add setup metadata to help PyPI flesh out content on pypi package page

Summary:
addresses comments in #19915
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19959

Differential Revision: D15149993

Pulled By: orionr

fbshipit-source-id: 0e438cfa1a311e89d4bed7ae9d7710a9f1b19a78

.circleci/config.yml

@@ -143,7 +143,7 @@ install_doc_push_script: &install_doc_push_script
     if [ "\$is_master_doc" = true ]; then
       find "\$install_path" -name "*.html" -print0 | xargs -0 perl -pi -w -e "s@master\s+\((\d\.\d\.[A-Fa-f0-9]+\+[A-Fa-f0-9]+)\s+\)@<a href='http://pytorch.org/docs/versions.html'>\1 \&#x25BC</a>@g"
     else
-      find "\$install_path" -name "*.html" -print0 | xargs -0 perl -pi -w -e "s@master\s+\((\d\.\d\.[A-Fa-f0-9]+\+[A-Fa-f0-9]+)\s+\)@<a href='http://pytorch.org/docs/versions.html'>\$version \&#x25BC</a>@g"
+      find "\$install_path" -name "*.html" -print0 | xargs -0 perl -pi -w -e "s@master\s+\((\d\.\d\.\S+)\s+\)@<a href='http://pytorch.org/docs/versions.html'>\$version \&#x25BC</a>@g"
     fi
 
     git add "\$install_path" || true
@@ -1169,11 +1169,11 @@ jobs:
             export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && ./doc_push_script.sh docs/master master") | docker exec -u jenkins -i "$id" bash) 2>&1'
 
           # stable release docs push. Due to some circleci limitations, we keep
-          # an eternal PR open (#16502) for merging v1.0.1 -> master for this job.
-          # XXX: The following code is only run on the v1.0.1 branch, which might
+          # an eternal PR open for merging v1.1.0 -> master for this job.
+          # XXX: The following code is only run on the v1.1.0 branch, which might
           # not be exactly the same as what you see here.
-          elif [[ "${CIRCLE_BRANCH}" == "v1.0.1" ]]; then
-            export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && ./doc_push_script.sh docs/stable 1.0.1") | docker exec -u jenkins -i "$id" bash) 2>&1'
+          elif [[ "${CIRCLE_BRANCH}" == "v1.1.0" ]]; then
+            export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && ./doc_push_script.sh docs/stable 1.1.0") | docker exec -u jenkins -i "$id" bash) 2>&1'
 
           # For open PRs: Do a dry_run of the docs build, don't push build
           else

.circleci/scripts/binary_linux_upload.sh

@@ -7,8 +7,8 @@ cat >/home/circleci/project/login_to_anaconda.sh <<EOL
 set +x
 echo "Trying to login to Anaconda"
 yes | anaconda login \
-    --username "$PYTORCH_BINARY_PJH5_CONDA_USERNAME" \
-    --password "$PYTORCH_BINARY_PJH5_CONDA_PASSWORD"
+    --username "$PYTORCH_BINARY_SOUMITH_CONDA_USERNAME" \
+    --password "$PYTORCH_BINARY_SOUMITH_CONDA_PASSWORD"
 set -x
 EOL
 chmod +x /home/circleci/project/login_to_anaconda.sh
@@ -24,7 +24,7 @@ pushd /home/circleci/project/final_pkgs
 if [[ "$PACKAGE_TYPE" == conda ]]; then
   retry conda install -yq anaconda-client
   retry timeout 30 /home/circleci/project/login_to_anaconda.sh
-  anaconda upload "$(ls)" -u pytorch --label main --no-progress --force
+  anaconda upload "$(ls)" -u pytorch-testing --label main --no-progress --force
 elif [[ "$PACKAGE_TYPE" == libtorch ]]; then
   retry pip install -q awscli
   s3_dir="s3://pytorch/libtorch/${PIP_UPLOAD_FOLDER}${DESIRED_CUDA}/"

.circleci/scripts/binary_macos_upload.sh

@@ -6,8 +6,8 @@ cat >/Users/distiller/project/login_to_anaconda.sh <<EOL
 set +x
 echo "Trying to login to Anaconda"
 yes | anaconda login \
-    --username "$PYTORCH_BINARY_PJH5_CONDA_USERNAME" \
-    --password "$PYTORCH_BINARY_PJH5_CONDA_PASSWORD"
+    --username "$PYTORCH_BINARY_SOUMITH_CONDA_USERNAME" \
+    --password "$PYTORCH_BINARY_SOUMITH_CONDA_PASSWORD"
 set -x
 EOL
 chmod +x /Users/distiller/project/login_to_anaconda.sh
@@ -24,7 +24,7 @@ pushd "$workdir/final_pkgs"
 if [[ "$PACKAGE_TYPE" == conda ]]; then
   retry conda install -yq anaconda-client
   retry /Users/distiller/project/login_to_anaconda.sh
-  retry anaconda upload "$(ls)" -u pytorch --label main --no-progress --force
+  retry anaconda upload "$(ls)" -u pytorch-testing --label main --no-progress --force
 elif [[ "$PACKAGE_TYPE" == libtorch ]]; then
   retry pip install -q awscli
   s3_dir="s3://pytorch/libtorch/${PIP_UPLOAD_FOLDER}${DESIRED_CUDA}/"

.circleci/scripts/binary_populate_env.sh

@@ -40,19 +40,19 @@ fi
 
 # Upload to parallel folder for gcc abis
 if [[ "$DESIRED_DEVTOOLSET" == 'devtoolset7' ]]; then
-  export PIP_UPLOAD_FOLDER='nightly/devtoolset7/'
+  export PIP_UPLOAD_FOLDER='devtoolset7/'
   if [[ "$PACKAGE_TYPE" == 'conda' ]]; then
     echo "We don't handle conda builds with gcc ABI of 1, since we don't"
     echo "want to add a new package name to the conda builds"
     exit 1
   fi
 else
-  export PIP_UPLOAD_FOLDER='nightly/'
+  export PIP_UPLOAD_FOLDER=''
 fi
 
 # We put this here so that OVERRIDE_PACKAGE_VERSION below can read from it
 export DATE="$(date -u +%Y%m%d)"
-export PYTORCH_BUILD_VERSION="1.1.0.dev$DATE"
+export PYTORCH_BUILD_VERSION="1.1.0"
 export PYTORCH_BUILD_NUMBER=1
 
 cat >>"$envfile" <<EOL
@@ -73,8 +73,8 @@ export PYTORCH_BUILD_VERSION="$PYTORCH_BUILD_VERSION"
 export PYTORCH_BUILD_NUMBER="$PYTORCH_BUILD_NUMBER"
 export OVERRIDE_PACKAGE_VERSION="$PYTORCH_BUILD_VERSION"
 
-export TORCH_PACKAGE_NAME='torch-nightly'
-export TORCH_CONDA_BUILD_FOLDER='pytorch-nightly'
+export TORCH_PACKAGE_NAME='torch'
+export TORCH_CONDA_BUILD_FOLDER='pytorch-1.1.0'
 
 export NO_FBGEMM=1
 export PIP_UPLOAD_FOLDER="$PIP_UPLOAD_FOLDER"

.circleci/verbatim-sources/header-section.yml

@@ -143,7 +143,7 @@ install_doc_push_script: &install_doc_push_script
     if [ "\$is_master_doc" = true ]; then
       find "\$install_path" -name "*.html" -print0 | xargs -0 perl -pi -w -e "s@master\s+\((\d\.\d\.[A-Fa-f0-9]+\+[A-Fa-f0-9]+)\s+\)@<a href='http://pytorch.org/docs/versions.html'>\1 \&#x25BC</a>@g"
     else
-      find "\$install_path" -name "*.html" -print0 | xargs -0 perl -pi -w -e "s@master\s+\((\d\.\d\.[A-Fa-f0-9]+\+[A-Fa-f0-9]+)\s+\)@<a href='http://pytorch.org/docs/versions.html'>\$version \&#x25BC</a>@g"
+      find "\$install_path" -name "*.html" -print0 | xargs -0 perl -pi -w -e "s@master\s+\((\d\.\d\.\S+)\s+\)@<a href='http://pytorch.org/docs/versions.html'>\$version \&#x25BC</a>@g"
     fi
 
     git add "\$install_path" || true

.circleci/verbatim-sources/job-specs-custom.yml

@@ -62,11 +62,11 @@
             export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && ./doc_push_script.sh docs/master master") | docker exec -u jenkins -i "$id" bash) 2>&1'
 
           # stable release docs push. Due to some circleci limitations, we keep
-          # an eternal PR open (#16502) for merging v1.0.1 -> master for this job.
-          # XXX: The following code is only run on the v1.0.1 branch, which might
+          # an eternal PR open for merging v1.1.0 -> master for this job.
+          # XXX: The following code is only run on the v1.1.0 branch, which might
           # not be exactly the same as what you see here.
-          elif [[ "${CIRCLE_BRANCH}" == "v1.0.1" ]]; then
-            export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && ./doc_push_script.sh docs/stable 1.0.1") | docker exec -u jenkins -i "$id" bash) 2>&1'
+          elif [[ "${CIRCLE_BRANCH}" == "v1.1.0" ]]; then
+            export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && ./doc_push_script.sh docs/stable 1.1.0") | docker exec -u jenkins -i "$id" bash) 2>&1'
 
           # For open PRs: Do a dry_run of the docs build, don't push build
           else

README.md

@@ -37,11 +37,12 @@ At a granular level, PyTorch is a library that consists of the following compone
 
 | Component | Description |
 | ---- | --- |
-| **torch** | a Tensor library like NumPy, with strong GPU support |
-| **torch.autograd** | a tape-based automatic differentiation library that supports all differentiable Tensor operations in torch |
-| **torch.nn** | a neural networks library deeply integrated with autograd designed for maximum flexibility |
-| **torch.multiprocessing** | Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training |
-| **torch.utils** | DataLoader and other utility functions for convenience |
+| [**torch**](https://pytorch.org/docs/stable/torch.html) | a Tensor library like NumPy, with strong GPU support |
+| [**torch.autograd**](https://pytorch.org/docs/stable/autograd.html) | a tape-based automatic differentiation library that supports all differentiable Tensor operations in torch |
+| [**torch.jit**](https://pytorch.org/docs/stable/jit.html) | a compilation stack (TorchScript) to create serializable and optimizable models from PyTorch code  |
+| [**torch.nn**](https://pytorch.org/docs/stable/nn.html) | a neural networks library deeply integrated with autograd designed for maximum flexibility |
+| [**torch.multiprocessing**](https://pytorch.org/docs/stable/multiprocessing.html) | Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training |
+| [**torch.utils**](https://pytorch.org/docs/stable/data.html) | DataLoader and other utility functions for convenience |
 
