Shortcuts

Source code for torch.nn.quantized.dynamic.modules.linear

from __future__ import absolute_import, division, print_function, unicode_literals
import torch
from ....modules.linear import Linear as NNLinear
import torch.nn.quantized as nnq
from torch.nn.quantized.modules.utils import _quantize_weight

[docs]class Linear(nnq.Linear): r""" A dynamic quantized linear module with quantized tensor as inputs and outputs. 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 :class:`torch.nn.Linear`, attributes will be randomly initialized at module creation time and will be overwritten later Attributes: weight (Tensor): the non-learnable quantized weights of the module which are of shape :math:`(\text{out\_features}, \text{in\_features})`. bias (Tensor): the non-learnable bias of the module of shape :math:`(\text{out\_features})`. If :attr:`bias` is ``True``, the values are initialized to zero. Examples:: >>> m = nn.quantized.dynamic.Linear(20, 30) >>> input = torch.randn(128, 20) >>> output = m(input) >>> print(output.size()) torch.Size([128, 30]) """ def __init__(self, in_features, out_features, bias_=True): super(Linear, self).__init__(in_features, out_features, bias_) # We don't muck around with buffers or attributes or anything here # to keep the module simple. *everything* is simply a Python attribute. # Serialization logic is explicitly handled in the below serialization and # deserialization modules def forward(self, x): # Note that we can handle self.bias == None case. Y = torch.ops.quantized.linear_dynamic( x, self._packed_params._packed_params) return Y.to(x.dtype) def _get_name(self): return 'DynamicQuantizedLinear' def extra_repr(self): return 'in_features={}, out_features={}'.format( self.in_features, self.out_features )
[docs] @classmethod def from_float(cls, mod): r"""Create a dynamic quantized module from a float module or qparams_dict Args: mod (Module): a float module, either produced by torch.quantization utilities or provided by the user """ assert type(mod) == NNLinear, 'nn.quantized.dynamic.Linear.from_float only works for nn.Linear' assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined' if mod.qconfig is not None and mod.qconfig.weight is not None: weight_observer = mod.qconfig.weight() else: # We have the circular import issues if we import the qconfig in the beginning of this file: # https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the # import until we need it. from torch.quantization.qconfig import default_dynamic_qconfig weight_observer = default_dynamic_qconfig.weight() assert weight_observer.dtype == torch.qint8, 'Weight observer must have dtype torch.qint8' weight_observer(mod.weight) qweight = _quantize_weight(mod.weight.float(), weight_observer) qlinear = Linear(mod.in_features, mod.out_features) qlinear.set_weight_bias(qweight, mod.bias) return qlinear

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources