replace custom_convert_fx API with convert callback

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/379

This diff ~~~prototypes~~~ implements replacing the `custom_convert_fx` API with a callback.

Reviewed By: LiamZhuuu

Differential Revision: D39859228

fbshipit-source-id: 34719d1758c4afa7e47930c12d3443813d3f4546

d2go/modeling/meta_arch/rcnn.py

@@ -61,9 +61,6 @@ class GeneralizedRCNN(_GeneralizedRCNN):
     def custom_prepare_fx(self, cfg, is_qat, example_input=None):
         return default_rcnn_custom_prepare_fx(self, cfg, is_qat, example_input)
 
-    def custom_convert_fx(self, cfg):
-        return default_rcnn_custom_convert_fx(self, cfg)
-
     def _cast_model_to_device(self, device):
         return _cast_detection_model(self, device)
 
@@ -309,7 +306,10 @@ def default_rcnn_custom_prepare_fx(self, cfg, is_qat, example_input=None):
 
     _fx_quant_prepare(model, cfg, is_qat, example_input)
 
-    return model
+    def convert_fx_callback(model):
+        return default_rcnn_custom_convert_fx(model, cfg)
+
+    return model, convert_fx_callback
 
 
 def _fx_quant_prepare(self, cfg, is_qat, example_input):

d2go/quantization/modeling.py

@@ -29,6 +29,8 @@ else:
 
 logger = logging.getLogger(__name__)
 
+_CONVERT_FX_CALLBACK_ATTRIBUTE = "_convert_fx_callback"
+
 
 def _is_observer_key(state_dict_key):
     observer_keys = ["activation_post_process", "weight_fake_quant"]
@@ -295,11 +297,7 @@ def default_custom_prepare_fx(cfg, model, is_qat, example_input=None):
         model = prepare_fx(model, qconfig_dict, (example_input,))
 
     logger.info("Setup the model with qconfig:\n{}".format(qconfig))
-    return model
-
-
-def default_custom_convert_fx(cfg, model):
-    return convert_fx(model)
+    return model, convert_fx
 
 
 def prepare_fake_quant_model(cfg, model, is_qat, example_input=None):
@@ -333,12 +331,28 @@ def prepare_fake_quant_model(cfg, model, is_qat, example_input=None):
             model = fuse_utils.swap_modules(model)
 
         if hasattr(model, "custom_prepare_fx"):
-            model = model.custom_prepare_fx(cfg, is_qat, example_input)
+            ret = model.custom_prepare_fx(cfg, is_qat, example_input)
+            if not (isinstance(ret, tuple) and len(ret) == 2):
+                raise ValueError(
+                    "`custom_prepare_fx` requires return model and convert_callback"
+                )
+            model, convert_fx_callback = ret
         else:
             logger.info(
                 "Using default implementation for custom_prepare_fx (FX graph mode)"
             )
-            model = default_custom_prepare_fx(cfg, model, is_qat, example_input)
+            model, convert_fx_callback = default_custom_prepare_fx(
+                cfg, model, is_qat, example_input
+            )
+
+        # HACK: store the convert_callback function as model attribute, which can be
+        # later accessed to convert fake quant model to quantized model. We'll find a
+        # better place to store this.
+        if hasattr(model, _CONVERT_FX_CALLBACK_ATTRIBUTE):
+            raise AttributeError(
+                f"{_CONVERT_FX_CALLBACK_ATTRIBUTE} is already set in model: {model}"
+            )
+        setattr(model, _CONVERT_FX_CALLBACK_ATTRIBUTE, convert_fx_callback)
 
     return model
 
@@ -352,10 +366,15 @@ def convert_to_quantized_model(cfg, fp32_model):
         int8_model = convert(fp32_model, inplace=False)
     else:
         # FX graph mode quantization
-        if hasattr(fp32_model, "custom_convert_fx"):
-            int8_model = fp32_model.custom_convert_fx(cfg)
-        else:
-            int8_model = convert_fx(fp32_model)
+        if not hasattr(fp32_model, _CONVERT_FX_CALLBACK_ATTRIBUTE):
+            raise AttributeError(
+                f"Can't find {_CONVERT_FX_CALLBACK_ATTRIBUTE} in model, please check "
+                f"`prepare_fake_quant_model` has been called: {fp32_model}"
+            )
+
+        convert_fx_callback = getattr(fp32_model, _CONVERT_FX_CALLBACK_ATTRIBUTE)
+        int8_model = convert_fx_callback(fp32_model)
+
     return int8_model
 
