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Commit 7a3b49e5 authored by Chao Liu's avatar Chao Liu
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Merge remote-tracking branch 'origin/develop' into contraction

parents e07b3d8e d3051d75
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test/gemm_reduce/gemm_reduce_fp16.cpp
View file @ 7a3b49e5
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream> #include <iostream>
#include "profile_gemm_reduce_impl.hpp" #include "profiler/include/profile_gemm_reduce_impl.hpp"
int main() int main()
{ {
......
test/gemm_split_k/gemm_split_k.cpp
View file @ 7a3b49e5
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream> #include <iostream>
#include <initializer_list> #include <initializer_list>
#include <cstdlib> #include <cstdlib>
#include <stdlib.h>
#include "config.hpp" #include "ck/ck.hpp"
#include "print.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "device.hpp" #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "host_tensor.hpp" #include "ck/tensor_operation/gpu/device/device_gemm_xdl_splitk.hpp"
#include "host_tensor_generator.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "device_tensor.hpp"
#include "host_gemm.hpp" #include "ck/library/utility/check_err.hpp"
#include "tensor_layout.hpp" #include "ck/library/host_tensor/device_memory.hpp"
#include "device_gemm_xdl_splitk.hpp" #include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/host_tensor/host_gemm.hpp"
enum struct GemmMatrixLayout enum struct GemmMatrixLayout
{ {
......
test/grouped_gemm/grouped_gemm_fp16.cpp
View file @ 7a3b49e5
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream> #include <iostream>
#include <numeric> #include <numeric>
#include <initializer_list> #include <initializer_list>
#include <cstdlib> #include <cstdlib>
#include <stdlib.h>
#include <half.hpp> #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "check_err.hpp" #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "config.hpp" #include "ck/tensor_operation/gpu/device/device_grouped_gemm_xdl.hpp"
#include "print.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "device.hpp"
#include "host_tensor.hpp" #include "ck/library/utility/check_err.hpp"
#include "host_tensor_generator.hpp" #include "ck/library/host_tensor/device_memory.hpp"
#include "host_gemm.hpp" #include "ck/library/host_tensor/host_tensor.hpp"
#include "device_tensor.hpp" #include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "device_grouped_gemm_xdl.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "element_wise_operation.hpp"
#include "reference_gemm.hpp"
#include "gemm_specialization.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
......
test/magic_number_division/magic_number_division.cpp
View file @ 7a3b49e5
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream> #include <iostream>
#include <numeric> #include <numeric>
#include <initializer_list> #include <initializer_list>
#include <cstdlib> #include <cstdlib>
#include <stdlib.h>
#include <half.hpp> #include "ck/ck.hpp"
#include "ck/utility/magic_division.hpp"
#include "check_err.hpp" #include "ck/library/utility/check_err.hpp"
#include "config.hpp" #include "ck/library/host_tensor/device_memory.hpp"
#include "magic_division.hpp" #include "ck/library/host_tensor/host_tensor.hpp"
#include "device.hpp" #include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "host_tensor.hpp"
#include "host_tensor_generator.hpp"
#include "device_tensor.hpp"
__global__ void gpu_magic_number_division(uint32_t magic_multiplier, __global__ void gpu_magic_number_division(uint32_t magic_multiplier,
uint32_t magic_shift, uint32_t magic_shift,
......
test/reduce/reduce_no_index.cpp
View file @ 7a3b49e5
#include "getopt.h" // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "host_common_util.hpp" #include <getopt.h>
#include "profile_reduce_impl.hpp"
#include "ck/library/host_tensor/host_common_util.hpp"
#include "profiler/include/profile_reduce_impl.hpp"
using namespace ck; using namespace ck;
......
test/reduce/reduce_with_index.cpp
View file @ 7a3b49e5
#include "getopt.h" // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "host_common_util.hpp" #include <getopt.h>
#include "profile_reduce_impl.hpp"
#include "ck/library/host_tensor/host_common_util.hpp"
#include "profiler/include/profile_reduce_impl.hpp"
using namespace ck; using namespace ck;
......
