gemini-code-assist[bot] commented on code in PR #19860:
URL: https://github.com/apache/tvm/pull/19860#discussion_r3449514729
##########
python/tvm/relax/frontend/tflite/tflite_frontend.py:
##########
@@ -6688,6 +6727,93 @@ def convert_embedding_lookup_sparse(self, op):
relax.op.greater(bucket_counts, relax.const(0.0, "float32")),
normalized, value_base
)
+ def convert_lsh_projection(self, op):
+ """Reject TFLite LSH_PROJECTION with a targeted diagnostic."""
+ from tflite.LSHProjectionOptions import LSHProjectionOptions
+ from tflite.LSHProjectionType import LSHProjectionType
+ from tflite.TensorType import TensorType
+
+ input_tensors = self.get_input_tensors(op)
+ output_tensors = self.get_output_tensors(op)
+ if len(input_tensors) not in (2, 3) or len(output_tensors) != 1:
+ raise tvm.error.OpNotImplemented(
+ "LSH_PROJECTION expects hash and input tensors, optional
weights, and one output"
+ )
+
+ hash_tensor, input_tensor = input_tensors[:2]
+ output_tensor = output_tensors[0]
+ hash_shape = to_int_list(self.get_tensor_shape(hash_tensor))
+ input_shape = to_int_list(self.get_tensor_shape(input_tensor))
+ output_shape = to_int_list(self.get_tensor_shape(output_tensor))
+ if hash_tensor.tensor.Type() != TensorType.FLOAT32:
+ raise tvm.error.OpNotImplemented("LSH_PROJECTION hash tensor must
be float32")
+ if len(hash_shape) != 2 or hash_shape[0] < 1 or hash_shape[1] > 32:
+ raise tvm.error.OpNotImplemented(
+ "LSH_PROJECTION hash tensor must be rank 2 with at most 32
bits"
+ )
+ if len(input_shape) < 1 or input_shape[0] < 1:
+ raise tvm.error.OpNotImplemented("LSH_PROJECTION input tensor must
be rank >= 1")
+ if len(input_tensors) == 3:
+ weight_tensor = input_tensors[2]
+ weight_shape = to_int_list(self.get_tensor_shape(weight_tensor))
+ if weight_tensor.tensor.Type() != TensorType.FLOAT32 or
weight_shape != [
+ input_shape[0]
+ ]:
+ raise tvm.error.OpNotImplemented(
+ "LSH_PROJECTION weights must be rank-1 float32 and match
input dimension 0"
+ )
Review Comment:
In TFLite, the weights tensor for `LSH_PROJECTION` is optional. If a model
does not provide weights, the third input index in the operator's inputs array
is `-1`, which causes `self.get_input_tensors(op)` to return `None` at index 2.
If `input_tensors[2]` is `None`, calling
`self.get_tensor_shape(weight_tensor)` will raise an `AttributeError` and
crash. We should check if `input_tensors[2]` is not `None` before attempting to
validate the weights.
```suggestion
if len(input_tensors) == 3 and input_tensors[2] is not None:
weight_tensor = input_tensors[2]
weight_shape = to_int_list(self.get_tensor_shape(weight_tensor))
if weight_tensor.tensor.Type() != TensorType.FLOAT32 or
weight_shape != [
input_shape[0]
]:
raise tvm.error.OpNotImplemented(
"LSH_PROJECTION weights must be rank-1 float32 and match
input dimension 0"
)
```
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