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Unverified Commit 6d8614ee authored by Adam Osewski's avatar Adam Osewski Committed by GitHub
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Softmax unit-test reduction across all and non innermost dims cases. (#406) 



* Add reduction across all dims cases.

* host softmax: handle all reduce

* Test cases when reduced dim is not innermost axis.

* Fix syntax.

* Test non innermost dim for fp32 and int8

* Group test suites wrt NumReduceDim.

* Additionally test failing cases.

* Throw error when Rank or NumReduceDims doesn't match arguments.

* Check reducedDims has correct values

* Move don't reuse DeviceReduceMultiblock IsSupportedArgument method.
Instead implement own. (in fact just get rid of one check to enable
reduction across inner dimensions).

* Reorganize unit tests to better cover use scenarios.

* Test input validation
* Test reduction of inner dimensions with custom op instances.

* Refactor fp32 and int8 unit tests.

* Fix FP32 instance template parameters.

* Add more instances.

* Instances with InSrcVectorDim=0.

* Do not initialize and copy data when arg not supported.

* ckProfiler Softmax use instance factory.

* Refactor device softmax IsSupported.

* Additionally add non-polymorphic api functions

* Split softmax instances into multiple files.

* Fix profiler.

* Reorganize tests to reuse profiler and cover edge cases.

* Clang-format

* I8 Softmax instances along with UT.

* Reuse type alias definitions from instance factory header.

* Clean included headers

* Fix variable names.

