mirror of
https://github.com/saymrwulf/pytorch.git
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278d398748
Summary: Add GPU version of math::Transpose Reviewed By: Yangqing Differential Revision: D6747958 fbshipit-source-id: 7047107609386c1ab53492381ca9bcf8bccd2924
462 lines
13 KiB
C++
462 lines
13 KiB
C++
/**
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* Copyright (c) 2016-present, Facebook, Inc.
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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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*/
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#include <iostream>
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#include <memory>
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#include <vector>
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#include <gtest/gtest.h>
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#include "caffe2/core/context.h"
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#include "caffe2/core/context_gpu.h"
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#include "caffe2/core/flags.h"
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#include "caffe2/operators/utility_ops.h"
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#include "caffe2/utils/math.h"
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CAFFE2_DECLARE_string(caffe_test_root);
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namespace caffe2 {
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void executeGpuBinaryOpTest(
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int shapex0,
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int shapex1,
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int shapey,
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std::function<float(int)> input0,
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std::function<float(int)> input1,
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std::function<void(
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int N0,
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int N1,
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const float* src0,
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const float* src1,
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float* dst,
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CUDAContext* context)> operation,
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std::function<float(int)> correct_output) {
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if (!HasCudaGPU())
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return;
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Workspace ws;
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DeviceOption option;
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option.set_device_type(CUDA);
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CUDAContext context(option);
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Blob* blobx0 = ws.CreateBlob("X0");
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Blob* blobx1 = ws.CreateBlob("X1");
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Blob* bloby = ws.CreateBlob("Y");
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Blob* bloby_host = ws.CreateBlob("Y_host");
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auto* tensorx0 = blobx0->GetMutable<Tensor<CUDAContext>>();
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auto* tensorx1 = blobx1->GetMutable<Tensor<CUDAContext>>();
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auto* tensory = bloby->GetMutable<Tensor<CUDAContext>>();
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vector<int> shapex0_vector{shapex0};
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vector<int> shapex1_vector{shapex1};
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vector<int> shapey_vector{shapey};
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tensorx0->Resize(shapex0_vector);
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tensorx1->Resize(shapex1_vector);
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tensory->Resize(shapey_vector);
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for (int i = 0; i < shapex0; i++) {
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math::Set<float, CUDAContext>(
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1, input0(i), tensorx0->mutable_data<float>() + i, &context);
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}
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for (int i = 0; i < shapex1; i++) {
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math::Set<float, CUDAContext>(
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1, input1(i), tensorx1->mutable_data<float>() + i, &context);
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}
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operation(
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shapex0,
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shapex1,
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tensorx0->template data<float>(),
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tensorx1->template data<float>(),
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tensory->mutable_data<float>(),
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&context);
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context.FinishDeviceComputation();
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// Copy result to CPU so we can inspect it
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auto* tensory_host = bloby_host->GetMutable<Tensor<CPUContext>>();
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tensory_host->CopyFrom<CUDAContext, CUDAContext>(*tensory, &context);
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context.FinishDeviceComputation();
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for (int i = 0; i < shapey; ++i) {
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EXPECT_EQ(tensory_host->data<float>()[i], correct_output(i));
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}
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}
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TEST(MathUtilGPUTest, testAddStripedBatch) {
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if (!HasCudaGPU())
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return;
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Workspace ws;
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DeviceOption option;
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option.set_device_type(CUDA);
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CUDAContext context(option);
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Blob* blobx = ws.CreateBlob("X");
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Blob* bloby = ws.CreateBlob("Y");
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Blob* bloby_host = ws.CreateBlob("Y_host");
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vector<int> shapex{33 * 9, 25};
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vector<int> shapey{33, 25};
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auto* tensorx = blobx->GetMutable<Tensor<CUDAContext>>();
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tensorx->Resize(shapex);
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int stripe = 33 * 25;
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vector<float> tot(33, 0.0);
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for (int j = 0; j < 9; j++) {
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// Have different values for each line
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for (int k = 0; k < 33; k++) {
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math::Set<float, CUDAContext>(
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33,
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1.0 + j + k,
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tensorx->mutable_data<float>() + j * stripe + k * 25,
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&context);
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tot[k] += 1.0 + j + k;
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}
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}
