[quant][graphmode] Add observers for dynamic quant (#35121)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35121
For dynamic quantization we insert observers at the input to mimic the quatization of activations that happens in the operator
Observer for weight is inserted similar to static quant
Test Plan:
python test/test_quantize_script.py
Sample output for single layer FC
.graph(%self : __torch__.___torch_mangle_4.M,
%x.2 : Tensor):
%_observer_1 : __torch__.torch.quantization.observer.MinMaxObserver = prim::GetAttr[name="_observer_1"](%self)
%x.1 : Tensor = prim::CallMethod[name="forward"](%_observer_1, %x.2)
%2 : __torch__.torch.nn.modules.linear.___torch_mangle_5.Linear = prim::GetAttr[name="fc"](%self)
%3 : Tensor = prim::CallMethod[name="forward"](%2, %x.1) # test/test_quantize_script.py:19:23
return (%3)
graph(%self : __torch__.torch.nn.modules.linear.___torch_mangle_5.Linear,
%input.1 : Tensor):
%2 : Function = prim::Constant[name="linear"]()
%3 : Tensor = prim::GetAttr[name="weight"](%self)
%_observer_0 : __torch__.torch.quantization.observer.MinMaxObserver = prim::GetAttr[name="_observer_0"](%self)
%7 : Tensor = prim::CallMethod[name="forward"](%_observer_0, %3)
%4 : Tensor = prim::GetAttr[name="bias"](%self)
%5 : Tensor = prim::CallFunction(%2, %input.1, %7, %4) # /home/supriyar/miniconda3/envs/pytorch_py3/lib/python3.7/site-packages/torch/nn/modules/linear.py:87:15
return (%5)
Imported from OSS
Differential Revision: D20599144
fbshipit-source-id: 9a8fa0e8655b9908826b981dce8a11d86efce5df
