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Unverified Commit 24af0144 authored by Po Yen Chen's avatar Po Yen Chen Committed by GitHub
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Merge branch 'develop' into gemm_layernorm_welford

parents 961f5e9e b79bbbc2
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test/batched_gemm/batched_gemm_int8.cpp 0 → 100644
View file @ 24af0144
// 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"
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;
} // namespace
int main()
{
int M = 256;
int N = 256;
int K = 128;
int BatchCount = 3;
bool pass = true;
pass = pass &&
ck::profiler::profile_batched_gemm_impl<ADataType, BDataType, CDataType, Row, Row, Row>(
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>(
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>(
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>(
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_gemm/test_batched_gemm_gemm_util.hpp
View file @ 24af0144
......@@ -5,7 +5,7 @@
#include <vector>
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_gemm_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_gemm_xdl_cshuffle.hpp"
#include "profiler/include/profile_batched_gemm_gemm_impl.hpp"
using ck::tensor_operation::device::GemmSpecialization;
......
test/batched_gemm_masking_scale_softmax_gemm_permute/CMakeLists.txt deleted 100644 → 0
View file @ 961f5e9e
add_custom_target(test_batched_gemm_masking_scale_softmax_gemm_permute)
add_gtest_executable(test_batched_gemm_masking_scale_softmax_gemm_permute_fp16 test_batched_gemm_masking_scale_softmax_gemm_permute_fp16.cpp)
target_link_libraries(test_batched_gemm_masking_scale_softmax_gemm_permute_fp16 PRIVATE utility device_batched_gemm_masking_scale_softmax_gemm_permute_instance)
add_dependencies(test_batched_gemm_masking_scale_softmax_gemm_permute test_batched_gemm_masking_scale_softmax_gemm_permute_fp16)
\ No newline at end of file
test/batched_gemm_softmax_gemm/test_batched_gemm_softmax_gemm_fp16.cpp
View file @ 24af0144
......@@ -9,9 +9,13 @@ class TestBatchedGemmSoftmaxGemmFP16 : public TestBatchedGemmSoftmaxGemm<Tuple>
{
};
using Masked = std::true_type;
using NoMask = std::false_type;
// clang-format off
using KernelTypes = ::testing::Types<
std::tuple<F16, F16, F16, F16, Row, Col, Row, Row>
std::tuple<F16, F16, F16, F16, Row, Col, Row, Row, NoMask>,
std::tuple<F16, F16, F16, F16, Row, Col, Row, Row, Masked>
>;
// clang-format on
......@@ -120,7 +124,6 @@ TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, DISABLED_Bench_FP16_IrregularK)
using ck::tensor_operation::device::GemmSpecialization;
// TODO: enable KPadding tests when it is implemented
TEST(TestBatchedGemmSoftmaxGemmInterface, GemmSpecializationSizeMatch)
{
int P = 120; // requires padding
......@@ -152,12 +155,12 @@ TEST(TestBatchedGemmSoftmaxGemmInterface, GemmSpecializationSizeMismatch)
// IsSupported(M, N, K, O)
// clang-format off
EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::Default>{}.IsSupported(128, 128, 120, 128));
// EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKPadding>{}.IsSupported(128, 128, 128, 120));
EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKPadding>{}.IsSupported(128, 128, 128, 120));
// Kernel can't support odd K size because SrcVectorDim == KDim and must satisfy SizeKRaw % ABSrcScalarPerVector == 0
// EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 129, 128));
// EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 130, 128));
EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 129, 128));
EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 130, 128));
// Kernel can't support odd O size because SrcVectorDim == ODim and must satisfy SizeORaw % B1SrcScalarPerVector == 0
// EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 128, 129));
EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 128, 129));
// clang-format on
}
......@@ -169,6 +172,5 @@ TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, AdhocTest)
{1020, 1020, 64, 128, 24},
{576, 576, 64, 64, 24},
};
this->bench_ = true;
this->Run();
}
test/batched_gemm_softmax_gemm/test_batched_gemm_softmax_gemm_util.hpp
View file @ 24af0144
......@@ -5,7 +5,7 @@
#include <vector>
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_softmax_gemm_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_softmax_gemm_xdl_cshuffle.hpp"
#include "profiler/include/profile_batched_gemm_softmax_gemm_impl.hpp"
using ck::tensor_operation::device::GemmSpecialization;
......@@ -20,14 +20,15 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
template <typename Tuple>
struct TestBatchedGemmSoftmaxGemm : public ::testing::Test
{
using ADataType = std::tuple_element_t<0, Tuple>;
using B0DataType = std::tuple_element_t<1, Tuple>;
using B1DataType = std::tuple_element_t<2, Tuple>;
using CDataType = std::tuple_element_t<3, Tuple>;
using ALayout = std::tuple_element_t<4, Tuple>;
using B0Layout = std::tuple_element_t<5, Tuple>;
using B1Layout = std::tuple_element_t<6, Tuple>;
using CLayout = std::tuple_element_t<7, Tuple>;
using ADataType = std::tuple_element_t<0, Tuple>;
using B0DataType = std::tuple_element_t<1, Tuple>;
using B1DataType = std::tuple_element_t<2, Tuple>;
using CDataType = std::tuple_element_t<3, Tuple>;
using ALayout = std::tuple_element_t<4, Tuple>;
using B0Layout = std::tuple_element_t<5, Tuple>;
using B1Layout = std::tuple_element_t<6, Tuple>;
using CLayout = std::tuple_element_t<7, Tuple>;
using MaskingType = std::tuple_element_t<8, Tuple>;
std::vector<std::vector<int>> lengths_ = {{256, 256, 64, 64, 4},
{256, 256, 128, 128, 4},
......@@ -54,7 +55,8 @@ struct TestBatchedGemmSoftmaxGemm : public ::testing::Test
ALayout,
B0Layout,
B1Layout,
CLayout>(
CLayout,
MaskingType::value>(
verify_, 1, false, bench_, M, N, K, O, BatchCount);
EXPECT_TRUE(pass);
......