test/reference_conv_fwd/reference_conv_fwd.cpp
View file @ 7a3b49e5
#include <cmath> // SPDX-License-Identifier: MIT
#include <cstdlib> // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <half.hpp>
#include <numeric> #include <cmath>
#include <type_traits> #include <cstdlib>
#include <vector> #include <numeric>
#include "gtest/gtest.h" #include <type_traits>
#include <vector>
#include "check_err.hpp" #include <gtest/gtest.h>
#include "config.hpp"
#include "conv_util.hpp" #include "ck/ck.hpp"
#include "element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "fill.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "host_tensor.hpp"
#include "reference_conv_fwd.hpp" #include "ck/library/utility/check_err.hpp"
#include "tensor_layout.hpp" #include "ck/library/utility/conv_util.hpp"
#include "ck/library/utility/fill.hpp"
namespace { #include "ck/library/host_tensor/host_tensor.hpp"
using InElementOp = ck::tensor_operation::element_wise::PassThrough; #include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
using OutElementOp = ck::tensor_operation::element_wise::PassThrough; namespace {
using InElementOp = ck::tensor_operation::element_wise::PassThrough;
template <ck::index_t NDim, using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
typename InDataType = float, using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
typename WeiDataType = float,
typename OutDataType = float, template <ck::index_t NDim,
typename InLayout = ck::tensor_layout::convolution::NHWC, typename InDataType = float,
typename WeiLayout = ck::tensor_layout::convolution::KYXC, typename WeiDataType = float,
typename OutLayout = ck::tensor_layout::convolution::NHWK, typename OutDataType = float,
typename FillInputOp = ck::utils::FillMonotonicSeq<InDataType>, typename InLayout = ck::tensor_layout::convolution::NHWC,
typename FillWeightsOp = ck::utils::FillConstant<WeiDataType>> typename WeiLayout = ck::tensor_layout::convolution::KYXC,
Tensor<OutDataType> typename OutLayout = ck::tensor_layout::convolution::NHWK,
run_reference_convolution_forward(const ck::utils::conv::ConvParams& params, typename FillInputOp = ck::utils::FillMonotonicSeq<InDataType>,
const FillInputOp& fill_input_op = FillInputOp{}, typename FillWeightsOp = ck::utils::FillConstant<WeiDataType>>
const FillWeightsOp& fill_weights_op = FillWeightsOp{0.5f}) Tensor<OutDataType>
{ run_reference_convolution_forward(const ck::utils::conv::ConvParams& params,
std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N_), const FillInputOp& fill_input_op = FillInputOp{},
static_cast<std::size_t>(params.C_)}; const FillWeightsOp& fill_weights_op = FillWeightsOp{0.5f})
input_dims.insert(std::end(input_dims), {
std::begin(params.input_spatial_lengths_), std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N_),
std::end(params.input_spatial_lengths_)); static_cast<std::size_t>(params.C_)};
input_dims.insert(std::end(input_dims),
std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K_), std::begin(params.input_spatial_lengths_),
static_cast<std::size_t>(params.C_)}; std::end(params.input_spatial_lengths_));
filter_dims.insert(std::end(filter_dims),
std::begin(params.filter_spatial_lengths_), std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K_),
std::end(params.filter_spatial_lengths_)); static_cast<std::size_t>(params.C_)};
filter_dims.insert(std::end(filter_dims),
const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths(); std::begin(params.filter_spatial_lengths_),
std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N_), std::end(params.filter_spatial_lengths_));
static_cast<std::size_t>(params.K_)};
output_dims.insert(std::end(output_dims), const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
std::begin(output_spatial_lengths), std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N_),
std::end(output_spatial_lengths)); static_cast<std::size_t>(params.K_)};
output_dims.insert(std::end(output_dims),
Tensor<InDataType> input(ck::utils::conv::get_host_tensor_descriptor(input_dims, InLayout{})); std::begin(output_spatial_lengths),
Tensor<WeiDataType> weights( std::end(output_spatial_lengths));
ck::utils::conv::get_host_tensor_descriptor(filter_dims, WeiLayout{}));
Tensor<OutDataType> host_output( Tensor<InDataType> input(ck::utils::conv::get_host_tensor_descriptor(input_dims, InLayout{}));
ck::utils::conv::get_host_tensor_descriptor(output_dims, OutLayout{})); Tensor<WeiDataType> weights(
ck::utils::conv::get_host_tensor_descriptor(filter_dims, WeiLayout{}));
fill_input_op(input.begin(), input.end()); Tensor<OutDataType> host_output(
fill_weights_op(weights.begin(), weights.end()); ck::utils::conv::get_host_tensor_descriptor(output_dims, OutLayout{}));
std::fill(host_output.begin(), host_output.end(), OutDataType(0.f));
fill_input_op(input.begin(), input.end());
auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<InDataType, fill_weights_op(weights.begin(), weights.end());
WeiDataType, std::fill(host_output.begin(), host_output.end(), OutDataType(0.f));
