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Commit f3b6e205 authored by Astha Rai's avatar Astha Rai
Browse files

fixed errors in test/profiler

parent 4dab86fe
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Showing with 65 additions and 80 deletions
example/44_elementwise_permute/elementwise_permute_3d.cpp
View file @ f3b6e205
......@@ -50,14 +50,20 @@ int main()
bool do_verification = true;
bool time_kernel = true;
const int N = 4;
/**const int N = 4;
const int C = 16;
const int H = 32;
const int W = 5;
const int D = 16;
const int D = 16;**/
std::vector<std::size_t> ncdhw = {N, C, D, H, W};
std::vector<std::size_t> nchwd = {N, C, H, W, D};
ck::index_t N = 4;
ck::index_t C = 16;
ck::index_t H = 32;
ck::index_t W = 5;
ck::index_t D = 16;
std::vector<ck::index_t> ncdhw = {N, C, D, H, W};
std::vector<ck::index_t> nchwd = {N, C, H, W, D};
Tensor<ADataType> a(ncdhw);
Tensor<BDataType> b(nchwd);
......
profiler/include/profiler/profile_transpose_impl.hpp
View file @ f3b6e205
......@@ -9,44 +9,56 @@
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm_splitk.hpp"
#include "ck/tensor_operation/gpu/device/device_elementwise.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_3d_impl.hpp"
#include "ck/library/tensor_operation_instance/gpu/gemm_splitk.hpp"
#include "ck/library/tensor_operation_instance/gpu/transpose_3d.hpp"
#include "ck/library/utility/check_err.hpp"
#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 {
namespace profiler {
template <typename ADataType, typename BDataType>
bool profile_gemm_splitk_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
int N,
int C,
int D,
int H,
int W)
template <typename HostTensorA, typename HostTensorB, typename Functor>
void host_elementwise4D(HostTensorB& B_nchwd, const HostTensorA& A_ncdhw, Functor functor)
{
for(std::size_t n = 0; n < A_ncdhw.mDesc.GetLengths()[0]; ++n)
for(std::size_t c = 0; c < A_ncdhw.mDesc.GetLengths()[1]; ++c)
for(std::size_t d = 0; d < A_ncdhw.mDesc.GetLengths()[2]; ++d)
for(std::size_t h = 0; h < A_ncdhw.mDesc.GetLengths()[3]; ++h)
for(std::size_t w = 0; w < A_ncdhw.mDesc.GetLengths()[4]; ++w)
{
auto a_val = A_ncdhw(n, c, d, h, w);
functor(B_nchwd(n, c, h, w, d), a_val);
}
}
template <typename ADataType, typename BDataType, index_t NumDim>
bool profile_transpose_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
ck::index_t N,
ck::index_t C,
ck::index_t D,
ck::index_t H,
ck::index_t W)
{
bool pass = true;
std::vector<std::size_t> ncdhw = {N, C, D, H, W};
std::vector<std::size_t> ndhwc = {N, D, H, W, C};
std::vector<ck::index_t> ncdhw = {N, C, D, H, W};
std::vector<ck::index_t> ndhwc = {N, D, H, W, C};
Tensor<ADataType> a(ncdhw);
Tensor<BDataType> b(ndhwc);
Tensor<BDataType> host_b(ndhwc);
// a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
std::array<void*, 1> output = {b_device_buf.GetDeviceBuffer()};
std::array<ck::index_t, 5> ab_lengths{N, C, H, W, D};
std::array<ck::index_t, 5> a_strides = {C * D * H * W, H * W, W, 1, D * H * W}; // N, C, D, H, W
std::array<ck::index_t, 5> b_strides = {C * H * W * D, H * W * D, W * D, D, 1}; // N, D, H, W, C
......@@ -63,25 +75,17 @@ bool profile_gemm_splitk_impl(int do_verification,
using ElementOp = ck::tensor_operation::element_wise::PassThrough;
const auto element_op = ElementOp{};
// const auto element_op = ElementOp{};
DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a.mData.data());
using DeviceOp =
ck::tensor_operation::device::DeviceElementwise3dImpl<ck::Tuple<ADataType>,
ck::Tuple<BDataType>,
ElementOp,
NumDim_m,
NumDim_n,
NumDim_k,
MPerThread,
NPerThread,
KPerThread,
ck::Sequence<InScalarPerVector>,
ck::Sequence<OutScalarPerVector>>;
std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
std::array<void*, 1> output = {b_device_buf.GetDeviceBuffer()};
using DeviceOp = ck::tensor_operation::device::
DeviceElementwise<ck::Tuple<ADataType>, ck::Tuple<BDataType>, ElementOp, NumDim>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
......@@ -91,19 +95,7 @@ bool profile_gemm_splitk_impl(int do_verification,
if(do_verification)
{
using ReferenceTransposeInstance = ck::tensor_operation::host::ReferenceTranspose
<< ck::Tuple<ADataType>,
ck::Tuple<BDataType>, ElementOp, NumDim_m, NumDim_n, NumDim_k, MPerThread, NPerThread,
KPerThread, ck::Sequence<InScalarPerVector>, ck::Sequence<OutScalarPerVector> > ;
