Aharrypotter commented on code in PR #19812:
URL: https://github.com/apache/tvm/pull/19812#discussion_r3426979645
##########
python/tvm/relax/frontend/tflite/tflite_frontend.py:
##########
@@ -5033,6 +5071,78 @@ def convert_bitcast(self, op):
return relax.op.memory.view(in_expr, shape=output_shape,
dtype=output_dtype)
+ def convert_rfft2d(self, op):
+ """Convert TFLite RFFT2D for static no-padding/no-truncation shapes."""
+ from tflite.TensorType import TensorType
+
+ input_tensors = self.get_input_tensors(op)
+ output_tensors = self.get_output_tensors(op)
+ if len(input_tensors) != 2 or len(output_tensors) != 1:
+ raise tvm.error.OpNotImplemented("RFFT2D expects two inputs and
one output")
+
+ data_tensor, fft_length_tensor = input_tensors
+ output_tensor = output_tensors[0]
+ if data_tensor.tensor.Type() != TensorType.FLOAT32:
+ raise tvm.error.OpNotImplemented("RFFT2D input must be float32")
+ if not self._is_tflite_complex64_type(output_tensor.tensor.Type()):
+ raise tvm.error.OpNotImplemented("RFFT2D output must be COMPLEX64")
+ if data_tensor.tensor.Sparsity() is not None or
fft_length_tensor.tensor.Sparsity() is not None:
+ raise tvm.error.OpNotImplemented("RFFT2D does not support sparse
inputs")
+
+ input_shape = tuple(to_int_list(self.get_tensor_shape(data_tensor)))
+ tflite_output_shape =
tuple(to_int_list(self.get_tensor_shape(output_tensor)))
+ if len(input_shape) < 2:
+ raise tvm.error.OpNotImplemented("RFFT2D input rank must be at
least 2")
+
+ try:
+ fft_length_value =
self.get_tensor_value_or_prefetched(fft_length_tensor)
+ except (ValueError, TypeError):
+ raise tvm.error.OpNotImplemented("RFFT2D requires a constant
fft_length") from None
Review Comment:
fixed
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