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parrot/lib/python3.10/site-packages/torch/ao/nn/intrinsic/__init__.py

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+# mypy: allow-untyped-defs
+from .modules import *  # noqa: F403
+from .modules.fused import _FusedModule  # noqa: F403
+
+# # Subpackages
+# from . import qat  # noqa: F403
+# from . import quantized  # noqa: F403
+
+__all__ = [
+    'ConvBn1d',
+    'ConvBn2d',
+    'ConvBn3d',
+    'ConvBnReLU1d',
+    'ConvBnReLU2d',
+    'ConvBnReLU3d',
+    'ConvReLU1d',
+    'ConvReLU2d',
+    'ConvReLU3d',
+    'LinearReLU',
+    'BNReLU2d',
+    'BNReLU3d',
+    'LinearBn1d',
+    'LinearLeakyReLU',
+    'LinearTanh',
+    'ConvAdd2d',
+    'ConvAddReLU2d',
+]
+
+# We are exposing all subpackages to the end-user.
+# Because of possible inter-dependency, we want to avoid
+# the cyclic imports, thus implementing lazy version
+# as per https://peps.python.org/pep-0562/
+def __getattr__(name):
+    if name in __all__:
+        import importlib
+        return importlib.import_module("." + name, __name__)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

parrot/lib/python3.10/site-packages/torch/ao/nn/intrinsic/modules/__init__.py

@@ -0,0 +1,38 @@
+from .fused import _FusedModule  # noqa: F401
+from .fused import ConvBn1d
+from .fused import ConvBn2d
+from .fused import ConvBn3d
+from .fused import ConvBnReLU1d
+from .fused import ConvBnReLU2d
+from .fused import ConvBnReLU3d
+from .fused import ConvReLU1d
+from .fused import ConvReLU2d
+from .fused import ConvReLU3d
+from .fused import LinearReLU
+from .fused import BNReLU2d
+from .fused import BNReLU3d
+from .fused import LinearBn1d
+from .fused import LinearLeakyReLU
+from .fused import LinearTanh
+from .fused import ConvAdd2d
+from .fused import ConvAddReLU2d
+
+__all__ = [
+    'ConvBn1d',
+    'ConvBn2d',
+    'ConvBn3d',
+    'ConvBnReLU1d',
+    'ConvBnReLU2d',
+    'ConvBnReLU3d',
+    'ConvReLU1d',
+    'ConvReLU2d',
+    'ConvReLU3d',
+    'LinearReLU',
+    'BNReLU2d',
+    'BNReLU3d',
+    'LinearBn1d',
+    'LinearLeakyReLU',
+    'LinearTanh',
+    'ConvAdd2d',
+    'ConvAddReLU2d',
+]

parrot/lib/python3.10/site-packages/torch/ao/nn/intrinsic/modules/fused.py

@@ -0,0 +1,155 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.nn import Conv1d, Conv2d, Conv3d, ReLU, Linear, BatchNorm1d, BatchNorm2d, BatchNorm3d
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+__all__ = ['ConvReLU1d', 'ConvReLU2d', 'ConvReLU3d', 'LinearReLU', 'ConvBn1d', 'ConvBn2d',
+           'ConvBnReLU1d', 'ConvBnReLU2d', 'ConvBn3d', 'ConvBnReLU3d', 'BNReLU2d', 'BNReLU3d',
+           'LinearBn1d', 'LinearLeakyReLU', 'LinearTanh', 'ConvAdd2d', 'ConvAddReLU2d']
+
+# Used for identifying intrinsic modules used in quantization
+class _FusedModule(torch.nn.Sequential):
+    pass
+
+class ConvReLU1d(_FusedModule):
+    r"""This is a sequential container which calls the Conv1d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, conv, relu):
+        assert type_before_parametrizations(conv) == Conv1d and type_before_parametrizations(relu) == ReLU, \
+            f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(relu)}'
+        super().__init__(conv, relu)
+
+class ConvReLU2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv2d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, conv, relu):
+        assert type_before_parametrizations(conv) == Conv2d and type_before_parametrizations(relu) == ReLU, \
+            f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(relu)}'
+        super().__init__(conv, relu)
+
+class ConvReLU3d(_FusedModule):
+    r"""This is a sequential container which calls the Conv3d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, conv, relu):
+        assert type_before_parametrizations(conv) == Conv3d and type_before_parametrizations(relu) == ReLU, \
+            f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(relu)}'
+        super().__init__(conv, relu)
+
+class LinearReLU(_FusedModule):
+    r"""This is a sequential container which calls the Linear and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, linear, relu):
+        assert type_before_parametrizations(linear) == Linear and type_before_parametrizations(relu) == ReLU, \
+            f'Incorrect types for input modules{type_before_parametrizations(linear)}{type_before_parametrizations(relu)}'
+        super().__init__(linear, relu)
+
+class ConvBn1d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 1d and Batch Norm 1d modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, conv, bn):
+        assert type_before_parametrizations(conv) == Conv1d and type_before_parametrizations(bn) == BatchNorm1d, \
+            f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}'
+        super().__init__(conv, bn)
+
+class ConvBn2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 2d and Batch Norm 2d modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, conv, bn):
+        assert type_before_parametrizations(conv) == Conv2d and type_before_parametrizations(bn) == BatchNorm2d, \
+            f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}'
+        super().__init__(conv, bn)
+
+class ConvBnReLU1d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 1d, Batch Norm 1d, and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, conv, bn, relu):
+        assert type_before_parametrizations(conv) == Conv1d and type_before_parametrizations(bn) == BatchNorm1d and \
+            type_before_parametrizations(relu) == ReLU, f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}{type_before_parametrizations(relu)}'  # noqa: B950
+        super().__init__(conv, bn, relu)
+
+class ConvBnReLU2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 2d, Batch Norm 2d, and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, conv, bn, relu):
+        assert type_before_parametrizations(conv) == Conv2d and type_before_parametrizations(bn) == BatchNorm2d and \
+            type_before_parametrizations(relu) == ReLU, f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}{type_before_parametrizations(relu)}'  # noqa: B950
+        super().__init__(conv, bn, relu)
+
+class ConvBn3d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 3d and Batch Norm 3d modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, conv, bn):
+        assert type_before_parametrizations(conv) == Conv3d and type_before_parametrizations(bn) == BatchNorm3d, \
+            f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}'
+        super().__init__(conv, bn)
+
+class ConvBnReLU3d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 3d, Batch Norm 3d, and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, conv, bn, relu):
+        assert type_before_parametrizations(conv) == Conv3d and type_before_parametrizations(bn) == BatchNorm3d and \
+            type_before_parametrizations(relu) == ReLU, f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}{type_before_parametrizations(relu)}'  # noqa: B950
+        super().__init__(conv, bn, relu)
+
+
+class BNReLU2d(_FusedModule):
+    r"""This is a sequential container which calls the BatchNorm 2d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, batch_norm, relu):
+        assert type_before_parametrizations(batch_norm) == BatchNorm2d and type_before_parametrizations(relu) == ReLU, \
+            f'Incorrect types for input modules{type_before_parametrizations(batch_norm)}{type_before_parametrizations(relu)}'
+        super().__init__(batch_norm, relu)
+
+class BNReLU3d(_FusedModule):
+    r"""This is a sequential container which calls the BatchNorm 3d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, batch_norm, relu):
+        assert type_before_parametrizations(batch_norm) == BatchNorm3d and type_before_parametrizations(relu) == ReLU, \
+            f'Incorrect types for input modules{type_before_parametrizations(batch_norm)}{type_before_parametrizations(relu)}'
+        super().__init__(batch_norm, relu)
+
+
+class LinearBn1d(_FusedModule):
+    r"""This is a sequential container which calls the Linear and BatchNorm1d modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, linear, bn):
+        assert type_before_parametrizations(linear) == Linear and type_before_parametrizations(bn) == BatchNorm1d, \
+            f'Incorrect types for input modules{type_before_parametrizations(linear)}{type_before_parametrizations(bn)}'
+        super().__init__(linear, bn)
+
+class LinearLeakyReLU(_FusedModule):
+    r"""This is a sequential container which calls the Linear and LeakyReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, linear, leaky_relu):
+        assert type(linear) == Linear and type(leaky_relu) == torch.nn.LeakyReLU, \
+            f'Incorrect types for input modules{type(linear)}{type(leaky_relu)}'
+        super().__init__(linear, leaky_relu)
+
+class LinearTanh(_FusedModule):
+    r"""This is a sequential container which calls the Linear and Tanh modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, linear, tanh):
+        assert type(linear) == Linear and type(tanh) == torch.nn.Tanh, \
+            f'Incorrect types for input modules{type(linear)}{type(tanh)}'
+        super().__init__(linear, tanh)
+
+class ConvAdd2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv2d modules with extra Add.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, conv, add):
+        super().__init__(conv)
+        self.add = add
+
+    def forward(self, x1, x2):
+        return self.add(self[0](x1), x2)
+
+class ConvAddReLU2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv2d, add, Relu.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, conv, add, relu):
+        super().__init__(conv)
+        self.add = add
+        self.relu = relu
+
+    def forward(self, x1, x2):
+        return self.relu(self.add(self[0](x1), x2))

