quant docs: add and clean up InstanceNorm{n}d

Fixes docstrings and adds to quantization docs for quantized InstanceNorm.

Test plan:
* build on Mac OS and inspect

ghstack-source-id: f4d38fdc7f9ea84057e680045394522c09093074
Pull Request resolved: #40345

docs/source/quantization.rst

@@ -243,6 +243,9 @@ Layers for the quantization-aware training
 * :class:`~torch.nn.qat.Hardswish` — Hardswish
 * :class:`~torch.nn.qat.LayerNorm` — LayerNorm
 * :class:`~torch.nn.qat.GroupNorm` — GroupNorm
+* :class:`~torch.nn.qat.InstanceNorm1d` — InstanceNorm1d
+* :class:`~torch.nn.qat.InstanceNorm2d` — InstanceNorm2d
+* :class:`~torch.nn.qat.InstanceNorm3d` — InstanceNorm3d
 
 ``torch.quantization``
 ~~~~~~~~~~~~~~~~~~~~~~
@@ -356,6 +359,9 @@ Quantized version of standard NN layers.
 * :class:`~torch.nn.quantized.Hardswish` — Hardswish
 * :class:`~torch.nn.quantized.LayerNorm` — LayerNorm. *Note: performance on ARM is not optimized*.
 * :class:`~torch.nn.quantized.GroupNorm` — GroupNorm. *Note: performance on ARM is not optimized*.
+* :class:`~torch.nn.quantized.InstanceNorm1d` — InstanceNorm1d. *Note: performance on ARM is not optimized*.
+* :class:`~torch.nn.quantized.InstanceNorm2d` — InstanceNorm2d. *Note: performance on ARM is not optimized*.
+* :class:`~torch.nn.quantized.InstanceNorm3d` — InstanceNorm3d. *Note: performance on ARM is not optimized*.
 
 ``torch.nn.quantized.dynamic``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -721,6 +727,20 @@ GroupNorm
 .. autoclass:: GroupNorm
     :members:
 
+InstanceNorm1d
+~~~~~~~~~~~~~~~
+.. autoclass:: InstanceNorm1d
+    :members:
+
+InstanceNorm2d
+~~~~~~~~~~~~~~~
+.. autoclass:: InstanceNorm2d
+    :members:
+
+InstanceNorm3d
+~~~~~~~~~~~~~~~
+.. autoclass:: InstanceNorm3d
+    :members:
 
 torch.nn.quantized
 ----------------------------
@@ -814,6 +834,21 @@ GroupNorm
 .. autoclass:: GroupNorm
     :members:
 
+InstanceNorm1d
+~~~~~~~~~~~~~~~
+.. autoclass:: InstanceNorm1d
+    :members:
+
+InstanceNorm2d
+~~~~~~~~~~~~~~~
+.. autoclass:: InstanceNorm2d
+    :members:
+
+InstanceNorm3d
+~~~~~~~~~~~~~~~
+.. autoclass:: InstanceNorm3d
+    :members:
+
 torch.nn.quantized.dynamic
 ----------------------------
 

torch/nn/qat/modules/normalization.py

@@ -47,8 +47,8 @@ def from_float(cls, mod, qconfig=None):
 
 class InstanceNorm1d(nn.InstanceNorm1d):
     r"""
-    A InstanceNorm1d module attached with FakeQuantize modules for both output
-    activation and weight, used for quantization aware training.
+    A InstanceNorm1d module attached with FakeQuantize modules for output
+    activation, used for quantization aware training.
 
     Similar to `torch.nn.InstanceNorm1d`, with FakeQuantize modules initialized to
     default.
@@ -92,8 +92,8 @@ def from_float(cls, mod, qconfig=None):
 
 class InstanceNorm2d(nn.InstanceNorm2d):
     r"""
-    A InstanceNorm2d module attached with FakeQuantize modules for both output
-    activation and weight, used for quantization aware training.
+    A InstanceNorm2d module attached with FakeQuantize modules for output
+    activation, used for quantization aware training.
 
     Similar to `torch.nn.InstanceNorm2d`, with FakeQuantize modules initialized to
     default.
@@ -137,8 +137,8 @@ def from_float(cls, mod, qconfig=None):
 
 class InstanceNorm3d(nn.InstanceNorm3d):
     r"""
-    A InstanceNorm3d module attached with FakeQuantize modules for both output
-    activation and weight, used for quantization aware training.
+    A InstanceNorm3d module attached with FakeQuantize modules for output
+    activation, used for quantization aware training.
 
     Similar to `torch.nn.InstanceNorm3d`, with FakeQuantize modules initialized to
     default.

torch/nn/quantized/modules/normalization.py

@@ -76,7 +76,12 @@ def from_float(cls, mod):
         return new_mod
 
 class InstanceNorm1d(torch.nn.InstanceNorm1d):
-    r"""This is the quantized version of `torch.nn.InstanceNorm1d`.
+    r"""This is the quantized version of :class:`~torch.nn.InstanceNorm1d`.
+
+    Additional args:
+        * **scale** - quantization scale of the output, type: double.
+        * **zero_point** - quantization zero point of the output, type: long.
+
     """
     def __init__(self, num_features, weight, bias, scale, zero_point,
                  eps=1e-5, momentum=0.1, affine=False,
@@ -106,7 +111,12 @@ def from_float(cls, mod):
         return new_mod
 
 class InstanceNorm2d(torch.nn.InstanceNorm2d):
-    r"""This is the quantized version of `torch.nn.InstanceNorm2d`.
+    r"""This is the quantized version of :class:`~torch.nn.InstanceNorm2d`.
+
+    Additional args:
+        * **scale** - quantization scale of the output, type: double.
+        * **zero_point** - quantization zero point of the output, type: long.
+
     """
     def __init__(self, num_features, weight, bias, scale, zero_point,
                  eps=1e-5, momentum=0.1, affine=False,
@@ -136,7 +146,12 @@ def from_float(cls, mod):
         return new_mod
 
 class InstanceNorm3d(torch.nn.InstanceNorm3d):
-    r"""This is the quantized version of `torch.nn.InstanceNorm2d`.
+    r"""This is the quantized version of :class:`~torch.nn.InstanceNorm3d`.
+
+    Additional args:
+        * **scale** - quantization scale of the output, type: double.
+        * **zero_point** - quantization zero point of the output, type: long.
+
     """
     def __init__(self, num_features, weight, bias, scale, zero_point,
                  eps=1e-5, momentum=0.1, affine=False,