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test_cudnn_convolution.cc
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test_cudnn_convolution.cc
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/************************************************************
*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*
*************************************************************/
#include "../src/model/layer/cudnn_convolution.h"
#ifdef USE_CUDNN
#include "gtest/gtest.h"
using singa::CudnnConvolution;
using singa::Shape;
TEST(CudnnConvolution, Setup) {
CudnnConvolution conv;
// EXPECT_EQ("CudnnConvolution", conv.layer_type());
singa::LayerConf conf;
singa::ConvolutionConf *convconf = conf.mutable_convolution_conf();
convconf->set_kernel_h(2);
convconf->set_kernel_w(2);
convconf->set_pad_h(1);
convconf->set_pad_w(1);
convconf->set_stride_h(1);
convconf->set_stride_w(1);
convconf->set_num_output(2);
convconf->set_bias_term(true);
// MB
convconf->set_workspace_byte_limit(256);
convconf->set_prefer("fastest");
conv.Setup(Shape{1, 3, 3}, conf);
EXPECT_EQ(2u, conv.kernel_h());
EXPECT_EQ(2u, conv.kernel_w());
EXPECT_EQ(1u, conv.pad_h());
EXPECT_EQ(1u, conv.pad_w());
EXPECT_EQ(1u, conv.stride_h());
EXPECT_EQ(1u, conv.stride_w());
EXPECT_EQ(2u, conv.num_filters());
EXPECT_EQ(true, conv.bias_term());
EXPECT_EQ(256u << 20, conv.workspace_byte_limit());
EXPECT_STREQ("fastest", conv.prefer().c_str());
EXPECT_EQ(1u, conv.channels());
EXPECT_EQ(3u, conv.height());
EXPECT_EQ(3u, conv.width());
}
TEST(CudnnConvolution, Forward) {
const size_t batchsize = 1, c = 1, h = 3, w = 3;
const float x[batchsize * c * h * w] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
6.0f, 7.0f, 8.0f, 9.0f};
auto cuda = std::make_shared<singa::CudaGPU>();
singa::Tensor in(singa::Shape{batchsize, c, h, w}, cuda);
in.CopyDataFromHostPtr(x, batchsize * c * h * w);
// Set weight and bias manually
const size_t num_filters = 1;
const size_t col_height = 1 * 3 * 3; // channels * kernel_w * kernel_h
const float we[num_filters * col_height] = {1.0f, 1.0f, 0.0f, 0.0f, 0.0f,
-1.0f, 0.0f, 1.0f, 0.0f};
singa::Tensor weight(singa::Shape{num_filters, col_height}, cuda);
weight.CopyDataFromHostPtr(we, col_height);
const float b[num_filters] = {1.0f};
singa::Tensor bias(singa::Shape{num_filters}, cuda);
bias.CopyDataFromHostPtr(b, num_filters);
CudnnConvolution conv;
conv.set_weight(weight);
conv.set_bias(bias);
singa::LayerConf conf;
singa::ConvolutionConf *convconf = conf.mutable_convolution_conf();
convconf->set_kernel_h(3);
convconf->set_kernel_w(3);
convconf->set_pad_h(1);
convconf->set_pad_w(1);
convconf->set_stride_h(2);
convconf->set_stride_w(2);
convconf->set_num_output(1);
convconf->set_bias_term(true);
// MB
convconf->set_workspace_byte_limit(256);
convconf->set_prefer("fastest");
conv.Setup(Shape{1, 3, 3}, conf);
// Parameter "flag" does not influence convolution
singa::Tensor out1 = conv.Forward(singa::kTrain, in);
out1.ToHost();
const float *outptr1 = out1.data<float>();
// Input: 3*3; kernel: 3*3; stride: 2*2; padding: 1*1.
