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[Relax][Frontend][TFLite] Support static hashtable find #19879

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Merged
tlopex merged 1 commit into from
Jun 24, 2026
Merged

[Relax][Frontend][TFLite] Support static hashtable find #19879

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135 changes: 132 additions & 3 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 @@ -711,6 +711,45 @@ def _has_tensor_buffer_data(tensor_wrapper):
and tensor_wrapper.buffer.DataLength() > 0
)

def _get_string_tensor_value(self, tensor_wrapper, op_name):
"""Decode a constant TFLite string tensor buffer."""
if not self._is_tflite_string_type(tensor_wrapper.tensor.Type()):
raise tvm.error.OpNotImplemented(f"{op_name} requires a TensorType.STRING tensor")
if not self._has_tensor_buffer_data(tensor_wrapper):
raise tvm.error.OpNotImplemented(f"{op_name} requires a constant string tensor")

data = bytes(tensor_wrapper.buffer.DataAsNumpy())
if len(data) < 4:
raise tvm.error.OpNotImplemented(f"{op_name} has an invalid string tensor buffer")

count = int(np.frombuffer(data, dtype="<i4", count=1)[0])
if count < 0:
raise tvm.error.OpNotImplemented(f"{op_name} has an invalid string tensor count")

header_size = 4 * (count + 2)
if len(data) < header_size:
raise tvm.error.OpNotImplemented(f"{op_name} has an invalid string tensor offsets")

offsets = np.frombuffer(data, dtype="<i4", count=count + 1, offset=4).astype(np.int64)
if np.any(offsets < header_size) or np.any(offsets > len(data)):
raise tvm.error.OpNotImplemented(f"{op_name} has out-of-bounds string tensor offsets")
if np.any(offsets[:-1] > offsets[1:]):
raise tvm.error.OpNotImplemented(f"{op_name} has non-monotonic string tensor offsets")

try:
values = [
data[int(offsets[i]) : int(offsets[i + 1])].decode("utf-8") for i in range(count)
]
except UnicodeDecodeError as e:
raise tvm.error.OpNotImplemented(f"{op_name} has invalid UTF-8 string data: {e}") from e
shape = self._get_tensor_shape_tuple(tensor_wrapper)
expected_count = math.prod(shape) if shape else 1
if expected_count != count:
raise tvm.error.OpNotImplemented(
f"{op_name} string tensor buffer count does not match its shape"
)
return np.array(values, dtype=object).reshape(shape)

def convert_hashtable(self, op):
"""Convert a TFLite HASHTABLE into an importer-local table handle."""
input_tensors = self.get_input_tensors(op)
Expand Down Expand Up @@ -759,21 +798,102 @@ def convert_hashtable_import(self, op):
):
raise tvm.error.OpNotImplemented("HASHTABLE_IMPORT requires constant keys and values")

if self._is_tflite_string_type(table_info["key_dtype"]):
keys = self._get_string_tensor_value(key_tensor, "HASHTABLE_IMPORT")
else:
keys = self.get_tensor_value(key_tensor)
if self._is_tflite_string_type(table_info["value_dtype"]):
values = self._get_string_tensor_value(value_tensor, "HASHTABLE_IMPORT")
else:
values = self.get_tensor_value(value_tensor)

if np.unique(keys).size != keys.size:
raise tvm.error.OpNotImplemented(
"HASHTABLE_IMPORT with duplicate keys is not supported"
)
Comment on lines +810 to +813

@gemini-code-assist gemini-code-assist Bot Jun 24, 2026

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medium

In HASHTABLE_IMPORT, there is no validation to ensure that the keys and values tensors have matching shapes. If a model has mismatched key and value shapes, zip in convert_hashtable_find will silently truncate the elements, leading to unexpected behavior or incorrect constant folding. We should explicitly validate that the shapes of keys and values match before proceeding.

        if keys.shape != values.shape:
            raise tvm.error.OpNotImplemented(
                "HASHTABLE_IMPORT requires keys and values to have the same shape"
            )

        if np.unique(keys).size != keys.size:
            raise tvm.error.OpNotImplemented(
                "HASHTABLE_IMPORT with duplicate keys is not supported"
            )

@Aharrypotter Aharrypotter Jun 24, 2026

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This case already be covered by the existing import-time guard just above
this block:

key_shape = self._get_tensor_shape_tuple(key_tensor)
value_shape = self._get_tensor_shape_tuple(value_tensor)
if key_shape != value_shape:
    raise tvm.error.OpNotImplemented("HASHTABLE_IMPORT requires keys and values same shape")

