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batched_gemm: use profiler in ctest (#163)

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[ROCm/composable_kernel commit: c8f3acf9c0]
This commit is contained in:
Jianfeng Yan
2022-03-30 21:32:49 -05:00
committed by GitHub
parent 71b19b4e4e
commit 18781affb9
5 changed files with 48 additions and 134 deletions
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2
include/ck/tensor_operation/gpu/device/device_gemm.hpp
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@@ -1,5 +1,7 @@
#pragma once
#include <iostream>
#include <vector>
#include "device_base.hpp"
namespace ck {

4
include/ck/tensor_operation/gpu/device/tensor_layout.hpp
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@@ -1,5 +1,4 @@
#ifndef TENSOR_LAYOUT_HPP
#define TENSOR_LAYOUT_HPP
#pragma once
namespace ck {
namespace tensor_layout {
@@ -128,4 +127,3 @@ std::ostream& operator<<(std::ostream& os, const Layout&)
} // namespace tensor_layout
} // namespace ck
#endif

7
library/include/ck/library/host_tensor/host_tensor_generator.hpp
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@@ -1,7 +1,8 @@
#ifndef HOST_TENSOR_GENERATOR_HPP
#define HOST_TENSOR_GENERATOR_HPP
#pragma once
#include <cmath>
#include <numeric>
#include "config.hpp"
template <typename T>
@@ -147,5 +148,3 @@ struct GeneratorTensor_Sequential
return dims[Dim];
}
};
#endif

19
profiler/include/profile_batched_gemm_impl.hpp
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@@ -1,6 +1,13 @@
#pragma once
#include <memory>
#include "config.hpp"
#include "element_wise_operation.hpp"
#include "tensor_layout.hpp"
#include "device.hpp"
#include "host_tensor_generator.hpp"
#include "device_gemm.hpp"
#include "reference_batched_gemm.hpp"
namespace ck {
@@ -52,7 +59,7 @@ template <typename ADataType,
typename ALayout,
typename BLayout,
typename CLayout>
void profile_batched_gemm_impl(int do_verification,
bool profile_batched_gemm_impl(int do_verification,
int init_method,
bool do_log,
int nrepeat,
@@ -64,6 +71,8 @@ void profile_batched_gemm_impl(int do_verification,
int StrideC,
int BatchCount = 1)
{
bool pass = true;
auto f_host_tensor_descriptor = [](std::size_t batch_count,
std::size_t row,
std::size_t col,
@@ -379,12 +388,14 @@ void profile_batched_gemm_impl(int do_verification,
{
bf16_to_f32_(c_g_m_n_device_result, *c_f32_g_m_n_device_result);
check_error(*c_f32_g_m_n_host_result, *c_f32_g_m_n_device_result);
float err = check_error(*c_f32_g_m_n_host_result, *c_f32_g_m_n_device_result);
pass = pass && (err < 1E-6);
}
else
{
check_error(c_g_m_n_host_result, c_g_m_n_device_result);
float err = check_error(c_g_m_n_host_result, c_g_m_n_device_result);
pass = pass && (err < 1E-6);
}
if(do_log)
@@ -408,6 +419,8 @@ void profile_batched_gemm_impl(int do_verification,
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_gemm_name << std::endl;
return pass;
}
} // namespace profiler

