[C++ API] Remove deprecated torch::nn::BatchNorm / FeatureDropout / modules_ordered_dict and torch::nn::init::Nonlinearity / FanMode (#34508)

Summary:
This PR is BC-breaking in the following way:
- The deprecated `torch::nn::BatchNorm` is removed in favor of `torch::nn::BatchNorm{1,2,3}d`
- The deprecated `torch::nn::FeatureDropout` is removed in favor of `torch::nn::Dropout{2,3}d`
- The deprecated `torch::nn::modules_ordered_dict` is removed. User should do `Sequential sequential({{"m1", MyModule(1)}, {"m2", MyModule(2)}})` instead.
- The deprecated `torch::nn::init::Nonlinearity` is removed, in favor of the following enums:
    - `torch::kLinear`
    - `torch::kConv1D`
    - `torch::kConv2D`
    - `torch::kConv3D`
    - `torch::kConvTranspose1D`
    - `torch::kConvTranspose2D`
    - `torch::kConvTranspose3D`
    - `torch::kSigmoid`
    - `torch::kTanh`
    - `torch::kReLU`
    - `torch::kLeakyReLU`
- The deprecated `torch::nn::init::FanMode` is removed, in favor of the following enums:
    - `torch::kFanIn`
    - `torch::kFanOut`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34508

Differential Revision: D20351601

Pulled By: yf225

fbshipit-source-id: cca0cd112f29a31bb023e348ca8f82780e42bea3

caffe2/CMakeLists.txt

@@ -612,7 +612,6 @@ if (NOT INTERN_BUILD_MOBILE OR NOT BUILD_CAFFE2_MOBILE)
       ${TORCH_SRC_DIR}/csrc/api/src/nn/modules/rnn.cpp
       ${TORCH_SRC_DIR}/csrc/api/src/nn/modules/upsampling.cpp
       ${TORCH_SRC_DIR}/csrc/api/src/nn/modules/container/functional.cpp
-      ${TORCH_SRC_DIR}/csrc/api/src/nn/modules/container/named_any.cpp
       ${TORCH_SRC_DIR}/csrc/api/src/nn/options/activation.cpp
       ${TORCH_SRC_DIR}/csrc/api/src/nn/options/adaptive.cpp
       ${TORCH_SRC_DIR}/csrc/api/src/nn/options/batchnorm.cpp

test/cpp/api/init.cpp

@@ -111,19 +111,19 @@ TEST(InitTest, CanInitializeTensorThatRequiresGrad) {
 
 TEST(InitTest, CalculateGainWithTanh) {
   double gain =
-      torch::nn::init::calculate_gain(torch::nn::init::Nonlinearity::Tanh);
+      torch::nn::init::calculate_gain(torch::kTanh);
   ASSERT_DOUBLE_EQ(gain, 5.0 / 3.0);
 }
 
 TEST(InitTest, CalculateGainWithRelu) {
   double gain =
-      torch::nn::init::calculate_gain(torch::nn::init::Nonlinearity::ReLU);
+      torch::nn::init::calculate_gain(torch::kReLU);
   ASSERT_DOUBLE_EQ(gain, std::sqrt(2.0));
 }
 
 TEST(InitTest, CalculateGainWithLeakyRelu) {
   double gain =
-      torch::nn::init::calculate_gain(torch::nn::init::Nonlinearity::LeakyReLU);
+      torch::nn::init::calculate_gain(torch::kLeakyReLU);
   ASSERT_DOUBLE_EQ(gain, std::sqrt(2.0 / (1 + pow(0.01, 2))));
 }
 
@@ -131,41 +131,3 @@ TEST(InitTest, CanInitializeCnnWithOrthogonal) {
   torch::nn::Conv2d conv_layer(torch::nn::Conv2dOptions(3, 2, 3).stride(2));
   torch::nn::init::orthogonal_(conv_layer->named_parameters()["weight"]);
 }
-
-#define NONLINEARITY_ENUM_LEGACY_WARNING_CHECK(func_name, enum_name, enum_torch_kname) \
-{ \
-  std::stringstream buffer; \
-  CerrRedirect cerr_redirect(buffer.rdbuf()); \
-  std::cerr << torch::nn::init::func_name(torch::nn::init::Nonlinearity::enum_name) << std::endl; \
-  ASSERT_EQ(count_substr_occurrences(buffer.str(), enum_torch_kname), 1); \
-}
-
-#define FANMODE_ENUM_LEGACY_WARNING_CHECK(func_name, enum_name, enum_torch_kname) \
-{ \
-  std::stringstream buffer; \
-  CerrRedirect cerr_redirect(buffer.rdbuf()); \
-  std::cerr << torch::nn::init::func_name(torch::randn({4, 5}), 0, torch::nn::init::FanMode::enum_name) << std::endl; \
-  ASSERT_EQ(count_substr_occurrences(buffer.str(), enum_torch_kname), 1); \
-}
-
-TEST(InitTest, NonlinearityLegacyEnum) {
-  NONLINEARITY_ENUM_LEGACY_WARNING_CHECK(calculate_gain, Linear, "torch::kLinear")
-  NONLINEARITY_ENUM_LEGACY_WARNING_CHECK(calculate_gain, Conv1D, "torch::kConv1D")
-  NONLINEARITY_ENUM_LEGACY_WARNING_CHECK(calculate_gain, Conv2D, "torch::kConv2D")
-  NONLINEARITY_ENUM_LEGACY_WARNING_CHECK(calculate_gain, Conv3D, "torch::kConv3D")
-  NONLINEARITY_ENUM_LEGACY_WARNING_CHECK(calculate_gain, ConvTranspose1D, "torch::kConvTranspose1D")
-  NONLINEARITY_ENUM_LEGACY_WARNING_CHECK(calculate_gain, ConvTranspose2D, "torch::kConvTranspose2D")
-  NONLINEARITY_ENUM_LEGACY_WARNING_CHECK(calculate_gain, ConvTranspose3D, "torch::kConvTranspose3D")
-  NONLINEARITY_ENUM_LEGACY_WARNING_CHECK(calculate_gain, Sigmoid, "torch::kSigmoid")
-  NONLINEARITY_ENUM_LEGACY_WARNING_CHECK(calculate_gain, Tanh, "torch::kTanh")
-  NONLINEARITY_ENUM_LEGACY_WARNING_CHECK(calculate_gain, ReLU, "torch::kReLU")
-  NONLINEARITY_ENUM_LEGACY_WARNING_CHECK(calculate_gain, LeakyReLU, "torch::kLeakyReLU")
-}
-
-TEST(InitTest, FanModeLegacyEnum) {
-  FANMODE_ENUM_LEGACY_WARNING_CHECK(kaiming_normal_, FanIn, "torch::kFanIn")
-  FANMODE_ENUM_LEGACY_WARNING_CHECK(kaiming_normal_, FanOut, "torch::kFanOut")
-
-  FANMODE_ENUM_LEGACY_WARNING_CHECK(kaiming_uniform_, FanIn, "torch::kFanIn")
-  FANMODE_ENUM_LEGACY_WARNING_CHECK(kaiming_uniform_, FanOut, "torch::kFanOut")
-}