EXPECT_EQ(4u, out1.Size());
EXPECT_EQ(3.0f, outptr1[0]);
EXPECT_EQ(7.0f, outptr1[1]);
EXPECT_EQ(-3.0f, outptr1[2]);
EXPECT_EQ(12.0f, outptr1[3]);
}
TEST(CudnnConvolution, Backward) {
// src_data
const size_t batchsize = 1, c = 1, src_h = 3, src_w = 3;
const float x[batchsize * c * src_h * src_w] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
6.0f, 7.0f, 8.0f, 9.0f};
auto cuda = std::make_shared<singa::CudaGPU>();
singa::Tensor in(singa::Shape{batchsize, c, src_h, src_w}, cuda);
in.CopyDataFromHostPtr(x, batchsize * c * src_h * src_w);
// Set weight_ and bias_ manually
const size_t num_filters = 1;
const size_t col_height = 1 * 3 * 3; // channels * kernel_w * kernel_h
const float we[num_filters * col_height] = {1.0f, 1.0f, 0.0f, 0.0f, 0.0f,
-1.0f, 0.0f, 1.0f, 0.0f};
singa::Tensor weight(singa::Shape{num_filters, col_height}, cuda);
weight.CopyDataFromHostPtr(we, col_height);
const float b[num_filters] = {1.0f};
singa::Tensor bias(singa::Shape{num_filters}, cuda);
bias.CopyDataFromHostPtr(b, num_filters);
CudnnConvolution conv;
conv.set_weight(weight);
conv.set_bias(bias);
singa::LayerConf conf;
singa::ConvolutionConf *convconf = conf.mutable_convolution_conf();
convconf->set_kernel_h(3);
convconf->set_kernel_w(3);
convconf->set_pad_h(1);
convconf->set_pad_w(1);
convconf->set_stride_h(2);
convconf->set_stride_w(2);
convconf->set_num_output(1);
convconf->set_bias_term(true);
convconf->set_workspace_byte_limit(256);
convconf->set_prefer("fastest");
conv.Setup(Shape{1, 3, 3}, conf);
// Parameter "flag" does not influence convolution
singa::Tensor out1 = conv.Forward(singa::kTrain, in);
// grad
const size_t grad_h = 2, grad_w = 2;
const float dy[batchsize * num_filters * grad_h * grad_w] = {0.1f, 0.2f, 0.3f,
0.4f};
singa::Tensor grad(singa::Shape{batchsize, num_filters, grad_h, grad_w},
cuda);
grad.CopyDataFromHostPtr(dy, batchsize * num_filters * grad_h * grad_w);
const auto ret = conv.Backward(singa::kTrain, grad);
singa::Tensor in_grad = ret.first;
in_grad.ToHost();
const float *dx = in_grad.data<float>();
const float *wptr = we;
EXPECT_EQ(9u, in_grad.Size());
EXPECT_EQ(dy[0] * wptr[4], dx[0]);
EXPECT_EQ(dy[0] * wptr[5] + dy[1] * wptr[3], dx[1]);
EXPECT_EQ(dy[1] * wptr[4], dx[2]);
EXPECT_EQ(dy[0] * wptr[7] + dy[2] * wptr[1], dx[3]);
EXPECT_EQ(
dy[0] * wptr[8] + dy[1] * wptr[6] + dy[2] * wptr[2] + dy[3] * wptr[0],
dx[4]);
EXPECT_EQ(dy[1] * wptr[7] + dy[3] * wptr[1], dx[5]);
EXPECT_EQ(dy[2] * wptr[4], dx[6]);
EXPECT_EQ(dy[2] * wptr[5] + dy[3] * wptr[3], dx[7]);
EXPECT_EQ(dy[3] * wptr[4], dx[8]);
singa::Tensor dw = ret.second[0];
singa::Tensor db = ret.second[1];
dw.ToHost();
db.ToHost();
const float *dbptr = db.data<float>();
EXPECT_EQ(dy[0] + dy[1] + dy[2] + dy[3], dbptr[0]);
const float *dwptr = dw.data<float>();
EXPECT_EQ(9u, dw.Size());
EXPECT_EQ(dy[3] * x[4], dwptr[0]);
EXPECT_EQ(dy[3] * x[5] + dy[2] * x[3], dwptr[1]);
