[quant] Add quantized Sigmoid module (#45883)

Summary: Pull Request resolved: #45883

Test Plan:
python test/test_quantization.py TestStaticQuantizedModule.test_sigmoid

Imported from OSS

Reviewed By: z-a-f

Differential Revision: D24129116

fbshipit-source-id: aa960549509c60374012f35b1f5be39e90418099

test/quantization/test_quantized_module.py

@@ -716,6 +716,9 @@ def test_elu(self):
     def test_leaky_relu(self):
         self._test_activation_module_impl("LeakyReLU", nn.LeakyReLU, nnq.LeakyReLU, {"negative_slope": 0.2})
 
+    def test_sigmoid(self):
+        self._test_activation_module_impl("Sigmoid", nn.Sigmoid, nnq.Sigmoid, {})
+
     @given(
         num_embeddings=st.integers(10, 50),
         embedding_dim=st.integers(5, 50).filter(lambda x: x % 4 == 0),

torch/nn/quantized/modules/__init__.py

@@ -2,7 +2,7 @@
 import torch
 from torch.nn.modules.pooling import MaxPool2d
 
-from .activation import ReLU, ReLU6, Hardswish, ELU, LeakyReLU
+from .activation import ReLU, ReLU6, Hardswish, ELU, LeakyReLU, Sigmoid
 from .batchnorm import BatchNorm2d, BatchNorm3d
 from .normalization import LayerNorm, GroupNorm, InstanceNorm1d, \
     InstanceNorm2d, InstanceNorm3d
@@ -100,6 +100,7 @@ def from_float(mod):
     'Hardswish',
     'ELU',
     'LeakyReLU',
+    'Sigmoid',
     'LayerNorm',
     'GroupNorm',
     'InstanceNorm1d',

torch/nn/quantized/modules/activation.py

@@ -149,3 +149,24 @@ def _get_name(self):
     def from_float(cls, mod):
         scale, zero_point = mod.activation_post_process.calculate_qparams()
         return cls(float(scale), int(zero_point), mod.negative_slope, mod.inplace)
+
+class Sigmoid(torch.nn.Sigmoid):
+    r"""This is the quantized equivalent of :class:`~torch.nn.LeakyReLU`.
+
+    Args:
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+    """
+
+    def __init__(self, output_scale: float, output_zero_point: int):
+        super().__init__()
+        self.output_scale = output_scale
+        self.output_zero_point = output_zero_point
+
+    def forward(self, input):
+        return torch.ops.quantized.sigmoid(input, self.output_scale, self.output_zero_point)
+
+    @classmethod
+    def from_float(cls, mod):
+        output_scale, output_zero_point = mod.activation_post_process.calculate_qparams()
+        return cls(float(output_scale), int(output_zero_point))