Add merge pooled emb docstring (pytorch#3172)

Summary:
X-link: facebookresearch/FBGEMM#266

As title

Pull Request resolved: pytorch#3172

Reviewed By: YazhiGao, shintaro-iwasaki

Differential Revision: D63391637

Pulled By: sryap

fbshipit-source-id: 7cd4e98b89b1a7b77bf468d1d8e0f084e6d0b09a

fbgemm_gpu/docs/src/fbgemm_gpu-python-api/pooled_embedding_ops.rst

@@ -0,0 +1,6 @@
+Pooled Embedding Operators
+==========================
+
+.. automodule:: fbgemm_gpu
+
+.. autofunction:: torch.ops.fbgemm.merge_pooled_embeddings

fbgemm_gpu/docs/src/index.rst

@@ -91,3 +91,4 @@ Table of Contents
 
    fbgemm_gpu-python-api/table_batched_embedding_ops.rst
    fbgemm_gpu-python-api/jagged_tensor_ops.rst
+   fbgemm_gpu-python-api/pooled_embedding_ops.rst

fbgemm_gpu/fbgemm_gpu/docs/__init__.py

@@ -7,6 +7,6 @@
 
 # Trigger the manual addition of docstrings to pybind11-generated operators
 try:
-    from . import jagged_tensor_ops, table_batched_embedding_ops  # noqa: F401
+    from . import jagged_tensor_ops, merge_pooled_embedding_ops  # noqa: F401
 except Exception:
     pass

fbgemm_gpu/fbgemm_gpu/docs/merge_pooled_embedding_ops.py

@@ -0,0 +1,36 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+
+from .common import add_docs
+
+add_docs(
+    torch.ops.fbgemm.merge_pooled_embeddings,
+    """
+merge_pooled_embeddings(pooled_embeddings, uncat_dim_size, target_device, cat_dim=1) -> Tensor
+
+Concatenate embedding outputs from different devices (on the same host)
+on to the target device.
+
+Args:
+    pooled_embeddings (List[Tensor]): A list of embedding outputs from
+        different devices on the same host. Each output has 2
+        dimensions.
+
+    uncat_dim_size (int): The size of the dimension that is not
+        concatenated, i.e., if `cat_dim=0`, `uncat_dim_size` is the size
+        of dim 1 and vice versa.
+
+    target_device (torch.device): The target device that aggregates all
+        the embedding outputs.
+
+    cat_dim (int = 1): The dimension that the tensors are concatenated
+
+Returns:
+    The concatenated embedding output (2D) on the target device
+    """,
+)