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Hip tensor permute unit test (#1068)

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* adding files for F32 example

* adding functioning implementation with scalar multiplication and unary operator support

* added fp 16 type check in unary square

* updating scalar multiplication as an operator

* functioning version with scalar operator

* changing strides for col major

* updated column major implementation

* working column major implementation

* cleaned up comments, rearranged/renamed files

* small edits to 3d transpose profiler

* adding test/profiler/instance files for hipTensor permute unit test

* added more test instances

* cleaned up errors, randomized input tensor, added more instances

* turned off time printouts

* removed conflicting transpose profiler

* rearranged some files

[ROCm/composable_kernel commit: 12a8883c48]
This commit is contained in:
arai713
2023-12-18 19:35:00 -08:00
committed by GitHub
parent 599986035e
commit 4a08e49813
10 changed files with 399 additions and 14 deletions
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11
example/44_elementwise_permute/elementwise_permute_4D_fp16_col.cpp
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@@ -1,5 +1,6 @@
#include <iostream>
#include <cstdlib>
#include <random>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
@@ -48,10 +49,8 @@ void host_elementwise4D(HostTensorB& B_nhwc,
for(std::size_t n = 0; n < N; ++n)
{
ADataType tmp_val;
// auto a_val = A_nchw(n, c, h, w);
auto a_val = A_nchw.mData[(n) + (c * N) + (h * C * N) + (w * H * C * N)];
functor_b(tmp_val, a_val);
// functor_a(B_nhwc(n, h, w, c), scale * tmp_val);
functor_a(B_nhwc.mData[(n) + (c * W * H * N) + (h * N) + (w * H * N)],
scale * tmp_val);
}
@@ -62,12 +61,14 @@ int main()
bool do_verification = true;
bool time_kernel = true;
std::vector<std::size_t> nchw = {4, 2, 1, 8};
std::vector<std::size_t> nhwc = {4, 1, 8, 2};
std::vector<std::size_t> nchw = {16, 8, 32, 64};
std::vector<std::size_t> nhwc = {16, 32, 64, 8};
Tensor<ADataType> a(nchw);
Tensor<BDataType> b(nhwc);
float scale = 1.f;
auto i = 0;
std::mt19937 gen(11939);
std::uniform_int_distribution<int> dis(0, 1);
for(std::size_t w = 0; w < a.mDesc.GetLengths()[3]; ++w)
for(std::size_t h = 0; h < a.mDesc.GetLengths()[2]; ++h)
for(std::size_t c = 0; c < a.mDesc.GetLengths()[1]; ++c)
@@ -75,7 +76,7 @@ int main()
{
a.mData[(n * nchw[1] * nchw[2] * nchw[3]) + (c * nchw[2] * nchw[3]) +
(h * nchw[3]) + w] = i;
i++;
i = dis(gen);
}
DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());

4
example/44_elementwise_permute/elementwise_permute_4D_fp32_col.cpp
View File

@@ -67,6 +67,8 @@ int main()
float scale = 1.f;
auto i = 0;
std::mt19937 gen(11939);
std::uniform_int_distribution<int> dis(0, 1);
for(std::size_t w = 0; w < a.mDesc.GetLengths()[3]; ++w)
for(std::size_t h = 0; h < a.mDesc.GetLengths()[2]; ++h)
for(std::size_t c = 0; c < a.mDesc.GetLengths()[1]; ++c)
@@ -74,7 +76,7 @@ int main()
{
a.mData[(n * nchw[1] * nchw[2] * nchw[3]) + (c * nchw[2] * nchw[3]) +
(h * nchw[3]) + w] = i;
i++;
i = dis(gen);
}
DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());

77
library/include/ck/library/tensor_operation_instance/gpu/permute_scale.hpp Normal file
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@@ -0,0 +1,77 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_elementwise_scale.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_permute_scale_f16_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F16>,
ck::Tuple<F16>,
PassThrough,
element_wise::UnarySquare,
Scale,
4>>>&);
void add_device_permute_scale_f32_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F32>,
ck::Tuple<F32>,
PassThrough,
element_wise::UnarySquare,
Scale,
4>>>&);
template <typename InDataTypeTuple,
typename OutDataTypeTuple,
typename ElementwiseOperation,
typename UnaryOperation,
typename Scale,
index_t NumDim>
struct DeviceOperationInstanceFactory<
ck::tensor_operation::device::DeviceElementwise<InDataTypeTuple,
OutDataTypeTuple,
ElementwiseOperation,
UnaryOperation,
Scale,
NumDim>>
{
using DeviceOp = DeviceElementwise<InDataTypeTuple,
OutDataTypeTuple,
ElementwiseOperation,
UnaryOperation,
Scale,
NumDim>;
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F32>> &&
is_same_v<OutDataTypeTuple, ck::Tuple<F32>>)
{
add_device_permute_scale_f32_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F16>> &&
is_same_v<OutDataTypeTuple, ck::Tuple<F16>>)
{
add_device_permute_scale_f16_instances(op_ptrs);
}
return op_ptrs;
}
};
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

