C++ API parity: LogSigmoid

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/27060

Test Plan: Imported from OSS

Differential Revision: D17682404

Pulled By: pbelevich

fbshipit-source-id: d60d64cd4caf1f56a2e05c516f91321d46ec9624

test/cpp/api/functional.cpp

@@ -347,3 +347,16 @@ TEST_F(FunctionalTest, LeakyReLU) {
     }
   }
 }
+
+TEST_F(FunctionalTest, LogSigmoid) {
+  const auto size = 3;
+  LogSigmoid model;
+  auto x = torch::linspace(-10.0, 10.0, size * size * size);
+  x.resize_({size, size, size});
+  auto y = F::logsigmoid(x);
+
+  ASSERT_EQ(y.ndimension(), 3);
+  ASSERT_EQ(y.sizes(), torch::IntArrayRef({size, size, size}));
+  auto y_exp = torch::log(torch::ones_like(x)/(torch::ones_like(x) + torch::exp(torch::neg(x))));
+  ASSERT_TRUE(torch::allclose(y, y_exp, 1e-4, 1e-7));
+}

test/cpp/api/modules.cpp

@@ -1099,6 +1099,23 @@ TEST_F(ModulesTest, LeakyReLU) {
   }
 }
 
+TEST_F(ModulesTest, LogSigmoid) {
+  const auto size = 3;
+  LogSigmoid model;
+  auto x = torch::linspace(-10.0, 10.0, size * size * size);
+  x.resize_({size, size, size}).set_requires_grad(true);
+  auto y = model(x);
+  torch::Tensor s = y.sum();
+
+  s.backward();
+  ASSERT_EQ(s.ndimension(), 0);
+
+  ASSERT_EQ(y.ndimension(), 3);
+  ASSERT_EQ(y.sizes(), torch::IntArrayRef({size, size, size}));
+  auto y_exp = torch::log(torch::ones_like(x)/(torch::ones_like(x) + torch::exp(torch::neg(x))));
+  ASSERT_TRUE(torch::allclose(y, y_exp, 1e-4, 1e-7));
+}
+
 TEST_F(ModulesTest, PrettyPrintIdentity) {
   ASSERT_EQ(c10::str(Identity()), "torch::nn::Identity()");
 }
@@ -1349,3 +1366,7 @@ TEST_F(ModulesTest, PrettyPrintLeakyReLU) {
       LeakyReLUOptions().negative_slope(0.42).inplace(true))),
     "torch::nn::LeakyReLU(negative_slope=0.42, inplace=true)");
 }
+
+TEST_F(ModulesTest, PrettyPrintLogSigmoid) {
+  ASSERT_EQ(c10::str(LogSigmoid()), "torch::nn::LogSigmoid()");
+}

torch/csrc/api/include/torch/nn/functional/activation.h

@@ -36,6 +36,10 @@ inline Tensor leaky_relu(Tensor& input, const LeakyReLUOptions& options) {
   }
 }
 
+inline Tensor logsigmoid(const Tensor& input) {
+  return torch::log_sigmoid(input);
+}
+
 } // namespace functional
 } // namespace nn
 } // namespace torch

torch/csrc/api/include/torch/nn/modules/activation.h

@@ -101,5 +101,24 @@ class TORCH_API LeakyReLUImpl : public torch::nn::Cloneable<LeakyReLUImpl> {
 
 TORCH_MODULE(LeakyReLU);
 
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ LogSigmoid ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+/// Applies the LogSigmoid function element-wise.
+/// See https://pytorch.org/docs/master/nn.html#torch.nn.LogSigmoid to learn
+/// about the exact behavior of this module.
+class TORCH_API LogSigmoidImpl : public torch::nn::Cloneable<LogSigmoidImpl> {
+ public:
+  LogSigmoidImpl() {}
+
+  Tensor forward(const Tensor& input);
+
+  void reset() override;
+
+  /// Pretty prints the `LogSigmoid` module into the given `stream`.
+  void pretty_print(std::ostream& stream) const override;
+};
+
+TORCH_MODULE(LogSigmoid);
+
 } // namespace nn
 } // namespace torch

torch/csrc/api/src/nn/modules/activation.cpp

@@ -84,5 +84,17 @@ void LeakyReLUImpl::pretty_print(std::ostream& stream) const {
   stream << ")";
 }
 
+// ============================================================================
+
+Tensor LogSigmoidImpl::forward(const Tensor& input) {
+  return F::logsigmoid(input);
+}
+
+void LogSigmoidImpl::reset() {}
+
+void LogSigmoidImpl::pretty_print(std::ostream& stream) const {
+  stream << "torch::nn::LogSigmoid()";
+}
+
 } // namespace nn
 } // namespace torch