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| * Licensed to the Apache Software Foundation (ASF) under one |
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| * to you under the Apache License, Version 2.0 (the |
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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 distributed under the License is distributed on an |
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| *************************************************************/ |
| #include "singa/singa_config.h" |
| #ifdef USE_CUDNN |
| |
| #include <cudnn.h> |
| #include <math.h> // exp |
| |
| #include "../src/model/layer/cudnn_softmax.h" |
| #include "gtest/gtest.h" |
| |
| // TODO(wangwei) add test for matrix input |
| using singa::CudnnSoftmax; |
| using singa::Shape; |
| TEST(CudnnSoftmax, Setup) { |
| CudnnSoftmax sft; |
| // EXPECT_EQ("CudnnSoftmax", sft.layer_type()); |
| |
| singa::LayerConf conf; |
| singa::SoftmaxConf* softmaxconf = conf.mutable_softmax_conf(); |
| softmaxconf->set_algorithm("fast"); |
| sft.Setup(Shape{1}, conf); |
| EXPECT_EQ(CUDNN_SOFTMAX_FAST, sft.Algorithm()); |
| } |
| |
| TEST(CudnnSoftmax, Forward1D) { |
| const float x[] = {1.f, 2.f, 0.f, -2.f, -3.f, -1.f}; |
| size_t n = sizeof(x) / sizeof(float); |
| auto cuda = std::make_shared<singa::CudaGPU>(); |
| singa::Shape shape = {n}; |
| singa::Tensor in(shape, cuda); |
| in.CopyDataFromHostPtr<float>(x, n); |
| |
| CudnnSoftmax sft; |
| singa::LayerConf conf; |
| singa::SoftmaxConf* softmaxconf = conf.mutable_softmax_conf(); |
| softmaxconf->set_algorithm("accurate"); |
| sft.Setup(Shape{1}, conf); |
| singa::Tensor out = sft.Forward(singa::kTrain, in); |
| out.ToHost(); |
| const float* yptr = out.data<float>(); |
| EXPECT_EQ(n, out.Size()); |
| |
| float* y = new float[n]; |
| float sigma = 0.f; |
| for (size_t i = 0; i < n; i++) sigma += exp(x[i]); |
| for (size_t i = 0; i < n; i++) y[i] = exp(x[i]) / sigma; |
| for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(y[i], yptr[i]); |
| delete[] y; |
| } |
| |
| TEST(CudnnSoftmax, Backward1D) { |
| const float x[] = {1.f, 2.f, 3.f, -2.f, -3.f, -1.f}; |
| size_t n = sizeof(x) / sizeof(float); |
| singa::Shape shape = {n}; |
| auto cuda = std::make_shared<singa::CudaGPU>(); |
| singa::Tensor in(shape, cuda); |
| in.CopyDataFromHostPtr<float>(x, n); |
| |
| CudnnSoftmax sft; |
| singa::LayerConf conf; |
| singa::SoftmaxConf* softmaxconf = conf.mutable_softmax_conf(); |
| softmaxconf->set_algorithm("accurate"); |
| sft.Setup(Shape{1}, conf); |
| |
| singa::Tensor out = sft.Forward(singa::kTrain, in); |
| out.ToHost(); |
| const float* yptr = out.data<float>(); |
| |
| const float grad[] = {2.f, -3.f, 1.f, 3.f, -1.f, -2.f}; |
| singa::Tensor out_diff(shape, cuda); |
| out_diff.CopyDataFromHostPtr<float>(grad, n); |
| const auto ret = sft.Backward(singa::kTrain, out_diff); |
| singa::Tensor in_diff = ret.first; |
| in_diff.ToHost(); |
| const float* xptr = in_diff.data<float>(); |
| |
| float* dx = new float[n]; |
| float sigma = 0.f; |
| for (size_t i = 0; i < n; i++) sigma += grad[i] * yptr[i]; |
| for (size_t i = 0; i < n; i++) dx[i] = (grad[i] - sigma) * yptr[i]; |
| for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(dx[i], xptr[i]); |
| delete[] dx; |
| } |
| |
| TEST(CudnnSoftmax, Forward2D) { |
| const float x[] = {1.f, 2.f, 0.f, -2.f, -3.f, -1.f}; |
| size_t n = sizeof(x) / sizeof(float); |
| size_t batch = 2, c = 3; |
| singa::Shape shape = {batch, c}; |
| auto cuda = std::make_shared<singa::CudaGPU>(); |
| singa::Tensor in(shape, cuda); |
| in.CopyDataFromHostPtr<float>(x, n); |
| |
| CudnnSoftmax sft; |
| singa::LayerConf conf; |
| singa::SoftmaxConf* softmaxconf = conf.mutable_softmax_conf(); |
| softmaxconf->set_algorithm("accurate"); |
| sft.Setup(Shape{c}, conf); |
| |
| singa::Tensor out = sft.Forward(singa::kTrain, in); |
| out.ToHost(); |
| const float* yptr = out.data<float>(); |
| EXPECT_EQ(n, out.Size()); |
| |
| float* y = new float[n]; |
| float* sigma = new float[batch]; |
| for (size_t i = 0; i < batch; i++) sigma[i] = 0.f; |
| for (size_t i = 0; i < n; i++) sigma[i / c] += exp(x[i]); |
| for (size_t i = 0; i < n; i++) y[i] = exp(x[i]) / sigma[i / c]; |
| for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(y[i], yptr[i]); |
| delete[] y; |
| delete[] sigma; |
| } |
| |
| TEST(CudnnSoftmax, Backward2D) { |
| const float x[] = {1.f, 2.f, 3.f, -2.f, -3.f, -1.f}; |
| size_t n = sizeof(x) / sizeof(float); |
| size_t batch = 2, c = 3; |
| auto cuda = std::make_shared<singa::CudaGPU>(); |
| singa::Shape shape = {batch, c}; |
| singa::Tensor in(shape, cuda); |
| in.CopyDataFromHostPtr<float>(x, n); |
| |
| CudnnSoftmax sft; |
| singa::LayerConf conf; |
| singa::SoftmaxConf* softmaxconf = conf.mutable_softmax_conf(); |
| softmaxconf->set_algorithm("accurate"); |
| sft.Setup(Shape{c}, conf); |
| |
| singa::Tensor out = sft.Forward(singa::kTrain, in); |
| out.ToHost(); |
| const float* yptr = out.data<float>(); |
| |
| const float grad[] = {2.f, -3.f, 1.f, 3.f, -1.f, -2.f}; |
| singa::Tensor out_diff(shape, cuda); |
| out_diff.CopyDataFromHostPtr<float>(grad, n); |
| const auto ret = sft.Backward(singa::kTrain, out_diff); |
| singa::Tensor in_diff = ret.first; |
| in_diff.ToHost(); |
| const float* xptr = in_diff.data<float>(); |
| |
| float* dx = new float[n]; |
| float* sigma = new float[batch]; |
| for (size_t i = 0; i < batch; i++) sigma[i] = 0.f; |
| for (size_t i = 0; i < n; i++) sigma[i / c] += grad[i] * yptr[i]; |
| for (size_t i = 0; i < n; i++) dx[i] = (grad[i] - sigma[i / c]) * yptr[i]; |
| for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(dx[i], xptr[i]); |
| delete[] dx; |
| delete[] sigma; |
| } |
| #endif // USE_CUDNN |