OutDataType,
InElementOp, auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<InDataType,
WeiElementOp, WeiDataType,
OutElementOp, OutDataType,
NDim>(); InElementOp,
auto ref_invoker = ref_conv.MakeInvoker(); WeiElementOp,
auto ref_argument = ref_conv.MakeArgument(input, OutElementOp,
weights, NDim>();
host_output, auto ref_invoker = ref_conv.MakeInvoker();
params.conv_filter_strides_, auto ref_argument = ref_conv.MakeArgument(input,
params.conv_filter_dilations_, weights,
params.input_left_pads_, host_output,
params.input_right_pads_, params.conv_filter_strides_,
InElementOp{}, params.conv_filter_dilations_,
WeiElementOp{}, params.input_left_pads_,
OutElementOp{}); params.input_right_pads_,
InElementOp{},
ref_invoker.Run(ref_argument); WeiElementOp{},
return host_output; OutElementOp{});
}
ref_invoker.Run(ref_argument);
} // anonymous namespace return host_output;
}
TEST(ReferenceConvolutionFWD, Conv2DNHWC)
{ } // anonymous namespace
ck::utils::conv::ConvParams params;
params.N_ = 1; TEST(ReferenceConvolutionFWD, Conv2DNHWC)
params.K_ = 1; {
params.C_ = 2; ck::utils::conv::ConvParams params;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3}; params.N_ = 1;
params.input_spatial_lengths_ = std::vector<ck::index_t>{6, 6}; params.K_ = 1;
params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1}; params.C_ = 2;
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1}; params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3};
params.input_left_pads_ = std::vector<ck::index_t>{0, 0}; params.input_spatial_lengths_ = std::vector<ck::index_t>{6, 6};
params.input_right_pads_ = std::vector<ck::index_t>{0, 0}; params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1};
auto out_tensor = run_reference_convolution_forward<2>(params); params.input_left_pads_ = std::vector<ck::index_t>{0, 0};
std::vector<std::size_t> ref_dims{1, 1, 4, 4}; params.input_right_pads_ = std::vector<ck::index_t>{0, 0};
std::vector<float> ref_data{130.5,
148.5, auto out_tensor = run_reference_convolution_forward<2>(params);
166.5, std::vector<std::size_t> ref_dims{1, 1, 4, 4};
184.5, std::vector<float> ref_data{130.5,
238.5, 148.5,
256.5, 166.5,
274.5, 184.5,
292.5, 238.5,
346.5, 256.5,
364.5, 274.5,
382.5, 292.5,
400.5, 346.5,
454.5, 364.5,
472.5, 382.5,
490.5, 400.5,
508.5}; 454.5,
EXPECT_TRUE(ck::utils::check_err( 472.5,
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!")); 490.5,
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!")); 508.5};
} EXPECT_TRUE(ck::utils::check_err(
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
TEST(ReferenceConvolutionFWD, Conv2DNHWCStridesDilationsPadding) EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
{ }
ck::utils::conv::ConvParams params;
params.N_ = 1; TEST(ReferenceConvolutionFWD, Conv2DNHWCStridesDilationsPadding)
params.K_ = 2; {
params.C_ = 2; ck::utils::conv::ConvParams params;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3}; params.N_ = 1;
params.input_spatial_lengths_ = std::vector<ck::index_t>{12, 12}; params.K_ = 2;
params.conv_filter_strides_ = std::vector<ck::index_t>{2, 2}; params.C_ = 2;
params.conv_filter_dilations_ = std::vector<ck::index_t>{2, 2}; params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3};
params.input_left_pads_ = std::vector<ck::index_t>{1, 1}; params.input_spatial_lengths_ = std::vector<ck::index_t>{12, 12};
params.input_right_pads_ = std::vector<ck::index_t>{1, 1}; params.conv_filter_strides_ = std::vector<ck::index_t>{2, 2};
params.conv_filter_dilations_ = std::vector<ck::index_t>{2, 2};
auto out_tensor = run_reference_convolution_forward<2>(params); params.input_left_pads_ = std::vector<ck::index_t>{1, 1};
std::vector<std::size_t> ref_dims = std::vector<std::size_t>{1, 2, 5, 5}; params.input_right_pads_ = std::vector<ck::index_t>{1, 1};
std::vector<float> ref_data{
210., 210., 327., 327., 351., 351., 375., 375., 399., 399., auto out_tensor = run_reference_convolution_forward<2>(params);
459., 459., 706.5, 706.5, 742.5, 742.5, 778.5, 778.5, 814.5, 814.5, std::vector<std::size_t> ref_dims = std::vector<std::size_t>{1, 2, 5, 5};
747., 747., 1138.5, 1138.5, 1174.5, 1174.5, 1210.5, 1210.5, 1246.5, 1246.5, std::vector<float> ref_data{
1035., 1035., 1570.5, 1570.5, 1606.5, 1606.5, 1642.5, 1642.5, 1678.5, 1678.5, 210., 210., 327., 327., 351., 351., 375., 375., 399., 399.,
1323., 1323., 2002.5, 2002.5, 2038.5, 2038.5, 2074.5, 2074.5, 2110.5, 2110.5}; 459., 459., 706.5, 706.5, 742.5, 742.5, 778.5, 778.5, 814.5, 814.5,
EXPECT_TRUE(ck::utils::check_err( 747., 747., 1138.5, 1138.5, 1174.5, 1174.5, 1210.5, 1210.5, 1246.5, 1246.5,
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!")); 1035., 1035., 1570.5, 1570.5, 1606.5, 1606.5, 1642.5, 1642.5, 1678.5, 1678.5,
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!")); 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!"));
TEST(ReferenceConvolutionFWD, Conv1DNWC) EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
{ }
