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Unverified Commit 98c80714 authored by Bartłomiej Kocot's avatar Bartłomiej Kocot Committed by GitHub
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Fix MNKPadding in gridwise_gemm_xdlops_v2r3 (#981)

parent ac9595a9
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Showing with 189 additions and 473 deletions
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp
View file @ 98c80714
...@@ -945,7 +945,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3_ext ...@@ -945,7 +945,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3_ext
} }
}(); }();
if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding) if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding)
{ {
return transform_tensor_descriptor(c_grid_desc_m_n, return transform_tensor_descriptor(c_grid_desc_m_n,
make_tuple(make_right_pad_transform(M, MPad - M), make_tuple(make_right_pad_transform(M, MPad - M),
......
test/batched_gemm/CMakeLists.txt
View file @ 98c80714
...@@ -2,22 +2,8 @@ list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942) ...@@ -2,22 +2,8 @@ list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
set(target 0) set(target 0)
foreach(gpu IN LISTS GPU_TARGETS) foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0) if(gpu IN_LIST gpu_list AND target EQUAL 0)
add_test_executable(test_batched_gemm_fp16 batched_gemm_fp16.cpp) add_gtest_executable(test_batched_gemm test_batched_gemm.cpp)
if(result EQUAL 0) target_link_libraries(test_batched_gemm PRIVATE utility device_batched_gemm_instance)
target_link_libraries(test_batched_gemm_fp16 PRIVATE utility device_batched_gemm_instance)
endif()
add_test_executable(test_batched_gemm_fp32 batched_gemm_fp32.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_fp32 PRIVATE utility device_batched_gemm_instance)
endif()
add_test_executable(test_batched_gemm_bf16 batched_gemm_bf16.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_bf16 PRIVATE utility device_batched_gemm_instance)
endif()
add_test_executable(test_batched_gemm_int8 batched_gemm_int8.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_int8 PRIVATE utility device_batched_gemm_instance)
endif()
set(target 1) set(target 1)
endif() endif()
endforeach() endforeach()
\ No newline at end of file
test/batched_gemm/batched_gemm_bf16.cpp deleted 100644 → 0
View file @ ac9595a9
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "profiler/profile_batched_gemm_impl.hpp"
#include "ck/library/tensor_operation_instance/gpu/batched_gemm.hpp"
namespace {
using ADataType = ck::bhalf_t;
using BDataType = ck::bhalf_t;
using CDataType = ck::bhalf_t;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
} // namespace
int main()
{
int M = 256;
int N = 256;
int K = 128;
int BatchCount = 3;
bool pass = true;
using namespace ck::tensor_operation::device;
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, K, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, K, N, M * K, K * N, M * N, BatchCount);
std::cout << "test BatchedGEMM bf16: " << (pass ? "Pass" : "Fail") << std::endl;
return pass ? 0 : 1;
}
test/batched_gemm/batched_gemm_fp16.cpp deleted 100644 → 0
View file @ ac9595a9
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "profiler/profile_batched_gemm_impl.hpp"
#include "ck/library/tensor_operation_instance/gpu/batched_gemm.hpp"
namespace {
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
} // namespace
int main()
{
int M = 512;
int N = 256;
int K = 128;
int BatchCount = 3;
bool pass = true;
using namespace ck::tensor_operation::device;
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, K, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, K, N, M * K, K * N, M * N, BatchCount);
std::cout << "test BatchedGEMM fp16: " << (pass ? "Pass" : "Fail") << std::endl;
return pass ? 0 : 1;
}
test/batched_gemm/batched_gemm_fp32.cpp deleted 100644 → 0
View file @ ac9595a9
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "profiler/profile_batched_gemm_impl.hpp"
#include "ck/library/tensor_operation_instance/gpu/batched_gemm.hpp"
namespace {
using ADataType = float;
using BDataType = float;
using CDataType = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
} // namespace
int main()
{
int M = 256;
int N = 256;
int K = 128;
int BatchCount = 3;
bool pass = true;
using namespace ck::tensor_operation::device;
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, K, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, K, N, M * K, K * N, M * N, BatchCount);
std::cout << "test BatchedGEMM fp32: " << (pass ? "Pass" : "Fail") << std::endl;
return pass ? 0 : 1;
