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Enable OpResolver with XNNPACK for quantizer #174

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Enable OpResolver with XNNPACK for quantizer #174

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2 changes: 1 addition & 1 deletion ai_edge_quantizer/model_validator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -224,7 +224,7 @@ def _setup_validation_interpreter(
"""

interpreter = utils.create_tfl_interpreter(
tflite_model=model, use_reference_kernel=use_reference_kernel
tflite_model=model
)
utils.invoke_interpreter_signature(
interpreter, signature_input, signature_key
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13 changes: 8 additions & 5 deletions ai_edge_quantizer/utils/tfl_interpreter_utils.py
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Expand Up @@ -30,15 +30,16 @@
def create_tfl_interpreter(
tflite_model: Union[str, bytes],
allocate_tensors: bool = True,
use_reference_kernel: bool = False,
use_xnnpack: bool = True,
num_threads: int = 4,
) -> tfl.Interpreter:
"""Creates a TFLite interpreter from a model file.
Args:
tflite_model: Model file path or bytes.
allocate_tensors: Whether to allocate tensors.
use_reference_kernel: Whether to use the reference kernel for the
interpreter.
use_xnnpack: Whether to use the XNNPACK delegate for the interpreter.
num_threads: The number of threads to use for the interpreter.
Returns:
A TFLite interpreter.
Expand All @@ -47,12 +48,14 @@ def create_tfl_interpreter(
with gfile.GFile(tflite_model, "rb") as f:
tflite_model = f.read()

if use_reference_kernel:
op_resolver = tfl.OpResolverType.BUILTIN_REF
if use_xnnpack:
op_resolver = tfl.OpResolverType.BUILTIN_WITH_XNNPACK
else:
op_resolver = tfl.OpResolverType.BUILTIN_WITHOUT_DEFAULT_DELEGATES

tflite_interpreter = tfl.Interpreter(
model_content=bytes(tflite_model),
num_threads=num_threads,
experimental_op_resolver_type=op_resolver,
experimental_preserve_all_tensors=True,
)
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