parrot/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/__init__.py

@@ -0,0 +1 @@
+from .modules import *  # noqa: F403

parrot/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/__init__.py

@@ -0,0 +1,31 @@
+from .linear_relu import LinearReLU
+from .linear_fused import LinearBn1d
+from .conv_fused import (
+    ConvBn1d,
+    ConvBn2d,
+    ConvBn3d,
+    ConvBnReLU1d,
+    ConvBnReLU2d,
+    ConvBnReLU3d,
+    ConvReLU1d,
+    ConvReLU2d,
+    ConvReLU3d,
+    update_bn_stats,
+    freeze_bn_stats,
+)
+
+__all__ = [
+    "LinearReLU",
+    "LinearBn1d",
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+    "ConvBn1d",
+    "ConvBn2d",
+    "ConvBn3d",
+    "ConvBnReLU1d",
+    "ConvBnReLU2d",
+    "ConvBnReLU3d",
+    "update_bn_stats",
+    "freeze_bn_stats",
+]

parrot/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/conv_fused.py

@@ -0,0 +1,826 @@
+# mypy: allow-untyped-defs
+import math
+import torch
+import torch.nn as nn
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.qat as nnqat
+import torch.nn.functional as F
+from torch.nn import init
+from torch.nn.utils import fuse_conv_bn_weights
+from torch.nn.modules.utils import _single, _pair, _triple
+from torch.nn.parameter import Parameter
+from typing import TypeVar
+
+__all__ = ['ConvBn1d', 'ConvBnReLU1d', 'ConvReLU1d', 'ConvBn2d', 'ConvBnReLU2d', 'ConvReLU2d', 'ConvBn3d',
+           'ConvBnReLU3d', 'ConvReLU3d', 'update_bn_stats', 'freeze_bn_stats']
+_BN_CLASS_MAP = {
+    1: nn.BatchNorm1d,
+    2: nn.BatchNorm2d,
+    3: nn.BatchNorm3d,
+}
+
+
+MOD = TypeVar('MOD', bound=nn.modules.conv._ConvNd)
+
+
+class _ConvBnNd(nn.modules.conv._ConvNd, nni._FusedModule):
+
+    _version = 2
+    _FLOAT_MODULE = MOD
+
+    def __init__(self,
+                 # ConvNd args
+                 in_channels, out_channels, kernel_size, stride,
+                 padding, dilation, transposed, output_padding,
+                 groups,
+                 bias,
+                 padding_mode,
+                 # BatchNormNd args
+                 # num_features: out_channels
+                 eps=1e-05, momentum=0.1,
+                 # affine: True
+                 # track_running_stats: True
+                 # Args for this module
+                 freeze_bn=False,
+                 qconfig=None,
+                 dim=2):
+        nn.modules.conv._ConvNd.__init__(self, in_channels, out_channels, kernel_size,
+                                         stride, padding, dilation, transposed,
+                                         output_padding, groups, False, padding_mode)
+        assert qconfig, 'qconfig must be provided for QAT module'
+        self.qconfig = qconfig
+        self.freeze_bn = freeze_bn if self.training else True
+        self.bn = _BN_CLASS_MAP[dim](out_channels, eps, momentum, True, True)
+        self.weight_fake_quant = self.qconfig.weight()
+        if bias:
+            self.bias = Parameter(torch.empty(out_channels))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_bn_parameters()
+
+        # this needs to be called after reset_bn_parameters,
+        # as they modify the same state
+        if self.training:
+            if freeze_bn:
+                self.freeze_bn_stats()
+            else:
+                self.update_bn_stats()
+        else:
+            self.freeze_bn_stats()
+
+        self._enable_slow_path_for_better_numerical_stability = False
+
+    def reset_running_stats(self):
+        self.bn.reset_running_stats()
+
+    def reset_bn_parameters(self):
+        self.bn.reset_running_stats()
+        init.uniform_(self.bn.weight)
+        init.zeros_(self.bn.bias)
+        # note: below is actually for conv, not BN
+        if self.bias is not None:
+            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / math.sqrt(fan_in)
+            init.uniform_(self.bias, -bound, bound)
+
+    def reset_parameters(self):
+        super().reset_parameters()
+
+    def update_bn_stats(self):
+        self.freeze_bn = False
+        self.bn.training = True
+        return self
+
+    def freeze_bn_stats(self):
+        self.freeze_bn = True
+        self.bn.training = False
+        return self
+
+    def _forward(self, input):
+        if self._enable_slow_path_for_better_numerical_stability:
+            return self._forward_slow(input)
+        return self._forward_approximate(input)
+
+    def _forward_approximate(self, input):
+        """Approximated method to fuse conv and bn. It requires only one forward pass.
+        conv_orig = conv / scale_factor where scale_factor = bn.weight / running_std
+        """
+        assert self.bn.running_var is not None
+        running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
+        scale_factor = self.bn.weight / running_std
+        weight_shape = [1] * len(self.weight.shape)
+        weight_shape[0] = -1
+        bias_shape = [1] * len(self.weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = self.weight_fake_quant(self.weight * scale_factor.reshape(weight_shape))
+        # using zero bias here since the bias for original conv
+        # will be added later
+        if self.bias is not None:
+            zero_bias = torch.zeros_like(self.bias, dtype=input.dtype)
+        else:
+            zero_bias = torch.zeros(self.out_channels, device=scaled_weight.device, dtype=input.dtype)
+        conv = self._conv_forward(input, scaled_weight, zero_bias)
+        conv_orig = conv / scale_factor.reshape(bias_shape)
+        if self.bias is not None:
+            conv_orig = conv_orig + self.bias.reshape(bias_shape)
+        conv = self.bn(conv_orig)
+        return conv
+
+    def _forward_slow(self, input):
+        """
+        A more accurate but slow method to compute conv bn fusion, following https://arxiv.org/pdf/1806.08342.pdf
+        It requires two forward passes but handles the case bn.weight == 0
+
+        Conv: Y = WX + B_c
+        Conv without bias: Y0 = WX = Y - B_c, Y = Y0 + B_c
+
+        Batch statistics:
+          mean_Y = Y.mean()
+                 = Y0.mean() + B_c
+          var_Y = (Y - mean_Y)^2.mean()
+                = (Y0 - Y0.mean())^2.mean()
+        BN (r: bn.weight, beta: bn.bias):
+          Z = r * (Y - mean_Y) / sqrt(var_Y + eps) + beta
+            = r * (Y0 - Y0.mean()) / sqrt(var_Y + eps) + beta
+
+        Fused Conv BN training (std_Y = sqrt(var_Y + eps)):
+          Z = (r * W / std_Y) * X + r * (B_c - mean_Y) / std_Y + beta
+            = (r * W / std_Y) * X - r * Y0.mean() / std_Y + beta
+
+        Fused Conv BN inference (running_std = sqrt(running_var + eps)):
+          Z = (r * W / running_std) * X - r * (running_mean - B_c) / running_std + beta
+
+        QAT with fused conv bn:
+          Z_train = fake_quant(r * W / running_std) * X * (running_std / std_Y) - r * Y0.mean() / std_Y + beta
+                  = conv(X, fake_quant(r * W / running_std)) * (running_std / std_Y) - r * Y0.mean() / std_Y + beta
+          Z_inference = conv(X, fake_quant(r * W / running_std)) - r * (running_mean - B_c) / running_std + beta