test/cpp/api/integration.cpp

@@ -284,10 +284,10 @@ TEST_F(IntegrationTest, MNISTBatchNorm_CUDA) {
   torch::manual_seed(0);
   auto model = std::make_shared<SimpleContainer>();
   auto conv1 = model->add(Conv2d(1, 10, 5), "conv1");
-  auto batchnorm2d = model->add(BatchNorm(10), "batchnorm2d");
+  auto batchnorm2d = model->add(BatchNorm2d(10), "batchnorm2d");
   auto conv2 = model->add(Conv2d(10, 20, 5), "conv2");
   auto linear1 = model->add(Linear(320, 50), "linear1");
-  auto batchnorm1 = model->add(BatchNorm(50), "batchnorm1");
+  auto batchnorm1 = model->add(BatchNorm1d(50), "batchnorm1");
   auto linear2 = model->add(Linear(50, 10), "linear2");
 
   auto forward = [&](torch::Tensor x) {

test/cpp/api/modulelist.cpp

@@ -158,7 +158,7 @@ TEST_F(ModuleListTest, SanityCheckForHoldingStandardModules) {
       Linear(10, 3),
       Conv2d(1, 2, 3),
       Dropout(0.5),
-      BatchNorm(5),
+      BatchNorm2d(5),
       Embedding(4, 10),
       LSTM(4, 5));
 }
@@ -210,7 +210,7 @@ TEST_F(ModuleListTest, HasReferenceSemantics) {
 }
 
 TEST_F(ModuleListTest, IsCloneable) {
-  ModuleList list(Linear(3, 4), Functional(torch::relu), BatchNorm(3));
+  ModuleList list(Linear(3, 4), Functional(torch::relu), BatchNorm1d(3));
   ModuleList clone = std::dynamic_pointer_cast<ModuleListImpl>(list->clone());
   ASSERT_EQ(list->size(), clone->size());
 
@@ -255,7 +255,7 @@ TEST_F(ModuleListTest, NestingIsPossible) {
 }
 
 TEST_F(ModuleListTest, CloneToDevice_CUDA) {
-  ModuleList list(Linear(3, 4), Functional(torch::relu), BatchNorm(3));
+  ModuleList list(Linear(3, 4), Functional(torch::relu), BatchNorm1d(3));
   torch::Device device(torch::kCUDA, 0);
   ModuleList clone =
       std::dynamic_pointer_cast<ModuleListImpl>(list->clone(device));
@@ -272,7 +272,7 @@ TEST_F(ModuleListTest, PrettyPrintModuleList) {
       Linear(10, 3),
       Conv2d(1, 2, 3),
       Dropout(0.5),
-      BatchNorm(5),
+      BatchNorm2d(5),
       Embedding(4, 10),
       LSTM(4, 5));
   ASSERT_EQ(
@@ -281,7 +281,7 @@ TEST_F(ModuleListTest, PrettyPrintModuleList) {
       "  (0): torch::nn::Linear(in_features=10, out_features=3, bias=true)\n"
       "  (1): torch::nn::Conv2d(1, 2, kernel_size=[3, 3], stride=[1, 1])\n"
       "  (2): torch::nn::Dropout(p=0.5, inplace=false)\n"
-      "  (3): torch::nn::BatchNorm(num_features=5, eps=1e-05, momentum=0.1, affine=true, track_running_stats=true)\n"
+      "  (3): torch::nn::BatchNorm2d(5, eps=1e-05, momentum=0.1, affine=true, track_running_stats=true)\n"
       "  (4): torch::nn::Embedding(num_embeddings=4, embedding_dim=10)\n"
       "  (5): torch::nn::LSTM(input_size=4, hidden_size=5, layers=1, dropout=0)\n"
       ")");
@@ -290,7 +290,7 @@ TEST_F(ModuleListTest, PrettyPrintModuleList) {
 TEST_F(ModuleListTest, RangeBasedForLoop) {
   torch::nn::ModuleList mlist(
     torch::nn::Linear(3, 4),
-    torch::nn::BatchNorm(4),
+    torch::nn::BatchNorm1d(4),
     torch::nn::Dropout(0.5)
   );
 

test/cpp/api/modules.cpp

@@ -1350,37 +1350,6 @@ TEST_F(ModulesTest, Dropout3d) {
   ASSERT_EQ(y.sum().item<float>(), 100);
 }
 
