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98 lines
3.0 KiB
98 lines
3.0 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 "paddle/fluid/memory/memcpy.h"
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#include "paddle/fluid/memory/memory.h"
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#include "paddle/fluid/platform/hostdevice.h"
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#include "paddle/fluid/platform/transform.h"
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template <typename T>
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class Scale {
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public:
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explicit Scale(const T& scale) : scale_(scale) {}
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HOSTDEVICE T operator()(const T& a) const { return a * scale_; }
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private:
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T scale_;
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};
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template <typename T>
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class Multiply {
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public:
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HOSTDEVICE T operator()(const T& a, const T& b) const { return a * b; }
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};
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using paddle::memory::Alloc;
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using paddle::memory::Copy;
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using paddle::platform::CPUPlace;
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using paddle::platform::CUDAPlace;
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using paddle::platform::CPUDeviceContext;
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using paddle::platform::CUDADeviceContext;
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using paddle::platform::Transform;
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TEST(Transform, CPUUnary) {
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CPUDeviceContext ctx;
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float buf[4] = {0.1, 0.2, 0.3, 0.4};
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Transform<CPUDeviceContext> trans;
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trans(ctx, buf, buf + 4, buf, Scale<float>(10));
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for (int i = 0; i < 4; ++i) {
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ASSERT_NEAR(buf[i], static_cast<float>(i + 1), 1e-5);
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}
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}
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TEST(Transform, GPUUnary) {
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CUDAPlace gpu0(0);
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CUDADeviceContext ctx(gpu0);
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float cpu_buf[4] = {0.1, 0.2, 0.3, 0.4};
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auto gpu_allocation = Alloc(gpu0, sizeof(float) * 4);
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float* gpu_buf = static_cast<float*>(gpu_allocation->ptr());
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Copy(gpu0, gpu_buf, CPUPlace(), cpu_buf, sizeof(cpu_buf), ctx.stream());
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Transform<CUDADeviceContext> trans;
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trans(ctx, gpu_buf, gpu_buf + 4, gpu_buf, Scale<float>(10));
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ctx.Wait();
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Copy(CPUPlace(), cpu_buf, gpu0, gpu_buf, sizeof(cpu_buf), ctx.stream());
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for (int i = 0; i < 4; ++i) {
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ASSERT_NEAR(cpu_buf[i], static_cast<float>(i + 1), 1e-5);
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}
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}
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TEST(Transform, CPUBinary) {
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int buf[4] = {1, 2, 3, 4};
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Transform<CPUDeviceContext> trans;
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CPUDeviceContext ctx;
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trans(ctx, buf, buf + 4, buf, buf, Multiply<int>());
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for (int i = 0; i < 4; ++i) {
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ASSERT_EQ((i + 1) * (i + 1), buf[i]);
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}
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}
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TEST(Transform, GPUBinary) {
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int buf[4] = {1, 2, 3, 4};
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CUDAPlace gpu0(0);
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CUDADeviceContext ctx(gpu0);
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auto gpu_allocation = Alloc(gpu0, sizeof(buf));
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int* gpu_buf = static_cast<int*>(gpu_allocation->ptr());
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Copy(gpu0, gpu_buf, CPUPlace(), buf, sizeof(buf), ctx.stream());
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Transform<CUDADeviceContext> trans;
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trans(ctx, gpu_buf, gpu_buf + 4, gpu_buf, gpu_buf, Multiply<int>());
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ctx.Wait();
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Copy(CPUPlace(), buf, gpu0, gpu_buf, sizeof(buf), ctx.stream());
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for (int i = 0; i < 4; ++i) {
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ASSERT_EQ((i + 1) * (i + 1), buf[i]);
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}
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}
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