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Commit d117a993 authored by Anthony Chang's avatar Anthony Chang
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test more padded cases

parent 5e1b0dd8
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Showing with 230 additions and 1 deletion
test/batched_gemm_gemm/test_batched_gemm_gemm_fp16.cpp
View file @ d117a993
...@@ -68,7 +68,6 @@ TYPED_TEST(TestBatchedGemmGemmFP16, Test_FP16_OddN) ...@@ -68,7 +68,6 @@ TYPED_TEST(TestBatchedGemmGemmFP16, Test_FP16_OddN)
this->Run(); this->Run();
} }
// Currently expected that no kernels can support this case
TYPED_TEST(TestBatchedGemmGemmFP16, Test_FP16_OddK) TYPED_TEST(TestBatchedGemmGemmFP16, Test_FP16_OddK)
{ {
this->lengths_ = std::vector<std::vector<int>>{ this->lengths_ = std::vector<std::vector<int>>{
......
test/batched_gemm_softmax_gemm/test_batched_gemm_softmax_gemm_fp16.cpp
View file @ d117a993
...@@ -19,6 +19,73 @@ TYPED_TEST_SUITE(TestBatchedGemmSoftmaxGemmFP16, KernelTypes); ...@@ -19,6 +19,73 @@ TYPED_TEST_SUITE(TestBatchedGemmSoftmaxGemmFP16, KernelTypes);
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16) { this->Run(); } TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16) { this->Run(); }
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_PadM)
{
this->lengths_ = std::vector<std::vector<int>>{
{136, 128, 32, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_PadN)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 136, 32, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_PadK)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 128, 40, 128, 1},
{128, 128, 136, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_PadO)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 128, 32, 136, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_OddM)
{
this->lengths_ = std::vector<std::vector<int>>{
{129, 128, 32, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_OddN)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 129, 32, 128, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_OddK)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 128, 33, 128, 1},
{128, 128, 129, 128, 1},
};
this->Run();
}
// If kernel B1Layout is RowMajor, expect not to support odd O size
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, Test_FP16_OddO)
{
this->lengths_ = std::vector<std::vector<int>>{
{128, 128, 32, 129, 1},
};
this->Run();
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, DISABLED_Bench_FP16) TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, DISABLED_Bench_FP16)
{ {
this->lengths_ = std::vector<std::vector<int>>{ this->lengths_ = std::vector<std::vector<int>>{
...@@ -38,6 +105,48 @@ TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, DISABLED_Bench_FP16) ...@@ -38,6 +105,48 @@ TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, DISABLED_Bench_FP16)
this->Run(); this->Run();
} }
using ck::tensor_operation::device::GemmSpecialization;
TEST(TestBatchedGemmSoftmaxGemmInterface, GemmSpecializationSizeMatch)
{
int P = 120; // requires padding
int Q = 128; // do not require padding
// 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));
// clang-format on
}
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));
// 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));
// 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));
// clang-format on
}
TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, AdhocTest) TYPED_TEST(TestBatchedGemmSoftmaxGemmFP16, AdhocTest)
{ {
this->lengths_ = std::vector<std::vector<int>>{ this->lengths_ = std::vector<std::vector<int>>{
......
test/batched_gemm_softmax_gemm/test_batched_gemm_softmax_gemm_util.hpp
View file @ d117a993
...@@ -4,7 +4,10 @@ ...@@ -4,7 +4,10 @@
#include <iostream> #include <iostream>
#include <vector> #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 "profiler/include/profile_batched_gemm_softmax_gemm_impl.hpp" #include "profiler/include/profile_batched_gemm_softmax_gemm_impl.hpp"
using ck::tensor_operation::device::GemmSpecialization;
template <ck::index_t N> template <ck::index_t N>
using I = ck::Number<N>; using I = ck::Number<N>;
...@@ -66,3 +69,121 @@ struct TestBatchedGemmSoftmaxGemm : public ::testing::Test ...@@ -66,3 +69,121 @@ struct TestBatchedGemmSoftmaxGemm : public ::testing::Test
} }
} }
}; };
template <GemmSpecialization GemmSpec>
struct DeviceInstanceWrapper_TNTT_FP16_M128_N128_K32_O128
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ALayout = Row;
using B0Layout = Col;
using B1Layout = Row;
using CLayout = Row;
using ADataType = F16;
using B0DataType = F16;
using B1DataType = F16;
using AccDataType = float;
using CShuffleDataType = float;
using CDataType = F16;
using AElementOp = PassThrough;
using B0ElementOp = PassThrough;
using Acc0ElementOp = PassThrough;
using B1ElementOp = PassThrough;
using CElementOp = PassThrough;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
// static constexpr auto GemmSpec = std::tuple_element_t<0, Tuple>::value;
using DeviceGemmGemmInstance =
ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemm_Xdl_CShuffle<
ALayout,
B0Layout,
B1Layout,
CLayout,
ADataType,
B0DataType,
B1DataType,
CDataType,
AccDataType,
CShuffleDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp,
B1ElementOp,
CElementOp,
GemmSpec,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
128, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
4, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<8, 32, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8>; // CShuffleBlockTransferScalarPerVector_NPerBlock
bool IsSupported(int M, int N, int K, int O)
{
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, // StrideA
0, // StrideB0
0, // StrideB1
0, // StrideC
0, // BatchStrideA
0, // BatchStrideB0
0, // BatchStrideB1
0, // BatchStrideC
PassThrough{}, // a_element_op
PassThrough{}, // b0_element_op
PassThrough{}, // acc0_element_op
PassThrough{}, // b1_element_op
PassThrough{}); // c_element_op
return gemm.IsSupportedArgument(argument);
}
};
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