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https://github.com/ROCm/composable_kernel.git
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ad57f6ef0b
* Wrap ck host utitlies in CK namespace. The CK and CK-Tile source code bases are incompatible because CK is not properly using namespaces everywhere. In particular, we need to put hip_check_error in the ck namespace. Move all functions in include/ck_/host_utility that were in global namespace into the ck namespace. There may be additional namespace problems like this, and it's possible we'll have namespace clashes. But it is good design to properly guard our to code bases (CK and CKTile) so that they can both coexist. Moreover, estabilishing this compatiblity is essential if we are going to allow the builder to instantiate kernels from either template library. * Add using declarations to test code. After moving some of the untils into the ck namespace, most examples and a few tests had to be updated to recognize the new namespace declarations. We add using declarations to individual compute units for functions that were previously in the global namespace. * Add using declarations to client examples.
213 lines
7.3 KiB
C++
213 lines
7.3 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <cstdlib>
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#include <iostream>
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#include <initializer_list>
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#include <vector>
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#include <gtest/gtest.h>
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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#include "ck/utility/common_header.hpp"
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#include "ck/wrapper/layout.hpp"
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#include "ck/wrapper/tensor.hpp"
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using ::ck::DeviceMem;
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using ::ck::launch_and_time_kernel;
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// Compare data in tensor with offset from layout.
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// Data and offset should match if physical memory has been initialized with
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// sequentially increasing values from 0.
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template <typename TensorType>
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__host__ __device__ bool TestTensorCheck3d(TensorType& tensor)
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{
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const auto& layout = ck::wrapper::layout(tensor);
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for(ck::index_t d = 0; d < ck::wrapper::size<0>(ck::wrapper::get<0>(layout)); d++)
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{
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for(ck::index_t h = 0; h < ck::wrapper::size<1>(ck::wrapper::get<0>(layout)); h++)
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{
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for(ck::index_t w = 0; w < ck::wrapper::size<1>(layout); w++)
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{
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const auto idx = ck::make_tuple(ck::make_tuple(d, h), w);
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if(tensor(idx) != layout(idx))
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{
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return false;
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}
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}
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}
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}
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return true;
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}
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template <typename TensorType>
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__host__ __device__ bool TestTensorCheck1d(TensorType& tensor, ck::index_t start_offset = 0)
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{
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const auto& layout = ck::wrapper::layout(tensor);
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for(ck::index_t w = 0; w < ck::wrapper::size<0>(layout); w++)
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{
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if(tensor(w) - start_offset != layout(ck::make_tuple(w)))
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{
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return false;
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}
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}
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return true;
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}
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template <ck::index_t nelems, typename TensorType>
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__host__ __device__ bool StaticTestTensorCheck1d(TensorType& tensor)
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{
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const auto& layout = ck::wrapper::layout(tensor);
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bool success = true;
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ck::static_for<0, nelems, 1>{}([&](auto w) {
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if(tensor(ck::Number<w.value>{}) != layout(ck::make_tuple(w.value)))
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{
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success = false;
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}
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});
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return success;
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}
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template <typename TensorType>
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__host__ __device__ void InitTensor(TensorType& tensor)
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{
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for(ck::index_t i = 0; i < ck::wrapper::size(ck::wrapper::layout(tensor)); i++)
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{
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tensor(i) = i;
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}
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}
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template <ck::index_t nelems, typename TensorType>
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__host__ __device__ void StaticInitTensor(TensorType& tensor)
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{
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ck::static_for<0, nelems, 1>{}([&](auto i) { tensor(ck::Number<i.value>{}) = i.value; });
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}
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// Tests
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TEST(TestTensor, ReadWriteHostMemory)
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{
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constexpr ck::index_t nelems = 8;
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std::array<ck::index_t, nelems> data;
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const auto layout = ck::wrapper::make_layout(ck::make_tuple(ck::make_tuple(2, 2), 2));
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auto tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Generic>(&data[0], layout);
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InitTensor(tensor);
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EXPECT_TRUE(TestTensorCheck1d(tensor));
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EXPECT_TRUE(TestTensorCheck3d(tensor));
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}
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__global__ void TestTensorReadWriteDevice(void* data, void* success)
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{
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constexpr ck::index_t nelems = 8;
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__shared__ ck::index_t p_shared[nelems];