+        """
+
+        assert self.bn.running_var is not None
+        assert self.bn.running_mean is not None
+
+        # using zero bias here since the bias for original conv
+        # will be added later
+        zero_bias = torch.zeros(self.out_channels, device=self.weight.device, dtype=input.dtype)
+
+        weight_shape = [1] * len(self.weight.shape)
+        weight_shape[0] = -1
+        bias_shape = [1] * len(self.weight.shape)
+        bias_shape[1] = -1
+
+        if self.bn.training:
+            # needed to compute batch mean/std
+            conv_out = self._conv_forward(input, self.weight, zero_bias)
+            # update bn statistics
+            with torch.no_grad():
+                conv_out_bias = (
+                    conv_out if self.bias is None else conv_out + self.bias.reshape(bias_shape)
+                )
+                self.bn(conv_out_bias)
+
+        # fused conv + bn without bias using bn running statistics
+        running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
+        scale_factor = self.bn.weight / running_std
+        scaled_weight = self.weight_fake_quant(
+            self.weight * scale_factor.reshape(weight_shape)
+        )
+        # fused conv without bias for inference: (r * W / running_std) * X
+        conv_bn = self._conv_forward(input, scaled_weight, zero_bias)
+
+        if self.bn.training:
+            avg_dims = [0] + list(range(2, len(self.weight.shape)))
+            batch_mean = conv_out.mean(avg_dims)  # type: ignore[possibly-undefined]
+            batch_var = torch.square(conv_out - batch_mean.reshape(bias_shape)).mean(
+                avg_dims
+            )
+            batch_std = torch.sqrt(batch_var + self.bn.eps)
+
+            # scale to use batch std in training mode
+            # conv(X, r * W / std_Y) = conv(X, r * W / running_std) * (running_std / std_Y)
+            unscale_factor = running_std / batch_std
+            conv_bn *= unscale_factor.reshape(bias_shape)
+
+            fused_mean = batch_mean
+            fused_std = batch_std
+        else:
+            fused_mean = self.bn.running_mean - (self.bias if self.bias is not None else 0)
+            fused_std = running_std
+
+        # fused bias = beta - r * mean / std
+        fused_bias = self.bn.bias - self.bn.weight * fused_mean / fused_std
+        conv_bn += fused_bias.reshape(bias_shape)
+
+        # HACK to let conv bias participate in loss to avoid DDP error (parameters
+        #   were not used in producing loss)
+        if self.bias is not None:
+            conv_bn += (self.bias - self.bias).reshape(bias_shape)
+
+        return conv_bn
+
+    def extra_repr(self):
+        # TODO(jerryzh): extend
+        return super().extra_repr()
+
+    def forward(self, input):
+        return self._forward(input)
+
+    def train(self, mode=True):
+        """
+        Batchnorm's training behavior is using the self.training flag. Prevent
+        changing it if BN is frozen. This makes sure that calling `model.train()`
+        on a model with a frozen BN will behave properly.
+        """
+        self.training = mode
+        if not self.freeze_bn:
+            for module in self.children():
+                module.train(mode)
+        return self
+
+    # ===== Serialization version history =====
+    #
+    # Version 1/None
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- gamma : Tensor
+    #   |--- beta : Tensor
+    #   |--- running_mean : Tensor
+    #   |--- running_var : Tensor
+    #   |--- num_batches_tracked : Tensor
+    #
+    # Version 2
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- bn : Module
+    #        |--- weight : Tensor (moved from v1.self.gamma)
+    #        |--- bias : Tensor (moved from v1.self.beta)
+    #        |--- running_mean : Tensor (moved from v1.self.running_mean)
+    #        |--- running_var : Tensor (moved from v1.self.running_var)
+    #        |--- num_batches_tracked : Tensor (moved from v1.self.num_batches_tracked)
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
+        version = local_metadata.get('version', None)
+        if version is None or version == 1:
+            # BN related parameters and buffers were moved into the BN module for v2
+            v2_to_v1_names = {
+                'bn.weight': 'gamma',
+                'bn.bias': 'beta',
+                'bn.running_mean': 'running_mean',
+                'bn.running_var': 'running_var',
+                'bn.num_batches_tracked': 'num_batches_tracked',
+            }
+            for v2_name, v1_name in v2_to_v1_names.items():
+                if prefix + v1_name in state_dict:
+                    state_dict[prefix + v2_name] = state_dict[prefix + v1_name]
+                    state_dict.pop(prefix + v1_name)
+                elif prefix + v2_name in state_dict:
+                    # there was a brief period where forward compatibility
+                    # for this module was broken (between
+                    # https://github.com/pytorch/pytorch/pull/38478
+                    # and https://github.com/pytorch/pytorch/pull/38820)
+                    # and modules emitted the v2 state_dict format while
+                    # specifying that version == 1. This patches the forward
+                    # compatibility issue by allowing the v2 style entries to
+                    # be used.
+                    pass
+                elif strict:
+                    missing_keys.append(prefix + v2_name)
+
+        super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
+                                      missing_keys, unexpected_keys, error_msgs)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a qat module from a float module or qparams_dict
+
+            Args: `mod` a float module, either produced by torch.ao.quantization utilities
+            or directly from user
+        """
+        # The ignore is because _FLOAT_MODULE is a TypeVar here where the bound
+        # has no __name__ (code is fine though)
+        assert type(mod) == cls._FLOAT_MODULE, 'qat.' + cls.__name__ + '.from_float only works for ' + \
+            cls._FLOAT_MODULE.__name__  # type: ignore[attr-defined]
+        assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
+        assert mod.qconfig, 'Input float module must have a valid qconfig'
+        qconfig = mod.qconfig
+        conv, bn = mod[0], mod[1]
+        qat_convbn = cls(conv.in_channels, conv.out_channels, conv.kernel_size,
+                         conv.stride, conv.padding, conv.dilation,
+                         conv.groups, conv.bias is not None,
+                         conv.padding_mode,
+                         bn.eps, bn.momentum,
+                         False,
+                         qconfig)
+        qat_convbn.weight = conv.weight
+        qat_convbn.bias = conv.bias
+        qat_convbn.bn.weight = bn.weight
+        qat_convbn.bn.bias = bn.bias
+        qat_convbn.bn.running_mean = bn.running_mean
+        qat_convbn.bn.running_var = bn.running_var