2
library/src/tensor_operation_instance/gpu/permute_scale/CMakeLists.txt Normal file
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@@ -0,0 +1,2 @@
add_instance_library(device_permute_scale_instance
device_permute_scale_instances.cpp)

56
library/src/tensor_operation_instance/gpu/permute_scale/device_permute_scale_instances.cpp Normal file
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@@ -0,0 +1,56 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_scale_impl.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F16 = ck::half_t;
using F32 = float;
using Pass = ck::tensor_operation::element_wise::PassThrough;
using UnaryOp = ck::tensor_operation::element_wise::UnarySquare;
using Scale = ck::tensor_operation::element_wise::Scale;
// clang-format off
using device_permute_scale_f16_instances =
std::tuple <
DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4, 1, ck::Sequence<1>, ck::Sequence<1>>,
DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4, 8, ck::Sequence<1>, ck::Sequence<1>>,
DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4, 4, ck::Sequence<1>, ck::Sequence<1>>,
DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4, 2, ck::Sequence<1>, ck::Sequence<1>>
>;
using device_permute_scale_f32_instances = std::tuple<
DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4, 1, ck::Sequence<1>, ck::Sequence<1>>,
DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4, 8, ck::Sequence<1>, ck::Sequence<1>>,
DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4, 4, ck::Sequence<1>, ck::Sequence<1>>,
DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4, 2, ck::Sequence<1>, ck::Sequence<1>>
>;
// clang-format on
void add_device_permute_scale_f16_instances(
std::vector<std::unique_ptr<
DeviceElementwise<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4>>>& instances)
{
add_device_operation_instances(instances, device_permute_scale_f16_instances{});
}
void add_device_permute_scale_f32_instances(
std::vector<std::unique_ptr<
DeviceElementwise<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4>>>& instances)
{
add_device_operation_instances(instances, device_permute_scale_f32_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

8
library/src/tensor_operation_instance/gpu/transpose/device_transpose_instances_3d.cpp
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@@ -19,22 +19,14 @@ void add_device_transpose_f16_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F16>, ck::Tuple<F16>, PassThrough, 5>>>&
instances)
{
#ifdef CK_ENABLE_FP16
add_device_operation_instances(instances, device_transpose_f16_instances{});
#else
ignore = instances;
#endif
}
void add_device_transpose_f32_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F32>, ck::Tuple<F32>, PassThrough, 5>>>&
instances)
{
#ifdef CK_ENABLE_FP32
add_device_operation_instances(instances, device_transpose_f32_instances{});
#else
ignore = instances;
#endif
}
} // namespace instance

1
test/CMakeLists.txt
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@@ -150,6 +150,7 @@ add_subdirectory(batched_gemm_multi_d)
add_subdirectory(grouped_convnd_bwd_data)
add_subdirectory(conv_tensor_rearrange)
add_subdirectory(transpose)
add_subdirectory(permute_scale)
add_subdirectory(wrapper)
if(GPU_TARGETS MATCHES "gfx11")
add_subdirectory(wmma_op)

6
test/permute_scale/CMakeLists.txt Normal file
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@@ -0,0 +1,6 @@
add_custom_target(test_permute)
add_gtest_executable(test_permute_scale test_permute_scale.cpp)
if(result EQUAL 0)
target_link_libraries(test_permute_scale PRIVATE utility device_permute_scale_instance)
add_dependencies(test_permute test_permute_scale)
endif()

36
test/permute_scale/test_permute_scale.cpp Normal file
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@@ -0,0 +1,36 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "test_permute_scale_impl.hpp"
using F16 = ck::half_t;
using F32 = float;
using ck::index_t;
template <typename Tuple>
class TestPermute : public ::testing::Test
{
protected:
using ADataType = std::tuple_element_t<0, Tuple>;
using BDataType = std::tuple_element_t<1, Tuple>;
void Run()
{
std::vector<std::vector<ck::index_t>> lengths = {
{4, 2, 1, 8}, {1, 1, 1, 1}, {16, 8, 32, 64}, {32, 64, 128, 128}};
for(auto length : lengths)
{
bool success =
ck::test_permute_scale_impl<ADataType, BDataType, 4>(true, 2, false, false, length);
EXPECT_TRUE(success);
}
}
};
using KernelTypes = ::testing::Types<std::tuple<F16, F16>, std::tuple<F32, F32>>;
TYPED_TEST_SUITE(TestPermute, KernelTypes);
TYPED_TEST(TestPermute, Test_FP16) { this->Run(); }
TYPED_TEST(TestPermute, Test_FP32) { this->Run(); }