* Add missing checks in Argument constructor.
Co-authored-by: default avatarAdam Osewski <aosewski@amd.com>
Co-authored-by: default avatarAnthony Chang <ac.chang@outlook.com>
parent 226bc02b
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Showing with 339 additions and 247 deletions
profiler/src/profile_softmax.cpp
View file @ 6d8614ee
......@@ -8,14 +8,10 @@
#include "profiler/include/profile_softmax_impl.hpp"
using ck::index_t;
using ck::profiler::NormDataType;
using ck::profiler::NormType;
using ck::profiler::SoftmaxDataType;
struct ArgParser
{
std::unordered_map<std::string, NormType> norm_dict = {{"batchnorm", NormType::BATCHNORM},
{"softmax", NormType::SOFTMAX}};
std::unordered_map<std::string, std::vector<int>> long_opts = {
{"length", {}}, {"stride", {}}, {"reduce", {}}, {"alpha", {}}, {"beta", {}}};
......@@ -50,7 +46,7 @@ struct ArgParser
void print_help()
{
std::cout << "arg1: tensor operation (batchnorm/softmax)\n"
std::cout << "arg1: tensor operation (softmax)\n"
<< "arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n"
<< "arg3: verification (0: no; 1: yes)\n"
<< "arg4: initialization (0: no init; 1: integer value; 2: decimal value)\n"
......@@ -64,7 +60,7 @@ void print_help()
<< std::endl;
}
int profile_normalization(int argc, char* argv[])
int profile_softmax(int argc, char* argv[])
{
if(argc <= 2)
{
......@@ -75,8 +71,7 @@ int profile_normalization(int argc, char* argv[])
ArgParser arg_parser;
// short unnamed options
const NormType norm_type = arg_parser.norm_dict[argv[1]];
const NormDataType data_type = static_cast<NormDataType>(std::stoi(argv[2]));
const SoftmaxDataType data_type = static_cast<SoftmaxDataType>(std::stoi(argv[2]));
const bool do_verification = std::stoi(argv[3]);
const int init_method = std::stoi(argv[4]);
const bool do_log = std::stoi(argv[5]);
......@@ -91,9 +86,10 @@ int profile_normalization(int argc, char* argv[])
arg_parser.long_opts["alpha"].empty() ? 1 : arg_parser.long_opts["alpha"][0];
const index_t beta = arg_parser.long_opts["beta"].empty() ? 0 : arg_parser.long_opts["beta"][0];
// Rank 3
if(length.size() == 3)
{
if(data_type == NormDataType::F16_F16)
if(data_type == SoftmaxDataType::F16_F16)
{
ck::profiler::profile_softmax_impl<ck::half_t, float, ck::half_t, 3>(do_verification,
init_method,
......@@ -103,10 +99,9 @@ int profile_normalization(int argc, char* argv[])
stride,
reduce,
float(alpha),
float(beta),
norm_type);
float(beta));
}
else if(data_type == NormDataType::F32_F32)
else if(data_type == SoftmaxDataType::F32_F32)
{
ck::profiler::profile_softmax_impl<float, float, float, 3>(do_verification,
init_method,
......@@ -116,17 +111,17 @@ int profile_normalization(int argc, char* argv[])
stride,
reduce,
float(alpha),
float(beta),
norm_type);
float(beta));
}
else
{
throw std::runtime_error("not implemented yet");
}
}
// Rank 4
else if(length.size() == 4)
{
if(data_type == NormDataType::F16_F16)
if(data_type == SoftmaxDataType::F16_F16)
{
ck::profiler::profile_softmax_impl<ck::half_t, float, ck::half_t, 4>(do_verification,
init_method,
......@@ -136,10 +131,9 @@ int profile_normalization(int argc, char* argv[])
stride,
reduce,
float(alpha),
float(beta),
norm_type);
float(beta));
}
else if(data_type == NormDataType::F32_F32)
else if(data_type == SoftmaxDataType::F32_F32)
{