auto ref_transpose = ReferenceTransposeInstance{};
auto ref_invoker = ref_transpose.MakeInvoker();
auto ref_argument =
ref_transpose
.MakeArgument(ab_lengths, {a_strides}, {b_strides}, input, output, element_op{})
ref_invoker.Run(ref_argument);
host_elementwise4D(host_b, a, ElementOp{});
}
std::string best_op_name;
......@@ -114,7 +106,7 @@ bool profile_gemm_splitk_impl(int do_verification,
for(auto& op_ptr : op_ptrs)
{
auto argument_ptr = op_ptr->MakeArgumentPointer(
ab_lengths, {a_strides}, {b_strides}, input, output, element_op{});
ab_lengths, {a_strides}, {b_strides}, input, output, ElementOp{});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
......@@ -128,9 +120,11 @@ bool profile_gemm_splitk_impl(int do_verification,
if(do_verification)
{
b_device_buf.FromDevice(b_device_result.mData.data());
b_device_buf.FromDevice(b.mData.data());
pass = pass & ck::utils::check_err(b_device_result, b_host_result);
// pass = pass & ck::utils::check_err(b_device_result, b_host_result);
pass &= ck::utils::check_err(
b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
if(do_log)
{
......@@ -158,7 +152,9 @@ bool profile_gemm_splitk_impl(int do_verification,
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
pass = pass & ck::utils::check_err(b_device_result, b_host_result);
// pass = pass & ck::utils::check_err(b_device_result, b_host_result);
pass &= ck::utils::check_err(
b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
if(tflops > best_tflops)
{
......@@ -173,22 +169,12 @@ bool profile_gemm_splitk_impl(int do_verification,
std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
}
}
}
if constexpr(is_same<BDataType, float>::value)
{
std::cout << "Best Perf for datatype = f32";
}
else if constexpr(is_same<BDataType, half_t>::value)
{
std::cout << "Best Perf for datatype = f16";
}
std::cout << " N = " << N << " C = " << C << " D = " << D << " H = " << H << " W = " << W << " : "
<< best_ave_time << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec << " GB/s, "
<< best_op_name << std::endl;
std::cout << " N = " << N << " C = " << C << " D = " << D << " H = " << H << " W = " << W
<< " : " << best_ave_time << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec
<< " GB/s, " << best_op_name << std::endl;
return pass;
return pass;
}
} // namespace profiler
......
test/CMakeLists.txt
View file @ f3b6e205
......@@ -156,6 +156,7 @@ add_subdirectory(pool)
add_subdirectory(batched_gemm_multi_d)
add_subdirectory(grouped_convnd_bwd_data)
add_subdirectory(conv_tensor_rearrange)
add_subdirectory(transpose)
if(GPU_TARGETS MATCHES "gfx11")
add_subdirectory(wmma_op)
endif()
test/transpose/test_transpose.cpp
View file @ f3b6e205
......@@ -5,19 +5,13 @@
#include "gtest/gtest.h"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "test_tranpose_util.hpp"
#include "test_transpose_util.hpp"
using F16 = ck::half_t;
using F32 = float;
enum struct MatrixLayout
{
NCDHW, // 0
NCHWD, // 1
};
template <typename Tuple>
class TestTranspose : public ck::test::TestTranspose<typename MatrixLayout<NCDHW>::type>
class TestTranspose : public ck::test::TestTranspose<Tuple>
{
};
......@@ -28,6 +22,6 @@ using KernelTypes = ::testing::Types<
>;
// clang-format on
TYPED_TEST_SUITE(TestGemmSplitK_MK_KN, KernelTypes);
TYPED_TEST_SUITE(TestTranspose, KernelTypes);
//#include "test_transpose_ut_cases.inc"
\ No newline at end of file
//#include "test_transpose_ut_cases.inc"
test/transpose/test_transpose_interface.cpp → test/transpose/test_transpose_util.hpp
View file @ f3b6e205
......@@ -24,8 +24,6 @@ class TestTranspose : public testing::Test
using F32 = float;
protected:
// using ALayout = std::tuple_element_t<0, Tuple>;
// using BLayout = std::tuple_element_t<1, Tuple>;
using ADataType = std::tuple_element_t<0, Tuple>;
using BDataType = std::tuple_element_t<1, Tuple>;
......@@ -42,11 +40,11 @@ class TestTranspose : public testing::Test
void RunSingle(const int N, const int C, const int D, const int H, const int W)
{
bool pass = ck::profiler::profile_transpose_impl<ADataType, BDataType, >(
bool pass = ck::profiler::profile_transpose_impl<ADataType, BDataType, 5>(
verify_, init_method_, log_, bench_, N, C, D, H, W);
EXPECT_TRUE(pass);
}
};
} // namespace test
} // namespace ck
\ No newline at end of file
} // namespace ck
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