test/batched_gemm_softmax_gemm_permute/CMakeLists.txt 0 → 100644
View file @ 24af0144
add_custom_target(test_batched_gemm_softmax_gemm_permute)
add_gtest_executable(test_batched_gemm_softmax_gemm_permute_fp16 test_batched_gemm_softmax_gemm_permute_fp16.cpp)
target_link_libraries(test_batched_gemm_softmax_gemm_permute_fp16 PRIVATE utility device_batched_gemm_softmax_gemm_permute_instance)
add_dependencies(test_batched_gemm_softmax_gemm_permute test_batched_gemm_softmax_gemm_permute_fp16)
\ No newline at end of file
test/batched_gemm_masking_scale_softmax_gemm_permute/test_batched_gemm_masking_scale_softmax_gemm_permute_fp16.cpp → test/batched_gemm_softmax_gemm_permute/test_batched_gemm_softmax_gemm_permute_fp16.cpp
View file @ 24af0144
......@@ -2,7 +2,7 @@
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "test_batched_gemm_masking_scale_softmax_gemm_permute_util.hpp"
#include "test_batched_gemm_softmax_gemm_permute_util.hpp"
template <typename Tuple>
class TestBatchedGemmMaskingScaleSoftmaxGemmPermuteFP16
......@@ -10,13 +10,18 @@ class TestBatchedGemmMaskingScaleSoftmaxGemmPermuteFP16
{
};
using I1_t = ck::Number<1>;
using I2_t = ck::Number<2>;
using MaskDisabled_t =
ck::integral_constant<MaskingSpecialization, MaskingSpecialization::MaskDisabled>;
using MaskOutUpperTriangle_t =
ck::integral_constant<MaskingSpecialization, MaskingSpecialization::MaskOutUpperTriangle>;
// clang-format off
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using CPermuteNumDims_G_M_O =
S<2, 1, 1>; // "using CLayout = Row" has been replaced by CPermuteNumDims_G_M_O
using KernelTypes = ::testing::Types<
std::tuple<F16, F16, F16, F16, Row, Col, Row, CPermuteNumDims_G_M_O>
std::tuple<I2_t, I1_t, I1_t, I1_t, I1_t, F16, F16, F16, F16, ck::Tuple<>, ck::Tuple<>, MaskDisabled_t>,
std::tuple<I2_t, I1_t, I1_t, I1_t, I1_t, F16, F16, F16, F16, ck::Tuple<>, ck::Tuple<>, MaskOutUpperTriangle_t>
>;
// clang-format on
......@@ -91,7 +96,7 @@ TYPED_TEST(TestBatchedGemmMaskingScaleSoftmaxGemmPermuteFP16, Test_FP16_OddO)
this->Run();
}
TYPED_TEST(TestBatchedGemmMaskingScaleSoftmaxGemmPermuteFP16, Bench_FP16_IrregularK)
TYPED_TEST(TestBatchedGemmMaskingScaleSoftmaxGemmPermuteFP16, DISABLED_Bench_FP16_IrregularK)
{
this->lengths_ = std::vector<std::vector<int>>{{256, 256, 160, 160, 1, 16},
{256, 64, 160, 64, 1, 16},
......@@ -125,7 +130,6 @@ TYPED_TEST(TestBatchedGemmMaskingScaleSoftmaxGemmPermuteFP16, DISABLED_Bench_FP1
using ck::tensor_operation::device::GemmSpecialization;
// TODO: enable KPadding tests when it is implemented
TEST(TestBatchedGemmMaskingScaleSoftmaxGemmPermuteInterface, GemmSpecializationSizeMatch)
{
int P = 120; // requires padding
......@@ -133,22 +137,22 @@ TEST(TestBatchedGemmMaskingScaleSoftmaxGemmPermuteInterface, GemmSpecializationS
// IsSupported(M, N, K, O)
// clang-format off
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::Default>{}.IsSupported(Q, Q, Q, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MPadding>{}.IsSupported(P, Q, Q, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NPadding>{}.IsSupported(Q, P, Q, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::KPadding>{}.IsSupported(Q, Q, P, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNPadding>{}.IsSupported(P, P, Q, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MKPadding>{}.IsSupported(P, Q, P, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NKPadding>{}.IsSupported(Q, P, P, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKPadding>{}.IsSupported(P, P, P, Q));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::OPadding>{}.IsSupported(Q, Q, Q, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MOPadding>{}.IsSupported(P, Q, Q, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NOPadding>{}.IsSupported(Q, P, Q, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::KOPadding>{}.IsSupported(Q, Q, P, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNOPadding>{}.IsSupported(P, P, Q, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MKOPadding>{}.IsSupported(P, Q, P, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NKOPadding>{}.IsSupported(Q, P, P, P));
EXPECT_TRUE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(P, P, P, P));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::Default>{}.IsSupported(Q, Q, Q, Q));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MPadding>{}.IsSupported(P, Q, Q, Q));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NPadding>{}.IsSupported(Q, P, Q, Q));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::KPadding>{}.IsSupported(Q, Q, P, Q));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNPadding>{}.IsSupported(P, P, Q, Q));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MKPadding>{}.IsSupported(P, Q, P, Q));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NKPadding>{}.IsSupported(Q, P, P, Q));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKPadding>{}.IsSupported(P, P, P, Q));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::OPadding>{}.IsSupported(Q, Q, Q, P));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MOPadding>{}.IsSupported(P, Q, Q, P));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NOPadding>{}.IsSupported(Q, P, Q, P));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::KOPadding>{}.IsSupported(Q, Q, P, P));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNOPadding>{}.IsSupported(P, P, Q, P));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MKOPadding>{}.IsSupported(P, Q, P, P));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::NKOPadding>{}.IsSupported(Q, P, P, P));