-TEST_F(ModulesTest, FeatureDropout) {
-  FeatureDropout dropout(0.5);
-  torch::Tensor x = torch::ones({10, 10}, torch::requires_grad());
-  torch::Tensor y = dropout(x);
-
-  y.backward(torch::ones_like(y));
-  ASSERT_EQ(y.ndimension(), 2);
-  ASSERT_EQ(y.size(0), 10);
-  ASSERT_EQ(y.size(1), 10);
-  ASSERT_LT(y.sum().item<float>(), 130); // Probably
-  ASSERT_GT(y.sum().item<float>(), 70); // Probably
-
-  dropout->eval();
-  y = dropout(x);
-  ASSERT_EQ(y.sum().item<float>(), 100);
-}
-
-TEST_F(ModulesTest, FeatureDropoutLegacyWarning) {
-  std::stringstream buffer;
-  torch::test::CerrRedirect cerr_redirect(buffer.rdbuf());
-
-  FeatureDropout bn(0.5);
-
-  ASSERT_EQ(
-    count_substr_occurrences(
-      buffer.str(),
-      "torch::nn::FeatureDropout module is deprecated"
-    ),
-  1);
-}
-
 TEST_F(ModulesTest, Parameters) {
   auto model = std::make_shared<NestedModel>();
   auto parameters = model->named_parameters();
@@ -1431,74 +1400,6 @@ TEST_F(ModulesTest, FunctionalArgumentBinding) {
   ASSERT_EQ(functional(torch::ones({})).item<float>(), 0);
 }
 
-TEST_F(ModulesTest, BatchNormStateful) {
-  BatchNorm bn(5);
-
-  // Is stateful by default.
-  ASSERT_TRUE(bn->options.track_running_stats());
-
-  ASSERT_TRUE(bn->running_mean.defined());
-  ASSERT_EQ(bn->running_mean.dim(), 1);
-  ASSERT_EQ(bn->running_mean.size(0), 5);
-
-  ASSERT_TRUE(bn->running_var.defined());
-  ASSERT_EQ(bn->running_var.dim(), 1);
-  ASSERT_EQ(bn->running_var.size(0), 5);
-
-  // Is affine by default.
-  ASSERT_TRUE(bn->options.affine());
-
-  ASSERT_TRUE(bn->weight.defined());
-  ASSERT_EQ(bn->weight.dim(), 1);
-  ASSERT_EQ(bn->weight.size(0), 5);
-
-  ASSERT_TRUE(bn->bias.defined());
-  ASSERT_EQ(bn->bias.dim(), 1);
-  ASSERT_EQ(bn->bias.size(0), 5);
-}
-TEST_F(ModulesTest, BatchNormStateless) {
-  BatchNorm bn(BatchNormOptions(5).track_running_stats(false).affine(false));
-
-  ASSERT_FALSE(bn->running_mean.defined());
-  ASSERT_FALSE(bn->running_var.defined());
-  ASSERT_FALSE(bn->weight.defined());
-  ASSERT_FALSE(bn->bias.defined());
-
-  ASSERT_THROWS_WITH(
-      bn(torch::ones({2, 5})),
-      "Calling BatchNorm::forward is only permitted "
-      "when the 'track_running_stats' option is true (was false). "
-      "Use BatchNorm::pure_forward instead.");
-}
-
-TEST_F(ModulesTest, BatchNormPureForward) {
-  BatchNorm bn(BatchNormOptions(5).affine(false));
-  bn->eval();
-
-  // Want to make sure we use the supplied values in `pure_forward` even if
-  // we are stateful.
-  auto input = torch::randn({2, 5});
-  auto mean = torch::randn(5);
-  auto variance = torch::rand(5);
-  auto output = bn->pure_forward(input, mean, variance);
-  auto expected = (input - mean) / torch::sqrt(variance + bn->options.eps());
-  ASSERT_TRUE(output.allclose(expected));
-}
-
-TEST_F(ModulesTest, BatchNormLegacyWarning) {
-  std::stringstream buffer;
-  torch::test::CerrRedirect cerr_redirect(buffer.rdbuf());
-
-  BatchNorm bn(5);
-
-  ASSERT_EQ(
-    count_substr_occurrences(
-      buffer.str(),
-      "torch::nn::BatchNorm module is deprecated"
-    ),
-  1);
-}
-
 TEST_F(ModulesTest, BatchNorm1dStateful) {
   BatchNorm1d bn(5);
 
@@ -4087,24 +3988,10 @@ TEST_F(ModulesTest, PrettyPrintDropout3d) {
   ASSERT_EQ(c10::str(Dropout3d(Dropout3dOptions().p(0.42).inplace(true))), "torch::nn::Dropout3d(p=0.42, inplace=true)");
 }
 
