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Remove RESTORE_ONNX_MODEL_INITIALIZERS and make it run default #3119

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Jun 29, 2024
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Remove RESTORE_ONNX_MODEL_INITIALIZERS and make it run default #3119

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7 changes: 1 addition & 6 deletions TrainingExtensions/torch/src/python/aimet_torch/onnx_utils.py
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Expand Up @@ -68,10 +68,6 @@
# this flag in such circumstances.
EXPORT_TO_ONNX_DIRECT = False

# Flag to enable restoring pruned initializers in ONNX graph
# By default, the flag is disabled because it is rare case we should restore initializers in most cases
RESTORE_ONNX_MODEL_INITIALIZERS = False

# runs the second pass of markers for non-leaf torch module and updates names of onnx ops belonging to
# non-leaf pytorch module
update_all_onnx_nodes_name = True
Expand Down Expand Up @@ -1125,8 +1121,7 @@ def load_simply_onnx_model(cls, filepath) -> onnx.ModelProto:
:return: Onnx model with optional simply pass
"""
onnx_model = onnx.load(filepath)
if RESTORE_ONNX_MODEL_INITIALIZERS:
onnx_model = restore_onnx_graph_initializers(onnx_model, inplace=True)
onnx_model = restore_onnx_graph_initializers(onnx_model, inplace=True)

if simplify_onnx_model:
onnx_model_simplified, check = onnxsim.simplify(onnx_model)
Expand Down
3 changes: 1 addition & 2 deletions TrainingExtensions/torch/src/python/aimet_torch/quantsim.py
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Expand Up @@ -642,8 +642,7 @@ def export_onnx_model_and_encodings(path: str, filename_prefix: str, original_mo
else:
kwargs = onnx_export_args
torch.onnx.export(original_model, dummy_input, onnx_path, **kwargs)
if onnx_utils.RESTORE_ONNX_MODEL_INITIALIZERS:
save_initializer_restored_onnx_graph(onnx_path, onnx_path)
save_initializer_restored_onnx_graph(onnx_path, onnx_path)
else:
# Create onnx model and obtain node to i/o tensor name map
OnnxSaver.create_onnx_model_with_pytorch_layer_names(onnx_path, original_model, dummy_input, is_conditional,
Expand Down
2 changes: 0 additions & 2 deletions TrainingExtensions/torch/test/python/test_onnx_utils.py
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Expand Up @@ -899,7 +899,6 @@ def test_set_node_name_for_multiple_p_relu_model(self, num_parameters):
with tempfile.TemporaryDirectory() as tmp_dir:
onnx_path = f"{tmp_dir}/multiple_p_relu_model.onnx"

onnx_utils.RESTORE_ONNX_MODEL_INITIALIZERS = True
onnx_utils.OnnxSaver.set_node_names(onnx_path, model, dummy_input)
onnx_model = onnx.load(onnx_path)

Expand All @@ -913,7 +912,6 @@ def test_set_node_name_for_multiple_p_relu_model(self, num_parameters):
assert name in valid_param_set

self.check_onnx_node_name_uniqueness(onnx_model)
onnx_utils.RESTORE_ONNX_MODEL_INITIALIZERS = False

def test_save_initializer_restored_onnx_graph(self):
model = MultiplePReluModel()
Expand Down
2 changes: 0 additions & 2 deletions TrainingExtensions/torch/test/python/test_quantizer.py
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Expand Up @@ -2533,7 +2533,6 @@ def test_export_to_onnx_for_multiple_p_relu_model(self, num_parameters, config_f
sim.compute_encodings(lambda m, _: m(dummy_input), None)
filename_prefix = "multiple_p_relu_model"

onnx_utils.RESTORE_ONNX_MODEL_INITIALIZERS = True
sim.export(tmp_dir, filename_prefix, dummy_input)
with open(f"{tmp_dir}/{filename_prefix}.encodings") as encodings_file:
encodings = json.load(encodings_file)
Expand All @@ -2547,7 +2546,6 @@ def test_export_to_onnx_for_multiple_p_relu_model(self, num_parameters, config_f
assert len(param_encodings[param_name]) == num_parameters
else: # Per-tensor
assert len(param_encodings[param_name]) == 1
onnx_utils.RESTORE_ONNX_MODEL_INITIALIZERS = False

def test_save_encodings_to_json(self):
model = ModelWithTwoInputsOneToAdd()
Expand Down
2 changes: 0 additions & 2 deletions TrainingExtensions/torch/test/python/v2/ab_test/test_quantizer_.py
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Expand Up @@ -2334,7 +2334,6 @@ def test_export_to_onnx_for_multiple_p_relu_model(self, num_parameters, config_f
sim.compute_encodings(lambda m, _: m(dummy_input), None)
filename_prefix = "multiple_p_relu_model"

onnx_utils.RESTORE_ONNX_MODEL_INITIALIZERS = True
sim.export(tmp_dir, filename_prefix, dummy_input)
with open(f"{tmp_dir}/{filename_prefix}.encodings") as encodings_file:
encodings = json.load(encodings_file)
Expand All @@ -2348,7 +2347,6 @@ def test_export_to_onnx_for_multiple_p_relu_model(self, num_parameters, config_f
assert len(param_encodings[param_name]) == num_parameters
else: # Per-tensor
assert len(param_encodings[param_name]) == 1
onnx_utils.RESTORE_ONNX_MODEL_INITIALIZERS = False

def test_save_encodings_to_json(self):
model = ModelWithTwoInputsOneToAdd()
Expand Down
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