150
test/batched_gemm/batched_gemm_fp16.cpp
View File

@@ -1,139 +1,41 @@
#include <half.hpp>
#include <tuple>
#include <vector>
#include "profile_batched_gemm_impl.hpp"
#include "batched_gemm_util.hpp"
#include "reference_batched_gemm.hpp"
#include "config.hpp"
#include "device.hpp"
#include "host_tensor.hpp"
#include "host_tensor_generator.hpp"
#include "device_tensor.hpp"
#include "device_batched_gemm_xdl.hpp"
#include "element_wise_operation.hpp"
#include "test_util.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceBatchedGemmPtr =
ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_batched_gemm_instance {
void add_device_batched_gemm_xdl_f16_f16_f16_gmk_gnk_gmn_instances(
std::vector<DeviceBatchedGemmPtr>& instances);
}
} // namespace device
} // namespace tensor_operation
} // namespace ck
#include <iostream>
namespace {
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 ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
using CLayout = ck::tensor_layout::gemm::RowMajor;
auto PrepareGemmTensor(const std::size_t batch_count,
const ck::batched_gemm_util::GemmParams& params)
{
auto f_host_tensor_descriptor =
[batch_count](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({row * stride, stride, 1}));
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({col * stride, 1, stride}));
}
};
Tensor<ADataType> a_g_m_k(
f_host_tensor_descriptor(params.M, params.K, params.StrideA, ALayout{}));
Tensor<BDataType> b_g_k_n(
f_host_tensor_descriptor(params.K, params.N, params.StrideB, BLayout{}));
Tensor<CDataType> c_g_m_n_host_result(
f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
Tensor<CDataType> c_g_m_n_device_result(
f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
a_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
b_g_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
return std::make_tuple(a_g_m_k, b_g_k_n, c_g_m_n_host_result, c_g_m_n_device_result);
}
bool TestBatchedGemm(const std::size_t batch_count, DeviceBatchedGemmPtr& gemmPtr)
{
// Arrange
ck::batched_gemm_util::GemmParams params;
params.M = 1024;
params.N = 1024;
params.K = 1024;
params.StrideA = 1024;
params.StrideB = 1024;
params.StrideC = 1024;
auto host_tensors = PrepareGemmTensor(batch_count, params);
const Tensor<ADataType>& a = std::get<0>(host_tensors);
const Tensor<BDataType>& b = std::get<1>(host_tensors);
Tensor<CDataType>& c_host = std::get<2>(host_tensors);
Tensor<CDataType>& c_device = std::get<3>(host_tensors);
auto a_element_op = PassThrough{};
auto b_element_op = PassThrough{};
auto c_element_op = PassThrough{};
using ReferenceBatchedGemmInstance =
ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
BDataType,
CDataType,
PassThrough,
PassThrough,
PassThrough>;
ck::batched_gemm_util::RunHostBatchedGemm<ReferenceBatchedGemmInstance>(
a, b, c_host, a_element_op, b_element_op, c_element_op);
// Act
ck::batched_gemm_util::RunDeviceBatchedGemm(
gemmPtr, params, a, b, c_device, a_element_op, b_element_op, c_element_op);
// Assert
// bool pass = test::check_err(
// c_device.mData, c_host.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
bool pass = check_error(c_device, c_host) < 0.007815f;
std::cout << (pass ? "SUCCESS" : "FAILURE") << std::endl;
return pass;
}
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
} // namespace
int main()
{
std::vector<DeviceBatchedGemmPtr> batched_gemm_ptrs;
ck::tensor_operation::device::device_batched_gemm_instance::
add_device_batched_gemm_xdl_f16_f16_f16_gmk_gnk_gmn_instances(batched_gemm_ptrs);
int M = 512;
int N = 256;
int K = 128;
int BatchCount = 3;
bool pass = true;
const std::size_t batch_count = 4;
for(auto& gemmPtr : batched_gemm_ptrs)
{
pass &= TestBatchedGemm(batch_count, gemmPtr);
}
pass = pass &&
ck::profiler::profile_batched_gemm_impl<ADataType, BDataType, CDataType, Row, Row, Row>(
true, 1, false, 1, M, N, K, K, N, N, BatchCount);
std::cout << "TestGemm ..... " << (pass ? "SUCCESS" : "FAILURE") << std::endl;
pass = pass &&
ck::profiler::profile_batched_gemm_impl<ADataType, BDataType, CDataType, Row, Col, Row>(
true, 1, false, 1, M, N, K, K, K, 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, 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, BatchCount);
std::cout << "test BatchedGEMM fp16: " << (pass ? "Pass" : "Fail") << std::endl;
return pass ? 0 : 1;
}