-TEST_F(ModulesTest, PrettyPrintFeatureDropout) {
-  ASSERT_EQ(c10::str(FeatureDropout()), "torch::nn::FeatureDropout(p=0.5, inplace=false)");
-  ASSERT_EQ(c10::str(FeatureDropout(0.42)), "torch::nn::FeatureDropout(p=0.42, inplace=false)");
-  ASSERT_EQ(c10::str(FeatureDropout(FeatureDropoutOptions().p(0.42).inplace(true))), "torch::nn::FeatureDropout(p=0.42, inplace=true)");
-}
-
 TEST_F(ModulesTest, PrettyPrintFunctional) {
   ASSERT_EQ(c10::str(Functional(torch::relu)), "torch::nn::Functional()");
 }
 
-TEST_F(ModulesTest, PrettyPrintBatchNorm) {
-  ASSERT_EQ(
-      c10::str(BatchNorm(
-          BatchNormOptions(4).eps(0.5).momentum(0.1).affine(false).track_running_stats(
-              true))),
-      "torch::nn::BatchNorm(num_features=4, eps=0.5, momentum=0.1, affine=false, track_running_stats=true)");
-}
-
 TEST_F(ModulesTest, PrettyPrintBatchNorm1d) {
   ASSERT_EQ(
       c10::str(BatchNorm1d(

test/cpp/api/sequential.cpp

@@ -14,7 +14,7 @@ using namespace torch::test;
 struct SequentialTest : torch::test::SeedingFixture {};
 
 TEST_F(SequentialTest, CanContainThings) {
-  Sequential sequential(Linear(3, 4), ReLU(), BatchNorm(3));
+  Sequential sequential(Linear(3, 4), ReLU(), BatchNorm1d(3));
 }
 
 TEST_F(SequentialTest, ConstructsFromSharedPointer) {
@@ -94,22 +94,6 @@ TEST_F(SequentialTest, ConstructsFromModuleHolder) {
   ASSERT_EQ(sequential->size(), 3);
 }
 
-TEST_F(SequentialTest, LegacyBuilderForOrderedDictOfNamedModules) {
-  std::stringstream buffer;
-  CerrRedirect cerr_redirect(buffer.rdbuf());
-
-  Sequential sequential_named(modules_ordered_dict({
-    {"m1", Linear(3, 4)},
-    {"m2", ReLU()},
-    {"m3", BatchNorm(3)}
-  }));
-  ASSERT_EQ(sequential_named->size(), 3);
-
-  ASSERT_EQ(
-    count_substr_occurrences(buffer.str(), "`torch::nn::modules_ordered_dict` is deprecated"),
-    1);
-}
-
 TEST_F(SequentialTest, PushBackAddsAnElement) {
   struct M : torch::nn::Module {
     explicit M(int value_) : value(value_) {}
@@ -291,7 +275,7 @@ TEST_F(SequentialTest, SanityCheckForHoldingStandardModules) {
       Linear(10, 3),
       Conv2d(1, 2, 3),
       Dropout(0.5),
-      BatchNorm(5),
+      BatchNorm2d(5),
       Embedding(4, 10),
       LSTM(4, 5));
 }
@@ -358,7 +342,7 @@ TEST_F(SequentialTest, HasReferenceSemantics) {
 }
 
 TEST_F(SequentialTest, IsCloneable) {
-  Sequential sequential(Linear(3, 4), Functional(torch::relu), BatchNorm(3));
+  Sequential sequential(Linear(3, 4), Functional(torch::relu), BatchNorm1d(3));
   Sequential clone =
       std::dynamic_pointer_cast<SequentialImpl>(sequential->clone());
   ASSERT_EQ(sequential->size(), clone->size());
@@ -398,7 +382,7 @@ TEST_F(SequentialTest, RegistersElementsAsSubmodules) {
 }
 