EXPECT_TRUE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(P, P, P, P));
// clang-format on
}
......@@ -156,13 +160,13 @@ TEST(TestBatchedGemmMaskingScaleSoftmaxGemmPermuteInterface, GemmSpecializationS
{
// IsSupported(M, N, K, O)
// clang-format off
EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::Default>{}.IsSupported(128, 128, 120, 128));
// EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKPadding>{}.IsSupported(128, 128, 128, 120));
EXPECT_FALSE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::Default>{}.IsSupported(128, 128, 120, 128));
EXPECT_FALSE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKPadding>{}.IsSupported(128, 128, 128, 120));
// Kernel can't support odd K size because SrcVectorDim == KDim and must satisfy SizeKRaw % ABSrcScalarPerVector == 0
// EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 129, 128));
// EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 130, 128));
EXPECT_FALSE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 129, 128));
EXPECT_FALSE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 130, 128));
// Kernel can't support odd O size because SrcVectorDim == ODim and must satisfy SizeORaw % B1SrcScalarPerVector == 0
// EXPECT_FALSE(DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 128, 129));
EXPECT_FALSE(DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128<GemmSpecialization::MNKOPadding>{}.IsSupported(128, 128, 128, 129));
// clang-format on
}
......@@ -174,6 +178,5 @@ TYPED_TEST(TestBatchedGemmMaskingScaleSoftmaxGemmPermuteFP16, AdhocTest)
{1020, 1020, 64, 128, 4, 6},
{576, 576, 64, 64, 4, 6},
};
this->bench_ = true;
this->Run();
}
test/batched_gemm_masking_scale_softmax_gemm_permute/test_batched_gemm_masking_scale_softmax_gemm_permute_util.hpp → test/batched_gemm_softmax_gemm_permute/test_batched_gemm_softmax_gemm_permute_util.hpp
View file @ 24af0144
......@@ -4,10 +4,14 @@
#include <iostream>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_softmax_gemm_permute_xdl_cshuffle.hpp"
#include "profiler/include/profile_batched_gemm_masking_scale_softmax_gemm_permute_impl.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_softmax_gemm_permute_xdl_cshuffle.hpp"
#include "profiler/include/profile_batched_gemm_softmax_gemm_permute_impl.hpp"
using ck::tensor_operation::device::GemmSpecialization;
using ck::tensor_operation::device::MaskingSpecialization;
using ck::tensor_operation::device::TensorSpecialization;
template <ck::index_t N>
using I = ck::Number<N>;
......@@ -20,14 +24,18 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
template <typename Tuple>
struct TestBatchedGemmMaskingScaleSoftmaxGemmPermute : public ::testing::Test
{
using ADataType = std::tuple_element_t<0, Tuple>;
using B0DataType = std::tuple_element_t<1, Tuple>;
using B1DataType = std::tuple_element_t<2, Tuple>;
using CDataType = std::tuple_element_t<3, Tuple>;
using ALayout = std::tuple_element_t<4, Tuple>;
using B0Layout = std::tuple_element_t<5, Tuple>;
using B1Layout = std::tuple_element_t<6, Tuple>;
using CPermuteNumDims_G_M_O = std::tuple_element_t<7, Tuple>;
using NumDimGType = std::tuple_element_t<0, Tuple>;
using NumDimMType = std::tuple_element_t<1, Tuple>;
using NumDimNType = std::tuple_element_t<2, Tuple>;
using NumDimKType = std::tuple_element_t<3, Tuple>;
using NumDimOType = std::tuple_element_t<4, Tuple>;
using ADataType = std::tuple_element_t<5, Tuple>;
using B0DataType = std::tuple_element_t<6, Tuple>;
using B1DataType = std::tuple_element_t<7, Tuple>;
using CDataType = std::tuple_element_t<8, Tuple>;
using Acc0BiasDataType = std::tuple_element_t<9, Tuple>;
using Acc1BiasDataType = std::tuple_element_t<10, Tuple>;
using MaskingType = std::tuple_element_t<11, Tuple>;
std::vector<std::vector<int>> lengths_ = {
{256, 256, 64, 64, 6, 4},
......@@ -42,15 +50,20 @@ struct TestBatchedGemmMaskingScaleSoftmaxGemmPermute : public ::testing::Test
void RunSingle(int M, int N, int K, int O, int G0, int G1)
{
bool pass = ck::profiler::profile_batched_gemm_masking_scale_softmax_gemm_permute_impl<
ADataType,
B0DataType,
B1DataType,
CDataType,
ALayout,
B0Layout,
B1Layout,
CPermuteNumDims_G_M_O>(verify_, 1, false, bench_, M, N, K, O, G0, G1);
bool pass =
ck::profiler::profile_batched_gemm_softmax_gemm_permute_impl<NumDimGType::value,
NumDimMType::value,
NumDimNType::value,
NumDimKType::value,
NumDimOType::value,
ADataType,
B0DataType,
B1DataType,
CDataType,
ck::Tuple<>,
ck::Tuple<>,
MaskingType::value>(
verify_, 1, false, bench_, M, N, K, O, G0, G1);
EXPECT_TRUE(pass);
}
......@@ -72,19 +85,13 @@ struct TestBatchedGemmMaskingScaleSoftmaxGemmPermute : public ::testing::Test
};
template <GemmSpecialization GemmSpec>
struct DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128
struct DeviceInstanceWrapper_G2M1N1K1O1_TNTT_FP16_M128_N128_K32_O128
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Scale = ck::tensor_operation::element_wise::Scale;
using ALayout = Row;
using B0Layout = Col;
using B1Layout = Row;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using CPermuteNumDims_G_M_O =
S<2, 1, 1>; // "using CLayout = Row" has been replaced by CPermuteNumDims_G_M_O
using ADataType = F16;
using B0DataType = F16;
