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Fix FP16 weight detection in NeuralNetworkBuilder.make_updatable (only first layer was checked) #2858

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Fix FP16 weight detection in NeuralNetworkBuilder.make_updatable (only first layer was checked) #2858

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15 changes: 8 additions & 7 deletions coremltools/models/neural_network/builder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -866,8 +866,9 @@ def _check_fp16_weight_param_exists(self, layers):

# uniDirectionalLSTM Layers
elif layer_type == "uniDirectionalLSTM":
return self._check_fp16_weight_params_lstms(lstm_wp=layer.uniDirectionalLSTM.weightParams,
has_peephole=layer.uniDirectionalLSTM.params.hasPeepholeVectors)
if self._check_fp16_weight_params_lstms(lstm_wp=layer.uniDirectionalLSTM.weightParams,
has_peephole=layer.uniDirectionalLSTM.params.hasPeepholeVectors):
return True

# biDirectionalLSTM Layers
elif layer_type == "biDirectionalLSTM":
Expand All @@ -878,19 +879,19 @@ def _check_fp16_weight_param_exists(self, layers):

# branch Layers
elif layer_type == "branch":
if len(layer.branch.ifBranch.float16Value) > 0:
if self._check_fp16_weight_param_exists(layer.branch.ifBranch.layers):
return True
if len(layer.branch.elseBranch.float16Value) > 0:
if self._check_fp16_weight_param_exists(layer.branch.elseBranch.layers):
return True

# loop Layers
elif layer_type == "loop":
if len(layer.loop.conditionNetwork.float16Value) > 0:
if self._check_fp16_weight_param_exists(layer.loop.conditionNetwork.layers):
return True
if len(layer.loop.bodyNetwork.float16Value) > 0:
if self._check_fp16_weight_param_exists(layer.loop.bodyNetwork.layers):
return True

return False
return False

def make_updatable(self, trainables):
"""
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2 changes: 1 addition & 1 deletion coremltools/test/pipeline/test_model_updatable.py
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Original file line number Diff line number Diff line change
Expand Up @@ -652,7 +652,7 @@ def test_nn_partial_fp16_make_updatable_quantized_layer_fail(self):
with self.assertRaises(ValueError):
q_nn_builder.make_updatable(["ip2"])

def test_nn_partial_fp16_make_updatable_fail(self):
def test_nn_updatable_fp16_quantize_fail(self):
nn_builder = self.create_base_builder()
model_path = os.path.join(self.model_dir, "updatable_creation.mlmodel")
print(model_path)
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