diff options
Diffstat (limited to 'ethosu/vela/tosa_supported_operators.py')
| -rw-r--r-- | ethosu/vela/tosa_supported_operators.py | 36 |
1 files changed, 35 insertions, 1 deletions
diff --git a/ethosu/vela/tosa_supported_operators.py b/ethosu/vela/tosa_supported_operators.py index c619f2f9..d3686160 100644 --- a/ethosu/vela/tosa_supported_operators.py +++ b/ethosu/vela/tosa_supported_operators.py @@ -54,11 +54,13 @@ class TosaSupportedOperators: # Supported data types # TODO will differ compared to TensorFlow Lite, currently set to the same supported_op_dtypes = set((DataType.uint8, DataType.int8, DataType.int16, DataType.int32)) # TODO add bool + tens_dim_range = (1, 65535) # TODO HW limitation, that is to be resolved in SW def __init__(self): # Setup the generic constraints. Note: the order matters self.generic_constraints = [] self.generic_constraints.append(TosaSupportedOperators.constraint_tens_dtype) + self.generic_constraints.append(TosaSupportedOperators.constraint_tens_dimension) # Setup specific constraints. Note: the order matters self.specific_constraints = defaultdict(list) @@ -69,6 +71,10 @@ class TosaSupportedOperators: for op_type in TosaSupportedOperators.depthwise_convolution_ops: self.specific_constraints[op_type].append(TosaSupportedOperators.constraint_depth_multiplier) + # Avgpool specific checks + for op_type in TosaSupportedOperators.avg_pooling_ops: + self.specific_constraints[op_type].append(TosaSupportedOperators.constraint_padding) + def is_operator_supported(self, op): ext_type = optype_to_tosa_op_type(op.type) if op.type not in TosaSupportedOperators.supported_operators: @@ -103,13 +109,41 @@ class TosaSupportedOperators: extra.append(f"Tensor '{tens.name}' has data type: {tens.dtype}") return valid, ", ".join(extra) + # TODO Duplicates check present for TFLite. But it is only temporarily added + # This is for a HW limitation, that is to be resolved in SW later on + @classmethod + @docstring_format_args(tens_dim_range) + def constraint_tens_dimension(cls, op): + "Tensor dimensions must be in the range [{}, {}]" + tens_min, tens_max = cls.tens_dim_range + valid = True + extra = [] + tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] + if not tensors: + tensors = [tens for tens in op.inputs if tens] + for tens in tensors: + if not all(tens_min <= dim <= tens_max for dim in tens.shape): + valid = False + extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}") + return valid, ", ".join(extra) + @staticmethod def constraint_ifm_producer(cls, op): "Input must be constant data" valid = op.ifm.ops and op.ifm.ops[0].type == Op.Const return valid, "Op has ifm with non-constant data" - # TODO duplicates TFLite_supported operators, but support for depth multiplier should be added at a later stage + @staticmethod + def constraint_padding(op): + # TODO Only support for when global scaling can be used. + # That is when there is padding no padding + "Avgpool only supported for no padding" + top, left, _, _ = op.attrs["explicit_padding"] + valid = top == 0 and left == 0 + + return valid, "Avgpool with pad_top {top} and pad_left {left}" + + # TODO duplicates tflite_supported operators, but support for depth multiplier should be added at a later stage @staticmethod def constraint_depth_multiplier(op): "For depth multipliers > 1, IFM channels must be 1 and OFM channels must be equal to the depth multiplier" |