......@@ -103,14 +110,17 @@ struct DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128
using DeviceGemmGemmInstance =
ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle<
ALayout,
B0Layout,
B1Layout,
CPermuteNumDims_G_M_O,
2,
1,
1,
1,
1,
ADataType,
B0DataType,
B1DataType,
CDataType,
ck::Tuple<>,
ck::Tuple<>,
AccDataType,
CShuffleDataType,
AElementOp,
......@@ -119,6 +129,10 @@ struct DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128
B1ElementOp,
CElementOp,
GemmSpec,
TensorSpecialization::Default, // ATensorSpec
TensorSpecialization::Default, // B0TensorSpec
TensorSpecialization::Default, // B1TensorSpec
TensorSpecialization::Default, // CTensorSpec
1,
256,
128, // MPerBlock
......@@ -159,29 +173,48 @@ struct DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
true>; // Masking
MaskingSpecialization::MaskOutUpperTriangle>; // MaskOutUpperTriangle
bool IsSupported(int M, int N, int K, int O)
{
const int G0 = 1, G1 = 1;
// A layout [G0, M, G1, K]
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M, K};
std::vector<ck::index_t> a_gs_ms_ks_strides{M * G1 * K, K, G1 * K, 1};
// B0 layout [G0, N, G1, K]
std::vector<ck::index_t> b0_gs_ns_ks_lengths{G0, G1, N, K};
std::vector<ck::index_t> b0_gs_ns_ks_strides{N * G1 * K, K, G1 * K, 1};
// B1 layout [G0, N, G1, O]
std::vector<ck::index_t> b1_gs_os_ns_lengths{G0, G1, O, N};
std::vector<ck::index_t> b1_gs_os_ns_strides{N * G1 * O, O, 1, G1 * O};
// C layout [G0, M, G1, O]
std::vector<ck::index_t> c_gs_ms_os_lengths{G0, G1, M, O};
std::vector<ck::index_t> c_gs_ms_os_strides{M * G1 * O, O, G1 * O, 1};
auto gemm = DeviceGemmGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument = gemm.MakeArgument(static_cast<ADataType*>(nullptr),
static_cast<B0DataType*>(nullptr),
static_cast<B1DataType*>(nullptr),
static_cast<CDataType*>(nullptr),
M,
N,
K,
O,
0, // BatchCount
{0, 0, M, O}, // gs ms ns lengths
{0, O, 0, 1}, // gs ms ns strides
0, // StrideA
0, // StrideB0
0, // StrideB1
0, // BatchStrideA
0, // BatchStrideB0
0, // BatchStrideB1
{}, // p_acc0_biases
{}, // p_acc1_biases
a_gs_ms_ks_lengths,
a_gs_ms_ks_strides,
b0_gs_ns_ks_lengths,
b0_gs_ns_ks_strides,
b1_gs_os_ns_lengths,
b1_gs_os_ns_strides,
c_gs_ms_os_lengths,
c_gs_ms_os_strides,
{}, // acc0_biases_gs_ms_ns_lengths
{}, // acc0_biases_gs_ms_ns_strides
{}, // acc1_biases_gs_ms_os_lengths
{}, // acc1_biases_gs_ms_os_strides
PassThrough{}, // a_element_op
PassThrough{}, // b0_element_op
Scale{1.f}, // acc0_element_op
......
test/convnd_bwd_data/convnd_bwd_data.cpp
View file @ 24af0144
......@@ -5,237 +5,89 @@
#include <iostream>
#include <initializer_list>
#include <vector>
#include <tuple>
#include <gtest/gtest.h>
#include "profiler/include/profile_conv_bwd_data_impl.hpp"
template <typename Tuple>
class TestConvndBwdData : public ::testing::Test
{
protected:
using DataType = std::tuple_element_t<0, Tuple>;
std::vector<ck::utils::conv::ConvParam> conv_params;
};
// 1d
TEST_F(TestConvndBwdData, Conv1dBwdData)
{
conv_params.clear();
conv_params.push_back({1, 1, 128, 128, 256, {1}, {14}, {2}, {1}, {0}, {0}});
conv_params.push_back({1, 1, 128, 128, 256, {3}, {28}, {1}, {1}, {1}, {1}});
conv_params.push_back({1, 1, 128, 128, 256, {1}, {3}, {1}, {1}, {0}, {0}});
for(auto& param : conv_params)
template <ck::index_t NDimSpatial>
void Run()
{
bool pass;
// fp32
pass = ck::profiler::profile_conv_bwd_data_impl<1,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK,
float,
float,
float>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// fp16
pass = ck::profiler::profile_conv_bwd_data_impl<1,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK,
ck::half_t,
ck::half_t,
ck::half_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// bf16
pass = ck::profiler::profile_conv_bwd_data_impl<1,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK,
ck::bhalf_t,
ck::bhalf_t,
ck::bhalf_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
for(auto& param : conv_params)
{
bool pass;
EXPECT_FALSE(conv_params.empty());
pass = ck::profiler::profile_conv_bwd_data_impl<
NDimSpatial,
ck::tuple_element_t<NDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::NDHWC>>,
ck::tuple_element_t<NDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::KZYXC>>,
ck::tuple_element_t<NDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::NWK,
ck::tensor_layout::convolution::NHWK,
ck::tensor_layout::convolution::NDHWK>>,
DataType,
DataType,
DataType>(true, // do_verification
1, // init_method integer value
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
}
}
};
// int8
pass = ck::profiler::profile_conv_bwd_data_impl<1,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK,
int8_t,
int8_t,
int8_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
using KernelTypes = ::testing::Types<std::tuple<float>,
std::tuple<ck::half_t>,
std::tuple<ck::bhalf_t>,
std::tuple<std::int8_t>>;
TYPED_TEST_SUITE(TestConvndBwdData, KernelTypes);
EXPECT_TRUE(pass);
}
// 1d
TYPED_TEST(TestConvndBwdData, Conv1dBwdData)
{
this->conv_params.clear();
this->conv_params.push_back({1, 1, 128, 128, 256, {1}, {14}, {2}, {1}, {0}, {0}});
this->conv_params.push_back({1, 1, 128, 128, 256, {3}, {28}, {1}, {1}, {1}, {1}});
this->conv_params.push_back({1, 1, 128, 128, 256, {1}, {3}, {1}, {1}, {0}, {0}});
this->template Run<1>();
}
// 2d
TEST_F(TestConvndBwdData, Conv2dBwdData)
TYPED_TEST(TestConvndBwdData, Conv2dBwdData)
{
conv_params.clear();
