ROCm/composable_kernel
1
0
Fork 0
mirror of https://github.com/ROCm/composable_kernel.git synced 2026-05-15 18:42:06 +00:00
Code Issues Packages Projects Releases Wiki Activity

Rangify constructor of HostTensorDescriptor & Tensor<> (#445)

Browse Source 
* Rangify STL algorithms

This commit adapts rangified std::copy(), std::fill() & std::transform()

* Rangify check_err()

By rangifying check_err(), we can not only compare values between
std::vector<>s, but also compare any ranges which have same value
type.

* Allow constructing Tensor<> like a HostTensorDescriptor

* Simplify Tensor<> object construction logics

* Remove more unnecessary 'HostTensorDescriptor' objects

* Re-format example code

* Re-write more HostTensorDescriptor ctor call

[ROCm/composable_kernel commit: 4a2a56c22f]
This commit is contained in:
Po Yen Chen
2022-11-12 01:36:01 +08:00
committed by GitHub
parent a4d6101e99
commit f2dd2e5b09
103 changed files with 657 additions and 649 deletions
Download Patch File Download Diff File Expand all files Collapse all files

19
test/gemm/gemm_util.hpp
View File

@@ -9,6 +9,7 @@
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
namespace ck {
@@ -128,15 +129,15 @@ struct TestGemm
{
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({stride, 1}));
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({1, stride}));
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
@@ -229,27 +230,27 @@ struct TestGemm
bool res = false;
if(std::is_same<CDataType, float>::value)
{
res = ck::utils::check_err(c_device.mData, c_host.mData);
res = ck::utils::check_err(c_device, c_host);
std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
}
else if(std::is_same<CDataType, ck::half_t>::value)
{
res = ck::utils::check_err(c_device.mData, c_host.mData);
res = ck::utils::check_err(c_device, c_host);
std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
}
else if(std::is_same<CDataType, ck::bhalf_t>::value)
{
res = ck::utils::check_err(c_device.mData, c_host.mData);
res = ck::utils::check_err(c_device, c_host);
std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
}
else if(std::is_same<CDataType, int8_t>::value)
{
res = ck::utils::check_err(c_device.mData, c_host.mData);
res = ck::utils::check_err(c_device, c_host);
std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
}
else if(std::is_same<CDataType, double>::value)
{
res = ck::utils::check_err(c_device.mData, c_host.mData);
res = ck::utils::check_err(c_device, c_host);
std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
}

9
test/gemm_split_k/gemm_split_k.cpp
View File

@@ -16,6 +16,7 @@
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/utility/host_gemm.hpp"
@@ -93,15 +94,15 @@ int test_gemm(const gemmArgs& args)
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, bool row_major) {
using namespace ck::literals;
if(row_major)
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({stride, 1}));
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({1, stride}));
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};

18
test/reference_conv_fwd/reference_conv_fwd.cpp
View File

@@ -12,6 +12,7 @@
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/fill.hpp"
#include "ck/library/utility/host_tensor.hpp"
@@ -54,7 +55,7 @@ run_reference_convolution_forward(const ck::utils::conv::ConvParam& conv_param,
fill_input_op(input.begin(), input.end());
fill_weights_op(weights.begin(), weights.end());
std::fill(host_output.begin(), host_output.end(), OutDataType(0.f));
ck::ranges::fill<OutDataType>(host_output, 0.f);
auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
InDataType,
@@ -122,7 +123,7 @@ TEST(ReferenceConvolutionFWD, Conv2DGNHWC)
508.5};
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv2DGNHWCStridesDilationsPadding)
@@ -149,7 +150,7 @@ TEST(ReferenceConvolutionFWD, Conv2DGNHWCStridesDilationsPadding)
1323., 1323., 2002.5, 2002.5, 2038.5, 2038.5, 2074.5, 2074.5, 2110.5, 2110.5};
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv1DGNWC)
@@ -178,7 +179,7 @@ TEST(ReferenceConvolutionFWD, Conv1DGNWC)
std::vector<float> ref_data{7.5, 13.5, 19.5, 25.5};
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv1DGNWCStridesDilationsPadding)
@@ -207,7 +208,7 @@ TEST(ReferenceConvolutionFWD, Conv1DGNWCStridesDilationsPadding)
std::vector<float> ref_data{9., 9., 19.5, 19.5, 31.5, 31.5, 43.5, 43.5, 55.5, 55.5};
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor, ref_data, "Error: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv1DGNWCSameOutputSize)
@@ -301,7 +302,7 @@ TEST(ReferenceConvolutionFWD, Conv1DGNWCSameOutputSize)
49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4, 49.4};
EXPECT_TRUE(ck::utils::check_err(
out_tensor2.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor2.mData, ref_data, "Error: incorrect results!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor2, ref_data, "Error: incorrect results!"));
}
#endif
@@ -340,8 +341,7 @@ TEST(ReferenceConvolutionFWD, Conv3DGNCDHW)
EXPECT_TRUE(ck::utils::check_err(out_tensor.mDesc.GetLengths(),
ref_dims,
"Error [case 1]: wrong output tensor dimensions!"));
EXPECT_TRUE(
ck::utils::check_err(out_tensor.mData, ref_data, "Error [case 1]: incorrect results!"));
EXPECT_TRUE(ck::utils::check_err(out_tensor, ref_data, "Error [case 1]: incorrect results!"));
}
TEST(ReferenceConvolutionFWD, Conv3DGNCDHWStridesDilations)
@@ -388,5 +388,5 @@ TEST(ReferenceConvolutionFWD, Conv3DGNCDHWStridesDilations)
ref_dims,
"Error [case 2]: wrong output tensor dimensions!"));
EXPECT_TRUE(ck::utils::check_err(
out_tensor.mData, ref_data, "Error [case 2]: incorrect results!", 1e-4f, 1e-6f));
out_tensor, ref_data, "Error [case 2]: incorrect results!", 1e-4f, 1e-6f));
}