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 1; TEST(ReferenceConvolutionFWD, Conv1DNWC)
params.N_ = 1; {
params.K_ = 1; ck::utils::conv::ConvParams params;
params.C_ = 2; params.num_dim_spatial_ = 1;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3}; params.N_ = 1;
params.input_spatial_lengths_ = std::vector<ck::index_t>{6}; params.K_ = 1;
params.conv_filter_strides_ = std::vector<ck::index_t>{1}; params.C_ = 2;
params.conv_filter_dilations_ = std::vector<ck::index_t>{1}; params.filter_spatial_lengths_ = std::vector<ck::index_t>{3};
params.input_left_pads_ = std::vector<ck::index_t>{0}; params.input_spatial_lengths_ = std::vector<ck::index_t>{6};
params.input_right_pads_ = std::vector<ck::index_t>{0}; params.conv_filter_strides_ = std::vector<ck::index_t>{1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1};
auto out_tensor = params.input_left_pads_ = std::vector<ck::index_t>{0};
run_reference_convolution_forward<1, params.input_right_pads_ = std::vector<ck::index_t>{0};
float,
float, auto out_tensor =
float, run_reference_convolution_forward<1,
ck::tensor_layout::convolution::NWC, float,
ck::tensor_layout::convolution::KXC, float,
ck::tensor_layout::convolution::NWK>(params); float,
std::vector<std::size_t> ref_dims{1, 1, 4}; ck::tensor_layout::convolution::NWC,
std::vector<float> ref_data{7.5, 13.5, 19.5, 25.5}; ck::tensor_layout::convolution::KXC,
EXPECT_TRUE(ck::utils::check_err( ck::tensor_layout::convolution::NWK>(params);
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!")); std::vector<std::size_t> ref_dims{1, 1, 4};
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!")); 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!"));
TEST(ReferenceConvolutionFWD, Conv1DNWCStridesDilationsPadding) EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
{ }
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 1; TEST(ReferenceConvolutionFWD, Conv1DNWCStridesDilationsPadding)
params.N_ = 1; {
params.K_ = 2; ck::utils::conv::ConvParams params;
params.C_ = 2; params.num_dim_spatial_ = 1;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3}; params.N_ = 1;
params.input_spatial_lengths_ = std::vector<ck::index_t>{12}; params.K_ = 2;
params.conv_filter_strides_ = std::vector<ck::index_t>{2}; params.C_ = 2;
params.conv_filter_dilations_ = std::vector<ck::index_t>{2}; params.filter_spatial_lengths_ = std::vector<ck::index_t>{3};
params.input_left_pads_ = std::vector<ck::index_t>{1}; params.input_spatial_lengths_ = std::vector<ck::index_t>{12};
params.input_right_pads_ = std::vector<ck::index_t>{1}; params.conv_filter_strides_ = std::vector<ck::index_t>{2};
params.conv_filter_dilations_ = std::vector<ck::index_t>{2};
auto out_tensor = params.input_left_pads_ = std::vector<ck::index_t>{1};
run_reference_convolution_forward<1, params.input_right_pads_ = std::vector<ck::index_t>{1};
float,
float, auto out_tensor =
float, run_reference_convolution_forward<1,
ck::tensor_layout::convolution::NWC, float,
ck::tensor_layout::convolution::KXC, float,
ck::tensor_layout::convolution::NWK>(params); float,
std::vector<std::size_t> ref_dims{1, 2, 5}; ck::tensor_layout::convolution::NWC,
std::vector<float> ref_data{9., 9., 19.5, 19.5, 31.5, 31.5, 43.5, 43.5, 55.5, 55.5}; ck::tensor_layout::convolution::KXC,
EXPECT_TRUE(ck::utils::check_err( ck::tensor_layout::convolution::NWK>(params);
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!")); std::vector<std::size_t> ref_dims{1, 2, 5};
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!")); 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!"));
TEST(ReferenceConvolutionFWD, Conv1DNWCSameOutputSize) EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
{ }
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 1; TEST(ReferenceConvolutionFWD, Conv1DNWCSameOutputSize)
params.N_ = 2; {
params.K_ = 16; ck::utils::conv::ConvParams params;
params.C_ = 4; params.num_dim_spatial_ = 1;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3}; params.N_ = 2;
params.input_spatial_lengths_ = std::vector<ck::index_t>{16}; params.K_ = 16;
params.conv_filter_strides_ = std::vector<ck::index_t>{1}; params.C_ = 4;
params.conv_filter_dilations_ = std::vector<ck::index_t>{1}; params.filter_spatial_lengths_ = std::vector<ck::index_t>{3};
params.input_left_pads_ = std::vector<ck::index_t>{1}; params.input_spatial_lengths_ = std::vector<ck::index_t>{16};
params.input_right_pads_ = std::vector<ck::index_t>{1}; params.conv_filter_strides_ = std::vector<ck::index_t>{1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1};
auto out_tensor2 = run_reference_convolution_forward<1, params.input_left_pads_ = std::vector<ck::index_t>{1};
float, params.input_right_pads_ = std::vector<ck::index_t>{1};
float,
float, auto out_tensor2 = run_reference_convolution_forward<1,
ck::tensor_layout::convolution::NWC, float,
ck::tensor_layout::convolution::KXC, float,
ck::tensor_layout::convolution::NWK>( float,
params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f}); ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
