Name	Size	Mode	Actions
__pycache__/	-	0755	rm
activation.py	4683	0644	editdlrm
batchnorm.py	2682	0644	editdlrm
conv.py	34625	0644	editdlrm
embedding_ops.py	11130	0644	editdlrm
functional_modules.py	8269	0644	editdlrm
linear.py	11930	0644	editdlrm
normalization.py	7351	0644	editdlrm
utils.py	2370	0644	editdlrm
__init__.py	3908	0644	editdlrm

import torch import torch.nn.quantized.functional class LayerNorm(torch.nn.LayerNorm): r"""This is the quantized version of :class:`~torch.nn.LayerNorm`. Additional args: * **scale** - quantization scale of the output, type: double. * **zero_point** - quantization zero point of the output, type: long. """ def __init__(self, normalized_shape, weight, bias, scale, zero_point, eps=1e-5, elementwise_affine=True, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super(LayerNorm, self).__init__( normalized_shape, eps=eps, elementwise_affine=elementwise_affine, **factory_kwargs) self.weight = weight self.bias = bias self.register_buffer('scale', torch.tensor(scale, **factory_kwargs)) self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs)) def forward(self, input): return torch.ops.quantized.layer_norm( input, self.normalized_shape, weight=self.weight, bias=self.bias, eps=self.eps, output_scale=self.scale, output_zero_point=self.zero_point) def _get_name(self): return 'QuantizedLayerNorm' @classmethod def from_float(cls, mod): scale, zero_point = mod.activation_post_process.calculate_qparams() new_mod = cls( mod.normalized_shape, mod.weight, mod.bias, float(scale), int(zero_point), mod.eps, mod.elementwise_affine) return new_mod class GroupNorm(torch.nn.GroupNorm): r"""This is the quantized version of :class:`~torch.nn.GroupNorm`. Additional args: * **scale** - quantization scale of the output, type: double. * **zero_point** - quantization zero point of the output, type: long. """ __constants__ = ['num_groups', 'num_channels', 'eps', 'affine'] def __init__(self, num_groups, num_channels, weight, bias, scale, zero_point, eps=1e-5, affine=True, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super(GroupNorm, self).__init__(num_groups, num_channels, eps, affine, **factory_kwargs) self.weight = weight self.bias = bias self.register_buffer('scale', torch.tensor(scale, **factory_kwargs)) self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs)) def forward(self, input): return torch.ops.quantized.group_norm( input, self.num_groups, self.weight, self.bias, self.eps, self.scale, self.zero_point) def _get_name(self): return 'QuantizedGroupNorm' @classmethod def from_float(cls, mod): scale, zero_point = mod.activation_post_process.calculate_qparams() new_mod = cls( mod.num_groups, mod.num_channels, mod.weight, mod.bias, float(scale), int(zero_point), mod.eps, mod.affine) return new_mod class InstanceNorm1d(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, track_running_stats=False, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super(InstanceNorm1d, self).__init__( num_features, eps, momentum, affine, track_running_stats, **factory_kwargs) self.weight = weight self.bias = bias self.register_buffer('scale', torch.tensor(scale, **factory_kwargs)) self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs)) def forward(self, input): return torch.ops.quantized.instance_norm( input, self.weight, self.bias, self.eps, self.scale, self.zero_point) def _get_name(self): return 'QuantizedInstanceNorm1d' @classmethod def from_float(cls, mod): scale, zero_point = mod.activation_post_process.calculate_qparams() new_mod = cls( mod.num_features, mod.weight, mod.bias, float(scale), int(zero_point), mod.eps, mod.affine) return new_mod class InstanceNorm2d(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, track_running_stats=False, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super(InstanceNorm2d, self).__init__( num_features, eps, momentum, affine, track_running_stats, **factory_kwargs) self.weight = weight self.bias = bias self.register_buffer('scale', torch.tensor(scale, **factory_kwargs)) self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs)) def forward(self, input): return torch.ops.quantized.instance_norm( input, self.weight, self.bias, self.eps, self.scale, self.zero_point) def _get_name(self): return 'QuantizedInstanceNorm2d' @classmethod def from_float(cls, mod): scale, zero_point = mod.activation_post_process.calculate_qparams() new_mod = cls( mod.num_features, mod.weight, mod.bias, float(scale), int(zero_point), mod.eps, mod.affine) return new_mod class InstanceNorm3d(torch.nn.InstanceNorm3d): 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, track_running_stats=False, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super(InstanceNorm3d, self).__init__( num_features, eps, momentum, affine, track_running_stats, **factory_kwargs) self.weight = weight self.bias = bias self.register_buffer('scale', torch.tensor(scale, **factory_kwargs)) self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs)) def forward(self, input): return torch.ops.quantized.instance_norm( input, self.weight, self.bias, self.eps, self.scale, self.zero_point) def _get_name(self): return 'QuantizedInstanceNorm3d' @classmethod def from_float(cls, mod): scale, zero_point = mod.activation_post_process.calculate_qparams() new_mod = cls( mod.num_features, mod.weight, mod.bias, float(scale), int(zero_point), mod.eps, mod.affine) return new_mod