support torch.sub/sign/abs in eager mode

Summary:
As title
Achieve by `vizard.quantization.functional. FloatFunctional` and `vizard.quantization.prepare.prepare_eager`

Differential Revision: D48377683

d2go/quantization/fx.py

@@ -10,16 +10,19 @@
 
 TORCH_VERSION: Tuple[int, ...] = tuple(int(x) for x in torch.__version__.split(".")[:2])
 if TORCH_VERSION > (1, 10):
-    from torch.ao.quantization.quantize import convert
+    from torch.ao.quantization.quantize import convert, prepare, prepare_qat
     from torch.ao.quantization.quantize_fx import convert_fx, prepare_fx, prepare_qat_fx
 else:
-    from torch.quantization.quantize import convert
+    from torch.quantization.quantize import convert, prepare, prepare_qat
     from torch.quantization.quantize_fx import convert_fx, prepare_fx, prepare_qat_fx
 
 
 @fb_overwritable()
 def get_prepare_fx_fn(cfg, is_qat):
-    return prepare_qat_fx if is_qat else prepare_fx
+    if cfg.QUANTIZATION.EAGER_MODE:
+        return prepare_qat if is_qat else prepare
+    else:
+        return prepare_qat_fx if is_qat else prepare_fx
 
 
 @fb_overwritable()

d2go/quantization/modeling.py

@@ -352,11 +352,8 @@ def prepare_fake_quant_model(cfg, model, is_qat, example_input=None):
             )
             model = default_prepare_for_quant(cfg, model)
         # NOTE: eager model needs to call prepare after `prepare_for_quant`
-        if is_qat:
-            torch.ao.quantization.prepare_qat(model, inplace=True)
-        else:
-            torch.ao.quantization.prepare(model, inplace=True)
-
+        prepare_fn = get_prepare_fx_fn(cfg, is_qat)
+        prepare_fn(model, inplace=True)
     else:
         # FX graph mode requires the model to be symbolically traceable, swap common
         # modules like SyncBN to FX-friendly version.