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ck::index_t* casted_data_ptr = static_cast<ck::index_t*>(data);
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bool* casted_success_ptr = static_cast<bool*>(success);
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const auto layout = ck::wrapper::make_layout(ck::make_tuple(ck::make_tuple(2, 2), 2));
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constexpr auto vgpr_layout =
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ck::wrapper::make_layout(make_tuple(ck::Number<nelems>{}), make_tuple(ck::Number<1>{}));
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auto tensor_global =
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ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(casted_data_ptr, layout);
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auto tensor_lds = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Lds>(p_shared, layout);
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auto tensor_vgpr =
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ck::wrapper::make_register_tensor<ck::wrapper::MemoryTypeEnum::Vgpr, ck::index_t>(
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vgpr_layout);
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InitTensor(tensor_global);
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InitTensor(tensor_lds);
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StaticInitTensor<nelems>(tensor_vgpr);
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*casted_success_ptr = TestTensorCheck1d(tensor_global);
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*casted_success_ptr &= TestTensorCheck3d(tensor_global);
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*casted_success_ptr &= TestTensorCheck1d(tensor_lds);
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*casted_success_ptr &= TestTensorCheck3d(tensor_lds);
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*casted_success_ptr &= StaticTestTensorCheck1d<nelems>(tensor_vgpr);
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}
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TEST(TestTensor, ReadWriteGlobalLdsRegistersMemory)
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{
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constexpr ck::index_t nelems = 8;
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std::array<ck::index_t, nelems> host_data;
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DeviceMem data_buf(nelems * sizeof(ck::index_t));
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data_buf.ToDevice(&host_data[0]);
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DeviceMem success_buf(sizeof(bool));
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launch_and_time_kernel(StreamConfig{},
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TestTensorReadWriteDevice,
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dim3(1),
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dim3(1),
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0,
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data_buf.GetDeviceBuffer(),
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success_buf.GetDeviceBuffer());
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bool success;
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success_buf.FromDevice(&success);
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EXPECT_TRUE(success);
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}
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TEST(TestTensor, Slicing)
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{
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constexpr ck::index_t nelems = 8;
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std::array<ck::index_t, nelems> data;
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const auto shape = ck::make_tuple(ck::make_tuple(2, 2), 2);
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const auto strides = ck::make_tuple(ck::make_tuple(1, 2), 4);
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const auto layout = ck::wrapper::make_layout(shape, strides);
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auto tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Generic>(&data[0], layout);
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InitTensor(tensor);
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auto tensor2x2x2 =
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tensor(ck::make_tuple(ck::wrapper::slice(2), ck::wrapper::slice(2)), ck::wrapper::slice(2));
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EXPECT_EQ(tensor2x2x2(0), layout(ck::make_tuple(ck::make_tuple(0, 0), 0)));
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EXPECT_EQ(ck::wrapper::rank(tensor2x2x2), 2);
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EXPECT_EQ(ck::wrapper::depth(tensor2x2x2), 2);
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EXPECT_EQ(ck::wrapper::size(tensor2x2x2), 8);
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EXPECT_TRUE(TestTensorCheck1d(tensor2x2x2));
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auto tensor2x2 = tensor(ck::make_tuple(1, ck::wrapper::slice(2)), ck::wrapper::slice(2));
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EXPECT_EQ(tensor2x2(0), layout(ck::make_tuple(ck::make_tuple(1, 0), 0)));
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EXPECT_EQ(ck::wrapper::rank(tensor2x2), 2);
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EXPECT_EQ(ck::wrapper::depth(tensor2x2), 2);
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EXPECT_EQ(ck::wrapper::size(tensor2x2), 4);
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EXPECT_TRUE(TestTensorCheck1d(tensor2x2));
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auto tensor1x1 = tensor(ck::make_tuple(1, ck::wrapper::slice(1, 2)), ck::wrapper::slice(1, 2));
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EXPECT_EQ(tensor1x1(0), layout(ck::make_tuple(ck::make_tuple(1, 1), 1)));
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EXPECT_EQ(rank(tensor1x1), 2);
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EXPECT_EQ(depth(tensor1x1), 2);
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EXPECT_EQ(size(tensor1x1), 1);
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EXPECT_TRUE(TestTensorCheck1d(tensor1x1));
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auto tensor2 = tensor(ck::make_tuple(1, 1), ck::wrapper::slice(0, 2));
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EXPECT_EQ(tensor2(0), layout(ck::make_tuple(ck::make_tuple(1, 1), 0)));
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EXPECT_EQ(ck::wrapper::rank(tensor2), 1);
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EXPECT_EQ(ck::wrapper::depth(tensor2), 1);
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EXPECT_EQ(ck::wrapper::size(tensor2), 2);
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EXPECT_TRUE(TestTensorCheck1d(tensor2));
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auto tensor2_v2 = tensor(2, ck::wrapper::slice(0, 2));
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EXPECT_EQ(tensor2_v2(0), layout(ck::make_tuple(2, 0)));
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EXPECT_EQ(ck::wrapper::rank(tensor2_v2), 1);
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EXPECT_EQ(ck::wrapper::depth(tensor2_v2), 1);
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EXPECT_EQ(ck::wrapper::size(tensor2_v2), 2);
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EXPECT_TRUE(TestTensorCheck1d(tensor2_v2));
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// negative indexing
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auto tensor1x2 = tensor(ck::make_tuple(1, ck::wrapper::slice(0, -2)), ck::wrapper::slice());
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EXPECT_EQ(tensor1x2(0), layout(ck::make_tuple(ck::make_tuple(1, 0), 0)));
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EXPECT_EQ(rank(tensor1x2), 2);
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EXPECT_EQ(depth(tensor1x2), 2);
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EXPECT_EQ(size(tensor1x2), 2);
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EXPECT_TRUE(TestTensorCheck1d(tensor1x2));
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}
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