 Usually one uses PyTorch either as:
 

aten/src/ATen/Declarations.cwrap

@@ -2688,6 +2688,7 @@
   types:
     - floating_point
   backends:
+    - CPU
     - CUDA
   return: self
   arguments:
@@ -2721,6 +2722,7 @@
 [[
   name: _th_geometric_
   backends:
+    - CPU
     - CUDA
   cname: geometric
   variants: function

aten/src/TH/generic/THTensorRandom.cpp

@@ -55,6 +55,12 @@ void THTensor_(cappedRandom)(THTensor *self, THGenerator *_generator, int64_t ma
   THTensor_(clampedRandom)(self, _generator, 0, max);
 }
 
+void THTensor_(geometric)(THTensor *self, THGenerator *_generator, double p)
+{
+  std::lock_guard<std::mutex> lock(_generator->mutex);
+  TH_TENSOR_APPLY(scalar_t, self, *self_data = (scalar_t)THRandom_geometric(_generator, p););
+}
+
 #if defined(TH_REAL_IS_FLOAT) || defined(TH_REAL_IS_DOUBLE)
 
 #if defined(TH_REAL_IS_FLOAT)

aten/src/TH/generic/THTensorRandom.h

@@ -5,6 +5,7 @@
 TH_API void THTensor_(random)(THTensor *self, THGenerator *_generator);
 TH_API void THTensor_(clampedRandom)(THTensor *self, THGenerator *_generator, int64_t min, int64_t max);
 TH_API void THTensor_(cappedRandom)(THTensor *self, THGenerator *_generator, int64_t max);
+TH_API void THTensor_(geometric)(THTensor *self, THGenerator *_generator, double p);
 
 #if defined(TH_REAL_IS_FLOAT) || defined(TH_REAL_IS_DOUBLE)
 TH_API void THTensor_(bernoulli_Tensor)(THTensor *self, THGenerator *_generator, THTensor *p);

cmake/Dependencies.cmake

@@ -338,6 +338,22 @@ if(BUILD_TEST)
   if (NOT CAFFE2_USE_MSVC_STATIC_RUNTIME)
       set(gtest_force_shared_crt ON CACHE BOOL "force shared crt on gtest" FORCE)
   endif()
+  # We need to replace googletest cmake scripts too.
+  # Otherwise, it will sometimes break the build.
+  # To make the git clean after the build, we make a backup first.
+  if (MSVC AND MSVC_Z7_OVERRIDE)
+    execute_process(
+      COMMAND ${CMAKE_COMMAND}
+              "-DFILENAME=${CMAKE_CURRENT_LIST_DIR}/../third_party/googletest/googletest/cmake/internal_utils.cmake"
+              "-DBACKUP=${CMAKE_CURRENT_LIST_DIR}/../third_party/googletest/googletest/cmake/internal_utils.cmake.bak"
+              "-DREVERT=0"
+              "-P"
+              "${CMAKE_CURRENT_LIST_DIR}/GoogleTestPatch.cmake"
+      RESULT_VARIABLE _exitcode)
+    if(NOT ${_exitcode} EQUAL 0)
+      message(WARNING "Patching failed for Google Test. The build may fail.")
+    endif()
+  endif()
 
   # Add googletest subdirectory but make sure our INCLUDE_DIRECTORIES
   # don't bleed into it. This is because libraries installed into the root conda
@@ -363,6 +379,21 @@ if(BUILD_TEST)
 
   # Recover build options.
   set(BUILD_SHARED_LIBS ${TEMP_BUILD_SHARED_LIBS} CACHE BOOL "Build shared libs" FORCE)
+
+  # To make the git clean after the build, we revert the changes here.
+  if (MSVC AND MSVC_Z7_OVERRIDE)
+    execute_process(
+      COMMAND ${CMAKE_COMMAND}
+              "-DFILENAME=${CMAKE_CURRENT_LIST_DIR}/../third_party/googletest/googletest/cmake/internal_utils.cmake"
+              "-DBACKUP=${CMAKE_CURRENT_LIST_DIR}/../third_party/googletest/googletest/cmake/internal_utils.cmake.bak"
+              "-DREVERT=1"
+              "-P"
+              "${CMAKE_CURRENT_LIST_DIR}/GoogleTestPatch.cmake"
+      RESULT_VARIABLE _exitcode)
+    if(NOT ${_exitcode} EQUAL 0)
+      message(WARNING "Reverting changes failed for Google Test. The build may fail.")
+    endif()
+  endif()
 endif()
 
 # ---[ FBGEMM

cmake/GoogleTestPatch.cmake

@@ -0,0 +1,24 @@
+# CMake file to replace the string contents in Google Test and Google Mock
+# Usage example:
+# Patch the cmake file
+#   cmake -DFILENAME=internal_utils.cmake
+#         -DBACKUP=internal_utils.cmake.bak
+#         -DREVERT=0 
+#         -P GoogleTestPatch.cmake 
+# Revert the changes
+#   cmake -DFILENAME=internal_utils.cmake
+#         -DBACKUP=internal_utils.cmake.bak
+#         -DREVERT=1 
+#         -P GoogleTestPatch.cmake 
+
+
+if(REVERT)
+  file(READ ${BACKUP} content)
+  file(WRITE ${FILENAME} "${content}")
+  file(REMOVE ${BACKUP})
+else(REVERT)
+  file(READ ${FILENAME} content)
+  file(WRITE ${BACKUP} "${content}")
+  string(REGEX REPLACE "[-/]Z[iI]" "/Z7" content "${content}")
+  file(WRITE ${FILENAME} "${content}")
+endif(REVERT)

docs/source/community/persons_of_interest.rst

@@ -63,7 +63,7 @@ Build + CI
 -  Jesse Hellemn (`pjh5 <https://github.com/pjh5>`__)
 -  Soumith Chintala (`soumith <https://github.com/soumith>`__)
 -  (sunsetting) Orion Reblitz-Richardson
-(`orionr <https://github.com/orionr>`__)
+   (`orionr <https://github.com/orionr>`__)
 
 Distributions & RNG
 ~~~~~~~~~~~~~~~~~~~

docs/source/distributions.rst

@@ -258,7 +258,7 @@ Probability distributions - torch.distributions
     :show-inheritance:
 
 :hidden:`LogitRelaxedBernoulli`
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. currentmodule:: torch.distributions.relaxed_bernoulli
 .. autoclass:: LogitRelaxedBernoulli
@@ -301,7 +301,7 @@ Probability distributions - torch.distributions
     :members:
     :undoc-members:
     :show-inheritance:
-    
+
 :hidden:`Weibull`
 ~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/jit.rst

@@ -151,7 +151,7 @@ net models. In particular, TorchScript supports:
 Unlike Python, each variable in TorchScript function must have a single static type.
 This makes it easier to optimize TorchScript functions.
 
-Example::
+Example (a type mismatch)::
 
     @torch.jit.script
     def an_error(x):
@@ -201,35 +201,34 @@ Example::
 
         @torch.jit.script_method
         def forward(self, x):
-            # type: (Tensor) -> Tuple[List[Tuple[Tensor, Tensor]], Dict[int, Tensor]]
+            # type: (Tensor) -> Tuple[List[Tuple[int, float]], Dict[str, int]]
 
-            # This annotates the list to be a `List[Tuple[Tensor, Tensor]]`
-            list_of_tuple = torch.jit.annotate(List[Tuple[Tensor, Tensor]], [])
+            # This annotates the list to be a `List[Tuple[int, float]]`
+            my_list = torch.jit.annotate(List[Tuple[int, float]], [])
             for i in range(10):
-                list_of_tuple.append((x, x))
+                my_list.append((x, x))
 
-            # This annotates the list to be a `Dict[int, Tensor]`
-            int_tensor_dict = torch.jit.annotate(Dict[int, Tensor], {})
-            return list_of_tuple, int_tensor_dict
+            my_dict = torch.jit.annotate(Dict[str, int], {})
+            return my_list, my_dict
 
 
 Optional Type Refinement
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
-TorchScript will refine the type of a variable of type Optional[T] when
-a comparison to None is made inside the conditional of an if statement.
-The compiler can reason about multiple None checks that are combined with
-AND, OR, or NOT. Refinement will also occur for else blocks of if statements
+TorchScript will refine the type of a variable of type ``Optional[T]`` when
+a comparison to ``None`` is made inside the conditional of an if-statement.
+The compiler can reason about multiple ``None`` checks that are combined with
+``and``, ``or``, and ``not``. Refinement will also occur for else blocks of if-statements
 that are not explicitly written.
 
 The expression must be emitted within the conditional; assigning
-a None check to a variable and using it in the conditional will not refine types.
+a ``None`` check to a variable and using it in the conditional will not refine types.
 
 
 Example::
 
   @torch.jit.script
-  def opt_unwrap(x, y, z):
+  def optional_unwrap(x, y, z):
     # type: (Optional[int], Optional[int], Optional[int]) -> int
     if x is None:
       x = 1
@@ -240,6 +239,66 @@ Example::
     return x
 
 
+Classes
+^^^^^^^^^^^^^^^^^^^^^^^^
+Python classes can be used in TorchScript if they are annotated with ``@torch.jit.script``,
+similar to how you would declare a TorchScript function: ::
+
+    @torch.jit.script
+    class Foo:
+      def __init__(self, x, y)
+        self.x = x
+
+      def aug_add_x(self, inc):
+        self.x += inc
+
+
+This subset is restricted:
+
+* All functions must be valid TorchScript functions (including ``__init__()``)
+* Classes must be new-style classes, as we use ``__new__()`` to construct them with pybind11
+* TorchScript classes are statically typed. Members are declared by assigning to
+  self in the ``__init__()`` method
+
+    For example, assigning outside of the ``__init__()`` method: ::
+
+        @torch.jit.script
+        class Foo:
+          def assign_x(self):
+            self.x = torch.rand(2, 3)
+
+    Will result in: ::
+
+        RuntimeError:
+        Tried to set nonexistent attribute: x. Did you forget to initialize it in __init__()?:
+        def assign_x(self):
+          self.x = torch.rand(2, 3)
+          ~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
+
+* No expressions except method definitions are allowed in the body of the class
+* No support for inheritance or any other polymorphism strategy, except for inheriting
+  from object to specify a new-style class
+
+After a class is defined, it can be used in both TorchScript and Python interchangeably
+like any other TorchScript type:
+
+::
+
+    @torch.jit.script
+    class Pair:
+      def __init__(self, first, second)
+        self.first = first
+        self.second = second
+
+    @torch.jit.script
+    def sum_pair(p):
+      # type : (Pair) -> Tensor
+      return p.first + p.second
+
+    p = Pair(torch.rand(2, 3), torch.rand(2, 3)
+    print(sum_pair(p))
+
+
 Expressions
 ~~~~~~~~~~~
 
@@ -255,8 +314,9 @@ List Construction
     ``[3, 4]``, ``[]``, ``[torch.rand(3), torch.rand(4)]``
 
     .. note::
-        an empty list is assumed have type ``List[Tensor]``.
+        An empty list is assumed have type ``List[Tensor]``.
         The types of other list literals are derived from the type of the members.
+        To denote an empty list of another type, use ``torch.jit.annotate``.
 