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auto* tensory = bloby->GetMutable<Tensor<CUDAContext>>();
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tensory->Resize(shapey);
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math::Set<float, CUDAContext>(
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stripe, 0.0, tensory->mutable_data<float>(), &context);
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math::AddStripedBatch<float, CUDAContext>(
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stripe,
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tensorx->template data<float>(),
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tensory->mutable_data<float>(),
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stripe,
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9,
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&context);
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context.FinishDeviceComputation();
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// Copy result to CPU so we can inspect it
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auto* tensory_host = bloby_host->GetMutable<Tensor<CPUContext>>();
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tensory_host->CopyFrom<CUDAContext, CUDAContext>(*tensory, &context);
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context.FinishDeviceComputation();
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for (int k = 0; k < 33; k++) {
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for (int i = 0; i < 25; i++) {
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EXPECT_EQ(tensory_host->data<float>()[k * 25 + i], tot[k]);
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}
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}
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}
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TEST(MathUtilGPUTest, testReduceMin) {
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executeGpuBinaryOpTest(
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6,
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1,
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1,
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[](int /*i*/) { return 11.0f; },
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[](int /*i*/) { return 0.0f; },
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[](int N0,
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int /*N1*/,
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const float* src0,
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const float* /*src1*/,
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float* dst,
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CUDAContext* context) {
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Tensor<CUDAContext> aux;
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math::ReduceMin<float, CUDAContext>(N0, src0, dst, &aux, context);
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},
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[](int /*i*/) { return 11.0f; });
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executeGpuBinaryOpTest(
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6,
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1,
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1,
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[](int i) { return i == 3 ? 11.0f : 17.0f; },
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[](int /*i*/) { return 0.0f; },
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[](int N0,
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int /*N1*/,
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const float* src0,
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const float* /*src1*/,
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float* dst,
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CUDAContext* context) {
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Tensor<CUDAContext> aux;
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math::ReduceMin<float, CUDAContext>(N0, src0, dst, &aux, context);
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},
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[](int /*i*/) { return 11.0f; });
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}
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TEST(MathUtilGPUTest, testReduceMax) {
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executeGpuBinaryOpTest(
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6,
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1,
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1,
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[](int /*i*/) { return 11.0f; },
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[](int /*i*/) { return 0.0f; },
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[](int N0,
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int /*N1*/,
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const float* src0,
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const float* /*src1*/,
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float* dst,
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CUDAContext* context) {
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Tensor<CUDAContext> aux;
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math::ReduceMax<float, CUDAContext>(N0, src0, dst, &aux, context);
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},
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[](int /*i*/) { return 11.0f; });
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executeGpuBinaryOpTest(
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6,
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1,
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1,
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[](int i) { return i == 3 ? 17.0f : 11.0f; },
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[](int /*i*/) { return 0.0f; },
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[](int N0,
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int /*N1*/,
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const float* src0,
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const float* /*src1*/,
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float* dst,
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CUDAContext* context) {
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Tensor<CUDAContext> aux;
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math::ReduceMax<float, CUDAContext>(N0, src0, dst, &aux, context);
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},
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[](int /*i*/) { return 17.0f; });
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}
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TEST(MathUtilGPUTest, testElemwiseMax) {
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executeGpuBinaryOpTest(
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13,
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13,
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13,
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[](int i) { return 2.0f - i; },
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[](int i) { return i - 6.0f; },
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[](int N0,
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int /*N1*/,
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const float* src0,
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const float* src1,
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float* dst,
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CUDAContext* context) {
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math::ElemwiseMax<float, CUDAContext>(N0, src0, src1, dst, context);
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},
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[](int i) { return std::max(2.0f - i, i - 6.0f); });
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}
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TEST(MathUtilGPUTest, testCopyVector) {
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executeGpuBinaryOpTest(
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6,