ck::profiler::profile_softmax_impl<float, float, float, 4>(do_verification,
init_method,
......@@ -149,8 +143,7 @@ int profile_normalization(int argc, char* argv[])
stride,
reduce,
float(alpha),
float(beta),
norm_type);
float(beta));
}
else
{
......
profiler/src/profiler.cpp
View file @ 6d8614ee
......@@ -20,7 +20,7 @@ int profile_conv_fwd_bias_relu_add(int, char*[]);
int profile_conv_bwd_data(int, char*[]);
int profile_conv_bwd_weight(int, char*[]);
int profile_grouped_conv_fwd(int, char*[]);
int profile_normalization(int, char*[]);
int profile_softmax(int, char*[]);
int profile_layernorm(int, char*[]);
int profile_groupnorm(int, char*[]);
int profile_reduce(int, char*[]);
......@@ -45,6 +45,7 @@ static void print_helper_message()
" conv_bwd_data: Convolution Backward Data\n"
" conv_bwd_weight: Convolution Backward Weight\n"
" grouped_conv_fwd: Grouped Convolution Forward\n"
" softmax: Softmax\n"
" reduce: Reduce\n");
// clang-format on
}
......@@ -129,9 +130,9 @@ int main(int argc, char* argv[])
{
return profile_reduce(argc, argv);
}
else if(strcmp(argv[1], "batchnorm") == 0 || strcmp(argv[1], "softmax") == 0)
else if(strcmp(argv[1], "softmax") == 0)
{
return profile_normalization(argc, argv);
return profile_softmax(argc, argv);
}
else if(strcmp(argv[1], "layernorm") == 0)
{
......
test/softmax/CMakeLists.txt
View file @ 6d8614ee
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)
add_gtest_executable(test_softmax_int8 test_softmax_int8.cpp)
target_link_libraries(test_softmax_fp32 PRIVATE utility)
target_link_libraries(test_softmax_fp16 PRIVATE utility)
target_link_libraries(test_softmax_int8 PRIVATE utility)
add_dependencies(test_softmax test_softmax_fp32)
add_dependencies(test_softmax test_softmax_fp16)
add_dependencies(test_softmax test_softmax_int8)
add_gtest_executable(test_softmax_rank3 test_softmax_rank3.cpp)
add_gtest_executable(test_softmax_rank4 test_softmax_rank4.cpp)
add_gtest_executable(test_softmax_interface test_softmax_interface.cpp)
target_link_libraries(test_softmax_rank3 PRIVATE utility device_softmax_instance)
target_link_libraries(test_softmax_rank4 PRIVATE utility device_softmax_instance)
target_link_libraries(test_softmax_interface PRIVATE utility device_softmax_instance)
add_dependencies(test_softmax test_softmax_rank3)
add_dependencies(test_softmax test_softmax_rank4)
add_dependencies(test_softmax test_softmax_interface)
test/softmax/test_softmax_fp16.cpp deleted 100644 → 0
View file @ 226bc02b
// 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, float, I<3>, I<1>, I<256>, I<8>, I<32>, I<1>, I<8>, I<1>, I<8>, I<4>>, // mixed precision
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<1>, I<256>, I<1>, I<256>, I<1>, I<16>, 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<32>, 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>>,
std::tuple<ck::half_t, float, ck::half_t, I<3>, I<2>, I<256>, I<1>, I<256>, I<1>, I<16>, 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<32>, 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 deleted 100644 → 0
View file @ 226bc02b
// 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, ck::half_t, I<3>, I<2>, I<256>, I<1>, I<256>, I<1>, I<8>, I<1>, I<4>, I<8>>, // mixed precision
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<1>, I<256>, I<1>, I<256>, I<1>, I<8>, I<1>, I<4>, I<4>>,