std::vector<std::size_t> ref_dims{2, 16, 16}; ck::tensor_layout::convolution::NWK>(
std::vector<float> ref_data{ params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
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, std::vector<std::size_t> ref_dims{2, 16, 16};
3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, std::vector<float> ref_data{
3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4,
5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4,
5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3,
8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3,
8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7,
10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7,
10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1,
12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1,
12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5,
15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5,
15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001,
17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001, 12.900001,
17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3,
20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3, 15.3,
20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7,
22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7,
22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1,
24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1, 20.1,
24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5,
27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5, 22.5,
27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002,
29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002, 24.900002,
29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001,
32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001, 27.300001,
32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7,
34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7, 29.7,
34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002,
23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002, 32.100002,
23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5,
27., 27., 27., 27., 27., 27., 27., 27., 34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5, 34.5,
27., 27., 27., 27., 27., 27., 27., 27., 23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8,
41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8,
41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 27., 27., 27., 27., 27., 27., 27., 27.,
44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 27., 27., 27., 27., 27., 27., 27., 27.,
44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7,
46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7, 41.7,
46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002,
48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002, 44.100002,
48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5,
51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5, 46.5,
51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998,
53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998, 48.899998,
53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3,
56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3, 51.3,
56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7,
58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7, 53.7,
58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002,
60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002, 56.100002,
60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5,
63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5, 58.5,
63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998,
65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998, 60.899998,
65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3,
68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3, 63.3,
68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7,
70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7, 65.7,
70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1,
72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1, 68.1,
72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5,
49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5, 70.5,
49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4}; 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9,
EXPECT_TRUE(ck::utils::check_err( 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9, 72.9,
out_tensor2.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!")); 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4,
EXPECT_TRUE(ck::utils::check_err(out_tensor2.mData, ref_data, "Error: incorrect results!")); 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!"));
TEST(ReferenceConvolutionFWD, Conv3DNCDHW) EXPECT_TRUE(ck::utils::check_err(out_tensor2.mData, ref_data, "Error: incorrect results!"));
{ }
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 3; TEST(ReferenceConvolutionFWD, Conv3DNCDHW)
params.N_ = 1; {
params.K_ = 1; ck::utils::conv::ConvParams params;