 Tuple Construction
 """"""""""""""""""
@@ -268,8 +328,9 @@ Dict Construction
     ``{'hello': 3}``, ``{}``, ``{'a': torch.rand(3), 'b': torch.rand(4)}``
 
     .. note::
-        an empty dict is assumed have type ``Dict[str, Tensor]``.
+        An empty dict is assumed have type ``Dict[str, Tensor]``.
         The types of other dict literals are derived from the type of the members.
+        To denote an empty dict of another type, use ``torch.jit.annotate``.
 
 Variables
 ^^^^^^^^^
@@ -341,10 +402,6 @@ Subscripts
 
   ``t[i:j, i]``
 
-  .. note::
-    TorchScript currently does not support mutating tensors in place, so any
-    tensor indexing can only appear on the right-hand size of an expression.
-
 Function Calls
 ^^^^^^^^^^^^^^
    Calls to built-in functions: ``torch.rand(3, dtype=torch.int)``
@@ -468,11 +525,6 @@ For loops with ``range``
         for i in range(10):
             x *= i
 
-    .. note::
-      Script currently does not support iterating over generic iterable
-      objects like lists or tensors. Script currently does not support start or
-      increment parameters to range. These will be added in a future version.
-
 For loops over tuples:
 
     ::
@@ -512,9 +564,9 @@ For loops over constant ``torch.nn.ModuleList``
                   return v
 
       .. note::
-          To use a module list inside a ``@script_method`` it must be marked
+          To use a ``nn.ModuleList`` inside a ``@script_method`` it must be marked
           constant by adding the name of the attribute to the ``__constants__``
-          list for the type. For loops over a ModuleList will unroll the body of the
+          list for the type. For loops over a ``nn.ModuleList`` will unroll the body of the
           loop at compile time, with each member of the constant module list.
 
 Return
@@ -557,17 +609,17 @@ To make writing TorchScript more convenient, we allow script code to refer
 to Python values in the surrounding scope. For instance, any time there is a
 reference to ``torch``, the TorchScript compiler is actually resolving it to the
 ``torch`` Python module when the function is declared.  These Python values are
-not a first class part of TorchScript. Instead they are desugared at compile-time
-into the primitive types that TorchScript supports. This section describes the
-rules that are used when accessing Python values in TorchScript. They depend
-on the dynamic type of the python valued referenced.
+not a first class part of TorchScript. Instead they are de-sugared at compile-time
+into the primitive types that TorchScript supports. This depends
+on the dynamic type of the Python valued referenced when compilation occurs.
+This section describes the rules that are used when accessing Python values in TorchScript.
 
 Functions
 ^^^^^^^^^
 
   TorchScript can call Python functions. This functionality is very useful when
-  incrementally converting a model into script. The model can be moved function-by-function
-  to script, leaving calls to Python functions in place. This way you can incrementally
+  incrementally converting a model to TorchScript. The model can be moved function-by-function
+  to TorchScript, leaving calls to Python functions in place. This way you can incrementally
   check the correctness of the model as you go.
 
   Example::
@@ -581,10 +633,37 @@ Functions
       def bar(x)
         return foo(x + 1)
 
-  .. note::
-    Attempting to call ``save`` on a ScriptModule that contains calls to Python
-    functions will fail. The intention is that this pathway is used for debugging
-    and the calls removed or turned into script functions before saving.
+  Attempting to call ``save`` on a ScriptModule that contains calls to Python
+  functions will fail. The intention is that this pathway is used for debugging
+  and the calls removed or turned into script functions before saving. If you
+  want to export a module with a Python function, add the ``@torch.jit.ignore``
+  decorator to the function which will replace these function calls with an
+  exception when the model is saved: ::
+
+      class M(torch.jit.ScriptModule):
+        def __init__(self):
+          super(M, self).__init__()
+
+        @torch.jit.script_method
+        def forward(self, x):
+          self.ignored_code(x)
+          return x + 2
+
+        @torch.jit.ignore
+        def ignored_code(self, x):
+          # non-TorchScript code
+          import pdb; pdb.set_trace()
+
+      m = M()
+      # Runs, makes upcall to Python to run `ignored_code`
+      m(torch.ones(2, 2))
+
+      # Replaces all calls to `ignored_code` with a `raise`
+      m.save("m.pt")
+      loaded = torch.jit.load("m.pt")
+
+      # This runs `ignored_code` after saving which will raise an Exception!
+      loaded(torch.ones(2, 2))
 
 
 Attribute Lookup On Python Modules
@@ -621,6 +700,7 @@ Python-defined Constants
     Supported constant Python Values are
 
     * ``int``
+    * ``float``
     * ``bool``
     * ``torch.device``
     * ``torch.layout``
@@ -629,6 +709,31 @@ Python-defined Constants
     * ``torch.nn.ModuleList`` which can be used in a TorchScript for loop
 
 
+Module Attributes
+^^^^^^^^^^^^^^^^^
+
+The ``torch.nn.Parameter`` wrapper and ``register_buffer`` can be used to assign
+tensors to a ``ScriptModule``. In a similar vein, attributes of any type can be
+assign on a ``ScriptModule`` by wrapping them with ``torch.jit.Attribute`` and
+specifying the type. All types available in TorchScript are supported. These
+attributes are mutable and are saved in a separate archive in the serialized
+model binary. Tensor attributes are semantically the same as buffers.
+
+Example::
+
+    class Foo(torch.jit.ScriptModule):
+      def __init__(self, a_dict):
+        super(Foo, self).__init__(False)
+        self.words = torch.jit.Attribute([], List[str])
+        self.some_dict = torch.jit.Attribute(a_dict, Dict[str, int])
+
+      @torch.jit.script_method
+      def forward(self, input):
+        # type: (str) -> int
+        self.words.append(input)
+        return self.some_dict[input]
+
+
 Debugging
 ~~~~~~~~~
 
@@ -655,21 +760,21 @@ Disable JIT for Debugging
 
         traced_fn(torch.rand(3, 4))
 
-    Debugging this script with PDB works except for when we invoke the @script
-    function. We can globally disable JIT, so that we can call the @script
+    Debugging this script with PDB works except for when we invoke the ``@torch.jit.script``
+    function. We can globally disable JIT, so that we can call the ``@torch.jit.script``
     function as a normal python function and not compile it. If the above script
     is called ``disable_jit_example.py``, we can invoke it like so::
 
         $ PYTORCH_JIT=0 python disable_jit_example.py
 
-    and we will be able to step into the @script function as a normal Python
+    and we will be able to step into the ``@torch.jit.script`` function as a normal Python
     function.
 
 
 Inspecting Code
 ^^^^^^^^^^^^^^^
 
-    TorchScript provides a code pretty-printer for all ScriptModule instances. This
+    TorchScript provides a code pretty-printer for all ``ScriptModule`` instances. This
     pretty-printer gives an interpretation of the script method's code as valid
     Python syntax. For example::
 
@@ -688,11 +793,11 @@ Inspecting Code
 
     A ``ScriptModule`` with a single ``forward`` method will have an attribute
     ``code``, which you can use to inspect the ``ScriptModule``'s code.
-    If the ScriptModule has more than one method, you will need to access
+    If the ``ScriptModule`` has more than one method, you will need to access
     ``.code`` on the method itself and not the module. We can inspect the
     code of a method named ``bar`` on a ScriptModule by accessing ``.bar.code``.
 
-    The example script abouve produces the code::
+    The example script above produces the code::
 
         def forward(self,
                     len: int) -> Tensor:
@@ -706,7 +811,7 @@ Inspecting Code
                 rv0 = rv1
             return rv0
 
-    This is TorchScript's interpretation of the code for the ``forward`` method.
+    This is TorchScript's compilation of the code for the ``forward`` method.
     You can use this to ensure TorchScript (tracing or scripting) has captured
     your model code correctly.
 
@@ -734,7 +839,7 @@ Interpreting Graphs
 
         print(foo.graph)
 
-    ``.graph`` follows the same rules described in the Inspecting Code section
+    ``.graph`` follows the same rules described in the `Inspecting Code`_ section
     with regard to ``forward`` method lookup.
 
     The example script above produces the graph::
@@ -949,9 +1054,9 @@ best practices?
       # ... later, when using the model:
 
       if use_gpu:
-         model = torch.jit.load("gpu.pth")
+        model = torch.jit.load("gpu.pth")
       else:
-         model = torch.jit.load("cpu.pth")
+        model = torch.jit.load("cpu.pth")
 
       model(input)
 
@@ -961,6 +1066,40 @@ best practices?
    the correct device information.
 
 
+Q: How do I store attributes on a ``ScriptModule``?
+
+    Say we have a model like: ::
+
+      class Model(torch.jit.ScriptModule):
+        def __init__(self):
+          super(Model, self).__init__()
+          self.x = 2
+
+        @torch.jit.script_method
+        def forward(self):
+          return self.x
+
+    If ``Model`` is instantiated it will result in a compilation error
+    since the compiler doesn't know about ``x``. There are 4 ways to inform the
+    compiler of attributes on ``ScriptModule``:
+
+    1. ``nn.Parameter`` - values wrapped in ``nn.Parameter`` will work as they
+    do on ``nn.Module``\s
+
+    2. ``register_buffer`` - values wrapped in ``register_buffer`` will work as
+    they do on ``nn.Module``\s
+
+    3. ``__constants__`` - adding a list called ``__constants__`` at the
+    class definition level will mark the contained names as constants. Constants
+    are saved directly in the code of the model. See
+    `Python-defined Constants`_.
+
+    4. ``torch.jit.Attribute`` - values wrapped in ``torch.jit.Attribute`` can
+    be any ``TorchScript`` type, be mutated and are saved outside of the code of
+    the model. See `Module Attributes`_.
+
+
+
 Builtin Functions
 ~~~~~~~~~~~~~~~~~
 

docs/source/nn.rst

@@ -530,7 +530,7 @@ Linear layers
 ----------------------------------
 
 :hidden:`Identity`
-~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: Identity
     :members:

docs/source/tensorboard.rst

@@ -20,6 +20,7 @@ and visualization by TensorBoard. For example:
 
 .. code:: python
 
+
     import torch
     import torchvision
     from torch.utils.tensorboard import SummaryWriter
@@ -31,7 +32,9 @@ and visualization by TensorBoard. For example:
     transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
     trainset = datasets.MNIST('mnist_train', train=True, download=True, transform=transform)
     trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
-    model = torchvision.models.vgg16(False)
+    model = torchvision.models.resnet50(False)
+    # Have ResNet model take in grayscale rather than RGB
+    model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
     images, labels = next(iter(trainloader))
 
     grid = torchvision.utils.make_grid(images)
@@ -39,8 +42,24 @@ and visualization by TensorBoard. For example:
     writer.add_graph(model, images)
     writer.close()
 
