expose example_input argument in setup_qat_model()

Summary:

Major changes
- **example_input** argument in **prepare_fake_quant_model()** is useful in certain cases. For example, in Argos model **custom_prepare_fx()** method under FX graph + QAT setup (D52760682), it is used to prepare example inputs to individual sub-modules by running one forward pass and bookkeeping the inputs to individual sub-modules. Therefore, we export argument **example_input** in **setup_qat_model()** function.
- For QAT model, currently we assert # of state dict keys (excluding observers) should be equal to # of state dict keys in the original model. However, when the assertion fails, it does not log useful information for debugging. We make changes to report what are the unique keys in each state dict.

Differential Revision: D52760688

d2go/quantization/modeling.py

@@ -5,7 +5,7 @@
 import copy
 import logging
 import math
-from typing import Any, Dict, Tuple
+from typing import Any, Dict, Optional, Tuple
 
 import detectron2.utils.comm as comm
 import torch
@@ -471,6 +471,7 @@ def setup_qat_model(
     enable_fake_quant: bool = False,
     enable_observer: bool = False,
     enable_learnable_observer: bool = False,
+    example_input: Optional[Any] = None,
 ):
     assert cfg.QUANTIZATION.QAT.FAKE_QUANT_METHOD in [
         "default",
@@ -490,7 +491,7 @@ def setup_qat_model(
     model_fp32_state_dict = model_fp32.state_dict()
 
     # prepare model for qat
-    model = prepare_fake_quant_model(cfg, model_fp32, True)
+    model = prepare_fake_quant_model(cfg, model_fp32, True, example_input=example_input)
 
     # make sure the proper qconfig are used in the model
     learnable_qat.check_for_learnable_fake_quant_ops(qat_method, model)
@@ -554,9 +555,23 @@ def _setup_non_qat_to_qat_state_dict_map(
         )
     )
 
-    assert len(new_state_dict_non_observer_keys_not_ignored) == len(
+    if not len(new_state_dict_non_observer_keys_not_ignored) == len(
         original_state_dict_shapes
-    ), f"keys in state dict of original and new qat model {len(new_state_dict_non_observer_keys_not_ignored)} vs {len(original_state_dict_shapes)}"
+    ):
+        a = set(new_state_dict_non_observer_keys_not_ignored)
+        b = set(original_state_dict_shapes.keys())
+        a_diff_b = a.difference(b)
+        b_diff_a = b.difference(a)
+        logger.info("unique keys in qat model state dict")
+        for key in a_diff_b:
+            logger.info(f"{key}")
+        logger.info("unique keys in original model state dict")
+        for key in b_diff_a:
+            logger.info(f"{key}")
+
+        raise RuntimeError(
+            f"an inconsistent number of keys in state dict of new qat and original model: {len(a)} vs {len(b)}"
+        )
 
     if is_eager_mode:
         for n_k, o_k in zip(