This runs before the keys and values are stored for HASHTABLE_FIND, so a
mismatched import should be rejected before the later zip path can silently
truncate anything. I do not think an extra keys.shape != values.shape check
changes the frontend behavior here.


hashtable_values = self.conversion_state["hashtable_values"]
table_key = table_info["table_key"]
if table_key not in hashtable_values:
hashtable_values[table_key] = {
"size": math.prod(key_shape) if key_shape else 1,
"key_dtype": table_info["key_dtype"],
"value_dtype": table_info["value_dtype"],
"keys": keys,
"values": values,
}
return None

def convert_hashtable_find(self, op):
"""Reject HASHTABLE_FIND until Relax can represent TFLite string tensors."""
raise tvm.error.OpNotImplemented(
"HASHTABLE_FIND requires TensorType.STRING support in Relax TFLite frontend"
"""Convert the constant-foldable string-to-int64 HASHTABLE_FIND subset."""
from tflite.TensorType import TensorType

input_tensors = self.get_input_tensors(op)
output_tensors = self.get_output_tensors(op)
if len(input_tensors) != 3 or len(output_tensors) != 1:
raise tvm.error.OpNotImplemented(
"HASHTABLE_FIND expects table, query, and default inputs with one output"
)

table_tensor, query_tensor, default_tensor = input_tensors
output_tensor = output_tensors[0]
table_info = self._get_hashtable_info_for_handle(table_tensor, "HASHTABLE_FIND")
table_key = table_info["table_key"]
hashtable_values = self.conversion_state["hashtable_values"]
if table_key not in hashtable_values:
raise tvm.error.OpNotImplemented(
"HASHTABLE_FIND requires a table initialized by a supported CALL_ONCE subgraph"
)
table_values = hashtable_values[table_key]

if (
query_tensor.tensor.Type() != table_values["key_dtype"]
or default_tensor.tensor.Type() != table_values["value_dtype"]
or output_tensor.tensor.Type() != table_values["value_dtype"]
):
raise tvm.error.OpNotImplemented("HASHTABLE_FIND key/value dtypes mismatch")

if not (
self._is_tflite_string_type(table_values["key_dtype"])
and table_values["value_dtype"] == TensorType.INT64
):
raise tvm.error.OpNotImplemented(
"HASHTABLE_FIND only supports constant string -> int64 tables"
)
if not self._has_tensor_buffer_data(query_tensor):
raise tvm.error.OpNotImplemented(
"HASHTABLE_FIND with runtime string queries is not supported"
)
if not self._has_tensor_buffer_data(default_tensor):
raise tvm.error.OpNotImplemented("HASHTABLE_FIND requires constant default values")

query_shape = self._get_tensor_shape_tuple(query_tensor)
output_shape = self._get_tensor_shape_tuple(output_tensor)
if output_shape != query_shape:
raise tvm.error.OpNotImplemented("HASHTABLE_FIND output shape must match query shape")

query_values = self._get_string_tensor_value(query_tensor, "HASHTABLE_FIND")
default_values = self.get_tensor_value(default_tensor)
default_shape = self._get_tensor_shape_tuple(default_tensor)
if default_shape == () or default_values.size == 1:
result = np.full(output_shape, int(default_values.item()), dtype=np.int64)
elif default_shape == query_shape:
result = default_values.astype(np.int64).copy()
else:
raise tvm.error.OpNotImplemented(
"HASHTABLE_FIND default value must be scalar or match query shape"
)

table_map = dict(
zip(
table_values["keys"].reshape(-1).tolist(),
table_values["values"].reshape(-1).astype(np.int64).tolist(),
)
)
for index, key in np.ndenumerate(query_values):
if key in table_map:
result[index] = table_map[key]
return relax.const(result.astype(np.int64), "int64")

def convert_hashtable_lookup(self, op):
"""Convert TFLite HASHTABLE_LOOKUP for non-string value tensors."""
Expand Down Expand Up @@ -8770,6 +8890,15 @@ def func(self, data):
dtype = "float32"
if shape is not None:
shape = tuple(shape) + (2,)
if dtype == "string":
# Relax has no string tensor type, so TFLite TensorType.STRING graph
# inputs cannot be represented. This also covers runtime string queries
# for ops like HASHTABLE_FIND, whose constant-foldable subset is handled
# in the op converter.
raise tvm.error.OpNotImplemented(
"Relax TFLite frontend does not support TensorType.STRING graph inputs "
"(e.g. runtime string queries)"
)
input_var = relax.Var(
name_hint=model_input_name,
ty=relax.TensorType(shape=shape, dtype=dtype),
Expand Down
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