-This can then be visualized with TensorBoard, which should be installed
-with ``pip install tensorboard`` or equivalent.
+This can then be visualized with TensorBoard, which should be installable
+and runnable with::
+
+    pip install tb-nightly  # Until 1.14 moves to the release channel
+    tensorboard --logdir=runs
 
 .. currentmodule:: torch.utils.tensorboard.writer
+
 .. autoclass:: SummaryWriter
+
+   .. automethod:: add_scalar
+   .. automethod:: add_histogram
+   .. automethod:: add_image
+   .. automethod:: add_figure
+   .. automethod:: add_video
+   .. automethod:: add_audio
+   .. automethod:: add_text
+   .. automethod:: add_graph
+   .. automethod:: add_embedding
+   .. automethod:: add_pr_curve
+   .. automethod:: add_custom_scalars

setup.py

@@ -229,7 +229,7 @@ cmake_python_include_dir = distutils.sysconfig.get_python_inc()
 # Version, create_version_file, and package_name
 ################################################################################
 package_name = os.getenv('TORCH_PACKAGE_NAME', 'torch')
-version = '1.1.0a0'
+version = '1.1.0'
 sha = 'Unknown'
 
 try:
@@ -243,8 +243,8 @@ if os.getenv('PYTORCH_BUILD_VERSION'):
     version = os.getenv('PYTORCH_BUILD_VERSION')
     if build_number > 1:
         version += '.post' + str(build_number)
-elif sha != 'Unknown':
-    version += '+' + sha[:7]
+# elif sha != 'Unknown':
+#     version += '+' + sha[:7]
 report("Building wheel {}-{}".format(package_name, version))
 
 
@@ -583,10 +583,16 @@ main_sources = ["torch/csrc/stub.cpp"]
 # before libcaffe2.so in the linker command.
 main_link_args.extend(CAFFE2_LIBS)
 
+install_requires=[]
+
+if sys.version_info[0] == 2:
+    install_requires.append('future')
+
 try:
     import numpy as np
     NUMPY_INCLUDE_DIR = np.get_include()
     USE_NUMPY = True
+    install_requires.append('numpy')
 except ImportError:
     USE_NUMPY = False
 
@@ -726,6 +732,7 @@ if __name__ == '__main__':
         cmdclass=cmdclass,
         packages=packages,
         entry_points=entry_points,
+        install_requires=install_requires,
         package_data={
             'torch': [
                 'py.typed',
@@ -818,6 +825,33 @@ if __name__ == '__main__':
                 'python/serialized_test/data/operator_test/*.zip',
             ]
         },
+        url='https://pytorch.org/',
+        download_url='https://github.com/pytorch/pytorch/tags',
+        author='PyTorch Team',
+        author_email='packages@pytorch.org',
+        # PyPI package information.
+        classifiers=[
+            'Development Status :: 5 - Production/Stable',
+            'Intended Audience :: Developers',
+            'Intended Audience :: Education',
+            'Intended Audience :: Science/Research',
+            'License :: OSI Approved :: BSD License',
+            'Programming Language :: C++',
+            'Programming Language :: Python :: 2',
+            'Programming Language :: Python :: 2.7',
+            'Programming Language :: Python :: 3',
+            'Programming Language :: Python :: 3.5',
+            'Programming Language :: Python :: 3.6',
+            'Programming Language :: Python :: 3.7',
+            'Topic :: Scientific/Engineering',
+            'Topic :: Scientific/Engineering :: Mathematics',
+            'Topic :: Scientific/Engineering :: Artificial Intelligence',
+            'Topic :: Software Development',
+            'Topic :: Software Development :: Libraries',
+            'Topic :: Software Development :: Libraries :: Python Modules',
+        ],
+        license='BSD',
+        keywords='pytorch machine learning',
     )
     if EMIT_BUILD_WARNING:
         print_box(build_update_message)

test/test_c10d.py

@@ -2176,20 +2176,27 @@ class DistributedDataParallelTest(MultiProcessTestCase):
         input = torch.rand([batch_size, 2], dtype=torch.float)
         target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)]).to(device_id)
 
-        def test_find_unused_parameters(find_unused_parameters):
-            model = DistributedDataParallel(
-                FindUnusedParametersModule().float().to(device_id),
-                device_ids=[device_id],
-                process_group=process_group,
-                find_unused_parameters=find_unused_parameters,
-            )
+        def test_find_unused_parameters(find_unused_parameters, test_default=False):
+            if test_default:
+                model = DistributedDataParallel(
+                    FindUnusedParametersModule().float().to(device_id),
+                    device_ids=[device_id],
+                    process_group=process_group,
+                )
+            else:
+                model = DistributedDataParallel(
+                    FindUnusedParametersModule().float().to(device_id),
+                    device_ids=[device_id],
+                    process_group=process_group,
+                    find_unused_parameters=find_unused_parameters,
+                )
 
             output, fc3 = model(input)
             output = fc3(output)
             loss = criterion(output, target)
             loss.backward()
 
-        # First test that the default behavior under these conditions is to
+        # First test that finding unused params under these conditions is to
         # trigger an error when `backward` is called (because fc3 is an unused
         # parameter and will therefore be marked ready twice).
         try:
@@ -2207,6 +2214,12 @@ class DistributedDataParallelTest(MultiProcessTestCase):
         except Exception as ex:
             self.fail("Unexpected exception: %s" % ex)
 
+        # Test find_unused_parameters defaults to False
+        try:
+            test_find_unused_parameters(True, test_default=True)
+        except Exception as ex:
+            self.fail("Unexpected exception: %s" % ex)
+
     @skip_if_not_nccl
     @skip_if_not_multigpu
     def test_multiple_outputs_multiple_backward(self):
@@ -2257,6 +2270,151 @@ class DistributedDataParallelTest(MultiProcessTestCase):
         loss2 = criterion(output2, target)
         loss2.backward()
 
+    @skip_if_not_nccl
+    @skip_if_not_multigpu
+    def test_no_used_parameters(self):
+        """
+        Note: this test can be sped up by only running it on a CPU module
+        once DistributedDataParallel supports them.
+        """
+        store = c10d.FileStore(self.file.name, self.world_size)
+        process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
+
+        class NoUsedParameters(nn.Module):
+            def __init__(self):
+                super(NoUsedParameters, self).__init__()
+
+                # Make sure this module has some parameters, only to then decide
+                # to never use them from the `forward` function.
+                self.fc1 = nn.Linear(2, 10, bias=False)
+                self.fc2 = nn.Linear(10, 4, bias=False)
+                self.fc3 = nn.Linear(4, 4, bias=False)
+                self.relu = nn.ReLU()
+
+            def forward(self, x):
+                return x * 0.0
+
+        device_id = gpus_for_rank(self.world_size)[self.rank][0]
+        model = DistributedDataParallel(
+            NoUsedParameters().float().to(device_id),
+            device_ids=[device_id],
+            process_group=process_group,
+            find_unused_parameters=True,
+        )
+
+        batch_size = 4
+        input = torch.rand([batch_size, 2], dtype=torch.float)
+
+        # After initialization, no parameter has their gradient set.
+        for p in model.parameters():
+            self.assertTrue(p.requires_grad)
+            self.assertIsNone(p.grad)
+
+        # Run `forward` function.
+        model(input)
+
+        # Because none of the parameters were used, we expect reduction for
+        # all parameters will be executed right when initializing the reducer.
+        # Once `forward` returns, all the parameter's gradients must be set.
+        for p in model.parameters():
+            self.assertTrue(p.requires_grad)
+            self.assertIsNotNone(p.grad)
+            self.assertTrue(torch.is_tensor(p.grad))
+            self.assertEqual(p.size(), p.grad.size())
+
+    @skip_if_not_nccl
+    @skip_if_not_multigpu
+    def test_no_grad(self):
+        """
+        Note: this test can be sped up by only running it on a CPU module
+        once DistributedDataParallel supports them.
+        """
+        store = c10d.FileStore(self.file.name, self.world_size)
+        process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
+
+        class NoGradModule(nn.Module):
+            def __init__(self):
+                super(NoGradModule, self).__init__()
+                self.fc1 = nn.Linear(2, 10, bias=False)
+                self.fc2 = nn.Linear(10, 4, bias=False)
+                self.relu = nn.ReLU()
+
+            def forward(self, x):
+                x = self.relu(self.fc1(x))
+                x = self.relu(self.fc2(x))
+                return F.softmax(x, dim=1)
+
+        device_id = gpus_for_rank(self.world_size)[self.rank][0]
+        model = DistributedDataParallel(
+            NoGradModule().float().to(device_id),
+            device_ids=[device_id],
+            process_group=process_group,
+        )
+
+        batch_size = 4
+        input = torch.rand([batch_size, 2], dtype=torch.float)
+
+        def check_no_grads():
+            for p in model.parameters():
+                self.assertTrue(p.requires_grad)
+                self.assertIsNone(p.grad)
+
+        # After initialization, no parameter has their gradient set.
+        check_no_grads()
+
+        # Run `forward` function with torch.no_grad()
+        with torch.no_grad():
+            output = model(input)
+            self.assertTrue(torch.is_tensor(output))
+
+        # No parameter should have their gradient set.
+        check_no_grads()
+
+    @skip_if_not_nccl
+    @skip_if_not_multigpu
+    def test_ignored_output(self):
+        """
+        Note: this test can be sped up by only running it on a CPU module
+        once DistributedDataParallel supports them.
+        """
+        store = c10d.FileStore(self.file.name, self.world_size)
+        process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
+
+        class IgnoredOutput(nn.Module):
+            def __init__(self):
+                super(IgnoredOutput, self).__init__()
+                self.fc1 = nn.Linear(2, 10, bias=False)
+                self.fc2 = nn.Linear(10, 4, bias=False)
+                self.relu = nn.ReLU()
+
+            def forward(self, x):
+                x = self.relu(self.fc1(x))
+                x = self.relu(self.fc2(x))
+                return F.softmax(x, dim=1)
+
+        device_id = gpus_for_rank(self.world_size)[self.rank][0]
+        model = DistributedDataParallel(
+            IgnoredOutput().float().to(device_id),
+            device_ids=[device_id],
+            process_group=process_group,
+        )
+
+        batch_size = 4
+        criterion = nn.CrossEntropyLoss()
+        input = torch.rand([batch_size, 2], dtype=torch.float)
+        target = torch.LongTensor([random.randrange(4) for _ in range(batch_size)]).to(device_id)
+
+        # Run a few iterations where we ignore the output.
+        for _ in range(4):
+            output = model(input)
+            del output
+
+        # Run a few iterations where we use the output.
+        for _ in range(4):
+            output = model(input)
+            loss = criterion(output, target)
+            loss.backward()
+
 
 class ReducerModule(nn.Module):
     def __init__(self):

test/test_jit.py

@@ -3648,20 +3648,14 @@ a")
         check_dynamic_indexing("[i:j, i]", consec((3, 3, 2)), 0, 2)
 
     def test_tensor_item(self):
-        def test_scalar_to_float_coercion(x):
-            return x.item() == 1
-
-        self.checkScript(test_scalar_to_float_coercion, (torch.tensor(1.0),))
-        self.checkScript(test_scalar_to_float_coercion, (torch.tensor(1),))
-
         def test_scalar_cast(x):
             scalar = x.item()
             return int(scalar), float(scalar)
 
         graph = torch.jit.script(test_scalar_cast).graph
         FileCheck().check("(int, float) = prim::TupleConstruct").run(graph)
-        self.checkScript(test_scalar_to_float_coercion, (torch.tensor(1.0),))
-        self.checkScript(test_scalar_to_float_coercion, (torch.tensor(1),))
+        self.checkScript(test_scalar_cast, (torch.tensor(1.0),))
+        self.checkScript(test_scalar_cast, (torch.tensor(1),))
 
         expected_str = r"Use int\(tensor\) or float\(tensor\) to retrieve"
         with self.assertRaisesRegex(RuntimeError, expected_str):
@@ -11730,6 +11724,8 @@ a")
 
