64_f64_f64_mk_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_f64_f64_f64_mk_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return res ? 0 : 1;
}

267
test/gemm/gemm_xdl_int8.cpp
View File

@@ -1,132 +1,135 @@
#include <algorithm>
#include <cstdlib>
#include <iostream>
#include <numeric>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "test/gemm/gemm_util.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceGemmNoOpPtr =
ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_gemm_instance {
void add_device_gemm_xdl_c_shuffle_i8_i8_i8_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_i8_i8_i8_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_i8_i8_i8_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_i8_i8_i8_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
} // namespace device_gemm_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
int main()
{
using ADataType = int8_t;
using BDataType = int8_t;
using CDataType = int8_t;
using AccDataType = int32_t;
using RowMajor = ck::tensor_layout::gemm::RowMajor;
using ColumnMajor = ck::tensor_layout::gemm::ColumnMajor;
std::vector<DeviceGemmNoOpPtr> gemmPtrs;
bool res = true;
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_i8_i8_i8_km_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_i8_i8_i8_km_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_i8_i8_i8_mk_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_i8_i8_i8_mk_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return res ? 0 : 1;
}
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <algorithm>
#include <cstdlib>
#include <iostream>
#include <numeric>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "test/gemm/gemm_util.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceGemmNoOpPtr =
ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_gemm_instance {
void add_device_gemm_xdl_c_shuffle_i8_i8_i8_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_i8_i8_i8_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_i8_i8_i8_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
void add_device_gemm_xdl_c_shuffle_i8_i8_i8_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
} // namespace device_gemm_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
int main()
{
using ADataType = int8_t;
using BDataType = int8_t;
using CDataType = int8_t;
using AccDataType = int32_t;
using RowMajor = ck::tensor_layout::gemm::RowMajor;
using ColumnMajor = ck::tensor_layout::gemm::ColumnMajor;
std::vector<DeviceGemmNoOpPtr> gemmPtrs;
bool res = true;
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_i8_i8_i8_km_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_i8_i8_i8_km_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
ColumnMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_i8_i8_i8_mk_kn_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
RowMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
gemmPtrs.clear();
ck::tensor_operation::device::device_gemm_instance::
add_device_gemm_xdl_c_shuffle_i8_i8_i8_mk_nk_mn_instances(gemmPtrs);
for(auto& gemmPtr : gemmPtrs)
{
res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
ADataType,
BDataType,
CDataType,
AccDataType,
RowMajor,
ColumnMajor,
RowMajor,
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return res ? 0 : 1;
}

3
test/gemm_reduce/gemm_reduce_fp16.cpp
View File

@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "profiler/include/profile_gemm_reduce_impl.hpp"

3
test/gemm_split_k/gemm_split_k.cpp
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@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <initializer_list>
#include <cstdlib>

3
test/grouped_gemm/grouped_gemm_fp16.cpp
View File

@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>

3
test/magic_number_division/magic_number_division.cpp
View File

@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>

3
test/reduce/reduce_no_index.cpp
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@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <getopt.h>
#include "ck/library/host_tensor/host_common_util.hpp"

3
test/reduce/reduce_with_index.cpp
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@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <getopt.h>
#include "ck/library/host_tensor/host_common_util.hpp"