+        # mypy error: Cannot determine type of 'num_batches_tracked'
+        qat_convbn.bn.num_batches_tracked = bn.num_batches_tracked  # type: ignore[has-type]
+        return qat_convbn
+
+    def to_float(self):
+        cls = type(self)
+        conv = cls._FLOAT_CONV_MODULE(  # type: ignore[attr-defined]
+            self.in_channels,
+            self.out_channels,
+            self.kernel_size,
+            self.stride,
+            self.padding,
+            self.dilation,
+            self.groups,
+            self.bias is not None,
+            self.padding_mode)
+        conv.weight = torch.nn.Parameter(self.weight.detach())
+        if self.bias is not None:
+            conv.bias = torch.nn.Parameter(self.bias.detach())
+
+        if cls._FLOAT_BN_MODULE:  # type: ignore[attr-defined]
+            # fuse bn into conv
+            assert self.bn.running_var is not None and self.bn.running_mean is not None
+            conv.weight, conv.bias = fuse_conv_bn_weights(
+                conv.weight,
+                conv.bias,
+                self.bn.running_mean,
+                self.bn.running_var,
+                self.bn.eps,
+                self.bn.weight,
+                self.bn.bias
+            )
+
+        if cls._FLOAT_RELU_MODULE:  # type: ignore[attr-defined]
+            modules = []
+            modules.append(conv)
+            relu = cls._FLOAT_RELU_MODULE()  # type: ignore[attr-defined]
+            modules.append(relu)
+            conv_relu = cls._FUSED_FLOAT_MODULE(*modules)  # type: ignore[attr-defined]
+            conv_relu.train(self.training)
+            return conv_relu
+        else:
+            conv.train(self.training)
+            return conv
+
+class ConvBn1d(_ConvBnNd, nn.Conv1d):
+    r"""
+    A ConvBn1d module is a module fused from Conv1d and BatchNorm1d,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv1d` and
+    :class:`torch.nn.BatchNorm1d`.
+
+    Similar to :class:`torch.nn.Conv1d`, with FakeQuantize modules initialized
+    to default.
+
+    Attributes:
+        freeze_bn:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_BN_MODULE = nn.BatchNorm1d
+    _FLOAT_RELU_MODULE: None = None
+    _FLOAT_MODULE = nni.ConvBn1d
+    _FLOAT_CONV_MODULE = nn.Conv1d
+
+    def __init__(self,
+                 # Conv1d args
+                 in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1,
+                 bias=None,
+                 padding_mode='zeros',
+                 # BatchNorm1d args
+                 # num_features: out_channels
+                 eps=1e-05, momentum=0.1,
+                 # affine: True
+                 # track_running_stats: True
+                 # Args for this module
+                 freeze_bn=False,
+                 qconfig=None):
+        kernel_size = _single(kernel_size)
+        stride = _single(stride)
+        padding = _single(padding)
+        dilation = _single(dilation)
+        _ConvBnNd.__init__(self, in_channels, out_channels, kernel_size, stride,
+                           padding, dilation, False, _single(0), groups, bias, padding_mode,
+                           eps, momentum, freeze_bn, qconfig, dim=1)
+
+class ConvBnReLU1d(ConvBn1d):
+    r"""
+    A ConvBnReLU1d module is a module fused from Conv1d, BatchNorm1d and ReLU,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv1d` and
+    :class:`torch.nn.BatchNorm1d` and :class:`torch.nn.ReLU`.
+
+    Similar to `torch.nn.Conv1d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    # base class defines _FLOAT_MODULE as "ConvBn1d"
+    _FLOAT_MODULE = nni.ConvBnReLU1d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE = nn.Conv1d
+    _FLOAT_BN_MODULE = nn.BatchNorm1d
+    _FLOAT_RELU_MODULE = nn.ReLU  # type: ignore[assignment]
+    # module class after fusing bn into conv
+    _FUSED_FLOAT_MODULE = nni.ConvReLU1d
+
+    def __init__(self,
+                 # Conv1d args
+                 in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1,
+                 bias=None,
+                 padding_mode='zeros',
+                 # BatchNorm1d args
+                 # num_features: out_channels
+                 eps=1e-05, momentum=0.1,
+                 # affine: True
+                 # track_running_stats: True
+                 # Args for this module
+                 freeze_bn=False,
+                 qconfig=None):
+        super().__init__(in_channels, out_channels, kernel_size, stride,
+                         padding, dilation, groups, bias,
+                         padding_mode, eps, momentum,
+                         freeze_bn,
+                         qconfig)
+
+    def forward(self, input):
+        return F.relu(ConvBn1d._forward(self, input))
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(mod, use_precomputed_fake_quant)
+
+class ConvReLU1d(nnqat.Conv1d, nni._FusedModule):
+    r"""A ConvReLU1d module is a fused module of Conv1d and ReLU, attached with
+    FakeQuantize modules for weight for
+    quantization aware training.
+
+    We combined the interface of :class:`~torch.nn.Conv1d` and
+    :class:`~torch.nn.BatchNorm1d`.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_MODULE = nni.ConvReLU1d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE = nn.Conv1d
+    _FLOAT_BN_MODULE: None = None
+    _FLOAT_RELU_MODULE = nn.ReLU
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1,
+                 bias=True, padding_mode='zeros',
+                 qconfig=None):
+        super().__init__(in_channels, out_channels, kernel_size,
+                         stride=stride, padding=padding, dilation=dilation,
+                         groups=groups, bias=bias, padding_mode=padding_mode,
+                         qconfig=qconfig)
+        assert qconfig, 'qconfig must be provided for QAT module'
+        self.qconfig = qconfig
+        self.weight_fake_quant = self.qconfig.weight()
+
+    def forward(self, input):
+        return F.relu(
+            self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias))
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(mod, use_precomputed_fake_quant=use_precomputed_fake_quant)
+
+class ConvBn2d(_ConvBnNd, nn.Conv2d):
+    r"""
+    A ConvBn2d module is a module fused from Conv2d and BatchNorm2d,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv2d` and
+    :class:`torch.nn.BatchNorm2d`.
+
+    Similar to :class:`torch.nn.Conv2d`, with FakeQuantize modules initialized
+    to default.
+
+    Attributes:
+        freeze_bn:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_MODULE = nni.ConvBn2d
+    _FLOAT_CONV_MODULE = nn.Conv2d
+    _FLOAT_BN_MODULE = nn.BatchNorm2d
+    _FLOAT_RELU_MODULE: None = None
+
+    def __init__(self,
+                 # ConvNd args
+                 in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1,
+                 bias=None,
+                 padding_mode='zeros',
+                 # BatchNorm2d args
+                 # num_features: out_channels
+                 eps=1e-05, momentum=0.1,
+                 # affine: True
+                 # track_running_stats: True