212
test/permute_scale/test_permute_scale_impl.hpp Normal file
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@@ -0,0 +1,212 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iomanip>
#include <random>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_elementwise_scale.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_scale_impl.hpp"
#include "ck/library/tensor_operation_instance/gpu/permute_scale.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"
namespace ck {
template <typename HostTensorA, typename HostTensorB, typename FunctorA, typename FunctorB>
void host_elementwise4D(HostTensorB& B_nhwc,
const HostTensorA& A_nchw,
FunctorA functor_a,
FunctorB functor_b,
float scale)
{
std::size_t N = A_nchw.mDesc.GetLengths()[0];
std::size_t C = A_nchw.mDesc.GetLengths()[1];
std::size_t H = A_nchw.mDesc.GetLengths()[2];
std::size_t W = A_nchw.mDesc.GetLengths()[3];
for(std::size_t w = 0; w < W; ++w)
for(std::size_t h = 0; h < H; ++h)
for(std::size_t c = 0; c < C; ++c)
for(std::size_t n = 0; n < N; ++n)
{
using tmp_type = ck::remove_reference_t<decltype(B_nhwc(0, 0))>;
tmp_type tmp_val = 0;
auto a_val = A_nchw.mData[(n) + (c * N) + (h * C * N) + (w * H * C * N)];
functor_b(tmp_val, a_val);
functor_a(B_nhwc.mData[(n) + (c * W * H * N) + (h * N) + (w * H * N)],
scale * tmp_val);
}
}
template <typename ADataType, typename BDataType, index_t NumDim>
bool test_permute_scale_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
std::vector<index_t> lengths)
{
bool pass = true;
using ElementOp = ck::tensor_operation::element_wise::PassThrough;
using UnaryOp = ck::tensor_operation::element_wise::UnarySquare;
using Scale = ck::tensor_operation::element_wise::Scale;
float scale = 2.f;
index_t N = lengths[0];
index_t C = lengths[1];
index_t H = lengths[2];
index_t W = lengths[3];
std::vector<ck::index_t> nchw = {N, C, H, W};
std::vector<ck::index_t> nhwc = {N, H, W, C};
Tensor<ADataType> a(nchw);
Tensor<BDataType> b(nhwc);
Tensor<BDataType> host_b(nhwc);
std::array<ck::index_t, 4> ab_lengths;
std::array<ck::index_t, 4> a_strides = {1,
static_cast<int>(nchw[0]),
static_cast<int>(nchw[0] * nchw[1]),
static_cast<int>(nchw[0] * nchw[1] * nchw[2])};
std::array<ck::index_t, 4> b_strides = {1,
static_cast<int>(nhwc[0] * nhwc[1] * nhwc[2]),
static_cast<int>(nhwc[0]),
static_cast<int>(nhwc[0] * nhwc[1])};
ck::ranges::copy(nchw, ab_lengths.begin());
std::cout << "A: " << a.mDesc << std::endl;
std::cout << "B: " << b.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1: a.GenerateTensorValue(GeneratorTensor_2<ADataType>{-1, 2}); break;
default: // a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}
std::mt19937 gen(11939);
std::uniform_int_distribution<int> dis(0, 1);
auto i = 0;
for(std::size_t w = 0; w < a.mDesc.GetLengths()[3]; ++w)
for(std::size_t h = 0; h < a.mDesc.GetLengths()[2]; ++h)
for(std::size_t c = 0; c < a.mDesc.GetLengths()[1]; ++c)
for(std::size_t n = 0; n < a.mDesc.GetLengths()[0]; ++n)
{
a.mData[(n * nchw[1] * nchw[2] * nchw[3]) + (c * nchw[2] * nchw[3]) +
(h * nchw[3]) + w] = i;
i = dis(gen);
}
}
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());
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,
UnaryOp,
Scale,
NumDim>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
std::string best_instance_name;
float best_ave_time = std::numeric_limits<float>::max();
float best_gb_per_sec = 0;
float best_tflops = 0;
if(do_verification)
{
host_elementwise4D(host_b, a, ElementOp{}, UnaryOp{}, scale);
}
for(auto& op_ptr : op_ptrs)
{
auto argument_ptr = op_ptr->MakeArgumentPointer(ab_lengths,
{a_strides},
{b_strides},
input,
output,
ElementOp{},
UnaryOp{},
Scale{scale});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
b_device_buf.SetZero();
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
if(do_verification)
{
b_device_buf.FromDevice(b.mData.data());
pass &= ck::utils::check_err(
b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
if(do_log)
{
LogRangeAsType<float>(std::cout << "a : ", a.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "b: ", b.mData, ",") << std::endl;
}
}
std::string op_name = op_ptr->GetTypeString();
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * nchw[0] * nchw[1] * nchw[2] * nchw[3];
std::size_t num_btype = sizeof(ADataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]) +
sizeof(BDataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]);
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
best_instance_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
}
}
if(time_kernel)
{
LogRange(std::cout << "length = ", lengths, ",") << ", ";
std::cout << "best perf = " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
<< best_instance_name << std::endl;
}
return true;
}
} // namespace ck