documentation: Add SMP 1.2.0 API docs

doc/api/training/smd_model_parallel.rst

@@ -43,6 +43,7 @@ Select a version to see the API documentation for version. To use the library, r
 .. toctree::
    :maxdepth: 1
 
+   smp_versions/v1_2_0.rst
    smp_versions/v1_1_0.rst
 
 It is recommended to use this documentation alongside `SageMaker Distributed Model Parallel

doc/api/training/smp_versions/v1.1.0/smd_model_parallel_common_api.rst

@@ -24,10 +24,12 @@ The following SageMaker distribute model parallel APIs are common across all fra
 
 
 .. function:: smp.init( )
+   :noindex:
 
    Initialize the library. Must be called at the beginning of training script.
 
 .. function:: @smp.step(non_split_inputs, input_split_axes, [*args, **kwargs])
+   :noindex:
 
    A decorator that must be placed over a function that represents a single
    forward and backward pass (for training use cases), or a single forward
@@ -159,6 +161,7 @@ The following SageMaker distribute model parallel APIs are common across all fra
 
 
 .. class:: StepOutput
+   :noindex:
 
 
    A class that encapsulates all versions of a ``tf.Tensor``
@@ -188,27 +191,32 @@ The following SageMaker distribute model parallel APIs are common across all fra
    post-processing operations on tensors.
 
    .. data:: StepOutput.outputs
+      :noindex:
 
       Returns a list of the underlying tensors, indexed by microbatch.
 
    .. function:: StepOutput.reduce_mean( )
+      :noindex:
 
       Returns a ``tf.Tensor``, ``torch.Tensor`` that averages the constituent ``tf.Tensor`` s
       ``torch.Tensor`` s. This is commonly used for averaging loss and gradients across microbatches.
 
    .. function:: StepOutput.reduce_sum( )
+      :noindex:
 
       Returns a ``tf.Tensor`` /
       ``torch.Tensor`` that sums the constituent
       ``tf.Tensor``\ s/\ ``torch.Tensor``\ s.
 
    .. function:: StepOutput.concat( )
+      :noindex:
 
       Returns a
       ``tf.Tensor``/``torch.Tensor`` that concatenates tensors along the
       batch dimension using ``tf.concat`` / ``torch.cat``.
 
    .. function:: StepOutput.stack( )
+      :noindex:
 
       Applies ``tf.stack`` / ``torch.stack``
       operation to the list of constituent ``tf.Tensor``\ s /
@@ -217,13 +225,15 @@ The following SageMaker distribute model parallel APIs are common across all fra
    **TensorFlow-only methods**
 
    .. function:: StepOutput.merge( )
+      :noindex:
 
       Returns a ``tf.Tensor`` that
       concatenates the constituent ``tf.Tensor``\ s along the batch
       dimension. This is commonly used for merging the model predictions
       across microbatches.
 
    .. function:: StepOutput.accumulate(method="variable", var=None)
+      :noindex:
 
       Functionally the same as ``StepOutput.reduce_mean()``. However, it is
       more memory-efficient, especially for large numbers of microbatches,
@@ -249,6 +259,7 @@ The following SageMaker distribute model parallel APIs are common across all fra
          ignored.
 
 .. _mpi_basics:
+   :noindex:
 
 MPI Basics
 ^^^^^^^^^^
@@ -271,7 +282,8 @@ The library exposes the following basic MPI primitives to its Python API:
 -  ``smp.get_dp_group()``: The list of ranks that hold different
    replicas of the same model partition.
 
-   .. _communication_api:
+.. _communication_api:
+   :noindex:
 
 Communication API
 ^^^^^^^^^^^^^^^^^
@@ -285,6 +297,7 @@ should involve.
 **Helper structures**
 
 .. data:: smp.CommGroup
+   :noindex:
 
    An ``enum`` that takes the values
    ``CommGroup.WORLD``, ``CommGroup.MP_GROUP``, and ``CommGroup.DP_GROUP``.
@@ -303,6 +316,7 @@ should involve.
       themselves.
 
 .. data:: smp.RankType
+   :noindex:
 
    An ``enum`` that takes the values
    ``RankType.WORLD_RANK``, ``RankType.MP_RANK``, and ``RankType.DP_RANK``.
@@ -318,6 +332,7 @@ should involve.
 **Communication primitives:**
 
 .. function:: smp.broadcast(obj, group)
+   :noindex:
 
    Sends the object to all processes in the
    group. The receiving process must call ``smp.recv_from`` to receive the
@@ -350,6 +365,7 @@ should involve.
           smp.recv_from(0, rank_type=smp.RankType.WORLD_RANK)
 
 .. function:: smp.send(obj, dest_rank, rank_type)
+   :noindex:
 
    Sends the object ``obj`` to
    ``dest_rank``, which is of a type specified by ``rank_type``.
@@ -373,6 +389,7 @@ should involve.
       ``recv_from`` call.
 