 TEST_F(SequentialTest, CloneToDevice_CUDA) {
-  Sequential sequential(Linear(3, 4), Functional(torch::relu), BatchNorm(3));
+  Sequential sequential(Linear(3, 4), Functional(torch::relu), BatchNorm1d(3));
   torch::Device device(torch::kCUDA, 0);
   Sequential clone =
       std::dynamic_pointer_cast<SequentialImpl>(sequential->clone(device));
@@ -415,7 +399,7 @@ TEST_F(SequentialTest, PrettyPrintSequential) {
       Linear(10, 3),
       Conv2d(1, 2, 3),
       Dropout(0.5),
-      BatchNorm(5),
+      BatchNorm2d(5),
       Embedding(4, 10),
       LSTM(4, 5));
   ASSERT_EQ(
@@ -424,7 +408,7 @@ TEST_F(SequentialTest, PrettyPrintSequential) {
       "  (0): torch::nn::Linear(in_features=10, out_features=3, bias=true)\n"
       "  (1): torch::nn::Conv2d(1, 2, kernel_size=[3, 3], stride=[1, 1])\n"
       "  (2): torch::nn::Dropout(p=0.5, inplace=false)\n"
-      "  (3): torch::nn::BatchNorm(num_features=5, eps=1e-05, momentum=0.1, affine=true, track_running_stats=true)\n"
+      "  (3): torch::nn::BatchNorm2d(5, eps=1e-05, momentum=0.1, affine=true, track_running_stats=true)\n"
       "  (4): torch::nn::Embedding(num_embeddings=4, embedding_dim=10)\n"
       "  (5): torch::nn::LSTM(input_size=4, hidden_size=5, layers=1, dropout=0)\n"
       ")");
@@ -433,7 +417,7 @@ TEST_F(SequentialTest, PrettyPrintSequential) {
       {"linear", Linear(10, 3)},
       {"conv2d", Conv2d(1, 2, 3)},
       {"dropout", Dropout(0.5)},
-      {"batchnorm", BatchNorm(5)},
+      {"batchnorm2d", BatchNorm2d(5)},
       {"embedding", Embedding(4, 10)},
       {"lstm", LSTM(4, 5)}
   });
@@ -443,7 +427,7 @@ TEST_F(SequentialTest, PrettyPrintSequential) {
       "  (linear): torch::nn::Linear(in_features=10, out_features=3, bias=true)\n"
       "  (conv2d): torch::nn::Conv2d(1, 2, kernel_size=[3, 3], stride=[1, 1])\n"
       "  (dropout): torch::nn::Dropout(p=0.5, inplace=false)\n"
-      "  (batchnorm): torch::nn::BatchNorm(num_features=5, eps=1e-05, momentum=0.1, affine=true, track_running_stats=true)\n"
+      "  (batchnorm2d): torch::nn::BatchNorm2d(5, eps=1e-05, momentum=0.1, affine=true, track_running_stats=true)\n"
       "  (embedding): torch::nn::Embedding(num_embeddings=4, embedding_dim=10)\n"
       "  (lstm): torch::nn::LSTM(input_size=4, hidden_size=5, layers=1, dropout=0)\n"
       ")");

tools/build_variables.bzl

@@ -254,7 +254,6 @@ torch_cpp_srcs = [
     "torch/csrc/api/src/nn/modules/rnn.cpp",
     "torch/csrc/api/src/nn/modules/upsampling.cpp",
     "torch/csrc/api/src/nn/modules/container/functional.cpp",
-    "torch/csrc/api/src/nn/modules/container/named_any.cpp",
     "torch/csrc/api/src/nn/options/activation.cpp",
     "torch/csrc/api/src/nn/options/adaptive.cpp",
     "torch/csrc/api/src/nn/options/batchnorm.cpp",

torch/csrc/api/include/torch/nn/init.h

@@ -8,24 +8,6 @@ namespace torch {
 namespace nn {
 namespace init {
 
-// This enum class is deprecated and will be removed in 1.5
-enum class Nonlinearity {
-  Linear,
-  Conv1D,
-  Conv2D,
-  Conv3D,
-  ConvTranspose1D,
-  ConvTranspose2D,
-  ConvTranspose3D,
-  Sigmoid,
-  Tanh,
-  ReLU,
-  LeakyReLU
-};
-
-// This enum class is deprecated and will be removed in 1.5
-enum class FanMode { FanIn, FanOut };
-
 using NonlinearityType = c10::variant<
   enumtype::kLinear,
   enumtype::kConv1D,
@@ -37,18 +19,12 @@ using NonlinearityType = c10::variant<
   enumtype::kSigmoid,
   enumtype::kTanh,
   enumtype::kReLU,
-  enumtype::kLeakyReLU,
+  enumtype::kLeakyReLU
-
-  // Support for this enum class is deprecated and will be removed in 1.5.
-  Nonlinearity
 >;
 
 using FanModeType = c10::variant<
   enumtype::kFanIn,
-  enumtype::kFanOut,
+  enumtype::kFanOut
-
-  // Support for this enum class is deprecated and will be removed in 1.5.
-  FanMode
 >;
 
 } // namespace init

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

@@ -12,73 +12,6 @@
 namespace torch {
 namespace nn {
 
-/// Applies [Batch Normalization](https://arxiv.org/abs/1502.03167) to an input.
-///
-/// Refer to the documentation for
-/// [`BatchNorm1d`](https://pytorch.org/docs/stable/nn.html#torch.nn.BatchNorm1d)
-/// in PyTorch to learn more about the exact semantics of this module, __but see
-/// the note below regarding differences between the Python and C++ API__.
-///
-/// \rst
-/// .. attention::
-///   In the Python API, there are separate implementations for 1-D, 2-D and 3-D
-///   BatchNorm. In C++, there is only one `BatchNorm` module, which works for
-///   any of these dimensions.
-/// \endrst
-class TORCH_API BatchNormImpl : public torch::nn::Cloneable<BatchNormImpl> {
- public:
-  explicit BatchNormImpl(int64_t num_features)
-      : BatchNormImpl(BatchNormOptions(num_features)) {}
-  explicit BatchNormImpl(const BatchNormOptions& options_);
-
-  void reset() override;
-
-  /// Pretty prints the `BatchNorm` module into the given `stream`.
-  void pretty_print(std::ostream& stream) const override;
-
-  /// Applies batch normalization on the `input` using the stored mean and
-  /// variance.
-  ///
-  /// The module must be constructed with `track_running_stats = true` when calling this
-  /// method, as the module will otherwise not store running statistics. If you
-  /// want to supply the mean and variance yourself, use `pure_forward`.
-  Tensor forward(const Tensor& input);
-
-  /// Applies batch normalization on the `input` using the given `mean` and
-  /// `variance` statistics.
-  Tensor pure_forward(
-      const Tensor& input,
-      const Tensor& mean,
-      const Tensor& variance);
-
-  /// The options with which this module was constructed.
-  BatchNormOptions options;
-
-  /// The learned weight.
-  /// Only defined if the `affine` option was `true` upon construction.
-  Tensor weight;
-
-  /// The learned bias.
-  /// Only defined if the `affine` option was `true` upon construction.
-  Tensor bias;
-
-  /// The running mean.
-  /// Only defined if the `track_running_stats` option was `true` upon construction.
-  Tensor running_mean;
-
-  /// The running variance.
-  /// Only defined if the `track_running_stats` option was `true` upon construction.
-  Tensor running_var;
-};
-
-/// A `ModuleHolder` subclass for `BatchNormImpl`.
-/// See the documentation for `BatchNormImpl` class to learn what methods it
-/// provides, or the documentation for `ModuleHolder` to learn about PyTorch's
-/// module storage semantics.
-TORCH_MODULE(BatchNorm);
-
-// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BatchNorm ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
 /// Base class for all (dimension-specialized) batchnorm and instancenorm modules.
 template <size_t D, typename Derived, typename DerivedOptions>
 class NormImplBase : public torch::nn::Cloneable<Derived> {
@@ -191,6 +124,8 @@ class BatchNormImplBase : public NormImplBase<D, Derived, BatchNormOptions> {
   void pretty_print(std::ostream& stream) const override;
 };
 
