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[TFLite] Cast operator adapted for MLIR-based convertor #7639

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Mar 19, 2021
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[TFLite] Cast operator adapted for MLIR-based convertor #7639

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[TFLite] Cast operator adapted for MLIR-based convertor
d-smirnov Mar 9, 2021
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d-smirnov Mar 16, 2021
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17 changes: 12 additions & 5 deletions python/tvm/relay/frontend/tflite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2336,11 +2336,18 @@ def convert_cast(self, op):
input_tensor = input_tensors[0]
in_expr = self.get_expr(input_tensor.tensor_idx)

assert op.BuiltinOptionsType() == BuiltinOptions.CastOptions
op_options = op.BuiltinOptions()
cast_options = CastOptions()
cast_options.Init(op_options.Bytes, op_options.Pos)
cast_dtype = cast_options.OutDataType()
# MLIR-based converter outputs no BuiltinOptions for Cast operator. In this
# case the output type can be derived from the Cast operator output tensor.
# When TOCO converter is used there will be "normal" BuiltinOptions.CastOptions
# with output type.
if op.BuiltinOptions() is not None:
assert op.BuiltinOptionsType() == BuiltinOptions.CastOptions
op_options = op.BuiltinOptions()
cast_options = CastOptions()
cast_options.Init(op_options.Bytes, op_options.Pos)
cast_dtype = cast_options.OutDataType()
else:
cast_dtype = self.get_output_tensors(op)[0].tensor.Type()
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Is this because the cast operator can be in 2 forms, one with the type specified by BuiltinOptions.CastOptions and the other type is where there is no output data type specified by BuiltinOptions.CastOptions but we can derive the output type of the cast operator based on the type of the output tensor for the Cast operator itself ?

Is that worth stating in the commit message or in a comment ?

Ramana


out = _op.cast(in_expr, self.get_tensor_type_str(cast_dtype))

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19 changes: 14 additions & 5 deletions tests/python/frontend/tflite/test_forward.py
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Expand Up @@ -647,19 +647,28 @@ def test_forward_transpose():
# ----


def _test_cast(data, cast_dtype):
def _test_cast(data, cast_dtype, use_mlir=False):
""" One iteration of CAST """
with tf.Graph().as_default():
in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
out = math_ops.cast(in_data, cast_dtype)
compare_tflite_with_tvm(data, "Placeholder:0", [in_data], [out])
compare_tflite_with_tvm(
data, "Placeholder:0", [in_data], [out], experimental_new_converter=use_mlir
)


def test_forward_cast():
""" CAST """
_test_cast(np.arange(6.0, dtype=np.float32).reshape((1, 6)), cast_dtype=tf.int32)
_test_cast(np.arange(6.0, dtype=np.float32).reshape((1, 6)), cast_dtype=tf.uint8)
_test_cast(np.arange(6.0, dtype=np.int32).reshape((1, 6)), cast_dtype=tf.int64)
for use_mlir in [False, True]:
_test_cast(
np.arange(6.0, dtype=np.float32).reshape((1, 6)), cast_dtype=tf.int32, use_mlir=use_mlir
)
_test_cast(
np.arange(6.0, dtype=np.float32).reshape((1, 6)), cast_dtype=tf.uint8, use_mlir=use_mlir
)
_test_cast(
np.arange(6.0, dtype=np.int32).reshape((1, 6)), cast_dtype=tf.int64, use_mlir=use_mlir
)


#######################################################################
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