std::tuple<float, float, float, I<3>, I<1>, I<256>, I<1>, I<256>, I<1>, I<16>, 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>>,
std::tuple<float, float, float, I<3>, I<2>, I<256>, I<1>, I<256>, I<1>, I<8>, I<1>, I<4>, I<4>>,
std::tuple<float, float, float, I<3>, I<2>, I<256>, I<1>, I<256>, I<1>, I<16>, 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_int8.cpp deleted 100644 → 0
View file @ 226bc02b
#include "gtest/gtest.h"
#include "test_softmax_util.hpp"
template <ck::index_t N>
using I = ck::Number<N>;
template <typename Tuple>
class TestSoftmaxINT8 : 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<int8_t, float, int8_t, I<3>, I<1>, I<256>, I<8>, I<32>, I<1>, I<16>, I<1>, I<16>, I<16>>,
std::tuple<int8_t, float, int8_t, I<3>, I<1>, I<256>, I<4>, I<64>, I<1>, I<16>, I<1>, I<16>, I<16>>,
std::tuple<int8_t, float, int8_t, I<3>, I<1>, I<256>, I<2>, I<128>, I<1>, I<16>, I<1>, I<16>, I<16>>,
std::tuple<int8_t, float, int8_t, I<3>, I<1>, I<256>, I<1>, I<256>, I<1>, I<16>, I<1>, I<16>, I<16>>,
std::tuple<int8_t, float, int8_t, I<3>, I<1>, I<256>, I<1>, I<256>, I<1>, I<32>, I<1>, I<16>, I<16>>,
std::tuple<int8_t, float, int8_t, I<3>, I<1>, I<256>, I<1>, I<256>, I<1>, I<64>, I<1>, I<16>, I<16>>,
std::tuple<int8_t, float, int8_t, I<3>, I<2>, I<256>, I<8>, I<32>, I<1>, I<16>, I<1>, I<16>, I<16>>,
std::tuple<int8_t, float, int8_t, I<3>, I<2>, I<256>, I<4>, I<64>, I<1>, I<16>, I<1>, I<16>, I<16>>,
std::tuple<int8_t, float, int8_t, I<3>, I<2>, I<256>, I<2>, I<128>, I<1>, I<16>, I<1>, I<16>, I<16>>,
std::tuple<int8_t, float, int8_t, I<3>, I<2>, I<256>, I<1>, I<256>, I<1>, I<16>, I<1>, I<16>, I<16>>,
std::tuple<int8_t, float, int8_t, I<3>, I<2>, I<256>, I<1>, I<256>, I<1>, I<32>, I<1>, I<16>, I<16>>,
std::tuple<int8_t, float, int8_t, I<3>, I<2>, I<256>, I<1>, I<256>, I<1>, I<64>, I<1>, I<16>, I<16>>
>;
// clang-format on
TYPED_TEST_SUITE(TestSoftmaxINT8, KernelTypes);
TYPED_TEST(TestSoftmaxINT8, Test_INT8) { this->Run(); }
test/softmax/test_softmax_interface.cpp 0 → 100644
View file @ 6d8614ee
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <stdexcept>
#include <vector>
#include "gtest/gtest.h"
#include "test_softmax_util.hpp"
class TestSoftmaxInterface : public ::testing::Test
{
protected:
template <ck::index_t Rank, ck::index_t NumReduceDims>
using SoftmaxInstance =
ck::DeviceSoftmaxInstanceWrapper<Rank, NumReduceDims, 256, 1, 256, 1, 8, 1, 8, 8>;
};
TEST_F(TestSoftmaxInterface, IncorrectReduceDims)
{
std::vector<ck::index_t> lengths{2, 128, 1536};
std::vector<ck::index_t> strides{128 * 1536, 1536, 1};
EXPECT_THROW((SoftmaxInstance<3, 1>{}.IsSupported(lengths, strides, {-1})), std::runtime_error);
EXPECT_THROW((SoftmaxInstance<3, 1>{}.IsSupported(lengths, strides, {3})), std::runtime_error);
EXPECT_THROW((SoftmaxInstance<3, 1>{}.IsSupported(lengths, strides, {0, 1})),
std::runtime_error);
EXPECT_THROW((SoftmaxInstance<3, 1>{}.IsSupported(lengths, strides, {})), std::runtime_error);
EXPECT_THROW((SoftmaxInstance<3, 2>{}.IsSupported(lengths, strides, {2, -1})),
std::runtime_error);
EXPECT_THROW((SoftmaxInstance<3, 2>{}.IsSupported(lengths, strides, {2, 4})),
std::runtime_error);
EXPECT_THROW((SoftmaxInstance<3, 2>{}.IsSupported(lengths, strides, {2})), std::runtime_error);
}
TEST_F(TestSoftmaxInterface, IncorrectLengthsSize)
{
std::vector<ck::index_t> lengths{128, 1536};