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1,
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6,
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[](int i) { return 5.0f - i; },
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[](int /*i*/) { return 0.0f; },
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[](int N0,
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int /*N1*/,
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const float* src0,
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const float* /*src1*/,
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float* dst,
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CUDAContext* context) {
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math::CopyVector<float, CUDAContext>(N0, src0, dst, context);
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},
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[](int i) { return 5.0f - i; });
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}
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namespace {
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class GemmBatchedGPUTest
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: public testing::TestWithParam<testing::tuple<bool, bool>> {
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protected:
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void SetUp() override {
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if (!HasCudaGPU()) {
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return;
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}
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option_.set_device_type(CUDA);
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cuda_context_ = make_unique<CUDAContext>(option_);
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Blob* X_blob = ws_.CreateBlob("X");
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Blob* W_blob = ws_.CreateBlob("W");
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Blob* Y_blob = ws_.CreateBlob("Y");
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X_ = X_blob->GetMutable<Tensor<CUDAContext>>();
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W_ = W_blob->GetMutable<Tensor<CUDAContext>>();
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Y_ = Y_blob->GetMutable<Tensor<CUDAContext>>();
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X_->Resize(std::vector<TIndex>{3, 5, 10});
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W_->Resize(std::vector<TIndex>{3, 6, 10});
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Y_->Resize(std::vector<TIndex>{3, 5, 6});
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math::Set<float, CUDAContext>(
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X_->size(), 1.0f, X_->mutable_data<float>(), cuda_context_.get());
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math::Set<float, CUDAContext>(
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W_->size(), 1.0f, W_->mutable_data<float>(), cuda_context_.get());
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trans_X_ = std::get<0>(GetParam());
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trans_W_ = std::get<1>(GetParam());
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}
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void RunGemmBatched(const float alpha, const float beta) {
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math::GemmBatched(
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trans_X_ ? CblasTrans : CblasNoTrans,
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trans_W_ ? CblasTrans : CblasNoTrans,
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3,
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5,
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6,
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10,
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alpha,
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X_->template data<float>(),
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W_->template data<float>(),
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beta,
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Y_->template mutable_data<float>(),
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cuda_context_.get());
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}
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void VerifyOutput(const float value) const {
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TensorCPU Y_cpu(*Y_);
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for (int i = 0; i < Y_cpu.size(); ++i) {
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EXPECT_FLOAT_EQ(value, Y_cpu.template data<float>()[i]);
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}
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}
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Workspace ws_;
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DeviceOption option_;
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std::unique_ptr<CUDAContext> cuda_context_;
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Tensor<CUDAContext>* X_ = nullptr;
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Tensor<CUDAContext>* W_ = nullptr;
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Tensor<CUDAContext>* Y_ = nullptr;
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bool trans_X_;
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bool trans_W_;
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};
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TEST_P(GemmBatchedGPUTest, GemmBatchedGPUFloatTest) {
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if (!HasCudaGPU()) {
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return;
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}
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RunGemmBatched(1.0f, 0.0f);
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VerifyOutput(10.0f);
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RunGemmBatched(1.0f, 0.5f);
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VerifyOutput(15.0f);
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RunGemmBatched(0.5f, 1.0f);
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VerifyOutput(20.0f);
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}
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INSTANTIATE_TEST_CASE_P(
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GemmBatchedGPUTrans,
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GemmBatchedGPUTest,
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testing::Combine(testing::Bool(), testing::Bool()));
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class TransposeGPUTest : public testing::Test {
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protected:
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void SetUp() override {
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if (!HasCudaGPU()) {
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return;
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}
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option_.set_device_type(CUDA);
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cuda_context_ = make_unique<CUDAContext>(option_);
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Blob* blob_x = ws_.CreateBlob("X");
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Blob* blob_y = ws_.CreateBlob("Y");
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Blob* blob_x_dims = ws_.CreateBlob("x_dims");
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Blob* blob_y_dims = ws_.CreateBlob("y_dims");
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Blob* blob_axes = ws_.CreateBlob("axes");
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X_ = blob_x->GetMutable<Tensor<CUDAContext>>();
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Y_ = blob_y->GetMutable<Tensor<CUDAContext>>();
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x_dims_device_ = blob_x_dims->GetMutable<Tensor<CUDAContext>>();
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y_dims_device_ = blob_y_dims->GetMutable<Tensor<CUDAContext>>();
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axes_device_ = blob_axes->GetMutable<Tensor<CUDAContext>>();