 
@@ -617,3 +636,15 @@ def _reset_qat_data_loader_if_needed(cfg, trainer, build_loader_func):
         # This method assumes the data loader can be replaced from trainer
         assert trainer.__class__ == SimpleTrainer
         trainer.reset_data_loader(lambda: build_loader_func(loader_cfg))
+
+
+def forward_custom_prepare_fx(root, sub_module_name, orig_ret):
+    """Helper function to forward return of `custom_prepare_fx` from sub module"""
+    new_sub_module, callback = orig_ret
+    setattr(root, sub_module_name, new_sub_module)
+
+    def new_callback(m):
+        setattr(m, sub_module_name, callback(getattr(m, sub_module_name)))
+        return m
+
+    return root, new_callback

d2go/runner/callbacks/quantization.py

@@ -8,6 +8,7 @@ from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
 
 import torch
 from d2go.config import CfgNode
+from d2go.quantization.modeling import prepare_fake_quant_model
 from d2go.utils.misc import mode
 from mobile_cv.arch.quantization.observer import update_stat as observer_update_stat
 from pytorch_lightning import LightningModule, Trainer
@@ -99,7 +100,10 @@ def checkpoint_has_prepared(checkpoint: Dict[str, Any]) -> bool:
 def maybe_prepare_for_quantization(model: LightningModule, checkpoint: Dict[str, Any]):
     if checkpoint_has_prepared(checkpoint) and not hasattr(model, PREPARED):
         # model has been prepared for QAT before saving into checkpoint
-        setattr(model, PREPARED, _deepcopy(model).custom_prepare_fx(is_qat=True))
+        copied = _deepcopy(model)
+        prepared = prepare_fake_quant_model(copied.cfg, copied.model, is_qat=True)
+        copied.model = prepared
+        setattr(model, PREPARED, copied)
 
 
 class QuantizationMixin(ABC):
@@ -162,8 +166,14 @@ class QuantizationMixin(ABC):
             The prepared Module to be used for quantized aware training.
         """
         is_qat = isinstance(self, QuantizationAwareTraining)
-        if hasattr(root, "custom_prepare_fx"):
-            return root.custom_prepare_fx(is_qat)
+        self._convert_fx_callback = None
+        if hasattr(root.model, "custom_prepare_fx"):
+            prepared, convert_fx_callback = root.model.custom_prepare_fx(
+                root.cfg, is_qat
+            )
+            self._convert_fx_callback = convert_fx_callback
+            root.model = prepared
+            return root
         prep_fn = prepare_qat_fx if is_qat else prepare_fx
         old_attrs = {
             attr: rgetattr(root, attr) for attr in attrs if rhasattr(root, attr)
@@ -203,8 +213,8 @@ class QuantizationMixin(ABC):
         Returns:
             The quantized model.
         """
-        if hasattr(root, "custom_convert_fx"):
-            return root.custom_convert_fx()
+        if self._convert_fx_callback is not None:
+            return self._convert_fx_callback(root)
         old_attrs = {
             attr: rgetattr(root, attr) for attr in attrs if rhasattr(root, attr)
         }

d2go/runner/lightning_task.py

@@ -17,10 +17,6 @@ from d2go.data.utils import update_cfg_if_using_adhoc_dataset
 from d2go.modeling.api import build_meta_arch
 from d2go.modeling.model_freezing_utils import set_requires_grad
 from d2go.optimizer import build_optimizer_mapper
-from d2go.quantization.modeling import (
-    default_custom_convert_fx,
-    default_custom_prepare_fx,
-)
 from d2go.runner.callbacks.quantization import maybe_prepare_for_quantization, PREPARED
 from d2go.runner.default_runner import (
     _get_tbx_writer,
@@ -505,26 +501,6 @@ class DefaultTask(pl.LightningModule):
                 self.ema_state.load_state_dict(checkpointed_state["model_ema"])
                 rank_zero_info("Loaded EMA state from checkpoint.")
 