 .. function:: smp.recv_from(src_rank, rank_type)
+   :noindex:
 
    Receive an object from a peer process. Can be used with a matching
    ``smp.send`` or a ``smp.broadcast`` call.
@@ -398,6 +415,7 @@ should involve.
       ``broadcast`` call, and the object is received.
 
 .. function:: smp.allgather(obj, group)
+   :noindex:
 
    A collective call that gathers all the
    submitted objects across all ranks in the specified ``group``. Returns a
@@ -431,6 +449,7 @@ should involve.
           out = smp.allgather(obj2, smp.CommGroup.MP_GROUP)  # returns [obj1, obj2]
 
 .. function:: smp.barrier(group=smp.WORLD)
+   :noindex:
 
    A statement that hangs until all
    processes in the specified group reach the barrier statement, similar to
@@ -452,12 +471,14 @@ should involve.
       processes outside that ``mp_group``.
 
 .. function:: smp.dp_barrier()
+   :noindex:
 
    Same as passing ``smp.DP_GROUP``\ to ``smp.barrier()``.
    Waits for the processes in the same \ ``dp_group`` as
    the current process to reach the same point in execution.
 
 .. function:: smp.mp_barrier()
+   :noindex:
 
    Same as passing ``smp.MP_GROUP`` to
    ``smp.barrier()``. Waits for the processes in the same ``mp_group`` as

doc/api/training/smp_versions/v1.1.0/smd_model_parallel_pytorch.rst

@@ -23,6 +23,7 @@ This API document assumes you use the following import statements in your traini
    to learn how to use the following API in your PyTorch training script.
 
 .. class:: smp.DistributedModel
+   :noindex:
 
    A sub-class of ``torch.nn.Module`` which specifies the model to be
    partitioned. Accepts a ``torch.nn.Module`` object ``module`` which is
@@ -157,6 +158,7 @@ This API document assumes you use the following import statements in your traini
    **Methods**
 
    .. function:: backward(tensors, grad_tensors)
+      :noindex:
 
       Triggers a distributed backward
       pass across model partitions. Example usage provided in the previous
@@ -165,42 +167,49 @@ This API document assumes you use the following import statements in your traini
       ``retain_grad`` and ``create_graph``  flags are not supported.
 
    .. function:: local_buffers( )
+      :noindex:
 
       Returns an iterator over buffers for the modules in
       the partitioned model that have been assigned to the current process.
 
    .. function:: local_named_buffers( )
+      :noindex:
 
       Returns an iterator over buffers for the
       modules in the partitioned model that have been assigned to the current
       process. This yields both the name of the buffer as well as the buffer
       itself.
 
    .. function:: local_parameters( )
+      :noindex:
 
       Returns an iterator over parameters for the
       modules in the partitioned model that have been assigned to the current
       process.
 
    .. function:: local_named_parameters( )
+      :noindex:
 
       Returns an iterator over parameters for
       the modules in the partitioned model that have been assigned to the
       current process. This yields both the name of the parameter as well as
       the parameter itself.
 
    .. function:: local_modules( )
+      :noindex:
 
       Returns an iterator over the modules in the
       partitioned model that have been assigned to the current process.
 
    .. function:: local_named_modules( )
+      :noindex:
 
       Returns an iterator over the modules in the
       partitioned model that have been assigned to the current process. This
       yields both the name of the module as well as the module itself.
 
    .. function:: local_state_dict( )
+      :noindex:
 
       Returns the ``state_dict`` that contains local
       parameters that belong to the current \ ``mp_rank``. This ``state_dict``
@@ -210,34 +219,39 @@ This API document assumes you use the following import statements in your traini
       partition, or to the entire model.
 
    .. function:: state_dict( )
+      :noindex:
 
       Returns the ``state_dict`` that contains parameters
       for the entire model. It first collects the \ ``local_state_dict``  and
       gathers and merges the \ ``local_state_dict`` from all ``mp_rank``\ s to
       create a full ``state_dict``.
 
    .. function:: load_state_dict( )
+      :noindex:
 
       Same as the ``torch.module.load_state_dict()`` ,
       except: It first gathers and merges the ``state_dict``\ s across
       ``mp_rank``\ s, if they are partial. The actual loading happens after the
       model partition so that each rank knows its local parameters.
 
    .. function:: register_post_partition_hook(hook)
+      :noindex:
 
       Registers a callable ``hook`` to
       be executed after the model is partitioned. This is useful in situations
       where an operation needs to be executed after the model partition during
-      the first call to ``smp.step``, but before the actual execution of the
+      the first call to ``smp.step`` but before the actual execution of the
       first forward pass. Returns a ``RemovableHandle`` object ``handle``,
       which can be used to remove the hook by calling ``handle.remove()``.
 
