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[Relax][Frontend][TFLite] Add explicit operator marker handling #19824

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[Relax][Frontend][TFLite] Add explicit operator marker handling #19824

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10 changes: 10 additions & 0 deletions python/tvm/relax/frontend/tflite/tflite_frontend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -219,6 +219,7 @@ def __init__(self, model, subgraph, exp_tab, ctx, conversion_state=None):
"DENSIFY": self.convert_densify,
"DEPTH_TO_SPACE": self.convert_depth_to_space,
"DEPTHWISE_CONV_2D": functools.partial(self.convert_conv, conv_type="depthwise"),
"DELEGATE": functools.partial(self.convert_operator_marker, op_name="DELEGATE"),
"DEQUANTIZE": self.convert_dequantize,
"DETECTION_POSTPROCESS": self.convert_detection_postprocess,
"DILATE": self.convert_dilate,
Expand Down Expand Up @@ -290,6 +291,9 @@ def __init__(self, model, subgraph, exp_tab, ctx, conversion_state=None):
"PACK": self.convert_pack,
"PAD": self.convert_pad,
"PADV2": self.convert_pad,
"PLACEHOLDER_FOR_GREATER_OP_CODES": functools.partial(
self.convert_operator_marker, op_name="PLACEHOLDER_FOR_GREATER_OP_CODES"
),
"POW": functools.partial(self._convert_elemwise, relax_op=_op.power),
"PRELU": self.convert_prelu,
"RANGE": self.convert_range,
Expand Down Expand Up @@ -479,6 +483,12 @@ def check_unsupported_ops(self):
if len(raise_msg) > 0:
raise tvm.error.OpNotImplemented(raise_msg)

def convert_operator_marker(self, op, op_name):
"""Reject TFLite marker builtins with an explicit diagnostic."""
raise tvm.error.OpNotImplemented(
f"TFLite operator marker {op_name} is not a Relax tensor operator"
)

def unbind(self, data, axis=1):
"""
This is a modified version compared to the one in common.py.
Expand Down
26 changes: 26 additions & 0 deletions tests/python/relax/test_frontend_tflite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4066,6 +4066,32 @@ def _get_builtin_operator(builtin_name):
return getattr(_tfl_builtin_operator, builtin_name)


def _build_tflite_operator_marker_model(builtin_name):
"""Build a minimal model containing a TFLite marker builtin."""
builder = flatbuffers.Builder(1024)
builtin_op = _get_builtin_operator(builtin_name)
op_code = _build_operator_code(builder, builtin_op)
tensors = [
_build_tensor(builder, 0, [1], tensor_type=_tfl_tensor_type.FLOAT32),
_build_tensor(builder, 0, [1], tensor_type=_tfl_tensor_type.FLOAT32),
]
op = _build_operator(builder, 0, [0], [1])
subgraph = _build_subgraph(builder, tensors=tensors, operators=[op], inputs=[0], outputs=[1])
return _finish_tflite_model(
builder,
subgraph=subgraph,
operator_codes=[op_code],
buffers=[_build_buffer(builder)],
)


@pytest.mark.parametrize("builtin_name", ["DELEGATE", "PLACEHOLDER_FOR_GREATER_OP_CODES"])
def test_operator_marker_unsupported(builtin_name):
"""TFLite marker builtins report explicit unsupported diagnostics."""
with pytest.raises(tvm.error.OpNotImplemented, match=f"TFLite operator marker {builtin_name}"):
_load_model_from_buffer(_build_tflite_operator_marker_model(builtin_name))
Comment on lines +4089 to +4092

@gemini-code-assist gemini-code-assist Bot Jun 18, 2026

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medium

To prevent test failures in environments with older versions of the tflite package (where DELEGATE or PLACEHOLDER_FOR_GREATER_OP_CODES might not be defined in _tfl_builtin_operator), we should check for their existence and skip the test if they are missing.

Suggested change
def test_operator_marker_unsupported(builtin_name):
"""TFLite marker builtins report explicit unsupported diagnostics."""
with pytest.raises(tvm.error.OpNotImplemented, match=f"TFLite operator marker {builtin_name}"):
_load_model_from_buffer(_build_tflite_operator_marker_model(builtin_name))
def test_operator_marker_unsupported(builtin_name):
"""TFLite marker builtins report explicit unsupported diagnostics."""
if not hasattr(_tfl_builtin_operator, builtin_name):
pytest.skip(f"{builtin_name} is not supported by the installed tflite package version.")
with pytest.raises(tvm.error.OpNotImplemented, match=f"TFLite operator marker {builtin_name}"):
_load_model_from_buffer(_build_tflite_operator_marker_model(builtin_name))



def _run_module(mod, *inputs):
tgt = tvm.target.Target("c")
ex = tvm.compile(mod, tgt)
Expand Down
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