781
test/reference_conv_fwd/reference_conv_fwd.cpp
View File

@@ -1,389 +1,392 @@
#include <cmath>
#include <cstdlib>
#include <numeric>
#include <type_traits>
#include <vector>
#include <gtest/gtest.h>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/conv_util.hpp"
#include "ck/library/utility/fill.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
namespace {
using InElementOp = ck::tensor_operation::element_wise::PassThrough;
using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
template <ck::index_t NDim,
typename InDataType = float,
typename WeiDataType = float,
typename OutDataType = float,
typename InLayout = ck::tensor_layout::convolution::NHWC,
typename WeiLayout = ck::tensor_layout::convolution::KYXC,
typename OutLayout = ck::tensor_layout::convolution::NHWK,
typename FillInputOp = ck::utils::FillMonotonicSeq<InDataType>,
typename FillWeightsOp = ck::utils::FillConstant<WeiDataType>>
Tensor<OutDataType>
run_reference_convolution_forward(const ck::utils::conv::ConvParams& params,
const FillInputOp& fill_input_op = FillInputOp{},
const FillWeightsOp& fill_weights_op = FillWeightsOp{0.5f})
{
std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N_),
static_cast<std::size_t>(params.C_)};
input_dims.insert(std::end(input_dims),
std::begin(params.input_spatial_lengths_),
std::end(params.input_spatial_lengths_));
std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K_),
static_cast<std::size_t>(params.C_)};
filter_dims.insert(std::end(filter_dims),
std::begin(params.filter_spatial_lengths_),
std::end(params.filter_spatial_lengths_));
const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N_),
static_cast<std::size_t>(params.K_)};
output_dims.insert(std::end(output_dims),
std::begin(output_spatial_lengths),
std::end(output_spatial_lengths));
Tensor<InDataType> input(ck::utils::conv::get_host_tensor_descriptor(input_dims, InLayout{}));
Tensor<WeiDataType> weights(
ck::utils::conv::get_host_tensor_descriptor(filter_dims, WeiLayout{}));
Tensor<OutDataType> host_output(
ck::utils::conv::get_host_tensor_descriptor(output_dims, OutLayout{}));
fill_input_op(input.begin(), input.end());
fill_weights_op(weights.begin(), weights.end());
std::fill(host_output.begin(), host_output.end(), OutDataType(0.f));
auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<InDataType,
WeiDataType,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
NDim>();
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(input,
weights,
host_output,
params.conv_filter_strides_,
params.conv_filter_dilations_,
params.input_left_pads_,
params.input_right_pads_,
InElementOp{},
WeiElementOp{},
OutElementOp{});
ref_invoker.Run(ref_argument);
return host_output;
}
} // anonymous namespace
TEST(ReferenceConvolutionFWD, Conv2DNHWC)
{
ck::utils::conv::ConvParams params;
params.N_ = 1;
params.K_ = 1;
params.C_ = 2;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{6, 6};
params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1};
params.input_left_pads_ = std::vector<ck::index_t>{0, 0};
params.input_right_pads_ = std::vector<ck::index_t>{0, 0};
auto out_tensor = run_reference_convolution_forward<2>(params);
std::vector<std::size_t> ref_dims{1, 1, 4, 4};
std::vector<float> ref_data{130.5,
148.5,
166.5,
184.5,
238.5,
256.5,
274.5,
292.5,
346.5,
364.5,
382.5,
400.5,
454.5,
472.5,
490.5,
508.5};
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv2DNHWCStridesDilationsPadding)
{
ck::utils::conv::ConvParams params;
params.N_ = 1;
params.K_ = 2;
params.C_ = 2;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{12, 12};
params.conv_filter_strides_ = std::vector<ck::index_t>{2, 2};
params.conv_filter_dilations_ = std::vector<ck::index_t>{2, 2};
params.input_left_pads_ = std::vector<ck::index_t>{1, 1};
params.input_right_pads_ = std::vector<ck::index_t>{1, 1};
auto out_tensor = run_reference_convolution_forward<2>(params);
std::vector<std::size_t> ref_dims = std::vector<std::size_t>{1, 2, 5, 5};
std::vector<float> ref_data{
210., 210., 327., 327., 351., 351., 375., 375., 399., 399.,
459., 459., 706.5, 706.5, 742.5, 742.5, 778.5, 778.5, 814.5, 814.5,
747., 747., 1138.5, 1138.5, 1174.5, 1174.5, 1210.5, 1210.5, 1246.5, 1246.5,
1035., 1035., 1570.5, 1570.5, 1606.5, 1606.5, 1642.5, 1642.5, 1678.5, 1678.5,
1323., 1323., 2002.5, 2002.5, 2038.5, 2038.5, 2074.5, 2074.5, 2110.5, 2110.5};
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv1DNWC)
{
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 1;
params.N_ = 1;
params.K_ = 1;
params.C_ = 2;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{6};
params.conv_filter_strides_ = std::vector<ck::index_t>{1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1};
params.input_left_pads_ = std::vector<ck::index_t>{0};
params.input_right_pads_ = std::vector<ck::index_t>{0};
auto out_tensor =
run_reference_convolution_forward<1,
float,
