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Some minor fix to unblock the Bert model quantization (#20787)

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Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20787

Set requires_grad=False for bias: this will block the jit tracing.
The as_type fix: The input tensor shape and output tensor shape will be different, which will trigger the assertion failure at https://fburl.com/0m8xy7tc.

Reviewed By: jamesr66a

Differential Revision: D15445092

fbshipit-source-id: 22da41a56ecb9ac092585d0cc1ff0658fb9d631b
This commit is contained in:
Lingyi Liu
2019-05-21 23:10:01 -07:00
committed by Facebook Github Bot
parent a501e7d5be
commit 70ecddfd76
1 changed files with 2 additions and 2 deletions
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4
torch/jit/quantized.py
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@ -20,7 +20,7 @@ class QuantizedLinear(torch.jit.ScriptModule):
self.weight = torch.nn.Parameter(self.weight, requires_grad=False)
self.col_offsets = torch.nn.Parameter(self.col_offsets, requires_grad=False)
assert other.bias is not None, 'QuantizedLinear requires a bias'
self.bias = torch.nn.Parameter(other.bias.clone().float())
self.bias = torch.nn.Parameter(other.bias.clone().float(), requires_grad=False)
self.register_buffer(
'packed_tensor_ptr',
@ -42,7 +42,7 @@ class QuantizedLinear(torch.jit.ScriptModule):
out = torch.fbgemm_linear_int8_weight(
input.float(), self.weight, self.packed_tensor_ptr, self.col_offsets,
self.scale, self.zero_point, self.bias)
return out.type_as(input)
return out.float()
def extra_repr(self):
repr = 'in_features={in_features}, out_features={out_features}, ' \