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Gemm+layernorm instance, ckProfiler, client example (#568)

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* Add gemm + layernorm instance

* Add ckProfiler

* Add test

* Add client example

* Detect if user forger to set the workrspace

* Use literal in the example

* [What] use builtin function for sqrt
[Why] compiler will not use v_sqrt_f64_e64 if we use ::sqrt()

* check gemm vaildity in IsSupportedArgument

* Add more testcases

* Merge duplicated folder in client example

* Print more infomation

* Use better kernel parameter for MS problem size

* clang format

* Add constexpr for if condition and remove redundant include

* Remove cstdlib and add constexpr
This commit is contained in:
rocking5566
2023-02-10 05:02:55 +08:00
committed by GitHub
parent 76d144fa7c
commit f7d28f3e4b
29 changed files with 1635 additions and 36 deletions
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7
test/gemm_layernorm/CMakeLists.txt Normal file
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add_custom_target(test_gemm_layernorm)
add_gtest_executable(test_gemm_add_relu_add_layernorm_fp16 test_gemm_add_relu_add_layernorm_fp16.cpp)
target_link_libraries(test_gemm_add_relu_add_layernorm_fp16 PRIVATE utility device_gemm_add_relu_add_layernorm_instance)
add_dependencies(test_gemm_layernorm test_gemm_add_relu_add_layernorm_fp16)

77
test/gemm_layernorm/test_gemm_add_relu_add_layernorm_fp16.cpp Normal file
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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "profiler/profile_gemm_add_relu_add_layernorm_impl.hpp"
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using F16 = ck::half_t;
using F32 = float;
using ck::index_t;
template <typename Tuple>
class TestGemmAddReluAddLayernorm : public ::testing::Test
{
protected:
using ADataType = std::tuple_element_t<0, Tuple>;
using BDataType = std::tuple_element_t<1, Tuple>;
using AccDataType = std::tuple_element_t<2, Tuple>;
using D0DataType = std::tuple_element_t<3, Tuple>;
using D1DataType = std::tuple_element_t<4, Tuple>;
using EMeanVarDataType = std::tuple_element_t<5, Tuple>;
using GammaDataType = std::tuple_element_t<6, Tuple>;
using BetaDataType = std::tuple_element_t<7, Tuple>;
using HDataType = std::tuple_element_t<8, Tuple>;
using ALayout = std::tuple_element_t<9, Tuple>;
using BLayout = std::tuple_element_t<10, Tuple>;
using D0Layout = std::tuple_element_t<11, Tuple>;
using D1Layout = std::tuple_element_t<12, Tuple>;
using HLayout = std::tuple_element_t<13, Tuple>;
void Run()
{
std::vector<std::vector<ck::index_t>> lengths = {
{1024, 1024, 1024}, {2048, 640, 640}, {1, 1, 1}};
for(auto length : lengths)
{
int M = length[0];
int N = length[1];
int K = length[2];
int StrideA = ck::is_same_v<ALayout, Row> ? K : M;
int StrideB = ck::is_same_v<BLayout, Row> ? N : K;
int StrideD0 = 0;
int StrideD1 = ck::is_same_v<D1Layout, Row> ? N : M;
int StrideH = ck::is_same_v<HLayout, Row> ? N : M;
bool success = ck::profiler::profile_gemm_add_relu_add_layernorm_impl<ADataType,
BDataType,
AccDataType,
D0DataType,
D1DataType,
EMeanVarDataType,
GammaDataType,
BetaDataType,
HDataType,
ALayout,
BLayout,
D0Layout,
D1Layout,
HLayout>(
true, 1, false, false, M, N, K, StrideA, StrideB, StrideD0, StrideD1, StrideH);
EXPECT_TRUE(success);
}
}
};
using KernelTypes = ::testing::Types<
std::tuple<F16, F16, F32, F16, F16, F16, F16, F16, F16, Row, Row, Row, Row, Row>,
std::tuple<F16, F16, F32, F16, F16, F16, F16, F16, F16, Row, Col, Row, Row, Row>,
std::tuple<F16, F16, F32, F16, F16, F16, F16, F16, F16, Col, Row, Row, Row, Row>,
std::tuple<F16, F16, F32, F16, F16, F16, F16, F16, F16, Col, Col, Row, Row, Row>>;
TYPED_TEST_SUITE(TestGemmAddReluAddLayernorm, KernelTypes);
TYPED_TEST(TestGemmAddReluAddLayernorm, Test_FP16) { this->Run(); }