std::vector<ck::index_t> strides{128 * 1536, 1536, 1};
std::vector<ck::index_t> reduce_dims{2};
EXPECT_THROW((SoftmaxInstance<3, 1>{}.IsSupported({128, 1536}, strides, reduce_dims)),
std::runtime_error);
EXPECT_THROW((SoftmaxInstance<3, 1>{}.IsSupported({}, strides, reduce_dims)),
std::runtime_error);
EXPECT_THROW((SoftmaxInstance<3, 1>{}.IsSupported({1, 8, 128, 1536}, strides, reduce_dims)),
std::runtime_error);
}
TEST_F(TestSoftmaxInterface, IncorrectStridesSize)
{
std::vector<ck::index_t> lengths{2, 128, 1536};
std::vector<ck::index_t> reduce_dims{2};
EXPECT_THROW((SoftmaxInstance<3, 1>{}.IsSupported(lengths, {1536, 1}, reduce_dims)),
std::runtime_error);
EXPECT_THROW((SoftmaxInstance<3, 1>{}.IsSupported(lengths, {}, reduce_dims)),
std::runtime_error);
EXPECT_THROW((SoftmaxInstance<3, 1>{}.IsSupported(lengths, {1, 2, 3, 4}, reduce_dims)),
std::runtime_error);
}
TEST_F(TestSoftmaxInterface, UnsupportedLengths)
{
using SoftmaxInstance1 = ck::DeviceSoftmaxInstanceWrapper<3, 1, 256, 1, 256, 1, 8, 1, 8, 4>;
EXPECT_FALSE(SoftmaxInstance1{}.IsSupported({2, 128, 1500}, {128 * 1500, 1500, 1}, {2}));
EXPECT_FALSE(SoftmaxInstance1{}.IsSupported({2, 127, 1536}, {127 * 1536, 1536, 1}, {2}));
EXPECT_FALSE(SoftmaxInstance1{}.IsSupported({2, 128, 1537}, {128 * 1537, 1537, 1}, {2}));
// Reduction of middle dimensions
using SoftmaxInstance2 = ck::DeviceSoftmaxInstanceWrapper<3, 3, 256, 8, 32, 8, 8, 0, 8, 4>;
EXPECT_FALSE(SoftmaxInstance2{}.IsSupported({2, 128, 1536}, {128 * 1536, 1536, 1}, {0, 1, 2}));
// Reduction of middle dimensions
using SoftmaxInstance3 = ck::DeviceSoftmaxInstanceWrapper<3, 1, 256, 8, 32, 8, 8, 0, 4, 8>;
EXPECT_FALSE(SoftmaxInstance3{}.IsSupported({2, 128, 1536}, {128 * 1536, 1536, 1}, {2}));
EXPECT_FALSE(SoftmaxInstance3{}.IsSupported({2, 128, 1537}, {128 * 1537, 1537, 1}, {1}));
EXPECT_FALSE(SoftmaxInstance3{}.IsSupported({2, 128, 1540}, {128 * 1540, 1540, 1}, {1}));
EXPECT_FALSE(SoftmaxInstance3{}.IsSupported({2, 127, 1536}, {127 * 1536, 1536, 1}, {1}));
}
TEST_F(TestSoftmaxInterface, UnsupportedInstance)
{
// Instance with InSrcVectorDim = 1, can't reduce middle dims if in/out vec size != 1
using SoftmaxInstance1 = ck::DeviceSoftmaxInstanceWrapper<3, 1, 256, 8, 32, 1, 8, 1, 8, 8>;
EXPECT_FALSE(SoftmaxInstance1{}.IsSupported({2, 128, 1024}, {128 * 1024, 1024, 1}, {0}));
}
test/softmax/test_softmax_rank3.cpp 0 → 100644
View file @ 6d8614ee
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <algorithm>
#include <stdexcept>
#include <vector>
#include "gtest/gtest.h"
#include "test_softmax_util.hpp"
template <ck::index_t N>
using I = ck::Number<N>;
using F16 = ck::half_t;
using F32 = float;
using I8 = int8_t;
template <typename Tuple>
class TestSoftmax : public ck::TestSoftmax<Tuple>
{
};
// clang-format off
using KernelTypes = ::testing::Types<
// InDataType, AccDataType, OutDataType, Rank
std::tuple< F16, F32, F16, I<3>>,
std::tuple< F32, F32, F32, I<3>>,
std::tuple< I8, F32, I8, I<3>>
>;
// clang-format on
TYPED_TEST_SUITE(TestSoftmax, KernelTypes);
#include "test_softmax_ut_cases.inc"
test/softmax/test_softmax_rank4.cpp 0 → 100644
View file @ 6d8614ee
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <algorithm>
#include <stdexcept>
#include <vector>
#include "gtest/gtest.h"
#include "test_softmax_util.hpp"
template <ck::index_t N>
using I = ck::Number<N>;
