wehub-resource-sync
222 lines
7.8 KiB
Plaintext
222 lines
7.8 KiB
Plaintext
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <gtest/gtest.h>
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#include <vector>
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#include "glog/logging.h"
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#include "paddle/phi/api/lib/utils/allocator.h"
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#include "paddle/phi/backends/context_pool.h"
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/funcs/broadcast_function.h"
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template <typename T>
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struct AddTernary_1 {
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inline HOSTDEVICE T operator()(T a, T b, T c) const { return a + b + c; }
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};
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template <typename T>
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struct AddTernary_2 {
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inline HOSTDEVICE T operator()(T a, T b, T c) const { return a + b + c; }
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};
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template <typename T>
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struct AddTernary_3 {
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inline HOSTDEVICE T operator()(T a, T b, T c) const { return a + b + c; }
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};
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template <typename T>
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void InitValue(T* data, size_t numel, const int val) {
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for (auto i = 0; i < numel; ++i) {
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data[i] = static_cast<T>(val);
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}
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}
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template <typename T, typename Func>
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void TestCase(const phi::GPUContext& dev_ctx,
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const phi::DDim& dim1,
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const phi::DDim& dim2,
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const phi::DDim& dim3,
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const phi::DDim& dim_out,
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const size_t times,
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Func compute) {
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phi::DataType dtype = phi::CppTypeToDataType<T>::Type();
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const auto alloc_cpu =
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std::make_unique<paddle::experimental::DefaultAllocator>(phi::CPUPlace());
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const auto alloc_gpu =
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std::make_unique<paddle::experimental::DefaultAllocator>(phi::GPUPlace());
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auto in1 = std::make_shared<phi::DenseTensor>(
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alloc_cpu.get(),
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phi::DenseTensorMeta(dtype, dim1, phi::DataLayout::NCHW));
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auto in2 = std::make_shared<phi::DenseTensor>(
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alloc_cpu.get(),
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phi::DenseTensorMeta(dtype, dim2, phi::DataLayout::NCHW));
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auto in3 = std::make_shared<phi::DenseTensor>(
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alloc_cpu.get(),
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phi::DenseTensorMeta(dtype, dim3, phi::DataLayout::NCHW));
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InitValue(in1->data<T>(), in1->numel(), 1);
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InitValue(in2->data<T>(), in2->numel(), 1);
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InitValue(in3->data<T>(), in3->numel(), 1);
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auto d_in1 = std::make_shared<phi::DenseTensor>(
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alloc_gpu.get(),
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phi::DenseTensorMeta(dtype, dim1, phi::DataLayout::NCHW));
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auto d_in2 = std::make_shared<phi::DenseTensor>(
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alloc_gpu.get(),
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phi::DenseTensorMeta(dtype, dim2, phi::DataLayout::NCHW));
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auto d_in3 = std::make_shared<phi::DenseTensor>(
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alloc_gpu.get(),
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phi::DenseTensorMeta(dtype, dim3, phi::DataLayout::NCHW));
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auto d_out = std::make_shared<phi::DenseTensor>(
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alloc_gpu.get(),
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phi::DenseTensorMeta(dtype, dim_out, phi::DataLayout::NCHW));
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phi::Copy(dev_ctx, *in1.get(), phi::GPUPlace(), false, d_in1.get());
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phi::Copy(dev_ctx, *in2.get(), phi::GPUPlace(), false, d_in2.get());
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phi::Copy(dev_ctx, *in3.get(), phi::GPUPlace(), false, d_in3.get());
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std::vector<const phi::DenseTensor*> inputs{
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d_in1.get(), d_in2.get(), d_in3.get()};
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std::vector<phi::DenseTensor*> outputs{d_out.get()};
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for (int i = 0; i < times; ++i) {
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phi::funcs::BroadcastKernel<T>(dev_ctx, inputs, &outputs, compute);
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}
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dev_ctx.Wait();
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}
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TEST(Broadcast, add) {
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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auto place = phi::GPUPlace();
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phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance();
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auto* dev_ctx = static_cast<const phi::GPUContext*>(pool.GetByPlace(place));
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size_t times = 10;
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do {
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auto dim1 = common::make_ddim({1, 2048, 3584});
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auto dim2 = common::make_ddim({1, 2048, 1});
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auto dim3 = common::make_ddim({1, 1, 3584});
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auto dim_out = common::make_ddim({1, 2048, 3584});
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TestCase<float>(
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*dev_ctx, dim1, dim2, dim3, dim_out, times, AddTernary_1<float>());
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TestCase<phi::dtype::float16>(*dev_ctx,
