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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/24231 As suggested in https://github.com/pytorch/pytorch/pull/23128#discussion_r309528932, we will add a default weight observer for the dynamic quantization path. We need to move `observer` and `qconfig` to a separate namespace. ghstack-source-id: 88583658 Differential Revision: D16781092 fbshipit-source-id: 5cd59c881a7f98b82704ca318b1e63650d73062a
81 lines
3.6 KiB
Python
81 lines
3.6 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import torch
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from ....modules.linear import Linear as NNLinear
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import torch.nn.quantized as nnq
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from torch._jit_internal import Optional
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class Linear(nnq.Linear):
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r"""
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A dynamic quantized linear module with quantized tensor as inputs and outputs.
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We adopt the same interface as `torch.nn.Linear`, please see
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https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation.
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Similar to :class:`torch.nn.Linear`, attributes will be randomly
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initialized at module creation time and will be overwritten later
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Attributes:
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weight (Tensor): the non-learnable quantized weights of the module which are of
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shape :math:`(\text{out\_features}, \text{in\_features})`.
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bias (Tensor): the non-learnable bias of the module of shape :math:`(\text{out\_features})`.
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If :attr:`bias` is ``True``, the values are initialized to zero.
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scale: `scale` parameter of weight Quantized Tensor, type: double
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zero_point: `zero_point` parameter for weight Quantized Tensor, type: long
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Examples::
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>>> m = nn.quantized.dynamic.Linear(20, 30)
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>>> input = torch.randn(128, 20)
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>>> output = m(input)
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>>> print(output.size())
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torch.Size([128, 30])
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"""
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__annotations__ = {'bias' : Optional[torch.Tensor]}
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def __init__(self, in_features, out_features, bias_=True):
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super(Linear, self).__init__(in_features, out_features, bias_)
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# We don't muck around with buffers or attributes or anything here
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# to keep the module simple. *everything* is simply a Python attribute.
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# Serialization logic is explicitly handled in the below serialization and
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# deserialization modules
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if bias_:
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del self.bias
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self.bias = torch.Tensor(out_features).float()
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else:
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self.bias = None
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def forward(self, x):
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# Note that we can handle self.bias == None case.
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Y = torch.ops.quantized.fbgemm_linear_dynamic(
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x, self._packed_weight,
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self.bias)
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return Y.to(x.dtype)
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@classmethod
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def from_float(cls, mod):
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r"""Create a dynamic quantized module from a float module or qparams_dict
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Args:
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mod (Module): a float module, either produced by torch.quantization
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utilities or provided by the user
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"""
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assert type(mod) == NNLinear, 'nn.quantized.dynamic.Linear.from_float only works for nn.Linear'
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assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
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if mod.qconfig is not None and mod.qconfig.weight() is not None:
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weight_observer = mod.qconfig.weight()
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else:
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# We have the circular import issues if we import the qconfig in the beginning of this file:
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# https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
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# import until we need it.
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from torch.quantization.QConfig import default_dynamic_qconfig
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weight_observer = default_dynamic_qconfig.weight()
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assert weight_observer.dtype == torch.qint8, 'Weight observer must have dtype torch.qint8'
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weight_observer(mod.weight)
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wt_scale, wt_zp = weight_observer.calculate_qparams()
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qweight = torch.quantize_linear(mod.weight.float(), float(wt_scale), int(wt_zp), torch.qint8)
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qlinear = Linear(mod.in_features, mod.out_features)
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qlinear.set_weight(qweight)
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qlinear.bias = mod.bias
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return qlinear
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