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

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 floating point tensor as inputs and outputs. We adopt the same interface as `torch.nn.Linear`, please see 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 floating point 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]) """ # version used in this class is different from the parent class nnq.Linear _version = 4 def __init__(self, in_features, out_features, bias_=True, dtype=torch.qint8): super(Linear, self).__init__(in_features, out_features, bias_, dtype=dtype) # 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 self.version = 4 def forward(self, x): # Note that we can handle self.bias == None case. if self._packed_params.dtype == torch.qint8: if self.version is None or self.version < 4: Y = torch.ops.quantized.linear_dynamic( x, self._packed_params._packed_params) else: Y = torch.ops.quantized.linear_dynamic( x, self._packed_params._packed_params, reduce_range=True) elif self._packed_params.dtype == torch.float16: Y = torch.ops.quantized.linear_dynamic_fp16( x, self._packed_params._packed_params) else: raise RuntimeError('Unsupported dtype on dynamic quantized linear!') return def _get_name(self): return 'DynamicQuantizedLinear' def extra_repr(self): extra_repr_str = 'in_features={}, out_features={}, dtype={}'.format( self.in_features, self.out_features, self._packed_params.dtype ) if self._packed_params.dtype == torch.qint8: extra_repr_str += ', qscheme={}'.format(self.weight().qscheme()) return extra_repr_str def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): version = local_metadata.get('version', None) self.version = version super(Linear, self)._load_from_state_dict(state_dict, prefix, local_metadata, False, missing_keys, unexpected_keys, error_msgs)
[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: # 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() dtype = weight_observer.dtype assert dtype in [torch.qint8, torch.float16], "The only supported dtypes for " \ "dynamic quantized linear are qint8 and float16 got: {}".format(dtype) weight_observer(mod.weight) if dtype == torch.qint8: qweight = _quantize_weight(mod.weight.float(), weight_observer) elif dtype == torch.float16: qweight = mod.weight.float() else: raise RuntimeError('Unsupported dtype specified for dynamic quantized Linear!') qlinear = Linear(mod.in_features, mod.out_features, dtype=dtype) qlinear.set_weight_bias(qweight, mod.bias) return qlinear


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources