split regressor's prepare_for_quant into eager and FX

Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/336

Reviewed By: jerryzh168

Differential Revision: D37860495

fbshipit-source-id: 1ce0bc7bc8071d3bfbe53cd61ed180da62e29327

d2go/quantization/modeling.py

@@ -285,9 +285,9 @@ def default_custom_convert_fx(cfg, model):
     return convert_fx(model)
 
 
-def convert_to_fake_quant_model(cfg, model, is_qat, example_input=None):
+def prepare_fake_quant_model(cfg, model, is_qat, example_input=None):
     """
-    Centralized function to convert fp32 model (D2Go's MetaArch) to fake quant model.
+    Centralized function to prepare fp32 model (D2Go's MetaArch) to fake quant model.
     """
     # TODO: create a warning for the direct use of `torch.ao.quantization.get_default_qconfig`
     # or `torch.ao.quantization.get_default_qat_qconfig` without calling D2Go's high-level
@@ -351,7 +351,7 @@ def post_training_quantize(cfg, model, data_loader):
         param.requires_grad = False
 
     example_input = next(iter(data_loader))
-    model = convert_to_fake_quant_model(cfg, model, False, example_input)
+    model = prepare_fake_quant_model(cfg, model, False, example_input)
     logger.info("Prepared the PTQ model for calibration:\n{}".format(model))
 
     # Option for forcing running calibration on GPU, works only when the model supports
@@ -412,7 +412,7 @@ def setup_qat_model(
     model_fp32_state_dict = model_fp32.state_dict()
 
     # prepare model for qat
-    model = convert_to_fake_quant_model(cfg, model_fp32, True)
+    model = prepare_fake_quant_model(cfg, model_fp32, True)
 
     # make sure the proper qconfig are used in the model
     learnable_qat.check_for_learnable_fake_quant_ops(qat_method, model)