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| * Licensed to the Apache Software Foundation (ASF) under one |
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| * http://www.apache.org/licenses/LICENSE-2.0 |
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| * software distributed under the License is distributed on an |
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| *************************************************************/ |
| |
| #include "gtest/gtest.h" |
| #include "singa/model/optimizer.h" |
| #include "singa/singa_config.h" |
| |
| TEST(SGD, ApplyWithoutMomentum) { |
| singa::SGD sgd; |
| const float v[4] = {0.1f, 0.2f, 0.3f, 0.4f}; |
| const float g[4] = {0.1f, 0.1f, 0.1f, 0.1f}; |
| |
| singa::Tensor value(singa::Shape{4}), grad(singa::Shape{4}); |
| value.CopyDataFromHostPtr(v, 4); |
| grad.CopyDataFromHostPtr(g, 4); |
| |
| float lr = 0.1f; |
| sgd.Apply(0, lr, "xx", grad, value); |
| |
| singa::Tensor v1 = value.Clone(); |
| const float* newv1 = v1.data<float>(); |
| for (int i = 0; i < 4; i++) { |
| EXPECT_FLOAT_EQ(newv1[i], v[i] - g[i] * lr); |
| } |
| |
| lr /= 2; |
| grad.CopyDataFromHostPtr(g, 4); |
| sgd.Apply(1, lr, "xx", grad, value); |
| singa::Tensor v2 = value.Clone(); |
| const float* newv2 = v2.data<float>(); |
| for (int i = 0; i < 4; i++) { |
| EXPECT_FLOAT_EQ(newv2[i], newv1[i] - g[i] * lr); |
| } |
| } |
| |
| TEST(SGD, ApplyWithMomentum) { |
| singa::SGD sgd; |
| float lr = 0.1f; |
| auto func = [](int step) { return step <= 5 ? 0.5f : 0.9f; }; |
| sgd.SetMomentumGenerator(func); |
| const float v[4] = {0.1f, 0.2f, 0.3f, 0.4f}; |
| const float g[4] = {0.01f, 0.02f, 0.03f, 0.04f}; |
| |
| singa::Tensor value(singa::Shape{4}), grad(singa::Shape{4}); |
| value.CopyDataFromHostPtr(v, 4); |
| grad.CopyDataFromHostPtr(g, 4); |
| |
| sgd.Apply(0, lr, "xx", grad, value); |
| |
| singa::Tensor v1 = value.Clone(); |
| const float* newv1 = v1.data<float>(); |
| for (int i = 0; i < 4; i++) { |
| EXPECT_FLOAT_EQ(newv1[i], v[i] - g[i] * lr); |
| } |
| |
| grad.CopyDataFromHostPtr(g, 4); |
| sgd.Apply(1, lr, "xx", grad, value); |
| singa::Tensor v2 = value.Clone(); |
| const float* newv2 = v2.data<float>(); |
| for (int i = 0; i < 4; i++) { |
| EXPECT_FLOAT_EQ(newv2[i], newv1[i] - (g[i] * lr + g[i] * lr * func(1))); |
| } |
| } |
| |
| #ifdef USE_CUDA |
| TEST(SGD, ApplyWithoutMomentumCuda) { |
| singa::SGD sgd; |
| const float v[4] = {0.1, 0.2, 0.3, 0.4}; |
| const float g[4] = {0.1, 0.1, 0.1, 0.1}; |
| |
| auto dev = std::make_shared<singa::CudaGPU>(); |
| singa::Tensor value(singa::Shape{4}, dev), grad(singa::Shape{4}, dev); |
| value.CopyDataFromHostPtr(v, 4); |
| grad.CopyDataFromHostPtr(g, 4); |
| |
| float lr = 0.1f; |
| sgd.Apply(0, lr, "xx", grad, value); |
| |
| singa::Tensor v1 = value.Clone(); |
| v1.ToHost(); |
| const float* newv1 = v1.data<float>(); |
| for (int i = 0; i < 4; i++) { |
| EXPECT_FLOAT_EQ(newv1[i], v[i] - g[i] * lr); |
| } |
| |
| lr /= 2; |
| grad.CopyDataFromHostPtr(g, 4); |
| sgd.Apply(1, lr, "xx", grad, value); |
| singa::Tensor v2 = value.Clone(); |
| v2.ToHost(); |
| const float* newv2 = v2.data<float>(); |
| for (int i = 0; i < 4; i++) { |
| EXPECT_FLOAT_EQ(newv2[i], newv1[i] - g[i] * lr); |
| } |
| } |
| |
| TEST(SGD, ApplyWithMomentumCuda) { |
| singa::SGD sgd; |
| float lr = 0.1f; |
| auto func = [](int step) { return step <= 5 ? 0.5f : 0.9f; }; |
| sgd.SetMomentumGenerator(func); |
| const float v[4] = {0.1, 0.2, 0.3, 0.4}; |
| const float g[4] = {0.01, 0.02, 0.03, 0.04}; |
| |
| auto dev = std::make_shared<singa::CudaGPU>(); |
| singa::Tensor value(singa::Shape{4}, dev), grad(singa::Shape{4}, dev); |
| value.CopyDataFromHostPtr(v, 4); |
| grad.CopyDataFromHostPtr(g, 4); |
| |
| sgd.Apply(0, lr, "xx", grad, value); |
| |
| singa::Tensor v1 = value.Clone(); |
| v1.ToHost(); |
| const float* newv1 = v1.data<float>(); |
| for (int i = 0; i < 4; i++) { |
| EXPECT_FLOAT_EQ(newv1[i], v[i] - g[i] * lr); |
| } |
| |
| grad.CopyDataFromHostPtr(g, 4); |
| sgd.Apply(1, lr, "xx", grad, value); |
| singa::Tensor v2 = value.Clone(); |
| v2.ToHost(); |
| const float* newv2 = v2.data<float>(); |
| for (int i = 0; i < 4; i++) { |
| EXPECT_FLOAT_EQ(newv2[i], newv1[i] - (g[i] * lr + g[i] * lr * func(1))); |
| } |
| } |
| #endif |