using F16 = ck::half_t;
using F32 = float;
using I8 = int8_t;
template <typename Tuple>
class TestSoftmax : public ck::TestSoftmax<Tuple>
{
};
// clang-format off
using KernelTypes = ::testing::Types<
// InDataType, AccDataType, OutDataType, Rank
std::tuple< F16, F32, F16, I<4>>,
std::tuple< F32, F32, F32, I<4>>,
std::tuple< I8, F32, I8, I<4>>
>;
// clang-format on
TYPED_TEST_SUITE(TestSoftmax, KernelTypes);
#include "test_softmax_ut_cases.inc"
test/softmax/test_softmax_ut_cases.inc 0 → 100644
View file @ 6d8614ee
#pragma once
TYPED_TEST(TestSoftmax, ReduceOutermostDim)
{
std::vector<ck::index_t> reduce_dims{this->Rank - 1};
this->Run(reduce_dims);
}
TYPED_TEST(TestSoftmax, ReduceMiddleDim)
{
for(int dim = 0; dim < this->Rank - 1; ++dim)
{
std::vector<ck::index_t> reduce_dims{dim};
this->Run(reduce_dims);
}
}
TYPED_TEST(TestSoftmax, ReduceMultipleDimsWithOutermost)
{
for(int dim = 0; dim < this->Rank - 1; ++dim)
{
std::vector<ck::index_t> reduce_dims{dim, this->Rank - 1};
this->Run(reduce_dims);
}
}
TYPED_TEST(TestSoftmax, ReduceMultipleMiddleDims)
{
std::vector<ck::index_t> reduce_dims{0, 1};
if(this->Rank >= 3)
{
this->Run(reduce_dims);
}
if(this->Rank >= 4)
{
reduce_dims = std::vector<ck::index_t>{0, 2};
this->Run(reduce_dims);
reduce_dims = std::vector<ck::index_t>{0, 1, 2};
this->Run(reduce_dims);
}
}
TYPED_TEST(TestSoftmax, ReduceAllDims)
{
std::vector<ck::index_t> reduce_dims(this->Rank);
std::iota(std::begin(reduce_dims), std::end(reduce_dims), 0);
this->Run(reduce_dims);
}
TYPED_TEST(TestSoftmax, ReduceOddLengths)
{
this->in_lengths_ = {{3, 63, 1032}};
if(this->Rank >= 4)
{
this->in_lengths_ = {{1, 3, 63, 1032}};
}
this->Run({this->Rank - 1});
this->Run({this->Rank - 2});
}
test/softmax/test_softmax_util.hpp
View file @ 6d8614ee
......@@ -3,19 +3,17 @@
#pragma once
#include <string>
#include <sstream>
#include <tuple>
#include <vector>
#include <iostream>
#include <gtest/gtest.h>
#include "ck/ck.hpp"
#include "ck/utility/number.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_softmax_impl.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
#include "include/ck/utility/data_type.hpp"
#include "profiler/include/profile_softmax_impl.hpp"
namespace ck {
......@@ -39,122 +37,106 @@ class TestSoftmax : public ::testing::Test
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 PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceInstance = tensor_operation::device::DeviceSoftmaxImpl<InDataType,
AccDataType,
OutDataType,
PassThrough,
PassThrough,
Rank,
NumReduceDim,
BlockSize,
MThreadClusterSize,
KThreadClusterSize,
MThreadSliceSize,
KThreadSliceSize,
InSrcVectorDim,
InSrcVectorSize,
OutDstVectorSize>;
TestSoftmax() : ref_instance_invoker_(ReferenceInstance{}.MakeInvoker()) {}
public:
std::vector<std::vector<index_t>> in_lengths_ = {{2, 128, 1024}, {4, 16, 8448}, {128, 128, 64}};
std::vector<std::vector<AccDataType>> scales_ = {{2, 0}, {0, 2}, {2, 2}};
bool bench_ = false; // measure kernel performance
bool verify_ = true;
void RunSingle(std::vector<index_t> in_length, AccDataType alpha, AccDataType beta)
void SetUp() override
{
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.GetElementSpaceSize());
DeviceMem out_dev(sizeof(OutDataType) * out.mDesc.GetElementSpaceSize());
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(),
PassThrough{},
PassThrough{});