float,
float,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK>(params);
std::vector<std::size_t> ref_dims{1, 1, 4};
std::vector<float> ref_data{7.5, 13.5, 19.5, 25.5};
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv1DNWCStridesDilationsPadding)
{
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 1;
params.N_ = 1;
params.K_ = 2;
params.C_ = 2;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{12};
params.conv_filter_strides_ = std::vector<ck::index_t>{2};
params.conv_filter_dilations_ = std::vector<ck::index_t>{2};
params.input_left_pads_ = std::vector<ck::index_t>{1};
params.input_right_pads_ = std::vector<ck::index_t>{1};
auto out_tensor =
run_reference_convolution_forward<1,
float,
float,
float,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK>(params);
std::vector<std::size_t> ref_dims{1, 2, 5};
std::vector<float> ref_data{9., 9., 19.5, 19.5, 31.5, 31.5, 43.5, 43.5, 55.5, 55.5};
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv1DNWCSameOutputSize)
{
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 1;
params.N_ = 2;
params.K_ = 16;
params.C_ = 4;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{16};
params.conv_filter_strides_ = std::vector<ck::index_t>{1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1};
params.input_left_pads_ = std::vector<ck::index_t>{1};
params.input_right_pads_ = std::vector<ck::index_t>{1};
auto out_tensor2 = run_reference_convolution_forward<1,
float,
float,
float,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK>(
params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
std::vector<std::size_t> ref_dims{2, 16, 16};
std::vector<float> ref_data{
1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4,
1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4,
3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3,
3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3,
5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7,
5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7,
8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1,
8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1,
10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5,
10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5,
12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001,
12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001,
15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3,
15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3,
17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7,
17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7,
20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1,
20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1,
22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5,
22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5,
24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002,
24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002,
27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001,
27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001,
29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7,
29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7,
32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002,
32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002,
34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5,
34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5,
23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8,
23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8,
27., 27., 27., 27., 27., 27., 27., 27.,
27., 27., 27., 27., 27., 27., 27., 27.,
41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7,
41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7,
44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002,
44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002,
46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5,
46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5,
48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998,
48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998,
51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3,
51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3,
53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7,
53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7,
56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002,
56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002,
58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5,
58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5,
60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998,
60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998,
63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3,
63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3,
65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7,