conv_params.push_back({2, 1, 128, 128, 256, {1, 1}, {7, 7}, {2, 2}, {1, 1}, {0, 0}, {0, 0}});
conv_params.push_back({2, 1, 128, 128, 256, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
conv_params.push_back({2, 1, 128, 128, 256, {1, 1}, {3, 3}, {1, 1}, {1, 1}, {0, 0}, {0, 0}});
for(auto& param : conv_params)
{
bool pass;
// fp32
pass = ck::profiler::profile_conv_bwd_data_impl<2,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
float,
float,
float>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// fp16
pass = ck::profiler::profile_conv_bwd_data_impl<2,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
ck::half_t,
ck::half_t,
ck::half_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// bf16
pass = ck::profiler::profile_conv_bwd_data_impl<2,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
ck::bhalf_t,
ck::bhalf_t,
ck::bhalf_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// int8
pass = ck::profiler::profile_conv_bwd_data_impl<2,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
int8_t,
int8_t,
int8_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
}
this->conv_params.clear();
this->conv_params.push_back(
{2, 1, 128, 128, 256, {1, 1}, {7, 7}, {2, 2}, {1, 1}, {0, 0}, {0, 0}});
this->conv_params.push_back(
{2, 1, 128, 128, 256, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
this->conv_params.push_back(
{2, 1, 128, 128, 256, {1, 1}, {3, 3}, {1, 1}, {1, 1}, {0, 0}, {0, 0}});
this->template Run<2>();
}
// 3d
TEST_F(TestConvndBwdData, Conv3dBwdData)
TYPED_TEST(TestConvndBwdData, Conv3dBwdData)
{
conv_params.clear();
conv_params.push_back(
this->conv_params.clear();
this->conv_params.push_back(
{3, 1, 128, 128, 256, {1, 1, 1}, {7, 7, 7}, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}});
conv_params.push_back(
this->conv_params.push_back(
{3, 1, 128, 128, 256, {3, 3, 3}, {14, 14, 3}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
conv_params.push_back(
this->conv_params.push_back(
{3, 1, 128, 128, 256, {1, 1, 1}, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}});
for(auto& param : conv_params)
{
bool pass;
// fp32
pass = ck::profiler::profile_conv_bwd_data_impl<3,
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK,
float,
float,
float>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// fp16
pass = ck::profiler::profile_conv_bwd_data_impl<3,
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK,
ck::half_t,
ck::half_t,
ck::half_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// bf16
pass = ck::profiler::profile_conv_bwd_data_impl<3,
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK,
ck::bhalf_t,
ck::bhalf_t,
ck::bhalf_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// int8
pass = ck::profiler::profile_conv_bwd_data_impl<3,
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK,
int8_t,
int8_t,
int8_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
}
this->template Run<3>();
}
test/convnd_bwd_weight/CMakeLists.txt deleted 100644 → 0
View file @ 961f5e9e
add_gtest_executable(test_convnd_bwd_weight convnd_bwd_weight.cpp)
target_link_libraries(test_convnd_bwd_weight PRIVATE utility device_conv1d_bwd_weight_instance device_conv2d_bwd_weight_instance device_conv3d_bwd_weight_instance)
test/convnd_bwd_weight/convnd_bwd_weight.cpp deleted 100644 → 0
View file @ 961f5e9e
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <iostream>
#include <initializer_list>
#include <vector>
#include <gtest/gtest.h>
#include "profiler/include/profile_conv_bwd_weight_impl.hpp"
class TestConvndBwdWeight : public ::testing::Test
{
protected:
std::vector<ck::utils::conv::ConvParam> conv_params;
};
// 1d
TEST_F(TestConvndBwdWeight, Conv1dBwdWeight)
{
conv_params.clear();
conv_params.push_back({1, 1, 128, 128, 256, {1}, {14}, {2}, {1}, {0}, {0}});
conv_params.push_back({1, 1, 128, 128, 256, {3}, {28}, {1}, {1}, {1}, {1}});
conv_params.push_back({1, 1, 128, 128, 256, {1}, {3}, {1}, {1}, {0}, {0}});
for(auto& param : conv_params)
{
bool pass;
// fp32
pass = ck::profiler::profile_conv_bwd_weight_impl<1,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK,
float,
float,
float>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param,
2);
EXPECT_TRUE(pass);
// fp16
pass = ck::profiler::profile_conv_bwd_weight_impl<1,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK,
ck::half_t,
ck::half_t,
ck::half_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param,
2);
EXPECT_TRUE(pass);
// bf16
pass = ck::profiler::profile_conv_bwd_weight_impl<1,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK,
ck::bhalf_t,
ck::bhalf_t,
ck::bhalf_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param,
2);
EXPECT_TRUE(pass);
}
}
// 2d
TEST_F(TestConvndBwdWeight, Conv2dBwdWeight)
{
conv_params.clear();
conv_params.push_back({2, 1, 128, 128, 256, {1, 1}, {7, 7}, {2, 2}, {1, 1}, {0, 0}, {0, 0}});
conv_params.push_back({2, 1, 32, 128, 256, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
conv_params.push_back({2, 1, 128, 128, 256, {1, 1}, {3, 3}, {1, 1}, {1, 1}, {0, 0}, {0, 0}});
for(auto& param : conv_params)
{
bool pass;
// fp32
pass = ck::profiler::profile_conv_bwd_weight_impl<2,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
float,
float,
float>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param,
2);
EXPECT_TRUE(pass);
// fp16
pass = ck::profiler::profile_conv_bwd_weight_impl<2,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
ck::half_t,
ck::half_t,