+                 # Args for this module
+                 freeze_bn=False,
+                 qconfig=None):
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+        _ConvBnNd.__init__(self, in_channels, out_channels, kernel_size, stride,
+                           padding, dilation, False, _pair(0), groups, bias, padding_mode,
+                           eps, momentum, freeze_bn, qconfig, dim=2)
+
+class ConvBnReLU2d(ConvBn2d):
+    r"""
+    A ConvBnReLU2d module is a module fused from Conv2d, BatchNorm2d and ReLU,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv2d` and
+    :class:`torch.nn.BatchNorm2d` and :class:`torch.nn.ReLU`.
+
+    Similar to `torch.nn.Conv2d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    # base class defines _FLOAT_MODULE as "ConvBn2d"
+    _FLOAT_MODULE = nni.ConvBnReLU2d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE = nn.Conv2d
+    _FLOAT_BN_MODULE = nn.BatchNorm2d
+    _FLOAT_RELU_MODULE = nn.ReLU  # type: ignore[assignment]
+    # module class after fusing bn into conv
+    _FUSED_FLOAT_MODULE = nni.ConvReLU2d
+
+    def __init__(self,
+                 # Conv2d args
+                 in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1,
+                 bias=None,
+                 padding_mode='zeros',
+                 # BatchNorm2d args
+                 # num_features: out_channels
+                 eps=1e-05, momentum=0.1,
+                 # affine: True
+                 # track_running_stats: True
+                 # Args for this module
+                 freeze_bn=False,
+                 qconfig=None):
+        super().__init__(in_channels, out_channels, kernel_size, stride,
+                         padding, dilation, groups, bias,
+                         padding_mode, eps, momentum,
+                         freeze_bn,
+                         qconfig)
+
+    def forward(self, input):
+        return F.relu(ConvBn2d._forward(self, input))
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(mod, use_precomputed_fake_quant)
+
+class ConvReLU2d(nnqat.Conv2d, nni._FusedModule):
+    r"""A ConvReLU2d module is a fused module of Conv2d and ReLU, attached with
+    FakeQuantize modules for weight for
+    quantization aware training.
+
+    We combined the interface of :class:`~torch.nn.Conv2d` and
+    :class:`~torch.nn.BatchNorm2d`.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_MODULE = nni.ConvReLU2d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE = nn.Conv2d
+    _FLOAT_BN_MODULE: None = None
+    _FLOAT_RELU_MODULE = nn.ReLU
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1,
+                 bias=True, padding_mode='zeros',
+                 qconfig=None):
+        super().__init__(in_channels, out_channels, kernel_size,
+                         stride=stride, padding=padding, dilation=dilation,
+                         groups=groups, bias=bias, padding_mode=padding_mode,
+                         qconfig=qconfig)
+        assert qconfig, 'qconfig must be provided for QAT module'
+        self.qconfig = qconfig
+        self.weight_fake_quant = self.qconfig.weight()
+
+    def forward(self, input):
+        return F.relu(
+            self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias))
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(mod, use_precomputed_fake_quant=use_precomputed_fake_quant)
+
+class ConvBn3d(_ConvBnNd, nn.Conv3d):
+    r"""
+    A ConvBn3d module is a module fused from Conv3d and BatchNorm3d,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv3d` and
+    :class:`torch.nn.BatchNorm3d`.
+
+    Similar to :class:`torch.nn.Conv3d`, with FakeQuantize modules initialized
+    to default.
+
+    Attributes:
+        freeze_bn:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_MODULE = nni.ConvBn3d
+    _FLOAT_CONV_MODULE = nn.Conv3d
+    _FLOAT_BN_MODULE = nn.BatchNorm3d
+    _FLOAT_RELU_MODULE: None = None
+
+    def __init__(
+        self,
+        # ConvNd args
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=None,
+        padding_mode="zeros",
+        # BatchNorm3d args
+        # num_features: out_channels
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+    ):
+        kernel_size = _triple(kernel_size)
+        stride = _triple(stride)
+        padding = _triple(padding)
+        dilation = _triple(dilation)
+        _ConvBnNd.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _triple(0),
+            groups,
+            bias,
+            padding_mode,
+            eps,
+            momentum,
+            freeze_bn,
+            qconfig,
+            dim=3,
+        )
+
+class ConvBnReLU3d(ConvBn3d):
+    r"""
+    A ConvBnReLU3d module is a module fused from Conv3d, BatchNorm3d and ReLU,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv3d` and
+    :class:`torch.nn.BatchNorm3d` and :class:`torch.nn.ReLU`.
+
+    Similar to `torch.nn.Conv3d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_MODULE = nni.ConvBnReLU3d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE = nn.Conv3d
+    _FLOAT_BN_MODULE = nn.BatchNorm3d
+    _FLOAT_RELU_MODULE = nn.ReLU  # type: ignore[assignment]
+    # module class after fusing bn into conv
+    _FUSED_FLOAT_MODULE = nni.ConvReLU3d
+
+    def __init__(
+        self,
+        # Conv3d args
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=None,
+        padding_mode="zeros",
+        # BatchNorm3d args
+        # num_features: out_channels
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode,
+            eps,
+            momentum,
+            freeze_bn,
+            qconfig,
+        )
+
+    def forward(self, input):
+        return F.relu(ConvBn3d._forward(self, input))
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(mod, use_precomputed_fake_quant=use_precomputed_fake_quant)
+
+class ConvReLU3d(nnqat.Conv3d, nni._FusedModule):
+    r"""A ConvReLU3d module is a fused module of Conv3d and ReLU, attached with
+    FakeQuantize modules for weight for
+    quantization aware training.
+
+    We combined the interface of :class:`~torch.nn.Conv3d` and
+    :class:`~torch.nn.BatchNorm3d`.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_MODULE = nni.ConvReLU3d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE = nn.Conv3d
+    _FLOAT_BN_MODULE: None = None
+    _FLOAT_RELU_MODULE = nn.ReLU
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        qconfig=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            qconfig=qconfig,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.weight_fake_quant = self.qconfig.weight()
+
+    def forward(self, input):
+        return F.relu(
+            self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(mod, use_precomputed_fake_quant=use_precomputed_fake_quant)
+
+def update_bn_stats(mod):
+    if type(mod) in {ConvBnReLU1d, ConvBnReLU2d, ConvBnReLU3d, ConvBn1d, ConvBn2d, ConvBn3d}:
+        mod.update_bn_stats()
+
+def freeze_bn_stats(mod):
+    if type(mod) in {ConvBnReLU1d, ConvBnReLU2d, ConvBnReLU3d, ConvBn1d, ConvBn2d, ConvBn3d}:
+        mod.freeze_bn_stats()