65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7,
68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1,
68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1,
70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5,
70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5,
72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9,
72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9,
49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4,
49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4};
EXPECT_TRUE(ck::utils::check_err(
out_tensor2.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor2.mData, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv3DNCDHW)
{
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 3;
params.N_ = 1;
params.K_ = 1;
params.C_ = 2;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3, 3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{6, 6, 6};
params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1, 1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1};
params.input_left_pads_ = std::vector<ck::index_t>{0, 0, 0};
params.input_right_pads_ = std::vector<ck::index_t>{0, 0, 0};
auto out_tensor = run_reference_convolution_forward<3,
float,
float,
float,
ck::tensor_layout::convolution::NCDHW,
ck::tensor_layout::convolution::KCZYX,
ck::tensor_layout::convolution::NKDHW>(
params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
std::vector<std::size_t> ref_dims{1, 1, 4, 4, 4};
std::vector<float> ref_data{
407.7, 410.40002, 413.09998, 415.80002, 423.90002, 426.6, 429.30002, 432.,
440.1, 442.80002, 445.5, 448.2, 456.30002, 459., 461.7, 464.40002,
504.90002, 507.6, 510.30002, 513., 521.1, 523.8, 526.5, 529.2001,
537.3, 540., 542.7001, 545.4, 553.5, 556.2001, 558.9, 561.6,
602.10004, 604.8, 607.5, 610.2, 618.3, 621., 623.7, 626.4,
634.5, 637.2, 639.9, 642.60004, 650.7, 653.4, 656.10004, 658.8,
699.3, 702., 704.7, 707.4, 715.5, 718.2, 720.9, 723.60004,
731.7, 734.4001, 737.10004, 739.8, 747.9001, 750.60004, 753.3, 756.};
EXPECT_TRUE(ck::utils::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error [case 1]: wrong output tensor dimensions!"));
EXPECT_TRUE(
ck::utils::check_err(out_tensor.mData, ref_data, "Error [case 1]: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv3DNCDHWStridesDilations)
{
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 3;
params.N_ = 1;
params.K_ = 2;
params.C_ = 2;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3, 3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{12, 12, 12};
params.conv_filter_strides_ = std::vector<ck::index_t>{3, 3, 3};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1};
params.input_left_pads_ = std::vector<ck::index_t>{0, 0, 0};
params.input_right_pads_ = std::vector<ck::index_t>{0, 0, 0};
auto out_tensor = run_reference_convolution_forward<3,
float,
float,
float,
ck::tensor_layout::convolution::NCDHW,
ck::tensor_layout::convolution::KCZYX,
ck::tensor_layout::convolution::NKDHW>(
params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
std::vector<std::size_t> ref_dims{1, 2, 4, 4, 4};
std::vector<float> ref_data{
2756.7002, 2764.7998, 2772.9001, 2781., 2853.9001, 2862., 2870.1, 2878.2002,
2951.1, 2959.2002, 2967.2998, 2975.4001, 3048.2998, 3056.4001, 3064.5, 3072.6,
3923.1, 3931.2, 3939.2998, 3947.4, 4020.2998, 4028.4001, 4036.5002, 4044.5999,
4117.5, 4125.6, 4133.7, 4141.8, 4214.7, 4222.8, 4230.9004, 4239.,
5089.5, 5097.5996, 5105.7, 5113.8, 5186.7, 5194.8, 5202.9, 5211.,
5283.9004, 5292., 5300.0996, 5308.2, 5381.0996, 5389.2, 5397.3, 5405.4004,
6255.9004, 6264.0005, 6272.1, 6280.2, 6353.1, 6361.2, 6369.301, 6377.4,
6450.301, 6458.4, 6466.5, 6474.6, 6547.5, 6555.6, 6563.699, 6571.801,
2756.7002, 2764.7998, 2772.9001, 2781., 2853.9001, 2862., 2870.1, 2878.2002,
2951.1, 2959.2002, 2967.2998, 2975.4001, 3048.2998, 3056.4001, 3064.5, 3072.6,
3923.1, 3931.2, 3939.2998, 3947.4, 4020.2998, 4028.4001, 4036.5002, 4044.5999,
4117.5, 4125.6, 4133.7, 4141.8, 4214.7, 4222.8, 4230.9004, 4239.,
5089.5, 5097.5996, 5105.7, 5113.8, 5186.7, 5194.8, 5202.9, 5211.,
5283.9004, 5292., 5300.0996, 5308.2, 5381.0996, 5389.2, 5397.3, 5405.4004,
6255.9004, 6264.0005, 6272.1, 6280.2, 6353.1, 6361.2, 6369.301, 6377.4,
6450.301, 6458.4, 6466.5, 6474.6, 6547.5, 6555.6, 6563.699, 6571.801};
EXPECT_TRUE(ck::utils::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error [case 2]: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mData, ref_data, "Error [case 2]: incorrect results!", 1e-4f, 1e-6f));
}
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <cmath>
#include <cstdlib>
#include <numeric>
#include <type_traits>
#include <vector>
#include <gtest/gtest.h>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/conv_util.hpp"
#include "ck/library/utility/fill.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
namespace {