ck::half_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param,
2);
EXPECT_TRUE(pass);
// bf16
pass = ck::profiler::profile_conv_bwd_weight_impl<2,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
ck::bhalf_t,
ck::bhalf_t,
ck::bhalf_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param,
2);
EXPECT_TRUE(pass);
}
}
// 3d
TEST_F(TestConvndBwdWeight, Conv3dBwdWeight)
{
conv_params.clear();
conv_params.push_back(
{3, 1, 128, 128, 256, {1, 1, 1}, {7, 7, 7}, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}});
conv_params.push_back(
{3, 1, 32, 128, 256, {3, 3, 3}, {14, 14, 3}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
conv_params.push_back(
{3, 1, 128, 128, 256, {1, 1, 1}, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}});
for(auto& param : conv_params)
{
bool pass;
// fp32
pass = ck::profiler::profile_conv_bwd_weight_impl<3,
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK,
float,
float,
float>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param,
2);
EXPECT_TRUE(pass);
// fp16
pass = ck::profiler::profile_conv_bwd_weight_impl<3,
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK,
ck::half_t,
ck::half_t,
ck::half_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param,
2);
EXPECT_TRUE(pass);
// bf16
pass = ck::profiler::profile_conv_bwd_weight_impl<3,
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK,
ck::bhalf_t,
ck::bhalf_t,
ck::bhalf_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param,
2);
EXPECT_TRUE(pass);
}
}
test/convnd_fwd/convnd_fwd.cpp
View file @ 24af0144
......@@ -5,237 +5,88 @@
#include <iostream>
#include <initializer_list>
#include <vector>
#include <tuple>
#include <gtest/gtest.h>
#include "profiler/include/profile_conv_fwd_impl.hpp"
template <typename Tuple>
class TestConvndFwd : public ::testing::Test
{
protected:
using DataType = std::tuple_element_t<0, Tuple>;
std::vector<ck::utils::conv::ConvParam> conv_params;
};
// 1d
TEST_F(TestConvndFwd, Conv1dFwd)
{
conv_params.clear();
conv_params.push_back({1, 1, 128, 128, 256, {1}, {14}, {2}, {1}, {0}, {0}});
conv_params.push_back({1, 1, 128, 128, 256, {3}, {28}, {1}, {1}, {1}, {1}});
conv_params.push_back({1, 1, 128, 128, 256, {1}, {3}, {1}, {1}, {0}, {0}});
for(auto& param : conv_params)
template <ck::index_t NDimSpatial>
void Run()
{
bool pass;
// fp32
pass = ck::profiler::profile_conv_fwd_impl<1,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK,
float,
float,
float>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// fp16
pass = ck::profiler::profile_conv_fwd_impl<1,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK,
ck::half_t,
ck::half_t,
ck::half_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// bf16
pass = ck::profiler::profile_conv_fwd_impl<1,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK,
ck::bhalf_t,
ck::bhalf_t,
ck::bhalf_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
for(auto& param : conv_params)
{
bool pass;
EXPECT_FALSE(conv_params.empty());
pass = ck::profiler::profile_conv_fwd_impl<
NDimSpatial,
ck::tuple_element_t<NDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::NDHWC>>,
ck::tuple_element_t<NDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::KZYXC>>,
ck::tuple_element_t<NDimSpatial - 1,
ck::Tuple<ck::tensor_layout::convolution::NWK,
ck::tensor_layout::convolution::NHWK,
ck::tensor_layout::convolution::NDHWK>>,
DataType,
DataType,
DataType>(true, // do_verification
1, // init_method integer value
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
}
}
};
// int8
pass = ck::profiler::profile_conv_fwd_impl<1,
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK,
int8_t,
int8_t,
int8_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
using KernelTypes = ::testing::Types<std::tuple<float>,
std::tuple<ck::half_t>,
std::tuple<ck::bhalf_t>,
std::tuple<std::int8_t>>;
TYPED_TEST_SUITE(TestConvndFwd, KernelTypes);
EXPECT_TRUE(pass);
}
// 1d
TYPED_TEST(TestConvndFwd, Conv1dFwd)
{
this->conv_params.clear();
this->conv_params.push_back({1, 1, 128, 128, 256, {1}, {14}, {2}, {1}, {0}, {0}});
this->conv_params.push_back({1, 1, 128, 128, 256, {3}, {28}, {1}, {1}, {1}, {1}});
this->conv_params.push_back({1, 1, 128, 128, 256, {1}, {3}, {1}, {1}, {0}, {0}});
this->template Run<1>();
}
// 2d
TEST_F(TestConvndFwd, Conv2dFwd)
TYPED_TEST(TestConvndFwd, Conv2dFwd)
{
conv_params.clear();
conv_params.push_back({2, 1, 128, 128, 256, {1, 1}, {7, 7}, {2, 2}, {1, 1}, {0, 0}, {0, 0}});
conv_params.push_back({2, 1, 128, 128, 256, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
conv_params.push_back({2, 1, 128, 128, 256, {1, 1}, {3, 3}, {1, 1}, {1, 1}, {0, 0}, {0, 0}});
for(auto& param : conv_params)
{
bool pass;
// fp32
pass = ck::profiler::profile_conv_fwd_impl<2,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
float,
float,
float>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// fp16
pass = ck::profiler::profile_conv_fwd_impl<2,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
ck::half_t,
ck::half_t,
ck::half_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// bf16
pass = ck::profiler::profile_conv_fwd_impl<2,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
ck::bhalf_t,
ck::bhalf_t,
ck::bhalf_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// int8
pass = ck::profiler::profile_conv_fwd_impl<2,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK,
int8_t,
int8_t,
int8_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
}
this->conv_params.clear();
