remove deprecated silicon quantization

Summary: EZ

Reviewed By: zhanghang1989

Differential Revision: D29000628

fbshipit-source-id: f954214dfe3a989fc145663f8bb1870812e78ce7

d2go/modeling/quantization.py

@@ -113,33 +113,12 @@ def add_quantization_default_configs(_C):
     _C.QUANTIZATION.PTQ.CALIBRATION_NUM_IMAGES = 1
     _C.QUANTIZATION.PTQ.CALIBRATION_FORCE_ON_GPU = False
 
-    # deprecated
-    _C.QUANTIZATION.SILICON_QAT = CfgNode()
-    _C.QUANTIZATION.SILICON_QAT.ENABLED = False
-
     # register deprecated and renamed keys
     _C.register_deprecated_key("QUANTIZATION.QAT.LOAD_PRETRAINED")
     _C.register_renamed_key("QUANTIZATION.QAT.BACKEND", "QUANTIZATION.BACKEND")
     _C.register_deprecated_key("QUANTIZATION.ENABLE_CUSTOM_QSCHEME")
-
-
-@contextlib.contextmanager
-def silicon_qat_build_model_context(cfg):
-    mock_ctx_managers = []
-    if cfg.QUANTIZATION.SILICON_QAT.ENABLED:
-        from mobile_cv.silicon_pytorch_qat.replace_op import mock_quant_ops
-
-        mock_ctx_managers.extend(
-            [
-                mock_quant_ops(quant_op="quant_add"),
-                mock_quant_ops(quant_op="quant_fbb_convbnrelu"),
-            ]
-        )
-
-    with contextlib.ExitStack() as stack:
-        for mgr in mock_ctx_managers:
-            stack.enter_context(mgr)
-        yield
+    _C.register_deprecated_key("QUANTIZATION.SILICON_QAT")
+    _C.register_deprecated_key("QUANTIZATION.SILICON_QAT.ENABLED")
 
 
 # TODO: model.to(device) might not work for detection meta-arch, this function is the
@@ -168,26 +147,29 @@ def _cast_detection_model(model, device):
 
 
 def add_d2_quant_mapping(mappings):
-    """ HACK: Add d2 specific module mapping for eager model quantization
-    """
+    """HACK: Add d2 specific module mapping for eager model quantization"""
     import torch.quantization.quantization_mappings as qm
+
     for k, v in mappings.items():
         if k not in qm.get_default_static_quant_module_mappings():
             qm.DEFAULT_STATIC_QUANT_MODULE_MAPPINGS[k] = v
         if k not in qm.get_default_qat_module_mappings():
             qm.DEFAULT_QAT_MODULE_MAPPINGS[k] = v
 
+
 # The `mock_quantization_type` decorate may not be needed anymore to unify
 # detectron2.layers modules and torch.nn modules since Pytorch 1.5. See comments on D23790034.
 def mock_quantization_type(quant_func):
-    import mock
     import builtins
     import functools
+
     import detectron2.layers as d2l
+    import mock
 
     type_mapping = {d2l.Linear: torch.nn.Linear}
     from d2go.utils.misc import check_version
-    if check_version(torch, '1.7.2', warning_only=True):
+
+    if check_version(torch, "1.7.2", warning_only=True):
         add_d2_quant_mapping(type_mapping)
 
     real_type = builtins.type
@@ -270,13 +252,14 @@ def default_prepare_for_quant(cfg, model):
 
     return model
 
+
 def default_prepare_for_quant_convert(cfg, model):
     return torch.quantization.quantize_fx.convert_fx(model)
 
 
 @mock_quantization_type
 def post_training_quantize(cfg, model, data_loader):
-    """ Calibrate a model, convert it to a quantized pytorch model """
+    """Calibrate a model, convert it to a quantized pytorch model"""
     model = copy.deepcopy(model)
     model.eval()
     # TODO: check why some parameters will have gradient
@@ -372,7 +355,9 @@ def setup_qat_model(cfg, model, enable_fake_quant=False, enable_observer=False):
     assert len(new_state_dict_non_observer_keys) == len(original_state_dict_shapes)
 
     if cfg.QUANTIZATION.EAGER_MODE:
-        for n_k, o_k in zip(new_state_dict_non_observer_keys, original_state_dict_shapes):
+        for n_k, o_k in zip(
+            new_state_dict_non_observer_keys, original_state_dict_shapes
+        ):
             assert new_state_dict_shapes[n_k] == original_state_dict_shapes[o_k]
         # _q_state_dict_map will store
         model._non_qat_to_qat_state_dict_map = dict(

d2go/runner/default_runner.py

@@ -36,7 +36,6 @@ from d2go.modeling.model_freezing_utils import (
 from d2go.modeling.quantization import (
     QATCheckpointer,
     setup_qat_model,
-    silicon_qat_build_model_context,
 )
 from d2go.optimizer import build_optimizer_mapper
 from d2go.utils.flop_calculator import add_print_flops_callback
@@ -151,7 +150,7 @@ class BaseRunner(object):
         torch._C._log_api_usage_once(identifier)
 
     def _initialize(self, cfg):
-        """ Runner should be initialized in the sub-process in ddp setting """
+        """Runner should be initialized in the sub-process in ddp setting"""
         if getattr(self, "_has_initialized", False):
             logger.warning("Runner has already been initialized, skip initialization.")
             return
@@ -174,10 +173,13 @@ class BaseRunner(object):
         from detectron2.config import get_cfg as get_d2_cfg
 
         cfg = get_d2_cfg()
-        cfg = CfgNode.cast_from_other_class(cfg)  # upgrade from D2's CfgNode to D2Go's CfgNode
+        cfg = CfgNode.cast_from_other_class(
+            cfg
+        )  # upgrade from D2's CfgNode to D2Go's CfgNode
 
         try:
             from d2go.runner import get_unintentional_added_configs_during_runner_import
+
             for key in get_unintentional_added_configs_during_runner_import():
                 cfg.register_deprecated_key(key)
         except ImportError:
@@ -242,10 +244,8 @@ class Detectron2GoRunner(BaseRunner):
         # build_model might modify the cfg, thus clone
         cfg = cfg.clone()
 
-        # silicon_qat_build_model_context is deprecated
-        with silicon_qat_build_model_context(cfg):
-            model = build_model(cfg)
-            model_ema.may_build_model_ema(cfg, model)
+        model = build_model(cfg)
+        model_ema.may_build_model_ema(cfg, model)
 
         if cfg.MODEL.FROZEN_LAYER_REG_EXP:
             set_requires_grad(model, cfg.MODEL.FROZEN_LAYER_REG_EXP, False)