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Add None check before dataclasses.replace. #297

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Add None check before dataclasses.replace. #297

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35 changes: 20 additions & 15 deletions ai_edge_quantizer/algorithms/utils/common_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -295,24 +295,28 @@ def _materialize_standard_op_with_same_as_input_scale(
op_tensor_params.append(input_tensor_params)
# Use input quantization params for all output tensors but without
# quantized_data in case the input is a constant tensor.
input_quant_params = dataclasses.replace(
input_tensor_params.consumers[0].parameters,
quantized_data=None,
)
if not isinstance(input_quant_params, qtyping.UniformQuantParams):
raise ValueError(
"_materialize_standard_op_with_same_as_input_scale only supports"
f" UniformQuantParams. For tensor {input_tensor_params.tensor_name},"
f" got {type(input_quant_params)}"
input_quant_params = input_tensor_params.consumers[0].parameters
if input_quant_params is not None:
input_quant_params = dataclasses.replace(
input_quant_params,
quantized_data=None,
)
if not isinstance(input_quant_params, qtyping.UniformQuantParams):
raise ValueError(
"_materialize_standard_op_with_same_as_input_scale only supports"
f" UniformQuantParams. For tensor {input_tensor_params.tensor_name},"
f" got {type(input_quant_params)}"
)
# Materialize each of the output tensors separately in case there are
# constants among them, requiring updating `quantized_data` first.
for output_tensor in output_tensors:
output_tensor_data = tfl_flatbuffer_utils.get_tensor_data(
output_tensor, graph_info.buffers
)
# Quantize constant inputs' data with the output quantization params.
if output_tensor_data is None:
if input_quant_params is None:
quant_params = None
elif output_tensor_data is None:
quant_params = input_quant_params
else:
quantized_data = uniform_quantize_tensor.uniform_quantize(
Expand All @@ -335,11 +339,12 @@ def _materialize_standard_op_with_same_as_input_scale(

# Change output qsv to be the same as input qsv. This is safe since TFL
# subgraph is acyclic.
input_tensor_qsv = tensor_name_to_qsv[input_tensor_params.tensor_name]
for output_tensor in output_tensors:
tensor_name_to_qsv[tfl_flatbuffer_utils.get_tensor_name(output_tensor)] = (
input_tensor_qsv
)
if input_tensor_params.tensor_name in tensor_name_to_qsv:
input_tensor_qsv = tensor_name_to_qsv[input_tensor_params.tensor_name]
for output_tensor in output_tensors:
tensor_name_to_qsv[
tfl_flatbuffer_utils.get_tensor_name(output_tensor)
] = input_tensor_qsv

return op_tensor_params

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