parrot/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_fused.py

@@ -0,0 +1,172 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.nn as nn
+import torch.ao.nn.intrinsic as nni
+import torch.nn.functional as F
+from torch.nn import init
+from torch.nn.parameter import Parameter
+from torch.nn.utils.fusion import fuse_linear_bn_weights
+
+__all__ = [
+    "LinearBn1d",
+]
+
+class LinearBn1d(nn.modules.linear.Linear, nni._FusedModule):
+    r"""
+    A LinearBn1d module is a module fused from Linear and BatchNorm1d, attached
+    with FakeQuantize modules for weight, used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Linear` and
+    :class:torch.nn.BatchNorm1d`.
+
+    Similar to :class:`torch.nn.Linear`, with FakeQuantize modules initialized
+    to default.
+
+    Attributes:
+        freeze_bn:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    def __init__(self,
+                 # Linear args
+                 in_features, out_features, bias=True,
+                 # BatchNorm1d args
+                 # num_features: out_features
+                 eps=1e-05, momentum=0.1,
+                 # affine: True
+                 # track_running_stats: True
+                 # Args for this module
+                 freeze_bn=False,
+                 qconfig=None):
+        nn.modules.linear.Linear.__init__(self, in_features, out_features, bias)
+        assert qconfig, 'qconfig must be provided for QAT module'
+        self.qconfig = qconfig
+        self.freeze_bn = freeze_bn if self.training else True
+        self.bn = nn.BatchNorm1d(out_features, eps, momentum, True, True)
+        self.weight_fake_quant = self.qconfig.weight()
+        if bias:
+            self.bias = Parameter(torch.empty(out_features))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_bn_parameters()
+
+        # this needs to be called after reset_bn_parameters,
+        # as they modify the same state
+        if self.training:
+            if freeze_bn:
+                self.freeze_bn_stats()
+            else:
+                self.update_bn_stats()
+        else:
+            self.freeze_bn_stats()
+
+    def reset_running_stats(self):
+        self.bn.reset_running_stats()
+
+    def reset_bn_parameters(self):
+        self.bn.reset_running_stats()
+        init.uniform_(self.bn.weight)
+        init.zeros_(self.bn.bias)
+
+    def reset_parameters(self):
+        super().reset_parameters()
+
+    def update_bn_stats(self):
+        self.freeze_bn = False
+        self.bn.training = True
+        return self
+
+    def freeze_bn_stats(self):
+        self.freeze_bn = True
+        self.bn.training = False
+        return self
+
+    def forward(self, input):
+        assert self.bn.running_var is not None
+
+        # Scale the linear weights by BN's running statistics to reduce
+        # weight jitter, see https://arxiv.org/pdf/1806.08342.pdf, page 18
+        # for motivation.
+        #
+        # Instead of
+        #
+        #   x1 = F.linear(x0, fq(w), b)
+        #   x2 = self.bn(x1)
+        #
+        # We have
+        #
+        #   # scale the weight by previous batch's running statistics
+        #   scale_factor = bn.w / bn.running_std_from_prev_batch
+        #   # do the linear transformation without bias
+        #   x1_scaled = F.linear(x0, fq(w * scale_factor), 0)
+        #   # reverse the scaling and add original bias
+        #   x1_orig = x1_scaled / scale_factor + b
+        #   x2 = self.bn(x1_orig)
+
+        running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
+        scale_factor = self.bn.weight / running_std
+        weight_shape = [1] * len(self.weight.shape)
+        weight_shape[0] = -1
+        bias_shape = [1] * len(self.weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = self.weight_fake_quant(self.weight * scale_factor.reshape(weight_shape))
+        if self.bias is not None:
+            zero_bias = torch.zeros_like(self.bias)
+        else:
+            zero_bias = torch.zeros(self.out_features, device=scaled_weight.device)
+        linear_out = F.linear(input, scaled_weight, zero_bias)
+        linear_out_orig = linear_out / scale_factor.reshape(bias_shape)
+        if self.bias is not None:
+            linear_out_orig = linear_out_orig + self.bias.reshape(bias_shape)
+        bn_out = self.bn(linear_out_orig)
+        return bn_out
+
+    def train(self, mode=True):
+        """
+        Batchnorm's training behavior is using the self.training flag. Prevent
+        changing it if BN is frozen. This makes sure that calling `model.train()`
+        on a model with a frozen BN will behave properly.
+        """
+        self.training = mode
+        if not self.freeze_bn:
+            for module in self.children():
+                module.train(mode)
+        return self
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a qat module from a float module or qparams_dict
+
+            Args: `mod' a float module, either produced by torch.ao.quantization
+            utilities or directly from user
+        """
+        assert type(mod) == nni.LinearBn1d, 'qat.' + cls.__name__ + \
+            '.from_float only works for ' + nni.LinearBn1d.__name__
+        assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
+        assert mod.qconfig, 'Input float module must have a valid config'
+        qconfig = mod.qconfig
+        linear, bn = mod[0], mod[1]
+        qat_linearbn = cls(linear.in_features, linear.out_features, linear.bias is not None,
+                           bn.eps, bn.momentum,
+                           False, qconfig)
+        qat_linearbn.weight = linear.weight
+        qat_linearbn.bias = linear.bias
+        qat_linearbn.bn.weight = bn.weight
+        qat_linearbn.bn.bias = bn.bias
+        qat_linearbn.bn.running_mean = bn.running_mean
+        qat_linearbn.bn.running_var = bn.running_var
+        qat_linearbn.bn.num_batches_tracked = bn.num_batches_tracked
+        return qat_linearbn
+
+    def to_float(self):
+        linear = torch.nn.Linear(self.in_features, self.out_features)
+        assert self.bn.running_var is not None and self.bn.running_mean is not None
+        linear.weight, linear.bias = fuse_linear_bn_weights(
+            self.weight,
+            self.bias,
+            self.bn.running_mean,
+            self.bn.running_var,
+            self.bn.eps,
+            self.bn.weight,
+            self.bn.bias)
+        return linear

parrot/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py

@@ -0,0 +1,49 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.qat as nnqat
+import torch.ao.nn.intrinsic as nni
+import torch.nn.functional as F
+
+class LinearReLU(nnqat.Linear, nni._FusedModule):
+    r"""
+    A LinearReLU module fused from Linear and ReLU modules, attached with
+    FakeQuantize modules for weight, used in
+    quantization aware training.
+
+    We adopt the same interface as :class:`torch.nn.Linear`.
+
+    Similar to `torch.ao.nn.intrinsic.LinearReLU`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight: fake quant module for weight
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.qat.LinearReLU(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+    _FLOAT_MODULE = nni.LinearReLU  # type: ignore[assignment]
+
+    def __init__(self, in_features, out_features, bias=True,
+                 qconfig=None):
+        super().__init__(in_features, out_features, bias, qconfig)
+
+    def forward(self, input):
+        return F.relu(F.linear(input, self.weight_fake_quant(self.weight), self.bias))
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(mod, use_precomputed_fake_quant)
+
+    def to_float(self):
+        linear = torch.nn.Linear(self.in_features, self.out_features, self.bias is not None)
+        linear.weight = torch.nn.Parameter(self.weight.detach())
+        if self.bias is not None:
+            linear.bias = torch.nn.Parameter(self.bias.detach())
+        relu = torch.nn.ReLU()
+        return torch.ao.nn.intrinsic.LinearReLU(linear, relu)

parrot/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/__init__.py

@@ -0,0 +1,14 @@
+from .modules import *  # noqa: F403
+
+__all__ = [
+    'BNReLU2d',
+    'BNReLU3d',
+    'ConvReLU1d',
+    'ConvReLU2d',
+    'ConvReLU3d',
+    'LinearReLU',
+    'LinearLeakyReLU',
+    'LinearTanh',
+    'ConvAdd2d',
+    'ConvAddReLU2d',
+]

parrot/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/__init__.py

@@ -0,0 +1 @@
+from .modules import *  # noqa: F403

parrot/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/__init__.py

@@ -0,0 +1,6 @@
+import torch
+from .linear_relu import LinearReLU
+
+__all__ = [
+    'LinearReLU',
+]