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dim1,
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dim2,
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dim3,
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dim_out,
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times,
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AddTernary_1<phi::dtype::float16>());
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TestCase<phi::dtype::bfloat16>(*dev_ctx,
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dim1,
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dim2,
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dim3,
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dim_out,
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times,
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AddTernary_1<phi::dtype::bfloat16>());
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TestCase<phi::dtype::complex<float>>(
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*dev_ctx,
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dim1,
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dim2,
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dim3,
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dim_out,
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times,
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AddTernary_1<phi::dtype::complex<float>>());
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TestCase<phi::dtype::complex<double>>(
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*dev_ctx,
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dim1,
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dim2,
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dim3,
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dim_out,
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times,
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AddTernary_1<phi::dtype::complex<double>>());
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} while (0);
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do {
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auto dim1 = common::make_ddim({1, 256, 4, 256, 256});
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auto dim2 = common::make_ddim({1, 256, 1, 1, 256});
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auto dim3 = common::make_ddim({1, 1, 4, 256, 256});
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auto dim_out = common::make_ddim({1, 256, 4, 256, 256});
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TestCase<float>(
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*dev_ctx, dim1, dim2, dim3, dim_out, times, AddTernary_2<float>());
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TestCase<phi::dtype::float16>(*dev_ctx,
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dim1,
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dim2,
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dim3,
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dim_out,
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times,
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AddTernary_2<phi::dtype::float16>());
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TestCase<phi::dtype::bfloat16>(*dev_ctx,
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dim1,
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dim2,
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dim3,
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dim_out,
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times,
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AddTernary_2<phi::dtype::bfloat16>());
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TestCase<phi::dtype::complex<float>>(
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*dev_ctx,
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dim1,
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dim2,
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dim3,
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dim_out,
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times,
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AddTernary_2<phi::dtype::complex<float>>());
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TestCase<phi::dtype::complex<double>>(
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*dev_ctx,
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dim1,
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dim2,
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dim3,
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dim_out,
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times,
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AddTernary_2<phi::dtype::complex<double>>());
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} while (0);
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do {
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auto dim1 = common::make_ddim({1, 256, 256});
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auto dim2 = common::make_ddim({1, 1, 256});
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auto dim3 = common::make_ddim({1, 256, 1});
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auto dim_out = common::make_ddim({1, 256, 256});
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TestCase<float>(
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*dev_ctx, dim1, dim2, dim3, dim_out, times, AddTernary_3<float>());
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TestCase<phi::dtype::float16>(*dev_ctx,
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dim1,
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dim2,
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dim3,
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dim_out,
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times,
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AddTernary_3<phi::dtype::float16>());
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TestCase<phi::dtype::bfloat16>(*dev_ctx,
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dim1,
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dim2,
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dim3,
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dim_out,
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times,
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AddTernary_3<phi::dtype::bfloat16>());
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TestCase<phi::dtype::complex<float>>(
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*dev_ctx,
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dim1,
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dim2,
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dim3,
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dim_out,
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times,
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AddTernary_3<phi::dtype::complex<float>>());
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TestCase<phi::dtype::complex<double>>(
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*dev_ctx,
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dim1,
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dim2,
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dim3,
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dim_out,
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times,
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AddTernary_3<phi::dtype::complex<double>>());
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} while (0);
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#endif
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}
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