using InElementOp = ck::tensor_operation::element_wise::PassThrough;
using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
template <ck::index_t NDim,
typename InDataType = float,
typename WeiDataType = float,
typename OutDataType = float,
typename InLayout = ck::tensor_layout::convolution::NHWC,
typename WeiLayout = ck::tensor_layout::convolution::KYXC,
typename OutLayout = ck::tensor_layout::convolution::NHWK,
typename FillInputOp = ck::utils::FillMonotonicSeq<InDataType>,
typename FillWeightsOp = ck::utils::FillConstant<WeiDataType>>
Tensor<OutDataType>
run_reference_convolution_forward(const ck::utils::conv::ConvParams& params,
const FillInputOp& fill_input_op = FillInputOp{},
const FillWeightsOp& fill_weights_op = FillWeightsOp{0.5f})
{
std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N_),
static_cast<std::size_t>(params.C_)};
input_dims.insert(std::end(input_dims),
std::begin(params.input_spatial_lengths_),
std::end(params.input_spatial_lengths_));
std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K_),
static_cast<std::size_t>(params.C_)};
filter_dims.insert(std::end(filter_dims),
std::begin(params.filter_spatial_lengths_),
std::end(params.filter_spatial_lengths_));
const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N_),
static_cast<std::size_t>(params.K_)};
output_dims.insert(std::end(output_dims),
std::begin(output_spatial_lengths),
std::end(output_spatial_lengths));
Tensor<InDataType> input(ck::utils::conv::get_host_tensor_descriptor(input_dims, InLayout{}));
Tensor<WeiDataType> weights(
ck::utils::conv::get_host_tensor_descriptor(filter_dims, WeiLayout{}));
Tensor<OutDataType> host_output(
ck::utils::conv::get_host_tensor_descriptor(output_dims, OutLayout{}));
fill_input_op(input.begin(), input.end());
fill_weights_op(weights.begin(), weights.end());
std::fill(host_output.begin(), host_output.end(), OutDataType(0.f));
auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<InDataType,
WeiDataType,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
NDim>();
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(input,
weights,
host_output,
params.conv_filter_strides_,
params.conv_filter_dilations_,
params.input_left_pads_,
params.input_right_pads_,
InElementOp{},
WeiElementOp{},
OutElementOp{});
ref_invoker.Run(ref_argument);
return host_output;
}
} // anonymous namespace
TEST(ReferenceConvolutionFWD, Conv2DNHWC)
{
ck::utils::conv::ConvParams params;
params.N_ = 1;
params.K_ = 1;
params.C_ = 2;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{6, 6};
params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1};
params.input_left_pads_ = std::vector<ck::index_t>{0, 0};
params.input_right_pads_ = std::vector<ck::index_t>{0, 0};
auto out_tensor = run_reference_convolution_forward<2>(params);
std::vector<std::size_t> ref_dims{1, 1, 4, 4};
std::vector<float> ref_data{130.5,
148.5,
166.5,
184.5,
238.5,
256.5,
274.5,
292.5,
346.5,
364.5,
382.5,
400.5,
454.5,
472.5,
490.5,
508.5};
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv2DNHWCStridesDilationsPadding)
{
ck::utils::conv::ConvParams params;
params.N_ = 1;
params.K_ = 2;
params.C_ = 2;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{12, 12};
params.conv_filter_strides_ = std::vector<ck::index_t>{2, 2};
params.conv_filter_dilations_ = std::vector<ck::index_t>{2, 2};
params.input_left_pads_ = std::vector<ck::index_t>{1, 1};
params.input_right_pads_ = std::vector<ck::index_t>{1, 1};
auto out_tensor = run_reference_convolution_forward<2>(params);
std::vector<std::size_t> ref_dims = std::vector<std::size_t>{1, 2, 5, 5};
std::vector<float> ref_data{
210., 210., 327., 327., 351., 351., 375., 375., 399., 399.,
459., 459., 706.5, 706.5, 742.5, 742.5, 778.5, 778.5, 814.5, 814.5,
747., 747., 1138.5, 1138.5, 1174.5, 1174.5, 1210.5, 1210.5, 1246.5, 1246.5,
1035., 1035., 1570.5, 1570.5, 1606.5, 1606.5, 1642.5, 1642.5, 1678.5, 1678.5,
1323., 1323., 2002.5, 2002.5, 2038.5, 2038.5, 2074.5, 2074.5, 2110.5, 2110.5};
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv1DNWC)
{
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 1;
params.N_ = 1;
params.K_ = 1;
params.C_ = 2;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{6};
params.conv_filter_strides_ = std::vector<ck::index_t>{1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1};
params.input_left_pads_ = std::vector<ck::index_t>{0};
params.input_right_pads_ = std::vector<ck::index_t>{0};
auto out_tensor =
run_reference_convolution_forward<1,
float,
float,
float,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK>(params);
std::vector<std::size_t> ref_dims{1, 1, 4};
std::vector<float> ref_data{7.5, 13.5, 19.5, 25.5};
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv1DNWCStridesDilationsPadding)
{
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 1;
params.N_ = 1;
params.K_ = 2;
params.C_ = 2;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{12};