params.C_ = 2; params.num_dim_spatial_ = 3;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3, 3}; params.N_ = 1;
params.input_spatial_lengths_ = std::vector<ck::index_t>{6, 6, 6}; params.K_ = 1;
params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1, 1}; params.C_ = 2;
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1}; params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3, 3};
params.input_left_pads_ = std::vector<ck::index_t>{0, 0, 0}; params.input_spatial_lengths_ = std::vector<ck::index_t>{6, 6, 6};
params.input_right_pads_ = std::vector<ck::index_t>{0, 0, 0}; params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1, 1};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1};
auto out_tensor = run_reference_convolution_forward<3, params.input_left_pads_ = std::vector<ck::index_t>{0, 0, 0};
float, params.input_right_pads_ = std::vector<ck::index_t>{0, 0, 0};
float,
float, auto out_tensor = run_reference_convolution_forward<3,
ck::tensor_layout::convolution::NCDHW, float,
ck::tensor_layout::convolution::KCZYX, float,
ck::tensor_layout::convolution::NKDHW>( float,
params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f}); ck::tensor_layout::convolution::NCDHW,
std::vector<std::size_t> ref_dims{1, 1, 4, 4, 4}; ck::tensor_layout::convolution::KCZYX,
std::vector<float> ref_data{ ck::tensor_layout::convolution::NKDHW>(
407.7, 410.40002, 413.09998, 415.80002, 423.90002, 426.6, 429.30002, 432., params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
440.1, 442.80002, 445.5, 448.2, 456.30002, 459., 461.7, 464.40002, std::vector<std::size_t> ref_dims{1, 1, 4, 4, 4};
504.90002, 507.6, 510.30002, 513., 521.1, 523.8, 526.5, 529.2001, std::vector<float> ref_data{
537.3, 540., 542.7001, 545.4, 553.5, 556.2001, 558.9, 561.6, 407.7, 410.40002, 413.09998, 415.80002, 423.90002, 426.6, 429.30002, 432.,
602.10004, 604.8, 607.5, 610.2, 618.3, 621., 623.7, 626.4, 440.1, 442.80002, 445.5, 448.2, 456.30002, 459., 461.7, 464.40002,
634.5, 637.2, 639.9, 642.60004, 650.7, 653.4, 656.10004, 658.8, 504.90002, 507.6, 510.30002, 513., 521.1, 523.8, 526.5, 529.2001,
699.3, 702., 704.7, 707.4, 715.5, 718.2, 720.9, 723.60004, 537.3, 540., 542.7001, 545.4, 553.5, 556.2001, 558.9, 561.6,
731.7, 734.4001, 737.10004, 739.8, 747.9001, 750.60004, 753.3, 756.}; 602.10004, 604.8, 607.5, 610.2, 618.3, 621., 623.7, 626.4,
EXPECT_TRUE(ck::utils::check_err(out_tensor.mDesc.GetLengths(), 634.5, 637.2, 639.9, 642.60004, 650.7, 653.4, 656.10004, 658.8,
ref_dims, 699.3, 702., 704.7, 707.4, 715.5, 718.2, 720.9, 723.60004,
"Error [case 1]: wrong output tensor dimensions!")); 731.7, 734.4001, 737.10004, 739.8, 747.9001, 750.60004, 753.3, 756.};
EXPECT_TRUE( EXPECT_TRUE(ck::utils::check_err(out_tensor.mDesc.GetLengths(),
ck::utils::check_err(out_tensor.mData, ref_data, "Error [case 1]: incorrect results!")); ref_dims,
} "Error [case 1]: wrong output tensor dimensions!"));
EXPECT_TRUE(
TEST(ReferenceConvolutionFWD, Conv3DNCDHWStridesDilations) ck::utils::check_err(out_tensor.mData, ref_data, "Error [case 1]: incorrect results!"));
{ }
ck::utils::conv::ConvParams params;
params.num_dim_spatial_ = 3; TEST(ReferenceConvolutionFWD, Conv3DNCDHWStridesDilations)
params.N_ = 1; {
params.K_ = 2; ck::utils::conv::ConvParams params;
params.C_ = 2; params.num_dim_spatial_ = 3;
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3, 3}; params.N_ = 1;
params.input_spatial_lengths_ = std::vector<ck::index_t>{12, 12, 12}; params.K_ = 2;
params.conv_filter_strides_ = std::vector<ck::index_t>{3, 3, 3}; params.C_ = 2;
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1}; params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3, 3};
params.input_left_pads_ = std::vector<ck::index_t>{0, 0, 0}; params.input_spatial_lengths_ = std::vector<ck::index_t>{12, 12, 12};
params.input_right_pads_ = std::vector<ck::index_t>{0, 0, 0}; params.conv_filter_strides_ = std::vector<ck::index_t>{3, 3, 3};
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1};
auto out_tensor = run_reference_convolution_forward<3, params.input_left_pads_ = std::vector<ck::index_t>{0, 0, 0};
float, params.input_right_pads_ = std::vector<ck::index_t>{0, 0, 0};
float,
float, auto out_tensor = run_reference_convolution_forward<3,
ck::tensor_layout::convolution::NCDHW, float,
ck::tensor_layout::convolution::KCZYX, float,
ck::tensor_layout::convolution::NKDHW>( float,
params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f}); ck::tensor_layout::convolution::NCDHW,
std::vector<std::size_t> ref_dims{1, 2, 4, 4, 4}; ck::tensor_layout::convolution::KCZYX,
std::vector<float> ref_data{ ck::tensor_layout::convolution::NKDHW>(
2756.7002, 2764.7998, 2772.9001, 2781., 2853.9001, 2862., 2870.1, 2878.2002, params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
2951.1, 2959.2002, 2967.2998, 2975.4001, 3048.2998, 3056.4001, 3064.5, 3072.6, std::vector<std::size_t> ref_dims{1, 2, 4, 4, 4};
3923.1, 3931.2, 3939.2998, 3947.4, 4020.2998, 4028.4001, 4036.5002, 4044.5999, std::vector<float> ref_data{