}
test/batched_gemm/batched_gemm_int8.cpp deleted 100644 → 0
View file @ ac9595a9
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "profiler/profile_batched_gemm_impl.hpp"
#include "ck/library/tensor_operation_instance/gpu/batched_gemm.hpp"
namespace {
using ADataType = int8_t;
using BDataType = int8_t;
using CDataType = int8_t;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
} // namespace
int main()
{
int M = 256;
int N = 256;
int K = 128;
int BatchCount = 3;
bool pass = true;
using namespace ck::tensor_operation::device;
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Row,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, K, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Row,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, N, N, M * K, K * N, M * N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_impl<ADataType,
BDataType,
CDataType,
Col,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Col,
Row,
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, K, N, M * K, K * N, M * N, BatchCount);
std::cout << "test BatchedGEMM int8: " << (pass ? "Pass" : "Fail") << std::endl;
return pass ? 0 : 1;
}
test/batched_gemm/test_batched_gemm.cpp 0 → 100644
View file @ 98c80714
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <iostream>
#include <initializer_list>
#include <tuple>
#include <vector>
#include <gtest/gtest.h>
#include "profiler/profile_batched_gemm_impl.hpp"
#include "ck/library/tensor_operation_instance/gpu/batched_gemm.hpp"
struct GemmParams
{
ck::index_t M;
ck::index_t N;
ck::index_t K;
ck::index_t BatchCount;
};
class TestBatchedGemm : public ::testing::Test
{
protected:
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
std::vector<GemmParams> params;
template <typename DataType>
void Run()
{
using namespace ck::tensor_operation::device;
bool pass = true;
for(auto& param : params)
{
const auto M = param.M;
const auto N = param.N;
const auto K = param.K;
const auto BatchCount = param.BatchCount;
pass =
pass && ck::profiler::profile_batched_gemm_impl<DataType,
DataType,
DataType,
Row,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Row,
Row,
DataType,
DataType,
DataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, N, N, M * K, K * N, M * N, BatchCount);
pass =
pass && ck::profiler::profile_batched_gemm_impl<DataType,
DataType,
DataType,
Row,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Row,
Col,
Row,
DataType,
DataType,
DataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, K, K, N, M * K, K * N, M * N, BatchCount);
pass =
pass && ck::profiler::profile_batched_gemm_impl<DataType,
DataType,
DataType,
Col,
Row,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Row,
Row,
DataType,
DataType,
DataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, N, N, M * K, K * N, M * N, BatchCount);
pass =
pass && ck::profiler::profile_batched_gemm_impl<DataType,
DataType,
DataType,
Col,
Col,
Row,
PassThrough,
PassThrough,
PassThrough,
DeviceBatchedGemm<Col,
Col,
Row,
DataType,
DataType,
DataType,
PassThrough,
PassThrough,
PassThrough>>(
true, 1, false, 1, M, N, K, M, K, N, M * K, K * N, M * N, BatchCount);
}
EXPECT_TRUE(pass);
}
};
#ifdef CK_ENABLE_INT8
TEST_F(TestBatchedGemm, i8)
{
this->params.push_back({64, 64, 64, 2});
this->params.push_back({64, 64, 64, 1});
this->params.push_back({60, 60, 60, 2});
this->params.push_back({68, 68, 68, 2});
this->params.push_back({40, 40, 40, 2});
this->params.push_back({256, 256, 128, 3});
this->template Run<int8_t>();
}
#endif
#ifdef CK_ENABLE_BF16
TEST_F(TestBatchedGemm, bf16)
{
this->params.push_back({64, 64, 64, 2});
this->params.push_back({64, 64, 64, 1});
this->params.push_back({60, 60, 60, 2});
this->params.push_back({68, 68, 68, 2});
this->params.push_back({40, 40, 40, 2});
this->params.push_back({256, 256, 128, 3});
this->template Run<ck::bhalf_t>();
}
#endif
#ifdef CK_ENABLE_FP16
TEST_F(TestBatchedGemm, fp16)
{
this->params.push_back({64, 64, 64, 2});
this->params.push_back({64, 64, 64, 1});
this->params.push_back({60, 60, 60, 2});
this->params.push_back({68, 68, 68, 2});
this->params.push_back({40, 40, 40, 2});
this->params.push_back({256, 256, 128, 3});
this->template Run<ck::half_t>();
}
#endif
#ifdef CK_ENABLE_FP32
TEST_F(TestBatchedGemm, fp32)
{
this->params.push_back({64, 64, 64, 2});
this->params.push_back({64, 64, 64, 1});
this->params.push_back({60, 60, 60, 2});
this->params.push_back({68, 68, 68, 2});
this->params.push_back({40, 40, 40, 2});
this->params.push_back({256, 256, 128, 3});
this->template Run<float>();
}
#endif
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