parrot/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py

@@ -0,0 +1,83 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.ao.nn.intrinsic
+import torch.ao.nn.intrinsic.qat
+import torch.ao.nn.quantized as nnq
+
+__all__ = [
+    "BNReLU2d",
+    "BNReLU3d"
+]
+
+class BNReLU2d(nnq.BatchNorm2d):
+    r"""
+    A BNReLU2d module is a fused module of BatchNorm2d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.BatchNorm2d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.BatchNorm2d
+
+    """
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.BNReLU2d
+
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None):
+        super().__init__(num_features, eps=eps, momentum=momentum, device=device, dtype=dtype)
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        return torch.ops.quantized.batch_norm2d_relu(
+            input, self.weight, self.bias, self.running_mean,
+            self.running_var, self.eps, self.scale, self.zero_point)
+
+    def _get_name(self):
+        return 'QuantizedBNReLU2d'
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        # TODO: Add qat support for BNReLU2d
+        return super().from_float(mod, use_precomputed_fake_quant=use_precomputed_fake_quant)
+
+    @classmethod
+    def from_reference(cls, bn_relu, output_scale, output_zero_point):
+        return super().from_reference(bn_relu[0], output_scale, output_zero_point)
+
+class BNReLU3d(nnq.BatchNorm3d):
+    r"""
+    A BNReLU3d module is a fused module of BatchNorm3d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.BatchNorm3d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.BatchNorm3d
+
+    """
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.BNReLU3d
+
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None):
+        super().__init__(num_features, eps=eps, momentum=momentum, device=device, dtype=dtype)
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, D, H, W)`!")
+        return torch.ops.quantized.batch_norm3d_relu(
+            input, self.weight, self.bias, self.running_mean,
+            self.running_var, self.eps, self.scale, self.zero_point)
+
+    def _get_name(self):
+        return 'QuantizedBNReLU3d'
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        # TODO: Add qat support for BNReLU3d
+        return super().from_float(mod, use_precomputed_fake_quant=use_precomputed_fake_quant)
+
+    @classmethod
+    def from_reference(cls, bn_relu, output_scale, output_zero_point):
+        return super().from_reference(bn_relu[0], output_scale, output_zero_point)

parrot/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py

@@ -0,0 +1,176 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.ao.nn.intrinsic
+import torch.ao.nn.intrinsic.qat
+import torch.nn.functional as F
+import torch.ao.nn.quantized as nnq
+
+from torch.nn.utils import fuse_conv_bn_weights
+
+__all__ = [
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+]
+
+_reverse_repeat_padding = nnq.modules.conv._reverse_repeat_padding
+
+# TODO: factor out the common parts to ConvNd
+class ConvReLU1d(nnq.Conv1d):
+    r"""
+    A ConvReLU1d module is a fused module of Conv1d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Conv1d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Conv1d
+
+    """
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.ConvReLU1d  # type: ignore[assignment]
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1, bias=True,
+                 padding_mode='zeros', device=None, dtype=None):
+        super().__init__(
+            in_channels, out_channels, kernel_size, stride=stride,
+            padding=padding, dilation=dilation, groups=groups, bias=bias,
+            padding_mode=padding_mode, device=device, dtype=dtype)
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 3:
+            raise ValueError("Input shape must be `(N, C, L)`!")
+        if self.padding_mode != 'zeros':
+            # Padding in Conv1d is stored as (p, p), need to get (p,)
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding[:1])
+            input = F.pad(input, _reversed_padding_repeated_twice,
+                          mode=self.padding_mode)
+        return torch.ops.quantized.conv1d_relu(
+            input, self._packed_params, self.scale, self.zero_point)
+
+    def _get_name(self):
+        return 'QuantizedConvReLU1d'
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        if type(mod) == torch.ao.nn.intrinsic.qat.ConvBnReLU1d:
+            assert mod.bn.running_var is not None and mod.bn.running_mean is not None
+            mod.weight, mod.bias = fuse_conv_bn_weights(
+                mod.weight, mod.bias, mod.bn.running_mean, mod.bn.running_var,
+                mod.bn.eps, mod.bn.weight, mod.bn.bias)
+        return super().from_float(mod, use_precomputed_fake_quant)
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        assert type(ref_qconv) != torch.ao.nn.intrinsic.ConvBnReLU1d, \
+            "BatchNorm1d should be fused into Conv1d before converting to reference module"
+        return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
+
+class ConvReLU2d(nnq.Conv2d):
+    r"""
+    A ConvReLU2d module is a fused module of Conv2d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Conv2d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Conv2d
+
+    """
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.ConvReLU2d  # type: ignore[assignment]
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1, bias=True,
+                 padding_mode='zeros', device=None, dtype=None):
+        super().__init__(
+            in_channels, out_channels, kernel_size, stride=stride,
+            padding=padding, dilation=dilation, groups=groups, bias=bias,
+            padding_mode=padding_mode, device=device, dtype=dtype)
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        if self.padding_mode != 'zeros':
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(input, _reversed_padding_repeated_twice,
+                          mode=self.padding_mode)
+        return torch.ops.quantized.conv2d_relu(
+            input, self._packed_params, self.scale, self.zero_point)
+
+    def _get_name(self):
+        return 'QuantizedConvReLU2d'
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        if type(mod) == torch.ao.nn.intrinsic.qat.ConvBnReLU2d:
+            assert mod.bn.running_var is not None and mod.bn.running_mean is not None
+            mod.weight, mod.bias = fuse_conv_bn_weights(
+                mod.weight, mod.bias, mod.bn.running_mean, mod.bn.running_var,
+                mod.bn.eps, mod.bn.weight, mod.bn.bias)
+        return super().from_float(mod, use_precomputed_fake_quant=use_precomputed_fake_quant)
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        assert type(ref_qconv) != torch.ao.nn.intrinsic.ConvBnReLU2d, \
+            "BatchNorm2d should be fused into Conv2d before converting to reference module"
+        return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
+
+
+class ConvReLU3d(nnq.Conv3d):
+    r"""
+    A ConvReLU3d module is a fused module of Conv3d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Conv3d`.
+
+    Attributes: Same as torch.ao.nn.quantized.Conv3d
+
+    """
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.ConvReLU3d  # type: ignore[assignment]
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1, bias=True,
+                 padding_mode='zeros', device=None, dtype=None):
+        assert padding_mode != 'reflect', "Conv3d does not support reflection padding"
+        super().__init__(
+            in_channels, out_channels, kernel_size, stride=stride,
+            padding=padding, dilation=dilation, groups=groups, bias=bias,
+            padding_mode=padding_mode, device=device, dtype=dtype)
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, D, H, W)`!")
+        if self.padding_mode != 'zeros':
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(input, _reversed_padding_repeated_twice,
+                          mode=self.padding_mode)
+        return torch.ops.quantized.conv3d_relu(
+            input, self._packed_params, self.scale, self.zero_point)
+
+    def _get_name(self):
+        return 'QuantizedConvReLU3d'
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        if type(mod) == torch.ao.nn.intrinsic.qat.ConvBnReLU3d:
+            assert mod.bn.running_var is not None and mod.bn.running_mean is not None
+            mod.weight, mod.bias = fuse_conv_bn_weights(
+                mod.weight,
+                mod.bias,
+                mod.bn.running_mean,
+                mod.bn.running_var,
+                mod.bn.eps,
+                mod.bn.weight,
+                mod.bn.bias,
+            )
+        return super().from_float(mod, use_precomputed_fake_quant=use_precomputed_fake_quant)
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        assert type(ref_qconv) != torch.ao.nn.intrinsic.ConvBnReLU3d, \
+            "BatchNorm3d should be fused into Conv3d before converting to reference module"
+        return super().from_reference(ref_qconv[0], output_scale, output_zero_point)