4117.5, 4125.6, 4133.7, 4141.8, 4214.7, 4222.8, 4230.9004, 4239., 2756.7002, 2764.7998, 2772.9001, 2781., 2853.9001, 2862., 2870.1, 2878.2002,
5089.5, 5097.5996, 5105.7, 5113.8, 5186.7, 5194.8, 5202.9, 5211., 2951.1, 2959.2002, 2967.2998, 2975.4001, 3048.2998, 3056.4001, 3064.5, 3072.6,
5283.9004, 5292., 5300.0996, 5308.2, 5381.0996, 5389.2, 5397.3, 5405.4004, 3923.1, 3931.2, 3939.2998, 3947.4, 4020.2998, 4028.4001, 4036.5002, 4044.5999,
6255.9004, 6264.0005, 6272.1, 6280.2, 6353.1, 6361.2, 6369.301, 6377.4, 4117.5, 4125.6, 4133.7, 4141.8, 4214.7, 4222.8, 4230.9004, 4239.,
6450.301, 6458.4, 6466.5, 6474.6, 6547.5, 6555.6, 6563.699, 6571.801, 5089.5, 5097.5996, 5105.7, 5113.8, 5186.7, 5194.8, 5202.9, 5211.,
2756.7002, 2764.7998, 2772.9001, 2781., 2853.9001, 2862., 2870.1, 2878.2002, 5283.9004, 5292., 5300.0996, 5308.2, 5381.0996, 5389.2, 5397.3, 5405.4004,
2951.1, 2959.2002, 2967.2998, 2975.4001, 3048.2998, 3056.4001, 3064.5, 3072.6, 6255.9004, 6264.0005, 6272.1, 6280.2, 6353.1, 6361.2, 6369.301, 6377.4,
3923.1, 3931.2, 3939.2998, 3947.4, 4020.2998, 4028.4001, 4036.5002, 4044.5999, 6450.301, 6458.4, 6466.5, 6474.6, 6547.5, 6555.6, 6563.699, 6571.801,
4117.5, 4125.6, 4133.7, 4141.8, 4214.7, 4222.8, 4230.9004, 4239., 2756.7002, 2764.7998, 2772.9001, 2781., 2853.9001, 2862., 2870.1, 2878.2002,
5089.5, 5097.5996, 5105.7, 5113.8, 5186.7, 5194.8, 5202.9, 5211., 2951.1, 2959.2002, 2967.2998, 2975.4001, 3048.2998, 3056.4001, 3064.5, 3072.6,
5283.9004, 5292., 5300.0996, 5308.2, 5381.0996, 5389.2, 5397.3, 5405.4004, 3923.1, 3931.2, 3939.2998, 3947.4, 4020.2998, 4028.4001, 4036.5002, 4044.5999,
6255.9004, 6264.0005, 6272.1, 6280.2, 6353.1, 6361.2, 6369.301, 6377.4, 4117.5, 4125.6, 4133.7, 4141.8, 4214.7, 4222.8, 4230.9004, 4239.,
6450.301, 6458.4, 6466.5, 6474.6, 6547.5, 6555.6, 6563.699, 6571.801}; 5089.5, 5097.5996, 5105.7, 5113.8, 5186.7, 5194.8, 5202.9, 5211.,
EXPECT_TRUE(ck::utils::check_err(out_tensor.mDesc.GetLengths(), 5283.9004, 5292., 5300.0996, 5308.2, 5381.0996, 5389.2, 5397.3, 5405.4004,
ref_dims, 6255.9004, 6264.0005, 6272.1, 6280.2, 6353.1, 6361.2, 6369.301, 6377.4,
"Error [case 2]: wrong output tensor dimensions!")); 6450.301, 6458.4, 6466.5, 6474.6, 6547.5, 6555.6, 6563.699, 6571.801};
EXPECT_TRUE(ck::utils::check_err( EXPECT_TRUE(ck::utils::check_err(out_tensor.mDesc.GetLengths(),
out_tensor.mData, ref_data, "Error [case 2]: incorrect results!", 1e-4f, 1e-6f)); 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));
}
test/softmax/CMakeLists.txt 0 → 100644
View file @ 7a3b49e5
add_custom_target(test_softmax)
add_gtest_executable(test_softmax_fp32 test_softmax_fp32.cpp)
add_gtest_executable(test_softmax_fp16 test_softmax_fp16.cpp)
target_link_libraries(test_softmax_fp32 PRIVATE host_tensor)
target_link_libraries(test_softmax_fp16 PRIVATE host_tensor)
add_dependencies(test_softmax test_softmax_fp32)
add_dependencies(test_softmax test_softmax_fp16)
\ No newline at end of file
test/softmax/test_softmax_fp16.cpp 0 → 100644
View file @ 7a3b49e5
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "test_softmax_util.hpp"
template <ck::index_t N>
using I = ck::Number<N>;
template <typename Tuple>
class TestSoftmaxFP16 : public ck::TestSoftmax<Tuple>
{
};
// clang-format off
using KernelTypes = ::testing::Types<
// InDataType, AccDataType, OutDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, InSrcVectorDim, InSrcVectorSize, OutDstVectorSize>
std::tuple<ck::half_t, float, ck::half_t, I<3>, I<1>, I<256>, I<8>, I<32>, I<1>, I<8>, I<1>, I<8>, I<8>>,
std::tuple<ck::half_t, float, ck::half_t, I<3>, I<1>, I<256>, I<4>, I<64>, I<1>, I<8>, I<1>, I<8>, I<8>>,
std::tuple<ck::half_t, float, ck::half_t, I<3>, I<1>, I<256>, I<2>, I<128>, I<1>, I<8>, I<1>, I<8>, I<8>>,
std::tuple<ck::half_t, float, ck::half_t, I<3>, I<1>, I<256>, I<1>, I<256>, I<1>, I<8>, I<1>, I<8>, I<8>>,
std::tuple<ck::half_t, float, ck::half_t, I<3>, I<2>, I<256>, I<8>, I<32>, I<1>, I<8>, I<1>, I<8>, I<8>>,
std::tuple<ck::half_t, float, ck::half_t, I<3>, I<2>, I<256>, I<4>, I<64>, I<1>, I<8>, I<1>, I<8>, I<8>>,
std::tuple<ck::half_t, float, ck::half_t, I<3>, I<2>, I<256>, I<2>, I<128>, I<1>, I<8>, I<1>, I<8>, I<8>>,
std::tuple<ck::half_t, float, ck::half_t, I<3>, I<2>, I<256>, I<1>, I<256>, I<1>, I<8>, I<1>, I<8>, I<8>>
>;
// clang-format on
TYPED_TEST_SUITE(TestSoftmaxFP16, KernelTypes);
TYPED_TEST(TestSoftmaxFP16, Test_FP16) { this->Run(); }
test/softmax/test_softmax_fp32.cpp 0 → 100644
View file @ 7a3b49e5
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "test_softmax_util.hpp"
template <ck::index_t N>
using I = ck::Number<N>;
template <typename Tuple>
class TestSoftmaxFP32 : public ck::TestSoftmax<Tuple>
{
};
// clang-format off
using KernelTypes = ::testing::Types<
// InDataType, AccDataType, OutDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, InSrcVectorDim, InSrcVectorSize, OutDstVectorSize>
std::tuple<float, float, float, I<3>, I<1>, I<256>, I<8>, I<32>, I<1>, I<4>, I<1>, I<4>, I<4>>,