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BatchNorm1d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 /// Applies the BatchNorm1d function.
 /// See https://pytorch.org/docs/master/nn.html#torch.nn.BatchNorm1d to learn
 /// about the exact behavior of this module.
@@ -217,6 +152,8 @@ class TORCH_API BatchNorm1dImpl : public BatchNormImplBase<1, BatchNorm1dImpl> {
 /// module storage semantics.
 TORCH_MODULE(BatchNorm1d);
 
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BatchNorm2d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 /// Applies the BatchNorm2d function.
 /// See https://pytorch.org/docs/master/nn.html#torch.nn.BatchNorm2d to learn
 /// about the exact behavior of this module.
@@ -243,6 +180,8 @@ class TORCH_API BatchNorm2dImpl : public BatchNormImplBase<2, BatchNorm2dImpl> {
 /// module storage semantics.
 TORCH_MODULE(BatchNorm2d);
 
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BatchNorm3d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 /// Applies the BatchNorm3d function.
 /// See https://pytorch.org/docs/master/nn.html#torch.nn.BatchNorm3d to learn
 /// about the exact behavior of this module.

torch/csrc/api/include/torch/nn/modules/container/functional.h

@@ -24,7 +24,7 @@ namespace nn {
 ///   Sequential sequential(
 ///     Linear(3, 4),
 ///     Functional(torch::relu),
-///     BatchNorm(3),
+///     BatchNorm1d(3),
 ///     Functional(torch::elu, /*alpha=*/1));
 /// \endrst
 ///

torch/csrc/api/include/torch/nn/modules/container/modulelist.h

@@ -15,7 +15,7 @@ namespace nn {
 ///
 ///   torch::nn::ModuleList mlist(
 ///     torch::nn::Linear(3, 4),
-///     torch::nn::BatchNorm(4),
+///     torch::nn::BatchNorm1d(4),
 ///     torch::nn::Dropout(0.5)
 ///   );
 ///
@@ -39,7 +39,7 @@ namespace nn {
 ///
 ///   torch::nn::ModuleList mlist(
 ///     torch::nn::Linear(3, 4),
-///     torch::nn::BatchNorm(4),
+///     torch::nn::BatchNorm1d(4),
 ///     torch::nn::Dropout(0.5)
 ///   );
 ///

torch/csrc/api/include/torch/nn/modules/container/named_any.h

@@ -91,15 +91,5 @@ class NamedAnyModule {
   AnyModule module_;
 };
 
-#ifndef DOXYGEN_SHOULD_SKIP_THIS
-
-C10_DEPRECATED_MESSAGE("`torch::nn::modules_ordered_dict` is deprecated. " \
-                       "To construct a `Sequential` with named submodules, " \
-                       "you can do `Sequential sequential({{\"m1\", MyModule(1)}, {\"m2\", MyModule(2)}})`")
-TORCH_API torch::OrderedDict<std::string, AnyModule> modules_ordered_dict(
-  std::initializer_list<NamedAnyModule> named_modules);
-
-#endif /* DOXYGEN_SHOULD_SKIP_THIS */
-
 } // namespace nn
 } // namespace torch

torch/csrc/api/include/torch/nn/modules/container/sequential.h

@@ -34,7 +34,7 @@ namespace nn {
 ///
 ///   torch::nn::Sequential seq(
 ///     torch::nn::Linear(3, 4),
-///     torch::nn::BatchNorm(4),
+///     torch::nn::BatchNorm1d(4),
 ///     torch::nn::Dropout(0.5)
 ///   );
 ///
@@ -69,7 +69,7 @@ namespace nn {
 ///
 ///   torch::nn::Sequential seq(
 ///     torch::nn::Linear(3, 4),
-///     torch::nn::BatchNorm(4),
+///     torch::nn::BatchNorm1d(4),
 ///     torch::nn::Dropout(0.5)
 ///   );
 ///

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

@@ -128,34 +128,6 @@ public:
 /// module storage semantics.
 TORCH_MODULE(Dropout3d);
 
-// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ FeatureDropout ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-/// Applies spatial [Dropout](https://arxiv.org/abs/1207.0580) to inputs with
-/// 2-D or 3-D features.
-///
-/// The equivalent in Python is
-/// [Dropout2d](https://pytorch.org/docs/stable/nn.html#torch.nn.Dropout2d) for
-/// 2-D features and
-/// [Dropout3d](https://pytorch.org/docs/stable/nn.html#torch.nn.Dropout3d) for
-/// 3-D features. This `FeatureDropout` module can instead deal with both 2-D
-/// and 3-D features.
-class TORCH_API FeatureDropoutImpl
-    : public detail::_DropoutNd<FeatureDropoutImpl> {
- public:
-  FeatureDropoutImpl(double p);
-
-  explicit FeatureDropoutImpl(const FeatureDropoutOptions& options_ = {});
-
-  /// During training, applies a noise mask to the input tensor.
-  /// During evaluation, applies an identity function.
-  Tensor forward(const Tensor& input);
-
-  /// Pretty prints the `FeatureDropout` module into the given `stream`.
-  void pretty_print(std::ostream& stream) const override;
-};
-
-TORCH_MODULE(FeatureDropout);
-
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ AlphaDropout ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 /// Applies Alpha Dropout over the input.

torch/csrc/api/include/torch/nn/options/dropout.h

@@ -39,9 +39,6 @@ using Dropout2dOptions = DropoutOptions;
 /// ```
 using Dropout3dOptions = DropoutOptions;
 
-/// Options for `FeatureDropout` module.
-using FeatureDropoutOptions = DropoutOptions;
-
 /// Options for `AlphaDropout` module.
 ///
 /// Example:

torch/csrc/api/src/nn/init.cpp

@@ -35,57 +35,6 @@ struct Fan {
   int64_t out;
 };
 
-#define COMPUTE_NONLINEARITY_ENUM(name) /* NOLINT(cppcoreguidelines-macro-usage) */ \
-case Nonlinearity::name: \
-  TORCH_WARN( \
-    "The enum value `torch::nn::init::Nonlinearity::", #name, "` is deprecated and will be removed in 1.5. ", \
-    "Please use `torch::k", #name, "` instead."); \
-  return torch::k##name;
-
-#define COMPUTE_FANMODE_ENUM(name) /* NOLINT(cppcoreguidelines-macro-usage) */ \
-case FanMode::name: \
-  TORCH_WARN( \
-    "The enum value `torch::nn::init::FanMode::", #name, "` is deprecated and will be removed in 1.5. ", \
-    "Please use `torch::k", #name, "` instead."); \
-  return torch::k##name;
-
-NonlinearityType _compute_nonlinearity_type(Nonlinearity nonlinearity) {
-  switch (nonlinearity) {
-    COMPUTE_NONLINEARITY_ENUM(Linear)
-    COMPUTE_NONLINEARITY_ENUM(Conv1D)
-    COMPUTE_NONLINEARITY_ENUM(Conv2D)
-    COMPUTE_NONLINEARITY_ENUM(Conv3D)
-    COMPUTE_NONLINEARITY_ENUM(ConvTranspose1D)
-    COMPUTE_NONLINEARITY_ENUM(ConvTranspose2D)
-    COMPUTE_NONLINEARITY_ENUM(ConvTranspose3D)
-    COMPUTE_NONLINEARITY_ENUM(Sigmoid)
-    COMPUTE_NONLINEARITY_ENUM(Tanh)
-    COMPUTE_NONLINEARITY_ENUM(ReLU)
-    COMPUTE_NONLINEARITY_ENUM(LeakyReLU)
-    default:
-      TORCH_INTERNAL_ASSERT(
-        false,
-        "The enum class `torch::nn::init::Nonlinearity` is deprecated, ",
-        "please don't add any new enum to it. ",
-        "Instead, add the new enum to `torch/csrc/api/include/torch/enum.h` ",
-        "and use `torch::kEnumName` to reference it.")
-  }
-}
-
-FanModeType _compute_fanmode_type(FanMode fanmode) {
-  switch (fanmode) {
-    COMPUTE_FANMODE_ENUM(FanIn);
-    COMPUTE_FANMODE_ENUM(FanOut);
-    default:
-      TORCH_INTERNAL_ASSERT(
-        false,
-        "The enum class `torch::nn::init::Nonlinearity` is deprecated, ",
-        "please don't add any new enum to it. ",
-        "Instead, add the new enum to `torch/csrc/api/include/torch/enum.h` ",
-        "and use `torch::kEnumName` to reference it.")
-  }
-}
-
 double calculate_kaiming_std(
     Tensor tensor,
     double a,
@@ -96,10 +45,6 @@ double calculate_kaiming_std(
   const auto gain = calculate_gain(nonlinearity, a);
   double std = 0.0;
 