params.conv_filter_strides_ = std::vector<ck::index_t>{2};
params.conv_filter_dilations_ = std::vector<ck::index_t>{2};
params.input_left_pads_ = std::vector<ck::index_t>{1};
params.input_right_pads_ = std::vector<ck::index_t>{1};
auto out_tensor =
run_reference_convolution_forward<1,
float,
float,
float,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK>(params);
std::vector<std::size_t> ref_dims{1, 2, 5};
std::vector<float> ref_data{9., 9., 19.5, 19.5, 31.5, 31.5, 43.5, 43.5, 55.5, 55.5};
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv1DNWCSameOutputSize)
{
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 1;
params.N_ = 2;
params.K_ = 16;
params.C_ = 4;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{16};
params.conv_filter_strides_ = std::vector<ck::index_t>{1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1};
params.input_left_pads_ = std::vector<ck::index_t>{1};
params.input_right_pads_ = std::vector<ck::index_t>{1};
auto out_tensor2 = run_reference_convolution_forward<1,
float,
float,
float,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK>(
params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
std::vector<std::size_t> ref_dims{2, 16, 16};
std::vector<float> ref_data{
1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4,
1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4,
3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3,
3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3,
5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7,
5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7,
8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1,
8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1,
10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5,
10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5,
12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001,
12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001,
15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3,
15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3,
17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7,
17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7,
20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1,
20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1,
22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5,
22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5,
24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002,
24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002,
27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001,
27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001,
29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7,
29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7,
32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002,
32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002,
34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5,
34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5,
23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8,
23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8,
27., 27., 27., 27., 27., 27., 27., 27.,
27., 27., 27., 27., 27., 27., 27., 27.,
41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7,
41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7,
44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002,
44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002,
46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5,
46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5,
48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998,
48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998,
51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3,
51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3,
53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7,
53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7,
56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002,
56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002,
58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5,
58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5,
60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998,
60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998,
63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3,
63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3,
65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7,
65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7,
68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1,
68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1,
70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5,
70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5,
72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9,
72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9,