this->conv_params.push_back(
{2, 1, 128, 128, 256, {1, 1}, {7, 7}, {2, 2}, {1, 1}, {0, 0}, {0, 0}});
this->conv_params.push_back(
{2, 1, 128, 128, 256, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
this->conv_params.push_back(
{2, 1, 128, 128, 256, {1, 1}, {3, 3}, {1, 1}, {1, 1}, {0, 0}, {0, 0}});
this->template Run<2>();
}
// 3d
TEST_F(TestConvndFwd, Conv3dFwd)
TYPED_TEST(TestConvndFwd, Conv3dFwd)
{
conv_params.clear();
conv_params.push_back(
this->conv_params.clear();
this->conv_params.push_back(
{3, 1, 128, 128, 256, {1, 1, 1}, {7, 7, 7}, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}});
conv_params.push_back(
this->conv_params.push_back(
{3, 1, 128, 128, 256, {3, 3, 3}, {14, 14, 3}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
conv_params.push_back(
this->conv_params.push_back(
{3, 1, 128, 128, 256, {1, 1, 1}, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}});
for(auto& param : conv_params)
{
bool pass;
// fp32
pass = ck::profiler::profile_conv_fwd_impl<3,
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK,
float,
float,
float>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// fp16
pass = ck::profiler::profile_conv_fwd_impl<3,
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK,
ck::half_t,
ck::half_t,
ck::half_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// bf16
pass = ck::profiler::profile_conv_fwd_impl<3,
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK,
ck::bhalf_t,
ck::bhalf_t,
ck::bhalf_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
// int8
pass = ck::profiler::profile_conv_fwd_impl<3,
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK,
int8_t,
int8_t,
int8_t>(true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param);
EXPECT_TRUE(pass);
}
this->template Run<3>();
}
test/elementwise_normalization/CMakeLists.txt 0 → 100644
View file @ 24af0144
add_custom_target(test_elementwise_normalization)
add_gtest_executable(test_elementwise_layernorm_fp16 test_elementwise_layernorm_fp16.cpp)
target_link_libraries(test_elementwise_layernorm_fp16 PRIVATE utility device_elementwise_normalization_instance)
add_dependencies(test_elementwise_normalization test_elementwise_layernorm_fp16)
test/elementwise_normalization/test_elementwise_layernorm_fp16.cpp 0 → 100644
View file @ 24af0144
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "profiler/include/profile_elementwise_layernorm_impl.hpp"
using F16 = ck::half_t;
using F32 = float;
using ck::index_t;
template <typename Tuple>
class TestElementwiseLayernorm : public ::testing::Test
{
protected:
using ADataType = std::tuple_element_t<0, Tuple>;
using BDataType = std::tuple_element_t<1, Tuple>;
using GammaDataType = std::tuple_element_t<2, Tuple>;
using BetaDataType = std::tuple_element_t<3, Tuple>;
using AccDataType = std::tuple_element_t<4, Tuple>;
using YDataType = std::tuple_element_t<5, Tuple>;
void Run()
{
// M, N
std::vector<std::vector<ck::index_t>> lengths = {
{1, 1}, {25, 16}, {39, 777}, {100, 200}, {1024, 1024}, {48 * 256, 2048}};
for(auto length : lengths)
{
bool success = ck::profiler::profile_elementwise_layernorm_impl<ADataType,
BDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType>(
true, 2, false, false, length);
EXPECT_TRUE(success);
}
}
};
using KernelTypes = ::testing::Types<
// ADataType, BDataType, GammaDataType, BetaDataType, AccDataType, YDataType>
std::tuple<F16, F16, F16, F16, F32, F16>>;
TYPED_TEST_SUITE(TestElementwiseLayernorm, KernelTypes);
TYPED_TEST(TestElementwiseLayernorm, Test_FP16) { this->Run(); }
test/gemm/CMakeLists.txt
View file @ 24af0144
......@@ -13,3 +13,13 @@ target_link_libraries(test_gemm_bf16 PRIVATE device_gemm_instance)
add_test_executable(test_gemm_int8 gemm_int8.cpp)
target_link_libraries(test_gemm_int8 PRIVATE utility)
target_link_libraries(test_gemm_int8 PRIVATE device_gemm_instance)
add_library(gemm_standalone_xdl_fp16_instances STATIC
instance/gemm_f16_nn_instance.cpp
instance/gemm_f16_nt_instance.cpp
instance/gemm_f16_tn_instance.cpp
instance/gemm_f16_tt_instance.cpp
)
add_test_executable(test_gemm_standalone_xdl_fp16 gemm_standalone_xdl_fp16.cpp)
target_link_libraries(test_gemm_standalone_xdl_fp16 PRIVATE gemm_standalone_xdl_fp16_instances utility)
target_include_directories(test_gemm_standalone_xdl_fp16 PRIVATE instance/)
test/gemm/gemm_bf16.cpp
View file @ 24af0144
......@@ -24,56 +24,11 @@
#include "test/gemm/gemm_util.hpp"
int main()
{
using ADataType = ck::bhalf_t;
using BDataType = ck::bhalf_t;
using CDataType = ck::bhalf_t;
using AccDataType = float;
using ADataType = ck::bhalf_t;
using BDataType = ck::bhalf_t;
using CDataType = ck::bhalf_t;
using AccDataType = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
#include "run_gemm_test.inc"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
auto test = [&](auto a_layout, auto b_layout, auto c_layout) {
bool pass = true;
using DeviceOp = ck::tensor_operation::device::DeviceGemm<decltype(a_layout),
decltype(b_layout),
decltype(c_layout),
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>;
const auto gemmPtrs =
ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
for(auto& gemmPtr : gemmPtrs)
{
pass &= ck::gemm_util::TestGemm<std::unique_ptr<DeviceOp>,
ADataType,
BDataType,
CDataType,
AccDataType,
decltype(a_layout),
decltype(b_layout),
decltype(c_layout),
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
return pass;
};
