[quant][pt2] Fix and rename move_model_to_eval (#108891)

test/inductor/test_inductor_freezing.py

@@ -629,7 +629,7 @@ def test_functional_constant_folding_after_dynamo_export(self):
             prepare_model(*example_inputs)
 
             convert_model = convert_pt2e(prepare_model)
-            convert_model.eval()
+            torch.ao.quantization.move_exported_model_to_eval(convert_model)
             compiler_model = compile_fx(convert_model, example_inputs)
 
             # First Run

test/inductor/test_mkldnn_pattern_matcher.py

@@ -112,7 +112,8 @@ def _test_common(
                 quantizer.set_global(xiq.get_default_x86_inductor_quantization_config())
                 prepare_model = prepare_pt2e(export_model, quantizer)
                 prepare_model(*inputs)
-                convert_model = convert_pt2e(prepare_model).eval()
+                convert_model = convert_pt2e(prepare_model)
+                torch.ao.quantization.move_exported_model_to_eval(convert_model)
                 _ = torch.compile(convert_model)(*inputs)
                 self.assertEqual(
                     counters["inductor"]["pattern_matcher_count"], matcher_count

test/quantization/pt2e/test_quantize_pt2e.py

@@ -361,7 +361,7 @@ def _verify_symmetric_qnnpack_qat_numerics(
         self.assertEqual(after_prepare_result_pt2e, after_prepare_result_fx)
 
         if verify_convert:
-            torch.ao.quantization.move_model_to_eval(model_pt2e)
+            torch.ao.quantization.move_exported_model_to_eval(model_pt2e)
             model_pt2e = convert_pt2e(model_pt2e)
             quant_result_pt2e = model_pt2e(*example_inputs)
             model_fx.eval()
@@ -2392,7 +2392,7 @@ def forward(self, x, y):
             non_ref_node_occurrence
         )
 
-    def test_move_model_to_eval(self):
+    def test_move_exported_model_to_eval(self):
         class M(torch.nn.Module):
             def __init__(self):
                 super().__init__()
@@ -2404,8 +2404,6 @@ def forward(self, x):
         example_inputs = (torch.randn(1),)
         m = M().train()
         m = capture_pre_autograd_graph(m, example_inputs)
-        m.graph.eliminate_dead_code()
-        m.recompile()
 
         # Assert that dropout op exists and is in train mode
         dropout_node = None
@@ -2417,13 +2415,43 @@ def forward(self, x):
         self.assertTrue(dropout_node.args[2])
 
         # Do the subgraph rewriting
-        torch.ao.quantization.move_model_to_eval(m)
+        torch.ao.quantization.move_exported_model_to_eval(m)
 
         # Assert that dropout op is now replaced with a clone op
         targets = [n.target for n in m.graph.nodes]
         self.assertTrue(torch.ops.aten.clone.default in targets)
         self.assertTrue(torch.ops.aten.native_dropout.default not in targets)
 
+    def test_disallow_eval_train(self):
+        m = TestHelperModules.ConvWithBNRelu(relu=True)
+        example_inputs = (torch.rand(3, 3, 5, 5),)
+
+        # Before export: this is OK
+        m.eval()
+        m.train()
+
+        # After export: this is not OK
+        m = capture_pre_autograd_graph(m, example_inputs)
+        with self.assertRaises(NotImplementedError):
+            m.eval()
+        with self.assertRaises(NotImplementedError):
+            m.train()
+
+        # After prepare: still not OK
+        quantizer = XNNPACKQuantizer()
+        m = prepare_qat_pt2e(m, quantizer)
+        with self.assertRaises(NotImplementedError):
+            m.eval()
+        with self.assertRaises(NotImplementedError):
+            m.train()
+
+        # After convert: still not OK
+        m = convert_pt2e(m)
+        with self.assertRaises(NotImplementedError):
+            m.eval()
+        with self.assertRaises(NotImplementedError):
+            m.train()
+
 