-  // Support for `torch::nn::init::FanMode` is deprecated and will be removed in 1.5.
-  if (c10::get_if<FanMode>(&mode)) {
-    mode = _compute_fanmode_type(c10::get<FanMode>(mode));
-  }
   if (c10::get_if<enumtype::kFanIn>(&mode)) {
     std = gain / std::sqrt(fan.in);
   } else {
@@ -110,10 +55,6 @@ double calculate_kaiming_std(
 } // namespace
 
 double calculate_gain(NonlinearityType nonlinearity, double param) {
-  // Support for `torch::nn::init::Nonlinearity` is deprecated and will be removed in 1.5.
-  if (c10::get_if<Nonlinearity>(&nonlinearity)) {
-    nonlinearity = _compute_nonlinearity_type(c10::get<Nonlinearity>(nonlinearity));
-  }
   if (c10::get_if<enumtype::kTanh>(&nonlinearity)) {
     return 5.0 / 3.0;  // NOLINT
   } else if (c10::get_if<enumtype::kReLU>(&nonlinearity)) {

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

@@ -16,69 +16,6 @@ namespace F = torch::nn::functional;
 namespace torch {
 namespace nn {
 
-BatchNormImpl::BatchNormImpl(const BatchNormOptions& options_) : options(options_) { // NOLINT(modernize-pass-by-value)
-  TORCH_WARN("torch::nn::BatchNorm module is deprecated and will be removed in 1.5. "
-             "Use BatchNorm{1,2,3}d instead.");
-  reset();
-}
-
-void BatchNormImpl::reset() {
-  if (options.affine()) {
-    weight = register_parameter(
-        "weight", torch::empty({options.num_features()}).uniform_());
-    bias = register_parameter("bias", torch::zeros({options.num_features()}));
-  }
-
-  if (options.track_running_stats()) {
-    running_mean =
-        register_buffer("running_mean", torch::zeros({options.num_features()}));
-    running_var =
-        register_buffer("running_var", torch::ones({options.num_features()}));
-  }
-}
-
-void BatchNormImpl::pretty_print(std::ostream& stream) const {
-  stream << std::boolalpha
-         << "torch::nn::BatchNorm(num_features=" << options.num_features()
-         << ", eps=" << options.eps() << ", momentum=" << options.momentum().value()
-         << ", affine=" << options.affine() << ", track_running_stats=" << options.track_running_stats()
-         << ")";
-}
-
-Tensor BatchNormImpl::forward(const Tensor& input) {
-  TORCH_CHECK(
-      options.track_running_stats(),
-      "Calling BatchNorm::forward is only permitted when "
-      "the 'track_running_stats' option is true (was false). "
-      "Use BatchNorm::pure_forward instead.");
-  return pure_forward(input, running_mean, running_var);
-}
-
-Tensor BatchNormImpl::pure_forward(
-    const Tensor& input,
-    const Tensor& mean,
-    const Tensor& variance) {
-  if (is_training()) {
-    const auto num_channels = input.dim() > 1 ? input.size(1) : 1;
-    TORCH_CHECK(
-        input.numel() / num_channels > 1,
-        "BatchNorm expected more than 1 value per channel when training!");
-  }
-
-  return torch::batch_norm(
-      input,
-      weight,
-      bias,
-      mean,
-      variance,
-      is_training(),
-      options.momentum().value(),
-      options.eps(),
-      torch::cuda::cudnn_is_available());
-}
-
-// ===========================================================================
-
 template <size_t D, typename Derived> 
 void BatchNormImplBase<D, Derived>::pretty_print(std::ostream& stream) const {
   stream << std::boolalpha

torch/csrc/api/src/nn/modules/container/named_any.cpp

@@ -1,20 +0,0 @@
-#include <torch/nn/modules/container/named_any.h>
-
-namespace torch {
-namespace nn {
-
-torch::OrderedDict<std::string, AnyModule> modules_ordered_dict(
-  std::initializer_list<NamedAnyModule> named_modules) {
-  TORCH_WARN(
-    "`torch::nn::modules_ordered_dict` is deprecated. "
-    "To construct a `Sequential` with named submodules, "
-    "you can do `Sequential sequential({{\"m1\", MyModule(1)}, {\"m2\", MyModule(2)}})`");
-  torch::OrderedDict<std::string, AnyModule> dict;
-  for (auto named_module : named_modules) {
-    dict.insert(named_module.name(), std::move(named_module.module()));
-  }
-  return dict;
-}
-
-} // namespace nn
-} // namespace torch

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

@@ -53,30 +53,6 @@ void Dropout3dImpl::pretty_print(std::ostream& stream) const {
 
 // ============================================================================
 
-FeatureDropoutImpl::FeatureDropoutImpl(double p)
-  : detail::_DropoutNd<FeatureDropoutImpl>(p) {
-  TORCH_WARN("torch::nn::FeatureDropout module is deprecated."
-             "Use Dropout{2,3}d instead.");
-}
-
-FeatureDropoutImpl::FeatureDropoutImpl(const FeatureDropoutOptions& options_)
-  : detail::_DropoutNd<FeatureDropoutImpl>(options_) {
-  TORCH_WARN("torch::nn::FeatureDropout module is deprecated."
-             "Use Dropout{2,3}d instead.");
-}
-
-Tensor FeatureDropoutImpl::forward(const Tensor& input) {
-  return torch::feature_dropout(input, options.p(), is_training());
-}
-
-void FeatureDropoutImpl::pretty_print(std::ostream& stream) const {
-  stream << std::boolalpha
-         << "torch::nn::FeatureDropout(p=" << options.p()
-         << ", inplace=" << options.inplace() << ")";
-}
-
-// ============================================================================
-
 Tensor AlphaDropoutImpl::forward(const Tensor& input) {
   return F::detail::alpha_dropout(input, options.p(), is_training(), /*inplace=*/false);
 }