std::tuple<float, float, float, I<3>, I<1>, I<256>, I<4>, I<64>, I<1>, I<4>, I<1>, I<4>, I<4>>,
std::tuple<float, float, float, I<3>, I<1>, I<256>, I<2>, I<128>, I<1>, I<4>, I<1>, I<4>, I<4>>,
std::tuple<float, float, float, I<3>, I<1>, I<256>, I<1>, I<256>, I<1>, I<4>, I<1>, I<4>, I<4>>,
std::tuple<float, float, float, I<3>, I<2>, I<256>, I<8>, I<32>, I<1>, I<4>, I<1>, I<4>, I<4>>,
std::tuple<float, float, float, I<3>, I<2>, I<256>, I<4>, I<64>, I<1>, I<4>, I<1>, I<4>, I<4>>,
std::tuple<float, float, float, I<3>, I<2>, I<256>, I<2>, I<128>, I<1>, I<4>, I<1>, I<4>, I<4>>,
std::tuple<float, float, float, I<3>, I<2>, I<256>, I<1>, I<256>, I<1>, I<4>, I<1>, I<4>, I<4>>
>;
// clang-format on
TYPED_TEST_SUITE(TestSoftmaxFP32, KernelTypes);
TYPED_TEST(TestSoftmaxFP32, Test_FP32) { this->Run(); }
test/softmax/test_softmax_util.hpp 0 → 100644
View file @ 7a3b49e5
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <vector>
#include <iostream>
#include <gtest/gtest.h>
#include "ck/ck.hpp"
#include "ck/utility/number.hpp"
#include "ck/tensor_operation/gpu/device/device_softmax.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
namespace ck {
template <typename Tuple>
class TestSoftmax : public ::testing::Test
{
protected:
using InDataType = std::tuple_element_t<0, Tuple>;
using AccDataType = std::tuple_element_t<1, Tuple>;
using OutDataType = std::tuple_element_t<2, Tuple>;
static constexpr index_t Rank = std::tuple_element_t<3, Tuple>{}.value;
static constexpr index_t NumReduceDim = std::tuple_element_t<4, Tuple>{}.value;
static constexpr index_t BlockSize = std::tuple_element_t<5, Tuple>{}.value;
static constexpr index_t MThreadClusterSize = std::tuple_element_t<6, Tuple>{}.value;
static constexpr index_t KThreadClusterSize = std::tuple_element_t<7, Tuple>{}.value;
static constexpr index_t MThreadSliceSize = std::tuple_element_t<8, Tuple>{}.value;
static constexpr index_t KThreadSliceSize = std::tuple_element_t<9, Tuple>{}.value;
static constexpr index_t InSrcVectorDim = std::tuple_element_t<10, Tuple>{}.value;
static constexpr index_t InSrcVectorSize = std::tuple_element_t<11, Tuple>{}.value;
static constexpr index_t OutDstVectorSize = std::tuple_element_t<12, Tuple>{}.value;
using ReferenceInstance =
tensor_operation::host::ReferenceSoftmax<InDataType, OutDataType, AccDataType>;
using DeviceInstance = tensor_operation::device::DeviceSoftmax<InDataType,
AccDataType,
OutDataType,
Rank,
NumReduceDim,
BlockSize,
MThreadClusterSize,
KThreadClusterSize,
MThreadSliceSize,
KThreadSliceSize,
InSrcVectorDim,
InSrcVectorSize,
OutDstVectorSize>;
TestSoftmax() : ref_instance_invoker_(ReferenceInstance{}.MakeInvoker()) {}
void RunSingle(std::vector<index_t> in_length, AccDataType alpha, AccDataType beta)
{
std::vector<index_t> reduce_dims(NumReduceDim);
std::iota(reduce_dims.begin(), reduce_dims.end(), Rank - NumReduceDim);
Tensor<InDataType> in(in_length);
Tensor<OutDataType> out(in_length);
in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
out.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
Tensor<OutDataType> out_ref(out);
DeviceMem in_dev(sizeof(InDataType) * in.mDesc.GetElementSpace());
DeviceMem out_dev(sizeof(OutDataType) * out.mDesc.GetElementSpace());
in_dev.ToDevice(in.mData.data());
out_dev.ToDevice(out.mData.data());
std::vector<index_t> i_in_lengths(in.mDesc.GetLengths().begin(),
in.mDesc.GetLengths().end());
std::vector<index_t> i_in_strides(in.mDesc.GetStrides().begin(),
in.mDesc.GetStrides().end());
auto device_instance = DeviceInstance{};
auto argument_ptr = device_instance.MakeArgumentPointer(i_in_lengths,
i_in_strides,
reduce_dims,
alpha,
beta,
in_dev.GetDeviceBuffer(),
out_dev.GetDeviceBuffer());
if(!device_instance.IsSupportedArgument(argument_ptr.get()))
{
FAIL() << "Unsupported argument";
}
auto invoker_ptr = device_instance.MakeInvokerPointer();
invoker_ptr->Run(argument_ptr.get());
ref_instance_invoker_.Run({in, out_ref, alpha, beta, Rank, reduce_dims});
out_dev.FromDevice(out.mData.data());
EXPECT_TRUE(ck::utils::check_err(out.mData, out_ref.mData));
}
void Run()
{
for(auto in_length : this->in_lengths_)
{
for(auto scale : this->scales_)
{
this->RunSingle(in_length, std::get<0>(scale), std::get<1>(scale));
}
}
}
std::vector<std::vector<index_t>> in_lengths_ = {{1, 8, 128}, {2, 128, 1024}, {3, 9, 1032}};
std::vector<std::tuple<AccDataType, AccDataType>> scales_ = {{1, 0}, {2, 2}, {0, 1}};
typename ReferenceInstance::Invoker ref_instance_invoker_;
};
} // namespace ck
test/space_filling_curve/space_filling_curve.cpp
View file @ 7a3b49e5
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <vector> #include <vector>
#include <iostream> #include <iostream>
#include <numeric> #include <numeric>
#include <cassert> #include <cassert>
#include "tensor_space_filling_curve.hpp" #include "ck/ck.hpp"
#include "ck/utility/common_header.hpp"
#include "ck/tensor_description/tensor_space_filling_curve.hpp"
using namespace ck; using namespace ck;
......
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