[docs][ao] Add missing docstrings for quantized_max_pool1d and quantized_max_pool2d...

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63242

These functions are part of the native functions namespace as well as the quantized namespace

Test Plan:
CI

Imported from OSS

Reviewed By: jerryzh168

Differential Revision: D30316430

fbshipit-source-id: cd9c839e5c1a961e3c6944e514c16fbc256a2f0c

diff --git a/docs/source/torch.rst b/docs/source/torch.rst
index febd119..88cbc69 100644 (file)
--- a/docs/source/torch.rst
+++ b/docs/source/torch.rst
@@ -353,6 +353,8 @@ Pointwise Ops
     positive
     pow
     quantized_batch_norm
+    quantized_max_pool1d
+    quantized_max_pool2d
     rad2deg
     real
     reciprocal

diff --git a/torch/_torch_docs.py b/torch/_torch_docs.py
index 35e1dce..6ab55aa 100644 (file)
--- a/torch/_torch_docs.py
+++ b/torch/_torch_docs.py
@@ -11272,6 +11272,70 @@ Example::
 """)
 
 
+add_docstr(torch.quantized_max_pool1d,
+           r"""
+quantized_max_pool1d(input, kernel_size, stride=[], padding=0, dilation=1, ceil_mode=False) -> Tensor
+
+Applies a 1D max pooling over an input quantized tensor composed of several input planes.
+
+Arguments:
+    input (Tensor): quantized tensor
+    kernel_size (list of int): the size of the sliding window
+    stride (``list of int``, optional): the stride of the sliding window
+    padding (``list of int``, opttional): padding to be added on both sides, must be >= 0 and <= kernel_size / 2
+    dilation (``list of int``, optional): The stride between elements within a sliding window, must be > 0. Default 1
+    ceil_mode (bool, optional):  If True, will use ceil instead of floor to compute the output shape.
+        Defaults to False.
+
+
+Returns:
+    Tensor: A quantized tensor with max_pool1d applied.
+
+Example::
+
+    >>> qx = torch.quantize_per_tensor(torch.rand(2, 2), 1.5, 3, torch.quint8)
+    >>> torch.quantized_max_pool1d(qx, [2])
+    tensor([[0.0000],
+            [1.5000]], size=(2, 1), dtype=torch.quint8,
+        quantization_scheme=torch.per_tensor_affine, scale=1.5, zero_point=3)
+""")
+
+
+add_docstr(torch.quantized_max_pool2d,
+           r"""
+quantized_max_pool2d(input, kernel_size, stride=[], padding=0, dilation=1, ceil_mode=False) -> Tensor
+
+Applies a 2D max pooling over an input quantized tensor composed of several input planes.
+
+Arguments:
+    input (Tensor): quantized tensor
+    kernel_size (``list of int``): the size of the sliding window
+    stride (``list of int``, optional): the stride of the sliding window
+    padding (``list of int``, optional): padding to be added on both sides, must be >= 0 and <= kernel_size / 2
+    dilation (``list of int``, optional): The stride between elements within a sliding window, must be > 0. Default 1
+    ceil_mode (bool, optional):  If True, will use ceil instead of floor to compute the output shape.
+        Defaults to False.
+
+
+Returns:
+    Tensor: A quantized tensor with max_pool2d applied.
+
+Example::
+
+    >>> qx = torch.quantize_per_tensor(torch.rand(2, 2, 2, 2), 1.5, 3, torch.quint8)
+    >>> torch.quantized_max_pool2d(qx, [2,2])
+    tensor([[[[1.5000]],
+
+            [[1.5000]]],
+
+
+            [[[0.0000]],
+
+            [[0.0000]]]], size=(2, 2, 1, 1), dtype=torch.quint8,
+        quantization_scheme=torch.per_tensor_affine, scale=1.5, zero_point=3)
+""")
+
+
 add_docstr(torch.Generator,
            r"""
 Generator(device='cpu') -> Generator