Dynamic quantization for bias. (#26057)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26057
bias is now unquantized (i.e. floating type) for qconv and qlinear. It is dynamically quantized by fbgemm.
TODO: Add some performance numbers.
Tests:
test:quantization
```
Summary (total time 8.41s):
PASS: 24
FAIL: 0
SKIP: 0
FATAL: 0
TIMEOUT: 0More details at https://our.intern.facebook.com/intern/buck/build/74d5f6f7-55c9-4350-a618-2013042fffd8
OMIT: 0
```
test:quantized
```
Summary (total time 13.21s):
PASS: 43
FAIL: 0
SKIP: 5
caffe2/test:quantized - test_qnnpack_maxpool2d (test_quantized.TestQNNPackOps)
caffe2/test:quantized - test_compare_tensor_scalar (test_quantized.TestComparatorOps)
caffe2/test:quantized - test_qnnpack_linear (test_quantized.TestQNNPackOps)
caffe2/test:quantized - test_qnnpack_relu (test_quantized.TestQNNPackOps)
caffe2/test:quantized - test_qnnpack_add (test_quantized.TestQNNPackOps)
FATAL: 0
TIMEOUT: 0
OMIT: 0
```
ghstack-source-id: 90166254
Test Plan:
buck test mode/dev caffe2/test:quantization
buck test mode/dev caffe2/test:quantized
Differential Revision: D17328028
fbshipit-source-id: d4a163d730d0f4a03e8e0faf7420710cf36eec09
