Update torchrec_intro_tutorial.py

Author: isururanawaka

intermediate_source/torchrec_intro_tutorial.py

@@ -200,7 +200,7 @@
 # In order to train models with massive embedding tables, sharding these
 # tables across GPUs is required, which then introduces a whole new set of
 # problems and opportunities in parallelism and optimization. Luckily, we have
-# the TorchRec library that has encountered, consolidated, and addressed
+# the TorchRec library <https://docs.pytorch.org/torchrec/overview.html>`__ that has encountered, consolidated, and addressed
 # many of these concerns. TorchRec serves as a **library that provides
 # primitives for large scale distributed embeddings**.
 #
@@ -496,11 +496,11 @@
 #
 # * **The module sharder**: This class exposes a ``shard`` API
 #   that handles sharding a TorchRec Module, producing a sharded module.
-#   * For ``EmbeddingBagCollection``, the sharder is `EmbeddingBagCollectionSharder <https://pytorch.org/torchrec/torchrec.distributed.html#torchrec.distributed.embeddingbag.EmbeddingBagCollectionSharder>`__
+#   * For ``EmbeddingBagCollection``, the sharder is `EmbeddingBagCollectionSharder `
 # * **Sharded module**: This class is a sharded variant of a TorchRec module.
 #   It has the same input/output as a the regular TorchRec module, but much
 #   more optimized and works in a distributed environment.
-#   * For ``EmbeddingBagCollection``, the sharded variant is `ShardedEmbeddingBagCollection <https://pytorch.org/torchrec/torchrec.distributed.html#torchrec.distributed.embeddingbag.ShardedEmbeddingBagCollection>`__
+#   * For ``EmbeddingBagCollection``, the sharded variant is `ShardedEmbeddingBagCollection`
 #
 # Every TorchRec module has an unsharded and sharded variant.
 #
@@ -619,7 +619,7 @@
 # Remember that TorchRec is a highly optimized library for distributed
 # embeddings. A concept that TorchRec introduces to enable higher
 # performance for training on GPU is a
-# `LazyAwaitable <https://pytorch.org/torchrec/torchrec.distributed.html#torchrec.distributed.types.LazyAwaitable>`__.
+# `LazyAwaitable `.
 # You will see ``LazyAwaitable`` types as outputs of various sharded
 # TorchRec modules. All a ``LazyAwaitable`` type does is delay calculating some
 # result as long as possible, and it does it by acting like an async type.
@@ -693,7 +693,7 @@ def _wait_impl(self) -> torch.Tensor:
 # order for distribution of gradients. ``input_dist``, ``lookup``, and
 # ``output_dist`` all depend on the sharding scheme. Since we sharded in a
 # table-wise fashion, these APIs are modules that are constructed by
-# `TwPooledEmbeddingSharding <https://pytorch.org/torchrec/torchrec.distributed.sharding.html#torchrec.distributed.sharding.tw_sharding.TwPooledEmbeddingSharding>`__.
+# `TwPooledEmbeddingSharding`.
 #
 
 sharded_ebc
@@ -742,7 +742,7 @@ def _wait_impl(self) -> torch.Tensor:
 # ``EmbeddingBagCollection`` to generate a
 # ``ShardedEmbeddingBagCollection`` module. This workflow is fine, but
 # typically when implementing model parallel,
-# `DistributedModelParallel <https://pytorch.org/torchrec/torchrec.distributed.html#torchrec.distributed.model_parallel.DistributedModelParallel>`__
+# `DistributedModelParallel`
 # (DMP) is used as the standard interface. When wrapping your model (in
 # our case ``ebc``), with DMP, the following will occur:
 #