     @unittest.skipIf(IS_WINDOWS or IS_SANDCASTLE, "NYI: TemporaryFileName support for Windows or Sandcastle")
     def test_attribute_unpickling(self):
+        tensor = torch.randn(2, 2)
+
         class M(torch.jit.ScriptModule):
             def __init__(self):
                 super(M, self).__init__()
@@ -11738,37 +11734,24 @@ a")
                 self.int = torch.jit.Attribute(99, int)
                 self.tuple = torch.jit.Attribute((1, 2, 3, 4), Tuple[int, int, int, int])
                 self.list = torch.jit.Attribute([(1, 2), (3, 4)], List[Tuple[int, int]])
-                self.tensor = torch.jit.Attribute(torch.randn(2, 2), torch.Tensor)
+                self.tensor = torch.jit.Attribute(tensor, torch.Tensor)
                 self.int_list = torch.jit.Attribute([1, 2, 3, 4], List[int])
 
             @torch.jit.script_method
             def forward(self):
                 return (self.table, self.float, self.int, self.tuple, self.list, self.int_list)
 
-        class TensorID(object):
-            def __setstate__(self, id):
-                self.id = id
-
-        class IntList(object):
-            def __setstate__(self, data):
-                self.data = data
-
-        class JitUnpickler(pickle.Unpickler):
-            def find_class(self, module, name):
-                if not module == '__main__':
-                    return None
-
-                if name == 'TensorID':
-                    return TensorID
-                elif name == 'IntList':
-                    return IntList
-
         with TemporaryFileName() as fname:
             M().save(fname)
             archive_name = os.path.basename(os.path.normpath(fname))
             archive = zipfile.ZipFile(fname, 'r')
             pickled_data = archive.read(os.path.join(archive_name, 'attributes.pkl'))
-            JitUnpickler(io.BytesIO(pickled_data)).load()
+            out = pickle.load(io.BytesIO(pickled_data))
+
+            self.assertEqual(out[0], {"I": "am", "a test": "test"})
+            self.assertEqual(out[1], 2.3)
+            self.assertEqual(out[2], 99)
+            self.assertEqual(out[6], [1, 2, 3, 4])
 
     @unittest.skipIf(IS_WINDOWS or IS_SANDCASTLE, "NYI: TemporaryFileName support for Windows or Sandcastle")
     def test_old_models_bc(self):

test/test_torch.py

@@ -891,6 +891,18 @@ class _TestTorchMixin(object):
     def test_max(self):
         self._testSelection(torch.max, max)
 
+    def test_log_normal(self):
+        for device in torch.testing.get_all_device_types():
+            a = torch.tensor([10], dtype=torch.float, device=device).log_normal_()
+            self.assertEqual(a.dtype, torch.float)
+            self.assertEqual(a.size(), torch.Size([1]))
+
+    def test_geometric(self):
+        for device in torch.testing.get_all_device_types():
+            a = torch.tensor([10], dtype=torch.float, device=device).geometric_(0.5)
+            self.assertEqual(a.dtype, torch.float)
+            self.assertEqual(a.size(), torch.Size([1]))
+
     @staticmethod
     def _test_max_with_inf(self, dtypes=(torch.float, torch.double), device='cpu'):
         for dtype in dtypes:

torch/csrc/distributed/c10d/reducer.cpp

@@ -45,6 +45,7 @@ Reducer::Reducer(
     : replicas_(std::move(replicas)),
       process_group_(std::move(process_group)),
       expect_autograd_hooks_(false),
+      require_finalize_(false),
       has_marked_unused_parameters_(false),
       next_bucket_(0),
       backward_stats_base_(0) {
@@ -160,6 +161,12 @@ void Reducer::mark_variable_ready(
   backward_stats_[replica_index][variable_index] =
       current_time_in_nanos() - backward_stats_base_;
 
+  // Any time we mark a variable ready (be it in line due to unused parameters,
+  // or via an autograd hook), we require a call to the finalize function. If
+  // this doesn't happen before the next iteration (or call to
+  // `prepare_for_backwards`), we know something is wrong.
+  require_finalize_ = true;
+
   const auto& bucket_index = variable_locators_[variable_index];
   auto& bucket = buckets_[bucket_index.bucket_index];
   auto& replica = bucket.replicas[replica_index];
@@ -228,12 +235,16 @@ void Reducer::mark_variable_ready(
     }
   }
 
-  // Queue function to finalize once the final bucket was marked ready.
+  // Run finalizer function once the final bucket was marked ready.
   if (next_bucket_ == buckets_.size()) {
-    // Autograd callbacks can only be registered while the engine is running.
-    AT_ASSERT(called_from_autograd);
-    torch::autograd::Engine::get_default_engine().queue_callback(
-        [=] { this->finalize_backward(); });
+    if (called_from_autograd) {
+      torch::autograd::Engine::get_default_engine().queue_callback([=] {
+        std::lock_guard<std::mutex> lock(this->mutex_);
+        this->finalize_backward();
+      });
+    } else {
+      finalize_backward();
+    }
   }
 }
 
@@ -375,6 +386,28 @@ void Reducer::prepare_for_backward(
   std::unordered_set<torch::autograd::Function*> seen;
   std::vector<torch::autograd::Function*> queue;
 
+  // Check that any prior reduction has finished.
+  // The variable `expect_autograd_hooks` is true until gradients for all
+  // parameters have been received and all buckets are ready.
+  if (require_finalize_) {
+    AT_ERROR(
+        "Expected to have finished reduction in the prior iteration before ",
+        "starting a new one. ",
+        "",
+        "This error indicates that your module has parameters that were ",
+        "not used in producing its output (the return value of `forward`). ",
+        "",
+        "You can enable unused parameter detection by passing the keyword "
+        "argument `find_unused_parameters=True` to ",
+        "`torch.nn.parallel.DistributedDataParallel`. ",
+        "",
+        "If you already have this argument set, then the distributed data ",
+        "parallel module wasn't able to locate the output tensors in the ",
+        "return value of your module's `forward` function. ",
+        "Please include the structure of the return value of `forward` of ",
+        "your module when reporting this issue (e.g. list, dict, iterable).");
+  }
+
   // Reset accounting.
   has_marked_unused_parameters_ = true;
   expect_autograd_hooks_ = true;
@@ -433,34 +466,16 @@ void Reducer::prepare_for_backward(
 }
 
 void Reducer::finalize_backward() {
-  std::lock_guard<std::mutex> lock(mutex_);
-
   // No longer expect autograd hooks to fire after this function returns.
   AT_ASSERT(expect_autograd_hooks_);
   expect_autograd_hooks_ = false;
 
+  // No longer require call to finalize after this function returns.
+  AT_ASSERT(require_finalize_);
+  require_finalize_ = false;
+
   // Check that all buckets were completed and had their work kicked off.
-  if (next_bucket_ < buckets_.size()) {
-    // If the reducer marked unused parameters and we STILL didn't get
-    // gradients for all module parameters, something is seriously wrong.
-    AT_ASSERT(!has_marked_unused_parameters_);
-    AT_ERROR(
-        "Expected to have gradients for all module parameters upon returning ",
-        "from the call to `torch.autograd.backward`. ",
-        "",
-        "This error indicates that your module has parameters that were ",
-        "not used in producing its output (the return value of `forward`). ",
-        "",
-        "You can enable unused parameter detection by passing the keyword "
-        "argument `find_unused_parameters=True` to ",
-        "`torch.nn.parallel.DistributedDataParallel`. ",
-        "",
-        "If you already have this argument set, then the distributed data ",
-        "parallel module wasn't able to locate the output tensors in the ",
-        "return value of your module's `forward` function. ",
-        "Please include the structure of the return value of `forward` of ",
-        "your module when reporting this issue (e.g. list, dict, iterable).");
-  }
+  AT_ASSERT(next_bucket_ == buckets_.size());
 
   // Wait for asynchronous reduction to complete and unflatten contents.
   for (auto& bucket : buckets_) {

torch/csrc/distributed/c10d/reducer.h

@@ -54,6 +54,7 @@ class Reducer {
   std::unordered_map<torch::autograd::Function*, std::tuple<int, int>> func_;
 
   bool expect_autograd_hooks_;
+  bool require_finalize_;
   bool has_marked_unused_parameters_;
   size_t next_bucket_;
 

torch/csrc/jit/README.md

@@ -1290,20 +1290,17 @@ During export a list of all the tensors in a model is created. Tensors can come
 
 ### `attributes.pkl`
 
-Attributes are all module properties that are not parameters or constants. Attributes are saved in a list in the order they were defined on the module. The list is stored as a Python `pickle` archive. `pickle`'s format was chosen due to:
-* **user friendliness** - the attributes file can be loaded in Python with `pickle` without having PyTorch installed
-* **size limits** - formats such as Protobuf empose size limits on total message size, whereas pickle limits are on individual values (e.g. strings cannot be longer than 4 GB)
-* **standard format** - `pickle` is a standard Python module with a reasonably simple format. The format is a program to be consumed by a stack machine that is detailed in Python's [`pickletools.py`](https://svn.python.org/projects/python/trunk/Lib/pickletools.py)
-* **built-in memoization** - for shared reference types (e.g. Tensor, string, lists, dicts)
-* **self describing** - a separate definition file is not needed to understand the pickled data
-* **eager mode save** - `torch.save()` already produces a `pickle` archive, so doing the same with attributes may ease unification of these formats in the future
+[pickler.h](pickler.h),
+[pickler.cpp](pickler.cpp),
+[torch/jit/_pickle.py](../../../torch/jit/_pickle.py)
+[caffe2/proto/torch.proto](../../../caffe2/proto/torch.proto)
 
