Add permute sparse data docstrings (#3178)

Summary:
X-link: facebookresearch/FBGEMM#274

As title

Pull Request resolved: #3178

Test Plan: See example https://deploy-preview-3178--pytorch-fbgemm-docs.netlify.app/fbgemm_gpu-python-api/sparse_ops

Reviewed By: shintaro-iwasaki

Differential Revision: D63458583

Pulled By: sryap

fbshipit-source-id: 3beb73e65e242c103428000ff26185335194035b

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

@@ -0,0 +1,8 @@
+Sparse Operators
+================
+
+.. automodule:: fbgemm_gpu
+
+.. autofunction:: torch.ops.fbgemm.permute_2D_sparse_data
+
+.. autofunction:: torch.ops.fbgemm.permute_1D_sparse_data

fbgemm_gpu/docs/src/index.rst

@@ -89,9 +89,10 @@ Table of Contents
    :maxdepth: 1
    :caption: FBGEMM_GPU Python Operators API
 
-   fbgemm_gpu-python-api/jagged_tensor_ops.rst
+   fbgemm_gpu-python-api/sparse_ops.rst
    fbgemm_gpu-python-api/pooled_embedding_ops.rst
    fbgemm_gpu-python-api/quantize_ops.rst
+   fbgemm_gpu-python-api/jagged_tensor_ops.rst
 
 .. _fbgemm-gpu.toc.api.python.modules:
 

fbgemm_gpu/fbgemm_gpu/docs/__init__.py

@@ -12,6 +12,7 @@
         merge_pooled_embedding_ops,
         permute_pooled_embedding_ops,
         quantize_ops,
+        sparse_ops,
     )
 except Exception:
     pass

fbgemm_gpu/fbgemm_gpu/docs/sparse_ops.py

@@ -0,0 +1,121 @@
+# 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.permute_2D_sparse_data,
+    """
+permute_2D_sparse_data(permute, lengths, values, weights=None, permuted_lengths_sum=None) -> Tuple[Tensor, Tensor, Optional[Tensor]]
+
+Permute 2D sparse data along the first dimension (dim 0). Note that 2D
+refers to the number of dense dimensions. The input data is actually 3D
+where the first two dimensions are dense and the last dimension is
+jagged (sparse). The data to permute over can be less or more and with or
+without repetitions.
+
+Args:
+    permute (Tensor): A 1D-tensor that describes how data is permuted along dim
+        0. `permute[i]` indicates that data at position `permute[i]` is moved
+        to position `i`. The length of this tensor is the total amount of data
+        in dim 0 to be permuted. The values in `permute` must be >= 0 and <
+        `lengths.shape[0]`
+
+    lengths (Tensor): A 2D-tensor that contains jagged shapes corresponding to
+        the other two dense dimensions. For example, in the case of the
+        embedding input, the 3D shape is (num features, batch size, bag size).
+        `lengths[t][b]` represents the bag size of feature `t` and sample `b`.
+
+    values (Tensor): A 1D-input-tensor to be permuted. The length of this
+        tensor must be equal to `lengths.sum()`. This tensor can be of any data
+        type.
+
+    weights (Optional[Tensor] = None): An optional 1D-float-tensor. It must
+        have the same length as `values`. It will be permuted the same way as
+        values
+
+    permuted_lengths_sum (Optional[int] = None): An optional value that
+        represents the total number of elements in the permuted data (output
+        shape). If not provided, the operator will compute this data which may
+        cause a device-host synchronization (if using GPU). Thus, it is
+        recommended to supply this value to avoid such the synchronization.
+
+Returns:
+    A tuple of permuted lengths, permuted indices and permuted weights
+
+**Example:**
+
+    >>> permute = torch.tensor([1, 0, 2], dtype=torch.int32, device="cuda")
+    >>> lengths = torch.tensor([[2, 3, 4, 5], [1, 2, 4, 8], [0, 3, 2, 3]], dtype=torch.int64, device="cuda")
+    >>> values = torch.randint(low=0, high=100, size=(lengths.sum().item(),), dtype=torch.int64, device="cuda")
+    >>> print(values)
+    tensor([29, 12, 61, 98, 56, 94,  5, 89, 65, 48, 71, 54, 40, 33, 78, 68, 42, 21,
+            60, 51, 15, 47, 48, 68, 52, 19, 38, 30, 38, 97, 97, 98, 18, 40, 42, 89,
+            66], device='cuda:0')
+    >>> torch.ops.fbgemm.permute_2D_sparse_data(permute, lengths, values)
+    (tensor([[1, 2, 4, 8],
+             [2, 3, 4, 5],
+             [0, 3, 2, 3]], device='cuda:0'),
+     tensor([78, 68, 42, 21, 60, 51, 15, 47, 48, 68, 52, 19, 38, 30, 38, 29, 12, 61,
+             98, 56, 94,  5, 89, 65, 48, 71, 54, 40, 33, 97, 97, 98, 18, 40, 42, 89,
+             66], device='cuda:0'),
+     None)
+    """,
+)
+
+add_docs(
+    torch.ops.fbgemm.permute_1D_sparse_data,
+    """
+permute_1D_sparse_data(permute, lengths, values, weights=None, permuted_lengths_sum=None) -> Tuple[Tensor, Tensor, Optional[Tensor]]
+
+Permute 1D sparse data. Note that 1D referrs to the number of dense dimensions.
+The input data is actually 2D where the first dimension is dense and the second
+dimension is jagged (sparse).  The data to permute over can be less or more and
+withh or without repetitions.
+
+Args:
+    permute (Tensor): A 1D-tensor that describes how data is permuted along dim
+        0. `permute[i]` indicates that data at position `permute[i]` is moved
+        to position `i`. The length of this tensor is the total amount of data
+        in dim 0 to be permuted. The values in `permute` must be >= 0 and <
+        `lengths.numel()`
+
+    lengths (Tensor): A 1D-tensor that contains jagged shapes corresponding to
+        the other dense dimension. `lengths[i]` represents the jagged shape of
+        data at position `i` in dim 0
+
+    values (Tensor): A 1D-input-tensor to be permuted. The length of this
+        tensor must be equal to `lengths.sum()`. This tensor can be of any data
+        type.
+
+    weights (Optional[Tensor] = None): An optional 1D-float-tensor. It must
+        have the same length as `values`. It will be permuted the same way as
+        values
+
+    permuted_lengths_sum (Optional[int] = None): An optional value that
+        represents the total number of elements in the permuted data (output
+        shape). If not provided, the operator will compute this data which may
+        cause a device-host synchronization (if using GPU). Thus, it is
+        recommended to supply this value to avoid such the synchronization.
+
+Returns:
+    A tuple of permuted lengths, permuted indices and permuted weights
+
+**Example:**
+    >>> permute = torch.tensor([1, 0, 3, 0], dtype=torch.int32, device="cuda")
+    >>> lengths = torch.tensor([2, 3, 4, 5], dtype=torch.int64, device="cuda")
+    >>> values = torch.randint(low=0, high=100, size=(lengths.sum().item(),), dtype=torch.int64, device="cuda")
+    >>> print(values)
+    tensor([ 1, 76, 24, 84, 94, 25, 15, 23, 31, 46,  9, 23, 34,  3],
+           device='cuda:0')
+    >>> torch.ops.fbgemm.permute_1D_sparse_data(permute, lengths, values)
+    (tensor([3, 2, 5, 2], device='cuda:0'),
+     tensor([24, 84, 94,  1, 76, 46,  9, 23, 34,  3,  1, 76], device='cuda:0'),
+     None)
+    """,
+)