 @skipIfNoQNNPACK
 class TestQuantizePT2EOps(QuantizationTestCase):

torch/ao/quantization/__init__.py

@@ -12,7 +12,7 @@
 from .quantize import *  # noqa: F403
 from .quantize_jit import *  # noqa: F403
 from .stubs import *  # noqa: F403
-from .pt2e.utils import move_model_to_eval
+from .pt2e.utils import move_exported_model_to_eval
 from typing import Union, List, Callable, Tuple, Optional
 from torch import Tensor
 import torch
@@ -120,7 +120,7 @@
     "get_quantized_operator",
     "get_static_quant_module_class",
     "load_observer_state_dict",
-    "move_model_to_eval",
+    "move_exported_model_to_eval",
     "no_observer_set",
     "per_channel_weight_observer_range_neg_127_to_127",
     "prepare",

torch/ao/quantization/pt2e/utils.py

@@ -1,3 +1,6 @@
+import operator
+import types
+
 import torch
 from torch.fx import (
     GraphModule,
@@ -6,14 +9,13 @@
 from torch.fx.subgraph_rewriter import replace_pattern_with_filters
 import torch.nn.functional as F
 from torch.nn.utils.fusion import fuse_conv_bn_weights
-import operator
 from typing import Any, Callable, Dict, Optional, Tuple, List, Union
 from torch.utils._pytree import LeafSpec
 
 __all__ = [
     "fold_bn_weights_into_conv_node",
     "get_aten_graph_module",
-    "move_model_to_eval",
+    "move_exported_model_to_eval",
     "remove_tensor_overload_for_qdq_ops",
 ]
 
@@ -200,6 +202,10 @@ def _replace_dropout_for_eval(m: GraphModule):
 
     See https://github.com/pytorch/pytorch/issues/103681.
     """
+    # Needed to ensure subgraph matches are self-contained
+    m.graph.eliminate_dead_code()
+    m.recompile()
+
     def dropout_train(x):
         return F.dropout(x, p=0.5, training=True)
 
@@ -390,9 +396,9 @@ def replacement(x_i8, scale, zero_point, quant_min, quant_max):
         node.args = new_args
     return gm
 
-# TODO: also support move_model_to_train
+# TODO: also support move_exported_model_to_train
 # TODO: also support standalone batchnorm
-def move_model_to_eval(m: GraphModule):
+def move_exported_model_to_eval(m: GraphModule):
     """
     Move an exported GraphModule to eval mode.
 
@@ -401,3 +407,20 @@ def move_model_to_eval(m: GraphModule):
     """
     _replace_dropout_for_eval(m)
     return m
+
+# TODO: Handle this in export itself and don't wrap the model in another GraphModule
+# in prepare and convert
+def _disallow_eval_train(model: GraphModule):
+    """
+    Disallow calling `model.train()` or `model.eval()` on the given GraphModule.
+    This is useful for exported models, where these methods don't actually behave as expected.
+    """
+    def _train(self, mode: bool = True):
+        raise NotImplementedError("Calling train() is not supported yet.")
+
+    def _eval(self, mode: bool = True):
+        raise NotImplementedError("Calling eval() is not supported yet.")
+
+    model.train = types.MethodType(_train, model)  # type: ignore[method-assign]
+    model.eval = types.MethodType(_eval, model)  # type: ignore[method-assign]
+    return model

torch/ao/quantization/quantize_pt2e.py

@@ -9,6 +9,7 @@
 from .pt2e.utils import (
     _get_node_name_to_scope,
     _fuse_conv_bn_,
+    _disallow_eval_train,
 )
 from .pt2e.representation import reference_representation_rewrite
 from .fx.prepare import prepare as fx_prepare
@@ -70,6 +71,7 @@ def prepare_pt2e(
     propagate_annotation(model)
     model = prepare(model, node_name_to_scope, is_qat=False)
     model.meta.update(original_graph_meta)
+    model = _disallow_eval_train(model)
     return model
 
 def prepare_qat_pt2e(
@@ -87,6 +89,7 @@ def prepare_qat_pt2e(
     _fuse_conv_bn_qat(model)
     model = prepare(model, node_name_to_scope, is_qat=True)
     model.meta.update(original_graph_meta)
+    model = _disallow_eval_train(model)
     return model
 
 def convert_pt2e(
@@ -100,4 +103,5 @@ def convert_pt2e(
         model = reference_representation_rewrite(model)
 
     model.meta.update(original_graph_meta)
+    model = _disallow_eval_train(model)
     return model