EXPECT_EQ(dy[2] * x[4], dwptr[2]);
EXPECT_EQ(dy[1] * x[1] + dy[3] * x[7], dwptr[3]);
EXPECT_FLOAT_EQ(dy[0] * x[0] + dy[1] * x[2] + dy[2] * x[6] + dy[3] * x[8],
dwptr[4]);
EXPECT_EQ(dy[0] * x[1] + dy[2] * x[7], dwptr[5]);
EXPECT_EQ(dy[1] * x[4], dwptr[6]);
EXPECT_EQ(dy[0] * x[3] + dy[1] * x[5], dwptr[7]);
EXPECT_EQ(dy[0] * x[4], dwptr[8]);
}
// Tests for prefer=autotune
TEST(CudnnConvolution_AT, Setup) {
CudnnConvolution conv;
// EXPECT_EQ("CudnnConvolution", conv.layer_type());
singa::LayerConf conf;
singa::ConvolutionConf *convconf = conf.mutable_convolution_conf();
convconf->set_kernel_h(2);
convconf->set_kernel_w(2);
convconf->set_pad_h(1);
convconf->set_pad_w(1);
convconf->set_stride_h(1);
convconf->set_stride_w(1);
convconf->set_num_output(2);
convconf->set_bias_term(true);
// MB
convconf->set_workspace_byte_limit(256);
convconf->set_prefer("autotune");
conv.Setup(Shape{1, 3, 3}, conf);
EXPECT_EQ(2u, conv.kernel_h());
EXPECT_EQ(2u, conv.kernel_w());
EXPECT_EQ(1u, conv.pad_h());
EXPECT_EQ(1u, conv.pad_w());
EXPECT_EQ(1u, conv.stride_h());
EXPECT_EQ(1u, conv.stride_w());
EXPECT_EQ(2u, conv.num_filters());
EXPECT_EQ(true, conv.bias_term());
EXPECT_EQ(256u << 20, conv.workspace_byte_limit());
EXPECT_STREQ("autotune", conv.prefer().c_str());
EXPECT_EQ(1u, conv.channels());
EXPECT_EQ(3u, conv.height());
EXPECT_EQ(3u, conv.width());
}
TEST(CudnnConvolution_AT, Forward) {
const size_t batchsize = 1, c = 1, h = 3, w = 3;
const float x[batchsize * c * h * w] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
6.0f, 7.0f, 8.0f, 9.0f};
auto cuda = std::make_shared<singa::CudaGPU>();
singa::Tensor in(singa::Shape{batchsize, c, h, w}, cuda);
in.CopyDataFromHostPtr(x, batchsize * c * h * w);
// Set weight and bias manually
const size_t num_filters = 1;
const float we[num_filters * batchsize * h * w] = {
1.0f, 1.0f, 0.0f, 0.0f, 0.0f, -1.0f, 0.0f, 1.0f, 0.0f};
singa::Tensor weight(singa::Shape{num_filters, batchsize * h * w}, cuda);
weight.CopyDataFromHostPtr(we, batchsize * h * w);
const float b[num_filters] = {1.0f};
singa::Tensor bias(singa::Shape{num_filters}, cuda);
bias.CopyDataFromHostPtr(b, num_filters);
CudnnConvolution conv;
conv.set_weight(weight);
conv.set_bias(bias);
singa::LayerConf conf;
singa::ConvolutionConf *convconf = conf.mutable_convolution_conf();
convconf->set_kernel_h(3);
convconf->set_kernel_w(3);
convconf->set_pad_h(1);
convconf->set_pad_w(1);
convconf->set_stride_h(2);
convconf->set_stride_w(2);
convconf->set_num_output(1);
convconf->set_bias_term(true);
// MB
convconf->set_workspace_byte_limit(256);
convconf->set_prefer("autotune");
conv.Setup(Shape{1, 3, 3}, conf);
// Parameter "flag" does not influence convolution
singa::Tensor out1 = conv.Forward(singa::kTrain, in);
out1.ToHost();
const float *outptr1 = out1.data<float>();
// Input: 3*3; kernel: 3*3; stride: 2*2; padding: 1*1.