-A given module may have many attributes of different types and many submodules, each with their own attributes. Attributes are recorded in `model.json`:
+Attributes are all module properties that are not parameters or constants. Attributes are saved in a list in the order they were defined on the module. A given module may have many attributes of different types and many submodules, each with their own attributes. Attribute metadata is recorded in `model.json`:
 * `type` - the full type of the attribute (in [Mypy syntax](https://mypy.readthedocs.io/en/latest/cheat_sheet_py3.html))
 * `name` - the attribute's name
 * `id` - the offset into the saved list of all model attributes
 
-`model.json`
+In `model.json`:
 ```json
 {
   "mainModule": {
@@ -1344,41 +1341,61 @@ A given module may have many attributes of different types and many submodules,
 }
 ```
 
-Attributes of the main module and its submodules are saved to a single file in the `zip` archive of a `.pt` file named `attributes.pkl`. A single file is used so that attributes can reference each other and shared values. Unpickling this will return a list of values corresponding to the attributes.
+Attributes of the main module and its submodules are saved to a single file in the `zip` archive of a `.pt` file named `attributes.pkl`. Attributes are stored as a Python `pickle` archive. `pickle`'s format was chosen due to:
+* **user friendliness** - the attributes file can be loaded in Python with `pickle`
+* **size limits** - formats such as Protobuf empose size limits on total message size, whereas pickle limits are on individual values (e.g. strings cannot be longer than 4 GB)
+* **standard format** - `pickle` is a standard Python module with a reasonably simple format. The format is a program to be consumed by a stack machine that is detailed in Python's [`pickletools.py`](https://svn.python.org/projects/python/trunk/Lib/pickletools.py)
+* **built-in memoization** - for shared reference types (e.g. Tensor, string, lists, dicts)
+* **self describing** - a separate definition file is not needed to understand the pickled data
+* **eager mode save** - `torch.save()` already produces a `pickle` archive, so doing the same with attributes avoids introducing yet another format
 
-All attributes are written into the `attributes.pkl` file with the exception of tensors, which store only a tensor table index (see "tensors" above). Classes are used to mark special data types, such as this tensor table index or specialized lists. To load the `attributes.pkl` file without PyTorch for inspection or manual editing, these classes must be defined, so a custom [`Unpickler`](https://docs.python.org/3/library/pickle.html#pickle.Unpickler) is necessary:
+[pickler.cpp](pickler.cpp) implements a subset of the Pickle format necessary for TorchScript models.
+
+
+A single file is used for the top level module and all submodules so that attributes can reference each other and share values. Unpickling `attributes.pkl`  will return a tuple of values corresponding to the attributes.
+
+All attributes are written into the `attributes.pkl` file with the exception of tensors, which store only a tensor table index (see "tensors" above). PyTorch functions defined in [torch/jit/_pickle.py](../../../torch/jit/_pickle.py) are used to mark special data types, such as this tensor table index or specialized lists. To load the `attributes.pkl` file, use the `pickle` module in Python:
+
+```python
+import pickle
+# attributes.pkl include references to functions in torch.jit._pickle
+import torch
+
+pickle.load(open("attributes.pkl", "rb"))
+```
+
+If for some reason you don't have PyTorch installed, you can still load `attributes.pkl` with a custom [`Unpickler`](https://docs.python.org/3/library/pickle.html#pickle.Unpickler):
 
 ```python
 import pickle
 
-# Tensor objects are stored as instances of this class
-class TensorID(object):
-    def __setstate__(self, id):
-        self.id = id
-
-# List[int] has internal specializations, and these are indicated with this class
-class IntList(object):
-    def __setstate__(self, data):
-        self.data = data
-
 class JitUnpickler(pickle.Unpickler):
     def find_class(self, module, name):
-        if not module == '__main__':
-            return None
+        if module != 'torch.jit._pickle':
+            raise RuntimeError("Unknown module")
 
-        if name == 'TensorID':
-            return TensorID
-        elif name == 'IntList':
-            return IntList
+        identity = lambda x: x
+        if name == 'build_tensor_from_id':
+            # Without the tensor table we can't do anything other than
+            # return the tensor ID
+            return identity
+        elif name == 'build_intlist':
+            return identity
 
-JitUnpickler(open("my_model/attributes.pkl", "rb")).load()
+print(JitUnpickler(open("out_dir/out/attributes.pkl", "rb")).load())
 ```
 
 #### Binary Format
 
 Running the following snippet produces a `ScriptModule` with several attributes.
+Python's `pickletools` module can be used to decode the binary blob of `attributes.pkl` into a human readable format.
 
 ```python
+import pickletools
+import zipfile
+import torch
+from typing import Tuple, List
+
 class M(torch.jit.ScriptModule):
     def __init__(self):
         super(M, self).__init__()
@@ -1391,50 +1408,46 @@ class M(torch.jit.ScriptModule):
     def forward(self):
         return (self.float, self.tuple, self.tensor, self.int_list)
 
-M().save("out.pt")
+M().save("out.zip")
+model_zip = zipfile.ZipFile("out.zip", 'r')
+model_zip.extractall("out_dir")
+pickletools.dis(open("out_dir/out/attributes.pkl", "rb"))
 ```
 
-In a terminal, Python's `pickletools` module can be used to decode the binary blob of `attributes.pkl` into a human readable format.
 
-```bash
-unzip -o out.pt
-python -m pickletools out/attributes.pkl
+The output of the above commands demonstrates the concepts described earlier. Attributes are wrapped in with `2: EMPTY_LIST` and appear in the order they are defined on the module. Functions for certain special types (e.g. `List[int]`, `Tensor`) can be seen at `37: GLOBAL` and `66: GLOBAL`, followed by data specific to that type, then finally by an instruction to build the object at `65: BUILD` and `113: BUILD` respectively.
 ```
-
-The output of the above commands demonstrates the concepts described earlier. Attributes are wrapped in with `2: EMPTY_LIST` and appear in the order they are defined on the module. Classes for certain special types (`List[int]`, `Tensor`) can be seen at `37: GLOBAL` and `66: GLOBAL`, followed by data specific to that type, then finally by an instruction to build the object at `65: BUILD` and `113: BUILD` respectively.
-```
-    0: \x80 PROTO      2
-    2: ]    EMPTY_LIST
-    3: (    MARK
-    4: G        BINFLOAT   2.3
-   13: (        MARK
-   14: J            BININT     1
-   19: J            BININT     2
-   24: J            BININT     3
-   29: J            BININT     4
-   34: t            TUPLE      (MARK at 13)
-   35: q        BINPUT     0
-   37: c        GLOBAL     '__main__ TensorID'
-   56: q        BINPUT     1
-   58: )        EMPTY_TUPLE
-   59: \x81     NEWOBJ
-   60: J        BININT     0
-   65: b        BUILD
-   66: c        GLOBAL     '__main__ IntList'
-   84: q        BINPUT     2
-   86: )        EMPTY_TUPLE
-   87: \x81     NEWOBJ
-   88: ]        EMPTY_LIST
-   89: q        BINPUT     3
-   91: (        MARK
-   92: J            BININT     1
-   97: J            BININT     2
-  102: J            BININT     3
-  107: J            BININT     4
-  112: e            APPENDS    (MARK at 91)
-  113: b        BUILD
-  114: e        APPENDS    (MARK at 3)
-  115: .    STOP
+  0: \x80 PROTO      2
+  2: (    MARK
+  3: G        BINFLOAT   2.3
+ 12: (        MARK
+ 13: K            BININT1    1
+ 15: K            BININT1    2
+ 17: K            BININT1    3
+ 19: K            BININT1    4
+ 21: t            TUPLE      (MARK at 12)
+ 22: q        BINPUT     0
+ 24: c        GLOBAL     'torch.jit._pickle build_tensor_from_id'
+ 64: q        BINPUT     1
+ 66: (        MARK
+ 67: K            BININT1    0
+ 69: t            TUPLE      (MARK at 66)
+ 70: R        REDUCE
+ 71: c        GLOBAL     'torch.jit._pickle build_intlist'
+104: q        BINPUT     2
+106: (        MARK
+107: ]            EMPTY_LIST
+108: (            MARK
+109: K                BININT1    1
+111: K                BININT1    2
+113: K                BININT1    3
+115: K                BININT1    4
+117: e                APPENDS    (MARK at 108)
+118: t            TUPLE      (MARK at 106)
+119: R        REDUCE
+120: q        BINPUT     3
+122: t        TUPLE      (MARK at 2)
+123: .    STOP
 highest protocol among opcodes = 2
 ```
 

torch/csrc/jit/pickler.cpp

@@ -6,6 +6,13 @@ namespace jit {
 using ::c10::IValue;
 
 PicklerClass getClass(const std::string& str) {
+  if (str == "build_tensor_from_id") {
+    return PicklerClass::TENSOR;
+  } else if (str == "build_intlist") {
+    return PicklerClass::INTLIST;
+  }
+
+  // TODO [unpickler refactor]
   if (str == "TensorID") {
     return PicklerClass::TENSOR;
   } else if (str == "IntList") {
@@ -15,8 +22,8 @@ PicklerClass getClass(const std::string& str) {
 }
 
 const std::string& getClassName(PicklerClass cls) {
-  static const std::string tensor_class("TensorID\n");
-  static const std::string intlist_class("IntList\n");
+  static const std::string tensor_class("build_tensor_from_id\n");
+  static const std::string intlist_class("build_intlist\n");
   switch (cls) {
     case PicklerClass::TENSOR:
       return tensor_class;
@@ -28,7 +35,7 @@ const std::string& getClassName(PicklerClass cls) {
 }
 
 const std::string& getModuleName() {
-  static const std::string module_name("__main__\n");
+  static const std::string module_name("torch.jit._pickle\n");
   return module_name;
 }
 
@@ -42,12 +49,11 @@ void Pickler::start() {
 
   // All attributes get pushed into a list and their indices saved in the
   // module def
-  push<OpCode>(OpCode::EMPTY_LIST);
   push<OpCode>(OpCode::MARK);
 }
 
 void Pickler::finish() {
-  push<OpCode>(OpCode::APPENDS);
+  push<OpCode>(OpCode::TUPLE);
   push<OpCode>(OpCode::STOP);
 }
 