parrot/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py

@@ -0,0 +1,178 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.quantized as nnq
+import torch.ao.nn.intrinsic as nni
+from torch.ao.nn.quantized.modules.utils import _quantize_weight
+
+__all__ = [
+    "LinearReLU",
+    "LinearLeakyReLU",
+    "LinearTanh",
+]
+
+class LinearReLU(nnq.Linear):
+    r"""
+    A LinearReLU module fused from Linear and ReLU modules
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Linear
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.intrinsic.LinearReLU(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+    _FLOAT_MODULE = nni.LinearReLU  # type: ignore[assignment]
+
+    def __init__(self, in_features, out_features, bias=True, dtype=torch.qint8):
+        super().__init__(in_features, out_features, bias, dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.quantized.linear_relu(
+            x, self._packed_params._packed_params, self.scale, self.zero_point)
+
+    def _get_name(self):
+        return 'QuantizedLinearReLU'
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(mod, use_precomputed_fake_quant)
+
+    @classmethod
+    def from_reference(cls, ref_linear_relu, output_scale, output_zero_point):
+        return super().from_reference(ref_linear_relu[0], output_scale, output_zero_point)
+
+class LinearLeakyReLU(nnq.Linear):
+    r"""
+    For onednn backend only
+    A LinearLeakyReLU module fused from Linear and LeakyReLU modules
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
+    Attributes:
+        Same as torch.ao.nn.quantized.Linear
+        + negative_slope
+    Examples::
+        >>> # xdoctest: +SKIP
+        >>> m = nn.intrinsic.LinearLeakyReLU(20, 30, 0.01)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+    _FLOAT_MODULE = nni.LinearLeakyReLU  # type: ignore[assignment]
+
+    def __init__(self, in_features, out_features, negative_slope, bias=True, dtype=torch.qint8):
+        super().__init__(in_features, out_features, bias, dtype)
+        self.negative_slope = negative_slope
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.quantized.linear_leaky_relu(
+            x, self._packed_params._packed_params, self.scale, self.zero_point, self.negative_slope)
+
+    def _get_name(self):
+        return 'QuantizedLinearLeakyReLU'
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        assert type(mod) == nni.LinearLeakyReLU, 'Input float module should be LinearLeakyReLU'
+        assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
+        activation_post_process = mod.activation_post_process
+        leaky_relu = mod[1]
+        mod = mod[0]
+        weight_post_process = mod.qconfig.weight()
+        weight_post_process(mod.weight)
+        dtype = weight_post_process.dtype
+        act_scale, act_zp = activation_post_process.calculate_qparams()  # type: ignore[union-attr,operator]
+        assert dtype == torch.qint8, 'Weight observer must have dtype torch.qint8'
+        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
+        qlinear_leaky_relu = cls(
+            mod.in_features,
+            mod.out_features,
+            leaky_relu.negative_slope,
+            dtype=dtype)
+        qlinear_leaky_relu.set_weight_bias(qweight, mod.bias)
+        qlinear_leaky_relu.scale = float(act_scale)
+        qlinear_leaky_relu.zero_point = int(act_zp)
+        return qlinear_leaky_relu
+
+    @classmethod
+    def from_reference(cls, ref_mod, output_scale, output_zero_point):
+        linear = ref_mod[0]
+        leaky_relu = ref_mod[1]
+        qlinear_leaky_relu = cls(
+            linear.in_features,
+            linear.out_features,
+            leaky_relu.negative_slope)
+        qweight = linear.get_quantized_weight()
+        qlinear_leaky_relu.set_weight_bias(qweight, linear.bias)
+        qlinear_leaky_relu.scale = float(output_scale)
+        qlinear_leaky_relu.zero_point = int(output_zero_point)
+        return qlinear_leaky_relu
+
+class LinearTanh(nnq.Linear):
+    r"""
+    A LinearTanh module fused from Linear and Tanh modules
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Linear
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.intrinsic.LinearTanh(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+    _FLOAT_MODULE = nni.LinearTanh  # type: ignore[assignment]
+
+    def __init__(self, in_features, out_features, bias=True, dtype=torch.qint8):
+        super().__init__(in_features, out_features, bias, dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.quantized.linear_tanh(
+            x, self._packed_params._packed_params, self.scale, self.zero_point)
+
+    def _get_name(self):
+        return 'QuantizedLinearTanh'
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        assert type(mod) == nni.LinearTanh, 'Input float module should be LinearTanh'
+        assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
+        activation_post_process = mod.activation_post_process
+        mod = mod[0]
+        weight_post_process = mod.qconfig.weight()
+        weight_post_process(mod.weight)
+        dtype = weight_post_process.dtype
+        act_scale, act_zp = activation_post_process.calculate_qparams()  # type: ignore[union-attr,operator]
+        assert dtype == torch.qint8, 'Weight observer must have dtype torch.qint8'
+        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
+        qlinear_tanh = cls(
+            mod.in_features,
+            mod.out_features,
+            dtype=dtype)
+        qlinear_tanh.set_weight_bias(qweight, mod.bias)
+        qlinear_tanh.scale = float(act_scale)
+        qlinear_tanh.zero_point = int(act_zp)
+        return qlinear_tanh
+
+    @classmethod
+    def from_reference(cls, ref_mod, output_scale, output_zero_point):
+        linear = ref_mod[0]
+        qlinear_tanh = cls(
+            linear.in_features,
+            linear.out_features)
+        qweight = linear.get_quantized_weight()
+        qlinear_tanh.set_weight_bias(qweight, linear.bias)
+        qlinear_tanh.scale = float(output_scale)
+        qlinear_tanh.zero_point = int(output_zero_point)
+        return qlinear_tanh

parrot/lib/python3.10/site-packages/torch/ao/nn/qat/__init__.py

@@ -0,0 +1 @@
+from .modules import *  # noqa: F403

parrot/lib/python3.10/site-packages/torch/ao/nn/qat/dynamic/__init__.py

@@ -0,0 +1 @@
+from .modules import *  # noqa: F403

parrot/lib/python3.10/site-packages/torch/ao/nn/qat/dynamic/modules/__init__.py

@@ -0,0 +1,3 @@
+from .linear import Linear
+
+__all__ = ["Linear"]

parrot/lib/python3.10/site-packages/torch/ao/nn/qat/dynamic/modules/linear.py

@@ -0,0 +1,26 @@
+# mypy: allow-untyped-defs
+import torch
+
+__all__ = ["Linear"]
+
+class Linear(torch.ao.nn.qat.Linear):
+    r"""
+    A linear module attached with FakeQuantize modules for weight,
+    used for dynamic quantization aware training.
+
+    We adopt the same interface as `torch.nn.Linear`, please see
+    https://pytorch.org/docs/stable/nn.html#torch.nn.Linear
+    for documentation.
+
+    Similar to `torch.nn.Linear`, with FakeQuantize modules initialized to
+    default.
+    """
+
+    def __init__(self, in_features, out_features, bias=True,
+                 qconfig=None, device=None, dtype=None) -> None:
+        super().__init__(in_features, out_features, bias, qconfig, device, dtype)
+        if not torch.ao.quantization.qconfig._activation_is_memoryless(qconfig):
+            raise ValueError(
+                "Dynamic QAT requires a memoryless observer." +
+                "This means a MovingAverage observer with averaging constant equal to 1"
+            )