49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4,
49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4};
EXPECT_TRUE(ck::utils::check_err(
out_tensor2.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor2.mData, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv3DNCDHW)
{
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 3;
params.N_ = 1;
params.K_ = 1;
params.C_ = 2;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3, 3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{6, 6, 6};
params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1, 1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1};
params.input_left_pads_ = std::vector<ck::index_t>{0, 0, 0};
params.input_right_pads_ = std::vector<ck::index_t>{0, 0, 0};
auto out_tensor = run_reference_convolution_forward<3,
float,
float,
float,
ck::tensor_layout::convolution::NCDHW,
ck::tensor_layout::convolution::KCZYX,
ck::tensor_layout::convolution::NKDHW>(
params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
std::vector<std::size_t> ref_dims{1, 1, 4, 4, 4};
std::vector<float> ref_data{
407.7, 410.40002, 413.09998, 415.80002, 423.90002, 426.6, 429.30002, 432.,
440.1, 442.80002, 445.5, 448.2, 456.30002, 459., 461.7, 464.40002,
504.90002, 507.6, 510.30002, 513., 521.1, 523.8, 526.5, 529.2001,
537.3, 540., 542.7001, 545.4, 553.5, 556.2001, 558.9, 561.6,
602.10004, 604.8, 607.5, 610.2, 618.3, 621., 623.7, 626.4,
634.5, 637.2, 639.9, 642.60004, 650.7, 653.4, 656.10004, 658.8,
699.3, 702., 704.7, 707.4, 715.5, 718.2, 720.9, 723.60004,
731.7, 734.4001, 737.10004, 739.8, 747.9001, 750.60004, 753.3, 756.};
EXPECT_TRUE(ck::utils::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error [case 1]: wrong output tensor dimensions!"));
EXPECT_TRUE(
ck::utils::check_err(out_tensor.mData, ref_data, "Error [case 1]: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv3DNCDHWStridesDilations)
{
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 3;
params.N_ = 1;
params.K_ = 2;
params.C_ = 2;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3, 3};
params.input_spatial_lengths_ = std::vector<ck::index_t>{12, 12, 12};
params.conv_filter_strides_ = std::vector<ck::index_t>{3, 3, 3};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1};
params.input_left_pads_ = std::vector<ck::index_t>{0, 0, 0};
params.input_right_pads_ = std::vector<ck::index_t>{0, 0, 0};
auto out_tensor = run_reference_convolution_forward<3,
float,
float,
float,
ck::tensor_layout::convolution::NCDHW,
ck::tensor_layout::convolution::KCZYX,
ck::tensor_layout::convolution::NKDHW>(
params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
std::vector<std::size_t> ref_dims{1, 2, 4, 4, 4};
std::vector<float> ref_data{
2756.7002, 2764.7998, 2772.9001, 2781., 2853.9001, 2862., 2870.1, 2878.2002,
2951.1, 2959.2002, 2967.2998, 2975.4001, 3048.2998, 3056.4001, 3064.5, 3072.6,
3923.1, 3931.2, 3939.2998, 3947.4, 4020.2998, 4028.4001, 4036.5002, 4044.5999,
4117.5, 4125.6, 4133.7, 4141.8, 4214.7, 4222.8, 4230.9004, 4239.,
5089.5, 5097.5996, 5105.7, 5113.8, 5186.7, 5194.8, 5202.9, 5211.,
5283.9004, 5292., 5300.0996, 5308.2, 5381.0996, 5389.2, 5397.3, 5405.4004,
6255.9004, 6264.0005, 6272.1, 6280.2, 6353.1, 6361.2, 6369.301, 6377.4,
6450.301, 6458.4, 6466.5, 6474.6, 6547.5, 6555.6, 6563.699, 6571.801,
2756.7002, 2764.7998, 2772.9001, 2781., 2853.9001, 2862., 2870.1, 2878.2002,
2951.1, 2959.2002, 2967.2998, 2975.4001, 3048.2998, 3056.4001, 3064.5, 3072.6,
3923.1, 3931.2, 3939.2998, 3947.4, 4020.2998, 4028.4001, 4036.5002, 4044.5999,
4117.5, 4125.6, 4133.7, 4141.8, 4214.7, 4222.8, 4230.9004, 4239.,
5089.5, 5097.5996, 5105.7, 5113.8, 5186.7, 5194.8, 5202.9, 5211.,
5283.9004, 5292., 5300.0996, 5308.2, 5381.0996, 5389.2, 5397.3, 5405.4004,
6255.9004, 6264.0005, 6272.1, 6280.2, 6353.1, 6361.2, 6369.301, 6377.4,
6450.301, 6458.4, 6466.5, 6474.6, 6547.5, 6555.6, 6563.699, 6571.801};
EXPECT_TRUE(ck::utils::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error [case 2]: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mData, ref_data, "Error [case 2]: incorrect results!", 1e-4f, 1e-6f));
}

3
test/softmax/test_softmax_fp16.cpp
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@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "test_softmax_util.hpp"

3
test/softmax/test_softmax_fp32.cpp
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@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "test_softmax_util.hpp"

3
test/softmax/test_softmax_util.hpp
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@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <vector>
#include <iostream>
#include <gtest/gtest.h>

3
test/space_filling_curve/space_filling_curve.cpp
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@@ -1,3 +1,6 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <vector>
#include <iostream>
#include <numeric>