@@ -92,17 +98,19 @@ void Pickler::addIValue(const IValue& ivalue) {
   }
 }
 
+/// Returns a void* uniquely identifying this IValue's data. For non-containers,
+/// returns nullptr.
 const void* Pickler::getPointer(const IValue& ivalue) {
   if (ivalue.isGenericDict()) {
-    return &(ivalue.toGenericDictRef());
+    return ivalue.toGenericDict().get();
   } else if (ivalue.isGenericList()) {
-    return &(ivalue.toGenericListRef());
+    return ivalue.toGenericList().get();
   } else if (ivalue.isTuple()) {
-    return &(ivalue.toTuple()->elements());
+    return ivalue.toTuple().get();
   } else if (ivalue.isString()) {
-    return &(ivalue.toStringRef());
+    return ivalue.toString().get();
   } else if (ivalue.isIntList()) {
-    return &(ivalue.toIntListRef());
+    return ivalue.toIntList().get();
   }
 
   return nullptr;
@@ -165,35 +173,48 @@ void Pickler::pushClass(PicklerClass cls) {
   } else {
     pushBinGet(memo_entry->second);
   }
-
-  push<OpCode>(OpCode::EMPTY_TUPLE);
-  push<OpCode>(OpCode::NEWOBJ);
 }
 
 void Pickler::pushTensor(const IValue& ivalue) {
   pushClass(PicklerClass::TENSOR);
 
   tensor_table_->push_back(ivalue.toTensor());
-  auto tensor_id = tensor_table_->size() - 1;
-  push<OpCode>(OpCode::BININT);
-  push<uint32_t>(tensor_id);
+  int64_t tensor_id = tensor_table_->size() - 1;
+  // Reduce arguments are spread (e.g. `*args`) before calling the global,
+  // so wrap in a tuple
+  push<OpCode>(OpCode::MARK);
+  addIValue(tensor_id);
+  push<OpCode>(OpCode::TUPLE);
 
-  push<OpCode>(OpCode::BUILD);
+  push<OpCode>(OpCode::REDUCE);
 }
 
 void Pickler::pushIntList(const IValue& ivalue) {
   pushClass(PicklerClass::INTLIST);
 
-  push<OpCode>(OpCode::EMPTY_LIST);
-  pushMemoization(ivalue);
+
+  // Reduce arguments are spread (e.g. `*args`) before calling the global,
+  // so wrap in a tuple
   push<OpCode>(OpCode::MARK);
 
+  push<OpCode>(OpCode::EMPTY_LIST);
+  // Mark list
+  push<OpCode>(OpCode::MARK);
+
+  // Add items
   for (const auto& item : ivalue.toIntListRef()) {
     addIValue(item);
   }
 
+  // Finish list
   push<OpCode>(OpCode::APPENDS);
-  push<OpCode>(OpCode::BUILD);
+
+  // Finish tuple
+  push<OpCode>(OpCode::TUPLE);
+
+  // Call reduce
+  push<OpCode>(OpCode::REDUCE);
+  pushMemoization(ivalue);
 }
 
 void Pickler::pushDouble(const IValue& ivalue) {
@@ -208,8 +229,6 @@ void Pickler::pushDouble(const IValue& ivalue) {
 }
 
 void Pickler::pushDict(const IValue& ivalue) {
-  auto dict = ivalue.toGenericDictRef();
-
   push<OpCode>(OpCode::EMPTY_DICT);
   pushMemoization(ivalue);
 
@@ -226,7 +245,7 @@ void Pickler::pushDict(const IValue& ivalue) {
 }
 
 void Pickler::pushMemoization(const void* item) {
-  AT_ASSERT(item != nullptr);
+  AT_CHECK(item != nullptr, "Pickler cannot memoize a nullptr");
   if (memo_id <= std::numeric_limits<uint8_t>::max()) {
     push<OpCode>(OpCode::BINPUT);
     push<uint8_t>(memo_id);
@@ -241,7 +260,14 @@ void Pickler::pushMemoization(const void* item) {
 }
 
 void Pickler::pushMemoization(const IValue& ivalue) {
-  pushMemoization(getPointer(ivalue));
+  auto ptr = getPointer(ivalue);
+  AT_CHECK(
+      ptr != nullptr,
+      "Pickler cannot memoize ",
+      ivalue.tagKind(),
+      " IValue ",
+      ivalue)
+  pushMemoization(ptr);
 }
 
 void Pickler::pushList(const IValue& ivalue) {
@@ -273,8 +299,17 @@ void Pickler::pushTuple(const IValue& ivalue) {
 
 std::vector<IValue> Unpickler::parse_ivalue_list() {
   run();
-  AT_ASSERT(stack_.size() == 1);
-  return stack_[0].toGenericListRef();
+  AT_CHECK(
+      stack_.size() == 1,
+      "Expected stack to end with a size of 1 but got ",
+      stack_.size());
+
+  auto value = stack_[0].ivalue();
+  if (value.isGenericList()) {
+    // TODO [unpickler refactor]
+    return value.toGenericListRef();
+  }
+  return value.toTuple()->elements();
 }
 
 double Unpickler::readFloat() {
@@ -294,7 +329,10 @@ double Unpickler::readFloat() {
 
 void Unpickler::run() {
   // Expect a PROTO opcode and protocol number at the start of blob
-  AT_ASSERT(readOpCode() == OpCode::PROTO);
+  AT_CHECK(
+      readOpCode() == OpCode::PROTO,
+      "Expected PROTO opcode at the start"
+      " of pickle archive");
   uint8_t protocol = read<uint8_t>();
   AT_CHECK(
       protocol == 2,
@@ -312,17 +350,18 @@ void Unpickler::run() {
   AT_ERROR("Overran buffer while unpickling data, didn't find STOP opcode");
 }
 
+
 OpCode Unpickler::readInstruction() {
   auto opcode = readOpCode();
   switch (opcode) {
     case OpCode::EMPTY_LIST: {
-      // Look back to see if the last opcode was an IntList class
       if (last_opcode_ == OpCode::NEWOBJ) {
+        // TODO [unpickler refactor] remove this case
         // It's a list specialization, the enum ID of which is on the stack
         AT_CHECK(
             stack_.size() > 0,
             "Unpickler found an empty stack when it expected a value");
-        auto value = stack_.back().toInt();
+        auto value = stack_.back().ivalue().toInt();
         AT_CHECK(
             value >= 0 && value <= std::numeric_limits<uint8_t>::max(),
             "Unpickler could not decode PicklerClass for ",
@@ -331,6 +370,14 @@ OpCode Unpickler::readInstruction() {
         if (cls == PicklerClass::INTLIST) {
           stack_.emplace_back(std::vector<int64_t>());
         }
+      } else if (stack_.size() > 0 && stack_.back().pickler_class_opt()) {
+        // Check if we're in a GLOBAL opcode and if so, if it's a list
+        // specialization
+        if (stack_.back().pickler_class() == PicklerClass::INTLIST) {
+          stack_.emplace_back(std::vector<int64_t>());
+        } else {
+          AT_ERROR("Unknown list specialization");
+        }
       } else {
         stack_.emplace_back(std::vector<IValue>());
       }
@@ -394,10 +441,14 @@ OpCode Unpickler::readInstruction() {
     case OpCode::TUPLE: {
       size_t start = marks_.back();
       marks_.pop_back();
-      IValue tup = c10::ivalue::Tuple::create(
-          std::vector<IValue>(stack_.begin() + start, stack_.end()));
-      stack_.resize(start);
-      stack_.push_back(tup);
+      auto tuple = c10::ivalue::Tuple::create({});
+      tuple->elements().reserve(stack_.size() - start);
+      auto start_it = stack_.begin() + start;
+      for (auto it = start_it; it != stack_.end(); ++it) {
+        tuple->elements().emplace_back(it->ivalue());
+      }
+      stack_.erase(start_it, stack_.end());
+      stack_.emplace_back(IValue(tuple));
     } break;
     case OpCode::EMPTY_DICT:
       stack_.emplace_back(c10::ivalue::UnorderedMap());
@@ -408,11 +459,11 @@ OpCode Unpickler::readInstruction() {
     case OpCode::SETITEMS: {
       size_t start = marks_.back();
       marks_.pop_back();
-      auto dict = stack_.at(start - 1).toGenericDict();
+      auto dict = stack_.at(start - 1).ivalue().toGenericDict();
       for (size_t i = start; i < stack_.size(); i += 2) {
-        dict->elements()[stack_[i]] = stack_[i + 1];
+        dict->elements()[stack_[i].ivalue()] = stack_[i + 1].ivalue();
       }
-      stack_.resize(start);
+      stack_.erase(stack_.begin() + start, stack_.end());
     } break;
     case OpCode::BINGET: {
       stack_.push_back(memo_table_.at(read<uint8_t>()));
@@ -423,35 +474,64 @@ OpCode Unpickler::readInstruction() {
     case OpCode::STOP:
       break;
     case OpCode::GLOBAL: {
-      AT_ASSERT(readString() == "__main__");
-      // Push class name to stack
-      stack_.emplace_back(static_cast<uint8_t>(getClass(readString())));
+      // Module name, it's not needed for anything
+      auto module_name = readString();
+      // TODO [unpickler refactor] __main__ isn't used by the pickler anymore
+      if (module_name == "__main__") {
+        stack_.emplace_back(static_cast<uint8_t>(getClass(readString())));
+      } else {
+        // Push class name to stack
+        stack_.emplace_back(getClass(readString()));
+      }
     } break;
     case OpCode::NEWOBJ: {
       // pop empty tuple
       stack_.pop_back();
     } break;
     case OpCode::BUILD: {
-      auto setitem_data = stack_.back();
+      // TODO: [unpickler refactor]
+      auto setitem_data = stack_.back().ivalue();
       stack_.pop_back();
 
+
       auto class_name =
-          static_cast<PicklerClass>(uint8_t(stack_.back().toInt()));
+        static_cast<PicklerClass>(uint8_t(stack_.back().ivalue().toInt()));
+      stack_.pop_back();
+
+      switch (class_name) {
+      case PicklerClass::TENSOR:
+        stack_.emplace_back(tensor_table_->at(setitem_data.toInt()));
+        break;
+      case PicklerClass::INTLIST:
+        stack_.emplace_back(setitem_data);
+        break;
+      default:
+        AT_ERROR("Unknown pickler class id");
+      }
+    } break;
+    case OpCode::REDUCE: {
+      // Pop reduce arg off the stack
+      auto data = stack_.back().ivalue().toTuple();
+      stack_.pop_back();
+
+      // Remove GLOBAL from stack
+      auto class_name = stack_.back().pickler_class();
       stack_.pop_back();
 
       switch (class_name) {
         case PicklerClass::TENSOR:
-          stack_.emplace_back(tensor_table_->at(setitem_data.toInt()));
+          stack_.emplace_back(
+              tensor_table_->at(data->elements().at(0).toInt()));
           break;
         case PicklerClass::INTLIST:
-          stack_.push_back(setitem_data);
+          stack_.emplace_back(data->elements().at(0).toIntListRef());
           break;
         default:
           AT_ERROR("Unknown pickler class id");
       }
     } break;
     default:
-      AT_ERROR("Unknown opcode for unpickling: ", static_cast<uint8_t>(opcode));
+      AT_ERROR("Unknown opcode for unpickling at ", reinterpret_cast<void*>(opcode),": ", static_cast<uint8_t>(opcode));
   }
   return opcode;
 }
@@ -460,19 +540,27 @@ void Unpickler::readList() {
   size_t start = marks_.back();
   marks_.pop_back();
   auto list_ivalue = stack_.at(start - 1);
-  if (list_ivalue.isIntList()) {
-    auto list = stack_.at(start - 1).toIntList();
-    auto num_elements = stack_.size() - start;
+  auto num_elements = stack_.size() - start;
+  if (list_ivalue.ivalue().isIntList()) {
+    auto list = stack_.at(start - 1).ivalue().toIntList();
     list->elements().reserve(num_elements);
     for (auto it = stack_.begin() + start; it != stack_.end(); ++it) {
-      list->elements().emplace_back(it->toInt());
+      list->elements().emplace_back(it->ivalue().toInt());
     }
   } else {
-    auto list = stack_.at(start - 1).toGenericList();
-    list->elements().insert(
-        list->elements().end(), stack_.begin() + start, stack_.end());
+    auto list = stack_.at(start - 1).ivalue().toGenericList();
+    list->elements().reserve(num_elements);
+    for (auto it = stack_.begin() + start; it != stack_.end(); ++it) {
+      list->elements().emplace_back(it->ivalue());
+    }
   }
-  stack_.resize(start);
+
+  stack_.erase(stack_.begin() + start, stack_.end());
+}
+
+inline bool is_valid_python_id_char(char c) {
+  return c == '_' || c == '.' || (c >= '0' && c <= '9') ||
+      (c >= 'a' && c <= 'z') || (c >= 'A' && c <= 'Z');
 }
 