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}
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void SetData(
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const std::vector<int>& x_dims,
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const std::vector<int>& y_dims,
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const std::vector<int>& axes,
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const std::vector<float>& x_data) {
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x_dims_device_->Resize(x_dims.size());
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cuda_context_->Copy<int, CPUContext, CUDAContext>(
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x_dims.size(), x_dims.data(), x_dims_device_->mutable_data<int>());
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y_dims_device_->Resize(y_dims.size());
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cuda_context_->Copy<int, CPUContext, CUDAContext>(
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y_dims.size(), y_dims.data(), y_dims_device_->mutable_data<int>());
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axes_device_->Resize(axes.size());
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cuda_context_->Copy<int, CPUContext, CUDAContext>(
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axes.size(), axes.data(), axes_device_->mutable_data<int>());
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X_->Resize(x_dims);
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Y_->Resize(y_dims);
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for (std::size_t i = 0; i < x_data.size(); ++i) {
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math::Set<float, CUDAContext>(
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1, x_data[i], X_->mutable_data<float>() + i, cuda_context_.get());
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}
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}
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void RunTranspose(const int num_axes, const int data_size) {
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math::Transpose<float, CUDAContext>(
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num_axes,
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x_dims_device_->data<int>(),
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y_dims_device_->data<int>(),
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axes_device_->data<int>(),
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data_size,
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X_->data<float>(),
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Y_->mutable_data<float>(),
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cuda_context_.get());
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cuda_context_->FinishDeviceComputation();
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}
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void VerifyResult(const std::vector<float>& expected_output) {
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Blob* blob_y_host = ws_.CreateBlob("Y_host");
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auto* Y_host = blob_y_host->GetMutable<TensorCPU>();
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Y_host->CopyFrom<CUDAContext, CUDAContext>(*Y_, cuda_context_.get());
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cuda_context_->FinishDeviceComputation();
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ASSERT_EQ(expected_output.size(), Y_host->size());
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for (std::size_t i = 0; i < expected_output.size(); ++i) {
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EXPECT_FLOAT_EQ(expected_output[i], Y_host->data<float>()[i]);
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}
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}
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Workspace ws_;
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DeviceOption option_;
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std::unique_ptr<CUDAContext> cuda_context_;
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Tensor<CUDAContext>* X_ = nullptr;
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Tensor<CUDAContext>* Y_ = nullptr;
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Tensor<CUDAContext>* x_dims_device_ = nullptr;
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Tensor<CUDAContext>* y_dims_device_ = nullptr;
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Tensor<CUDAContext>* axes_device_ = nullptr;
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};
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TEST_F(TransposeGPUTest, TransposeGPUFloatTest) {
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if (!HasCudaGPU()) {
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return;
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}
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{
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// Test for 1D transpose.
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const std::vector<int> x_dims = {3};
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const std::vector<int> y_dims = {3};
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const std::vector<int> axes = {0};
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SetData(x_dims, y_dims, axes, {1.0f, 2.0f, 3.0f});
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RunTranspose(1, 3);
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VerifyResult({1.0f, 2.0f, 3.0f});
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}
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{
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// Test for 2D transpose.
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const std::vector<int> x_dims = {2, 3};
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const std::vector<int> y_dims = {3, 2};
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const std::vector<int> axes = {1, 0};
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SetData(x_dims, y_dims, axes, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f});
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RunTranspose(2, 6);
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VerifyResult({1.0f, 4.0f, 2.0f, 5.0f, 3.0f, 6.0f});
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}
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{
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// Test for 3D transpose.
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const std::vector<int> x_dims = {2, 2, 2};
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const std::vector<int> y_dims = {2, 2, 2};
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const std::vector<int> axes1 = {1, 2, 0};
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SetData(
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x_dims,
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y_dims,
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axes1,
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{1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f});
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RunTranspose(3, 8);
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VerifyResult({1.0f, 5.0f, 2.0f, 6.0f, 3.0f, 7.0f, 4.0f, 8.0f});
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const std::vector<int> axes2 = {1, 0, 2};
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SetData(
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x_dims,
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y_dims,
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axes2,
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{1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f});
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RunTranspose(3, 8);
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VerifyResult({1.0f, 2.0f, 5.0f, 6.0f, 3.0f, 4.0f, 7.0f, 8.0f});
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}
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}
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} // namespace
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} // namespace caffe2
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