if(!device_instance.IsSupportedArgument(argument_ptr.get()))
if constexpr(Rank == 4)
{
// std::cout << "Skipped due to unsupported argument: "
// << "input lengths = [" << serialize_range(in_length) << "], "
// << "scaler = [" << alpha << ", " << beta << "]." << std::endl;
return;
in_lengths_ = std::vector<std::vector<index_t>>{
{1, 2, 128, 1024}, {2, 4, 16, 8448}, {1, 128, 128, 64}};
}
auto invoker_ptr = device_instance.MakeInvokerPointer();
invoker_ptr->Run(argument_ptr.get());
ref_instance_invoker_.Run({in, out_ref, alpha, beta, reduce_dims});
out_dev.FromDevice(out.mData.data());
bool pass;
if(std::is_same<InDataType, int8_t>::value)
{
EXPECT_TRUE(pass = ck::utils::check_err(
out.mData, out_ref.mData, "Error: Incorrect results!", 0, 1));
}
else
void RunSingle(std::vector<index_t> in_length,
std::vector<index_t> reduce_dims,
AccDataType alpha,
AccDataType beta)
{
EXPECT_TRUE(pass = ck::utils::check_err(out.mData, out_ref.mData));
int init_method = 1; // integer value initialization
bool log = false;
std::vector<ck::index_t> strides; // intenionally empty, to get packed layout.
bool pass = ck::profiler::profile_softmax_impl<InDataType, AccDataType, OutDataType, Rank>(
verify_, init_method, log, bench_, in_length, strides, reduce_dims, alpha, beta);
EXPECT_TRUE(pass);
}
if(!pass)
void Run(std::vector<index_t> reduce_dims = {})
{
FAIL() << "Failure in input lengths = [" << serialize_range(in_length) << "], "
<< "scaler = [" << alpha << ", " << beta << "].";
}
if(reduce_dims.empty())
{
reduce_dims.push_back(Rank - 1);
}
void Run()
{
for(auto in_length : this->in_lengths_)
{
for(auto scale : this->scales_)
{
this->RunSingle(in_length, scale[0], scale[1]);
this->RunSingle(in_length, reduce_dims, scale[0], scale[1]);
}
}
}
};
std::vector<std::vector<index_t>> in_lengths_ = {
{1, 8, 128}, {2, 128, 1024}, {3, 9, 1032}, {4, 4, 2048}, {8, 1, 8192}};
std::vector<std::vector<AccDataType>> scales_ = {{1, 0}, {1, 1}, {0, 1}, {2, 2}};
template <index_t Rank,
index_t NumReduceDim,
index_t BlockSize,
index_t MThreadClusterSize,
index_t KThreadClusterSize,
index_t MThreadSliceSize,
index_t KThreadSliceSize,
index_t InSrcVectorDim,
index_t InSrcVectorSize,
index_t OutDstVectorSize>
struct DeviceSoftmaxInstanceWrapper
{
using F16 = half_t;
using F32 = float;
using Pass = tensor_operation::element_wise::PassThrough;
typename ReferenceInstance::Invoker ref_instance_invoker_;
using InDataType = F16;
using AccDataType = F32;
using OutDataType = F16;
using InElementOp = Pass;
using AccElementOp = Pass;
using DeviceSoftmaxInstance = tensor_operation::device::DeviceSoftmaxImpl<InDataType,
AccDataType,
OutDataType,
InElementOp,
AccElementOp,
Rank,
NumReduceDim,
BlockSize,
MThreadClusterSize,
KThreadClusterSize,
MThreadSliceSize,
KThreadSliceSize,
InSrcVectorDim,
InSrcVectorSize,
OutDstVectorSize>;
bool IsSupported(const std::vector<index_t> in_lengths,
const std::vector<index_t> in_strides,
const std::vector<index_t> reduce_dims) const
{
auto softmax = DeviceSoftmaxInstance{};
auto argument = softmax.MakeArgument(in_lengths,
in_strides,
reduce_dims,
1, // alpha
1, // beta
nullptr, // in_dev
nullptr, // in_out
Pass{}, // in elementwise op
Pass{}); // acc elementwise op
return softmax.IsSupportedArgument(argument);
}
};
} // namespace ck
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