 // Read a newline terminated string
@@ -487,7 +575,12 @@ std::string Unpickler::readString() {
     }
 
     // Simple check just in case there is no terminating '\n'
-    AT_ASSERT(c >= '0' && c <= 'z');
+    AT_CHECK(
+        is_valid_python_id_char(c),
+        "Found character '",
+        uint8_t(c),
+        "' in string, "
+        "strings must be qualified Python identifiers");
 
     // Increment after to exclude newline from string
     ++n;

torch/csrc/jit/pickler.h

@@ -1,3 +1,5 @@
+#pragma once
+
 #include <string>
 #include <vector>
 
@@ -119,7 +121,7 @@ class Pickler {
   // the left of a '::', its type cannot be deduced by the compiler so one must
   // explicitly instantiate the template, i.e. push<int>(int) works, push(int)
   // does not)
-  template<typename T>
+  template <typename T>
   void push(typename std::common_type<T>::type value) {
     const char* begin = reinterpret_cast<const char*>(&value);
     stack_.insert(stack_.end(), begin, begin + sizeof(T));
@@ -140,6 +142,39 @@ class Pickler {
   uint32_t memo_id = 0;
 };
 
+// An item in the unpickler stack. There needs to be a way to differentiate
+// between a GLOBAL item (PicklerClass) and a normal value item (IValue)
+struct StackItem {
+  StackItem(IValue ivalue)
+      : pickler_class_(c10::nullopt), ivalue_(std::move(ivalue)) {}
+  StackItem(PicklerClass pickler_class)
+      : pickler_class_(pickler_class), ivalue_(c10::nullopt) {}
+
+  IValue ivalue() {
+    return *ivalue_;
+  }
+
+  PicklerClass pickler_class() {
+    return *pickler_class_;
+  }
+
+  c10::optional<IValue> ivalue_opt() {
+    return ivalue_;
+  }
+
+  c10::optional<PicklerClass> pickler_class_opt() {
+    return pickler_class_;
+  }
+
+ private:
+  c10::optional<PicklerClass> pickler_class_;
+  c10::optional<IValue> ivalue_;
+};
+
+// [unpickler refactor] there is some cruft around OpCode::BUILD,
+// OpCode::NEWOBJ, and the last_opcode_ member below that should be deleted at
+// some point, the Pickler doesn't produce it and it's only around to support
+// models saved before 1.1
 class Unpickler {
   TH_DISALLOW_COPY_AND_ASSIGN(Unpickler);
 
@@ -176,12 +211,14 @@ class Unpickler {
   OpCode readOpCode();
   void readList();
 
-  std::vector<IValue> stack_;
-  std::vector<IValue> memo_table_;
+  std::vector<StackItem> stack_;
+  std::vector<StackItem> memo_table_;
   std::vector<size_t> marks_;
   const uint8_t* bytes_;
   const uint8_t* end_ptr_;
   const std::vector<at::Tensor>* tensor_table_;
+
+  // [unpickler refactor]
   OpCode last_opcode_;
 };
 

torch/csrc/jit/script/schema_matching.cpp

@@ -107,10 +107,6 @@ Value* tryConvertToType(
         DeviceObjType::get()->isSubtypeOf(concrete_type)) {
       return graph.insert(aten::device, {value}, {}, loc);
     }
-    if (concrete_type == FloatType::get() &&
-        value->type() == NumberType::get()) {
-      return graph.insert(prim::Float, {value}, {}, loc);
-    }
   }
 
   return value;

torch/jit/__init__.py

@@ -9,7 +9,6 @@ import torch._jit_internal as _jit_internal
 from torch._six import with_metaclass, get_function_from_type, \
     string_classes
 from torch._jit_internal import ignore  # noqa: F401
-from torch.jit._pickle import Unpickler  # noqa: F401
 from ..nn.modules.utils import _single, _pair, _triple, _quadruple, \
     _list_with_default
 import torch.testing
@@ -99,7 +98,8 @@ def load(f, map_location=None, _extra_files=DEFAULT_EXTRA_FILES_MAP):
         Returns:
             A ``ScriptModule`` object.
 
-        Example:
+        Example: ::
+
             torch.jit.load('scriptmodule.pt')
 
             # Load ScriptModule from io.BytesIO object
@@ -178,7 +178,8 @@ def save(m, f, _extra_files=DEFAULT_EXTRA_FILES_MAP):
 
             Please use something like ``io.BytesIO`` instead.
 
-        Example:
+        Example: ::
+
             m = torch.jit.ScriptModule()
 
             # Save to file
@@ -1069,13 +1070,13 @@ if _enabled:
         The core data structure in TorchScript is the ``ScriptModule``. It is an
         analogue of torch's ``nn.Module`` and represents an entire model as a tree of
         submodules. Like normal modules, each individual module in a ``ScriptModule`` can
-        have submodules, parameters, and methods. In ``nn.Module``s methods are implemented
-        as Python functions, but in ``ScriptModule``s methods are implemented as
+        have submodules, parameters, and methods. In ``nn.Module``\s methods are implemented
+        as Python functions, but in ``ScriptModule``\s methods are implemented as
         TorchScript functions,  a statically-typed subset of Python that contains all
         of PyTorch's built-in Tensor operations. This difference allows your
         ScriptModules code to run without the need for a Python interpreter.
 
-        ``ScriptModule``s be created in two ways:
+        ``ScriptModule``\s be created in two ways:
 
         **Tracing:**
 
@@ -1132,9 +1133,9 @@ if _enabled:
             You can write TorchScript code directly using Python syntax. You do this
             using the ``@torch.jit.script`` decorator (for functions) or
             ``@torch.jit.script_method`` decorator (for methods) on subclasses of
-            ScriptModule. With this decorator the body of the annotated function is
+            ``ScriptModule``. With this decorator the body of the annotated function is
             directly translated into TorchScript. TorchScript itself is a subset of
-            the Python language, so not all features in python work, but we provide
+            the Python language, so not all features in Python work, but we provide
             enough functionality to compute on tensors and do control-dependent
             operations.
 

torch/jit/_pickle.py

@@ -1,24 +1,8 @@
-import pickle
+def build_intlist(data):
+    return data
 
 
-class TensorID(object):
-    def __setstate__(self, id):
-        self.id = id
-
-
-class IntList(object):
-    def __setstate__(self, data):
-        self.data = data
-
-
-class Unpickler(pickle.Unpickler):
-    def find_class(self, module, name):
-        if not module == '__main__':
-            return None
-
-        if name == 'TensorID':
-            return TensorID
-        elif name == 'IntList':
-            return IntList
-        elif name == 'LiteralTensor':
-            return LiteralTensor
+def build_tensor_from_id(data):
+    if isinstance(data, int):
+        # just the id, can't really do anything
+        return data

torch/nn/parallel/distributed.py

@@ -198,7 +198,7 @@ class DistributedDataParallel(Module):
                                        Parameters that don't receive gradients as
                                        part of this graph are preemptively marked
                                        as being ready to be reduced.
-                                       (default: ``True``)
+                                       (default: ``False``)
         check_reduction: when setting to ``True``, it enables DistributedDataParallel
                          to automatically check if the previous iteration's
                          backward reductions were successfully issued at the
@@ -220,7 +220,7 @@ class DistributedDataParallel(Module):
     def __init__(self, module, device_ids=None,
                  output_device=None, dim=0, broadcast_buffers=True,
                  process_group=None, bucket_cap_mb=25,
-                 find_unused_parameters=True,
+                 find_unused_parameters=False,
                  check_reduction=False):
 
         super(DistributedDataParallel, self).__init__()
@@ -380,14 +380,16 @@ class DistributedDataParallel(Module):
         else:
             output = self.module(*inputs, **kwargs)
 
-        # We'll return the output object verbatim since it is a freeform object.
-        # We need to find any tensors in this object, though, because we need to
-        # figure out which parameters were used during this forward pass,
-        # to ensure we short circuit reduction for any unused parameters.
-        if self.find_unused_parameters:
-            self.reducer.prepare_for_backward(list(_find_tensors(output)))
-        else:
-            self.reducer.prepare_for_backward([])
+        if torch.is_grad_enabled():
+            # We'll return the output object verbatim since it is a freeform
+            # object. We need to find any tensors in this object, though,
+            # because we need to figure out which parameters were used during
+            # this forward pass, to ensure we short circuit reduction for any
+            # unused parameters. Only if `find_unused_parameters` is set.
+            if self.find_unused_parameters:
+                self.reducer.prepare_for_backward(list(_find_tensors(output)))
+            else:
+                self.reducer.prepare_for_backward([])
         return output
 
     def scatter(self, inputs, kwargs, device_ids):

torch/utils/tensorboard/writer.py

@@ -459,7 +459,7 @@ class SummaryWriter(object):
             walltime (float): Optional override default walltime (time.time())
               seconds after epoch of event
         Shape:
-            vid_tensor: :math:`(N, T, C, H, W)`.
+            vid_tensor: :math:`(N, T, C, H, W)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`.
         """
         self._get_file_writer().add_summary(
             video(tag, vid_tensor, fps), global_step, walltime)
@@ -714,7 +714,7 @@ class SummaryWriter(object):
     def add_custom_scalars(self, layout):
         """Create special chart by collecting charts tags in 'scalars'. Note that this function can only be called once
         for each SummaryWriter() object. Because it only provides metadata to tensorboard, the function can be called
-        before or after the training loop. See ``examples/demo_custom_scalars.py`` for more.
+        before or after the training loop.
 
         Args:
             layout (dict): {categoryName: *charts*}, where *charts* is also a dictionary