EXPECT_EQ(4u, out1.Size());
EXPECT_EQ(3.0f, outptr1[0]);
EXPECT_EQ(7.0f, outptr1[1]);
EXPECT_EQ(-3.0f, outptr1[2]);
EXPECT_EQ(12.0f, outptr1[3]);
}
TEST(CudnnConvolution_AT, Backward) {
// src_data
const size_t batchsize = 1, c = 1, src_h = 3, src_w = 3;
const float x[batchsize * c * src_h * src_w] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
6.0f, 7.0f, 8.0f, 9.0f};
auto cuda = std::make_shared<singa::CudaGPU>();
singa::Tensor in(singa::Shape{batchsize, c, src_h, src_w}, cuda);
in.CopyDataFromHostPtr(x, batchsize * c * src_h * src_w);
// Set weight_ and bias_ manually
const size_t num_filters = 1;
const float we[num_filters * batchsize * src_h * src_w] = {
1.0f, 1.0f, 0.0f, 0.0f, 0.0f, -1.0f, 0.0f, 1.0f, 0.0f};
singa::Tensor weight(singa::Shape{num_filters, batchsize * src_h * src_w},
cuda);
weight.CopyDataFromHostPtr(we, batchsize * src_h * src_w);
const float b[num_filters] = {1.0f};
singa::Tensor bias(singa::Shape{num_filters}, cuda);
bias.CopyDataFromHostPtr(b, num_filters);
CudnnConvolution conv;
conv.set_weight(weight);
conv.set_bias(bias);
singa::LayerConf conf;
singa::ConvolutionConf *convconf = conf.mutable_convolution_conf();
convconf->set_kernel_h(3);
convconf->set_kernel_w(3);
convconf->set_pad_h(1);
convconf->set_pad_w(1);
convconf->set_stride_h(2);
convconf->set_stride_w(2);
convconf->set_num_output(1);
convconf->set_bias_term(true);
convconf->set_workspace_byte_limit(256);
convconf->set_prefer("autotune");
conv.Setup(Shape{1, 3, 3}, conf);
// Parameter "flag" does not influence convolution
singa::Tensor out1 = conv.Forward(singa::kTrain, in);
// grad
const size_t grad_h = 2, grad_w = 2;
const float dy[batchsize * num_filters * grad_h * grad_w] = {0.1f, 0.2f, 0.3f,
0.4f};
singa::Tensor grad(singa::Shape{batchsize, num_filters, grad_h, grad_w},
cuda);
grad.CopyDataFromHostPtr(dy, batchsize * num_filters * grad_h * grad_w);
const auto ret = conv.Backward(singa::kTrain, grad);
singa::Tensor in_grad = ret.first;
in_grad.ToHost();
const float *dx = in_grad.data<float>();
const float *wptr = we;
EXPECT_EQ(9u, in_grad.Size());
EXPECT_EQ(dy[0] * wptr[4], dx[0]);
EXPECT_EQ(dy[0] * wptr[5] + dy[1] * wptr[3], dx[1]);
EXPECT_EQ(dy[1] * wptr[4], dx[2]);
EXPECT_EQ(dy[0] * wptr[7] + dy[2] * wptr[1], dx[3]);
EXPECT_EQ(
dy[0] * wptr[8] + dy[1] * wptr[6] + dy[2] * wptr[2] + dy[3] * wptr[0],
dx[4]);
EXPECT_EQ(dy[1] * wptr[7] + dy[3] * wptr[1], dx[5]);
EXPECT_EQ(dy[2] * wptr[4], dx[6]);
EXPECT_EQ(dy[2] * wptr[5] + dy[3] * wptr[3], dx[7]);
EXPECT_EQ(dy[3] * wptr[4], dx[8]);
singa::Tensor dw = ret.second[0];
singa::Tensor db = ret.second[1];
dw.ToHost();
db.ToHost();
const float *dbptr = db.data<float>();
EXPECT_EQ(dy[0] + dy[1] + dy[2] + dy[3], dbptr[0]);
const float *dwptr = dw.data<float>();
EXPECT_EQ(9u, dw.Size());
EXPECT_EQ(dy[3] * x[4], dwptr[0]);
EXPECT_EQ(dy[3] * x[5] + dy[2] * x[3], dwptr[1]);
EXPECT_EQ(dy[2] * x[4], dwptr[2]);
EXPECT_EQ(dy[1] * x[1] + dy[3] * x[7], dwptr[3]);
EXPECT_FLOAT_EQ(dy[0] * x[0] + dy[1] * x[2] + dy[2] * x[6] + dy[3] * x[8],
dwptr[4]);
EXPECT_EQ(dy[0] * x[1] + dy[2] * x[7], dwptr[5]);
EXPECT_EQ(dy[1] * x[4], dwptr[6]);
EXPECT_EQ(dy[0] * x[3] + dy[1] * x[5], dwptr[7]);
EXPECT_EQ(dy[0] * x[4], dwptr[8]);
}
#endif // USE_CUDNN