-    .. function:: cpu( )
+   .. function:: cpu( )
+      :noindex:
 
       Allgathers parameters and buffers across all ``mp_rank``\ s and moves them
       to the CPU.
 
 .. class:: smp.DistributedOptimizer
+   :noindex:
 
    **Parameters**
    - ``optimizer``
@@ -246,13 +260,15 @@ This API document assumes you use the following import statements in your traini
    returns ``optimizer`` with the following methods overridden:
 
    .. function:: state_dict( )
+      :noindex:
 
       Returns the ``state_dict`` that contains optimizer state for the entire model.
       It first collects the ``local_state_dict`` and gathers and merges
       the ``local_state_dict`` from all ``mp_rank``s to create a full
       ``state_dict``.
 
    .. function::  load_state_dict( )
+      :noindex:
 
       Same as the ``torch.optimizer.load_state_dict()`` , except:
 
@@ -262,6 +278,7 @@ This API document assumes you use the following import statements in your traini
             rank knows its local parameters.
 
    .. function::  local_state_dict( )
+      :noindex:
 
       Returns the ``state_dict`` that contains the
       local optimizer state that belongs to the current \ ``mp_rank``. This
@@ -308,70 +325,79 @@ This API document assumes you use the following import statements in your traini
               self.child3 = Child3()                # child3 on default_partition
 
 .. function:: smp.get_world_process_group( )
+   :noindex:
 
    Returns a ``torch.distributed`` ``ProcessGroup`` that consists of all
    processes, which can be used with the ``torch.distributed`` API.
    Requires ``"ddp": True`` in SageMaker Python SDK parameters.
 
 .. function:: smp.get_mp_process_group( )
+   :noindex:
 
    Returns a ``torch.distributed`` ``ProcessGroup`` that consists of the
    processes in the ``MP_GROUP`` which contains the current process, which
    can be used with the \ ``torch.distributed`` API. Requires
    ``"ddp": True`` in SageMaker Python SDK parameters.
 
 .. function:: smp.get_dp_process_group( )
+   :noindex:
 
    Returns a ``torch.distributed`` ``ProcessGroup`` that consists of the
    processes in the ``DP_GROUP`` which contains the current process, which
    can be used with the \ ``torch.distributed`` API. Requires
    ``"ddp": True`` in SageMaker Python SDK parameters.
 
 .. function:: smp.is_initialized( )
+   :noindex:
 
    Returns ``True`` if ``smp.init`` has already been called for the
    process, and ``False`` otherwise.
 
 .. function::smp.is_tracing( )
+   :noindex:
 
    Returns ``True`` if the current process is running the tracing step, and
    ``False`` otherwise.
 
 .. data:: smp.nn.FusedLayerNorm
+   :noindex:
 
    `Apex Fused Layer Norm <https://nvidia.github.io/apex/layernorm.html>`__ is currently not
    supported by the library. ``smp.nn.FusedLayerNorm`` replaces ``apex``
    ``FusedLayerNorm`` and provides the same functionality. This requires
    ``apex`` to be installed on the system.
 
 .. data:: smp.optimizers.FusedNovoGrad
-
+   :noindex:
 
    `Fused Novo Grad optimizer <https://nvidia.github.io/apex/optimizers.html#apex.optimizers.FusedNovoGrad>`__ is
    currently not supported by the library. ``smp.optimizers.FusedNovoGrad`` replaces ``apex`` ``FusedNovoGrad``
    optimizer and provides the same functionality. This requires ``apex`` to
    be installed on the system.
 
 .. data:: smp.optimizers.FusedLamb
-
+   :noindex:
 
    `FusedLamb optimizer <https://nvidia.github.io/apex/optimizers.html#apex.optimizers.FusedLAMB>`__
    currently doesn’t work with the library. ``smp.optimizers.FusedLamb`` replaces
    ``apex`` ``FusedLamb`` optimizer and provides the same functionality.
    This requires ``apex`` to be installed on the system.
 
 .. data:: smp.amp.GradScaler
+   :noindex:
 
    `Torch AMP Gradscaler <https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler>`__
    currently doesn’t work with the library. ``smp.amp.GradScaler`` replaces
    ``torch.amp.GradScaler`` and provides the same functionality.
 
 .. _pytorch_saving_loading:
+   :noindex:
 
 APIs for Saving and Loading
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. function:: smp.save( )
+   :noindex:
 
    Saves an object. This operation is similar to ``torch.save()``, except
    it has an additional keyword argument, ``partial``, and accepts only
@@ -394,6 +420,7 @@ APIs for Saving and Loading
       override the defaultprotocol.
 
 .. function:: smp.load( )
+   :noindex:
 
    Loads an object saved with ``smp.save()`` from a file.
 
@@ -418,6 +445,7 @@ APIs for Saving and Loading
       Should be used when loading a model trained with the library.
 
 .. _pytorch_saving_loading_instructions:
+   :noindex:
 
 General Instruction For Saving and Loading
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^