-    # TODO: remove custom_prepare_fx/custom_convert_fx from LightningModule
-
-    def custom_prepare_fx(self, is_qat) -> pl.LightningModule:
-        if hasattr(self.model, "custom_prepare_fx"):
-            self.model = self.model.custom_prepare_fx(
-                self.cfg, is_qat, example_input=None
-            )
-        else:
-            self.model = default_custom_prepare_fx(
-                self.cfg, self.model, is_qat, example_input=None
-            )
-        return self
-
-    def custom_convert_fx(self) -> pl.LightningModule:
-        if hasattr(self.model, "custom_convert_fx"):
-            self.model = self.model.custom_convert_fx(self.cfg)
-        else:
-            self.model = default_custom_convert_fx(self.cfg, self.model)
-        return self
-
 
 # TODO(T123654122): subclass of DefaultTask will be refactored
 class GeneralizedRCNNTask(DefaultTask):

d2go/utils/misc.py

@@ -6,7 +6,7 @@ import logging
 import os
 import warnings
 from contextlib import contextmanager
-from typing import Any, Dict, Iterator
+from typing import Any, Callable, Dict, Iterator, Optional
 
 # @manual=//vision/fair/detectron2/detectron2:detectron2
 import detectron2.utils.comm as comm
@@ -129,16 +129,27 @@ def _log_api_usage(identifier: str):
     torch._C._log_api_usage_once("d2go." + identifier)
 
 
-def inplace_delegate(self, api_name, sub_module_name, *args, **kwargs):
+def inplace_delegate(
+    self,
+    api_name: str,
+    sub_module_name: str,
+    setter_fn: Optional[Callable],
+    *args,
+    **kwargs,
+) -> Any:
     """Helper function to delegate API calls to its submodule"""
 
     sub_module = getattr(self, sub_module_name)
     api_name = f"delegate_{api_name}"
     if hasattr(sub_module, api_name):
         func = getattr(sub_module, api_name)
-        # Assume the return of `func` will replace the submodule
-        setattr(self, sub_module_name, func(*args, **kwargs))
-        return self
+        orig_ret = func(*args, **kwargs)
+        if setter_fn is None:
+            # Assume the return of `func` will replace the submodule
+            setattr(self, sub_module_name, orig_ret)
+            return self
+        else:
+            return setter_fn(self, sub_module_name, orig_ret)
     else:
         raise RuntimeError(
             f"It seems the {sub_module_name} doesn't implement {api_name},"

d2go/utils/testing/meta_arch_helper.py

@@ -59,11 +59,12 @@ class DetMetaArchForTest(torch.nn.Module):
             {"": set_backend_and_create_qconfig(cfg, is_train=self.training)},
             example_inputs,
         )
-        return self
 
-    def custom_convert_fx(self, cfg):
-        self.avgpool = convert_fx(self.avgpool)
-        return self
+        def convert_fx_callback(model):
+            model.avgpool = convert_fx(model.avgpool)
+            return model
+
+        return self, convert_fx_callback
 
 
 def get_det_meta_arch_cfg(cfg, dataset_name, output_dir):

tests/runner/test_runner_lightning_task.py

@@ -214,17 +214,19 @@ class TestLightningTask(unittest.TestCase):
                     example_inputs,
                     self.custom_config_dict,
                 )
-                return self
 
-            def custom_convert_fx(self, cfg):
-                self.avgpool = convert_fx(
-                    self.avgpool, convert_custom_config=self.custom_config_dict
-                )
-                return self
+                def convert_fx_callback(model):
+                    model.avgpool = convert_fx(
+                        model.avgpool, convert_custom_config=model.custom_config_dict
+                    )
+                    return model
+
+                return self, convert_fx_callback
 
         cfg = self._get_cfg(tmp_dir)
         cfg.MODEL.META_ARCHITECTURE = "QuantizableDetMetaArchForTest"
         cfg.QUANTIZATION.QAT.ENABLED = True
+        cfg.QUANTIZATION.EAGER_MODE = False
         task = GeneralizedRCNNTask(cfg)
 
         callbacks = [