bool pass = test(Row{}, Row{}, Row{}) && test(Row{}, Col{}, Row{}) &&
test(Col{}, Row{}, Row{}) && test(Col{}, Col{}, Row{});
std::cout << "TestGemm ..... " << (pass ? "SUCCESS" : "FAILURE") << std::endl;
return pass ? 0 : 1;
}
int main() { return run_gemm_test(); }
test/gemm/gemm_fp16.cpp
View file @ 24af0144
......@@ -24,56 +24,11 @@
#include "test/gemm/gemm_util.hpp"
int main()
{
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
#include "run_gemm_test.inc"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
auto test = [&](auto a_layout, auto b_layout, auto c_layout) {
bool pass = true;
using DeviceOp = ck::tensor_operation::device::DeviceGemm<decltype(a_layout),
decltype(b_layout),
decltype(c_layout),
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>;
const auto gemmPtrs =
ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
for(auto& gemmPtr : gemmPtrs)
{
pass &= ck::gemm_util::TestGemm<std::unique_ptr<DeviceOp>,
ADataType,
BDataType,
CDataType,
AccDataType,
decltype(a_layout),
decltype(b_layout),
decltype(c_layout),
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
return pass;
};
bool pass = test(Row{}, Row{}, Row{}) && test(Row{}, Col{}, Row{}) &&
test(Col{}, Row{}, Row{}) && test(Col{}, Col{}, Row{});
std::cout << "TestGemm ..... " << (pass ? "SUCCESS" : "FAILURE") << std::endl;
return pass ? 0 : 1;
}
int main() { return run_gemm_test(); }
test/gemm/gemm_fp32.cpp
View file @ 24af0144
......@@ -24,56 +24,11 @@
#include "test/gemm/gemm_util.hpp"
int main()
{
using ADataType = float;
using BDataType = float;
using CDataType = float;
using AccDataType = float;
using ADataType = float;
using BDataType = float;
using CDataType = float;
using AccDataType = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
#include "run_gemm_test.inc"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
auto test = [&](auto a_layout, auto b_layout, auto c_layout) {
bool pass = true;
using DeviceOp = ck::tensor_operation::device::DeviceGemm<decltype(a_layout),
decltype(b_layout),
decltype(c_layout),
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>;
const auto gemmPtrs =
ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
for(auto& gemmPtr : gemmPtrs)
{
pass &= ck::gemm_util::TestGemm<std::unique_ptr<DeviceOp>,
ADataType,
BDataType,
CDataType,
AccDataType,
decltype(a_layout),
decltype(b_layout),
decltype(c_layout),
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
return pass;
};
bool pass = test(Row{}, Row{}, Row{}) && test(Row{}, Col{}, Row{}) &&
test(Col{}, Row{}, Row{}) && test(Col{}, Col{}, Row{});
std::cout << "TestGemm ..... " << (pass ? "SUCCESS" : "FAILURE") << std::endl;
return pass ? 0 : 1;
}
int main() { return run_gemm_test(); }
test/gemm/gemm_fp64.cpp
View file @ 24af0144
......@@ -24,56 +24,11 @@
#include "test/gemm/gemm_util.hpp"
int main()
{
using ADataType = double;
using BDataType = double;
using CDataType = double;
using AccDataType = double;
using ADataType = double;
using BDataType = double;
using CDataType = double;
using AccDataType = double;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
#include "run_gemm_test.inc"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
auto test = [&](auto a_layout, auto b_layout, auto c_layout) {
bool pass = true;
using DeviceOp = ck::tensor_operation::device::DeviceGemm<decltype(a_layout),
decltype(b_layout),
decltype(c_layout),
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>;
const auto gemmPtrs =
ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
for(auto& gemmPtr : gemmPtrs)
{
pass &= ck::gemm_util::TestGemm<std::unique_ptr<DeviceOp>,
ADataType,
BDataType,
CDataType,
AccDataType,
decltype(a_layout),
decltype(b_layout),
decltype(c_layout),
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
return pass;
};
bool pass = test(Row{}, Row{}, Row{}) && test(Row{}, Col{}, Row{}) &&
test(Col{}, Row{}, Row{}) && test(Col{}, Col{}, Row{});
std::cout << "TestGemm ..... " << (pass ? "SUCCESS" : "FAILURE") << std::endl;
return pass ? 0 : 1;
}
int main() { return run_gemm_test(); }
test/gemm/gemm_int8.cpp
View file @ 24af0144
......@@ -24,56 +24,11 @@
#include "test/gemm/gemm_util.hpp"
int main()
{
using ADataType = int8_t;
using BDataType = int8_t;
using CDataType = int8_t;
using AccDataType = int32_t;
using ADataType = int8_t;
using BDataType = int8_t;
using CDataType = int8_t;
using AccDataType = int32_t;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
#include "run_gemm_test.inc"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
auto test = [&](auto a_layout, auto b_layout, auto c_layout) {
bool pass = true;
using DeviceOp = ck::tensor_operation::device::DeviceGemm<decltype(a_layout),
decltype(b_layout),
decltype(c_layout),
ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>;
const auto gemmPtrs =
ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
for(auto& gemmPtr : gemmPtrs)
{
pass &= ck::gemm_util::TestGemm<std::unique_ptr<DeviceOp>,
ADataType,
BDataType,
CDataType,
AccDataType,
decltype(a_layout),
decltype(b_layout),
decltype(c_layout),
PassThrough,
PassThrough,
PassThrough>{}(gemmPtr);
}
return pass;
};
bool pass = test(Row{}, Row{}, Row{}) && test(Row{}, Col{}, Row{}) &&
test(Col{}, Row{}, Row{}) && test(Col{}, Col{}, Row{});
std::cout << "TestGemm ..... " << (pass ? "SUCCESS" : "FAILURE") << std::endl;
return pass ? 0 : 1;
}
int main() { return run_gemm_test(); }
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