[quant][graphmode][fx] Support non_traceable_module/module_class (#46298)

jerryzh168 · facebook-github-bot · commit a06b95b2badc · 2020-10-19T18:50:08.000-07:00
Summary: Pull Request resolved: #46298 Allow user to specify a list of qualified names for non traceable submodule or type of the non traceable submodule See quantize_fx.py for api Test Plan: Imported from OSS Reviewed By: vkuzo Differential Revision: D24294210 fbshipit-source-id: eb1e309065e3dfbf31e63507aaed73587f0dae29
diff --git a/test/quantization/test_quantize_fx.py b/test/quantization/test_quantize_fx.py
@@ -761,6 +761,59 @@ def forward(self, x):
             ref_res = ref_m(data)
             self.assertEqual(res, ref_res)
 
+    @skipIfNoFBGEMM
+    def test_non_traceable_module(self):
+        class NonTraceable(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x):
+                for k in x.keys():
+                    print(x[k])
+                return x
+
+        class NonTraceable2(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x):
+                # data dependent control flow is not traceable
+                for i in x:
+                    print(i)
+                return x
+
+        class M(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.m1 = NonTraceable()
+                self.m2 = NonTraceable2()
+
+            def forward(self, x):
+                x = self.m1(x)
+                x = self.m2(x)
+                return x
+
+        m = M().eval()
+        qconfig_dict = {"": default_qconfig}
+        prepare_custom_config_dict = {
+            "non_traceable_module_name": [
+                "m1"
+            ],
+            "non_traceable_module_class": [
+                NonTraceable2
+            ]
+        }
+        m = prepare_fx(
+            m, qconfig_dict,
+            prepare_custom_config_dict=prepare_custom_config_dict)
+
+        node_occurrence = {
+            ns.call_module(NonTraceable) : 1,
+            ns.call_module(NonTraceable2) : 1,
+        }
+        # make sure these modules are not traced
+        self.checkGraphModuleNodes(m, expected_node_occurrence=node_occurrence)
+
 class TestQuantizeFxOps(QuantizationTestCase):
     """Unit tests for individual ops
     """
diff --git a/torch/quantization/quantize_fx.py b/torch/quantization/quantize_fx.py
@@ -1,6 +1,5 @@
 import torch
 from torch.fx import GraphModule  # type: ignore
-from torch.fx import symbolic_trace  # type: ignore
 from torch.fx.symbolic_trace import Tracer  # type: ignore
 from .fx import Fuser  # noqa: F401
 from .fx import Quantizer  # noqa: F401
@@ -24,16 +23,16 @@ def _fuse_fx(graph_module, inplace=False):
     return fuser.fuse(graph_module, inplace)
 
 class CustomTracer(Tracer):
-    def __init__(self, standalone_modules, custom_module_classes):
+    def __init__(self, skipped_module_names, skipped_module_classes):
         super().__init__()
-        self.standalone_modules = standalone_modules
-        self.custom_module_classes = custom_module_classes
+        self.skipped_module_names = skipped_module_names
+        self.skipped_module_classes = skipped_module_classes
 
     def is_leaf_module(self, m, module_qualified_name):
         return (m.__module__.startswith('torch.nn') and
                 not isinstance(m, torch.nn.Sequential)) or \
-            module_qualified_name in self.standalone_modules or \
-            type(m) in self.custom_module_classes
+            module_qualified_name in self.skipped_module_names or \
+            type(m) in self.skipped_module_classes
 
 
 def _prepare_fx(model, qconfig_dict, inplace, prepare_custom_config_dict=None, is_standalone_module=False):
@@ -50,17 +49,19 @@ def _prepare_fx(model, qconfig_dict, inplace, prepare_custom_config_dict=None, i
     if prepare_custom_config_dict is None:
         prepare_custom_config_dict = {}
 
+    skipped_module_names = prepare_custom_config_dict.get("non_traceable_module_name", [])
+    skipped_module_classes = prepare_custom_config_dict.get("non_traceable_module_class", [])
+
     # symbolically trace the model
-    if is_standalone_module:
-        # standlone module is traced before quantizing standalone modules
-        graph_module = symbolic_trace(model)
-    else:
-        standalone_modules = prepare_custom_config_dict.get('standalone_module_name', [])
+    if not is_standalone_module:
+        # standalone module and custom module config are applied in top level module
+        standalone_module_names = prepare_custom_config_dict.get('standalone_module_name', [])
+        skipped_module_names += standalone_module_names
         custom_module_config = prepare_custom_config_dict.get('float_to_observed_custom_module_class', {})
         custom_module_classes = list(custom_module_config.keys())
-        # skipping tracing standalone modules when tracing top level module
-        tracer = CustomTracer(standalone_modules, custom_module_classes)
-        graph_module = GraphModule(model, tracer.trace(model))
+        skipped_module_classes += custom_module_classes
+    tracer = CustomTracer(skipped_module_names, skipped_module_classes)
+    graph_module = GraphModule(model, tracer.trace(model))
     graph_module = _fuse_fx(graph_module, inplace)
     quantizer = Quantizer()
     return quantizer.prepare(
@@ -156,7 +157,17 @@ def prepare_fx(model, qconfig_dict, inplace=False, prepare_custom_config_dict=No
         # float custom module to observed custom module
         "float_to_observed_custom_module_class": {
            CustomModule: ObservedCustomModule
-        }
+        },
+
+        # the qualified names for the submodule that are not symbolically traceable
+        "non_traceable_module_name": [
+           "non_traceable_module"
+        ],
+
+        # the module classes that are not symbolically traceable
+        "non_traceable_module_class": [
+           NonTraceableModule
+        ]
       }
 
 
