diff options
Diffstat (limited to 'ethosu/vela/supported_operators.py')
| -rw-r--r-- | ethosu/vela/supported_operators.py | 69 |
1 files changed, 67 insertions, 2 deletions
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py index f2c2eb9e..2e35d771 100644 --- a/ethosu/vela/supported_operators.py +++ b/ethosu/vela/supported_operators.py @@ -82,6 +82,7 @@ class SupportedOperators: binary_elem_wise_add_mul_sub = set((Op.Add, Op.Mul, Op.Sub,)) binary_elem_wise_main_ops = binary_elem_wise_min_max_ops | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops elem_wise_main_ops = binary_elem_wise_main_ops | unary_elem_wise_main_ops + pad_ops = set((Op.Pad,)) supported_int32_tensor_ops = ( set((Op.ReduceSum, Op.CLZ,)) | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops ) @@ -101,10 +102,11 @@ class SupportedOperators: shapeless_input_ops = binary_elem_wise_main_ops | set((Op.Split, Op.SplitV,)) per_axis_quant_ops = convolution_like_ops # per-axis/channel quantization only currently supported for conv ops supported_fused_activations = relu_ops | set((Op.Tanh, Op.Sigmoid, Op.LUT,)) - supported_operators = npu_pre_ops | mac_main_ops | elem_wise_main_ops | npu_post_ops | memory_only_ops + supported_operators = npu_pre_ops | mac_main_ops | elem_wise_main_ops | pad_ops | npu_post_ops | memory_only_ops # Supported data types supported_op_dtypes = set((DataType.uint8, DataType.int8, DataType.int16, DataType.int32)) supported_bias_dtypes = set((DataType.int32, DataType.int64)) + supported_pad_dtypes = set((DataType.int32, DataType.int64)) # Defined ranges for allowed values: tens_dim_range = (1, 65535) stride_range = (1, 3) @@ -115,6 +117,8 @@ class SupportedOperators: filter_range = (1, 8) filter_height_range = (1, 256) filter_product_range = (1, 256 * 256) + # Supported consumers + supported_pad_consumers = convolution_ops | depthwise_convolution_ops def __init__(self): # Setup the generic constraints. Note: the order matters @@ -251,6 +255,16 @@ class SupportedOperators: # FullyConnected specific checks: self.specific_constraints[Op.FullyConnected].append(SupportedOperators.constraint_fc_output_2d) + # Pad specific checks: + self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_matching_in_out_types) + self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_matching_quantization_parameters) + self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_input_count) + self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_shape) + self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_padding_dimensions) + self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_type) + self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_constant) + self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_ofm) + def is_operator_supported(self, op): ext_type = optype_to_builtintype(op.type) if op.type not in SupportedOperators.supported_operators: @@ -769,6 +783,57 @@ class SupportedOperators: return valid, f"Op has {inputs} inputs" @staticmethod + def constraint_pad_input_count(op): + "Number of input tensors must be exactly 2" + inputs = len(op.inputs) + valid = inputs == 2 + return valid, f"Op has {inputs} inputs" + + @staticmethod + def constraint_pad_shape(op): + "The padding tensor must have the shape [4,2]" + valid = op.inputs[1].shape == [4, 2] + return valid, f"The pad tensor has the shape: {op.inputs[1].shape}" + + @classmethod + @docstring_format_args([_list_formatter(supported_pad_dtypes)]) + def constraint_pad_type(cls, op): + "Pad tensor must be of type: {}" + pad_tensor = op.inputs[1] + valid = pad_tensor.dtype in cls.supported_pad_dtypes + return valid, f"Tensor '{pad_tensor.name}' has data type: {pad_tensor.dtype}" + + @staticmethod + def constraint_padding_dimensions(op): + "The pad tensor can only pad width and height" + pad_tensor = op.inputs[1].values + valid = sum(pad_tensor[0, :]) + sum(pad_tensor[-1, :]) == 0 + return valid, f"First dimension padding: {pad_tensor[0,:]}, last dimension padding: {pad_tensor[-1,:]}" + + @staticmethod + def constraint_pad_constant(op): + pad_tensor = op.inputs[1].values + valid = pad_tensor is not None + return valid, f"Op has non-constant padding tensor: {op.inputs[1].values}" + + @classmethod + @docstring_format_args([_optype_formatter(supported_pad_consumers)]) + def constraint_pad_ofm(cls, op): + "Must be followed by one of the following operator types: {}" + consumers = op.ofm.consumers() + consumers_to_pad = 0 + for consumer in consumers: + if consumer.type in cls.supported_pad_consumers: + if consumer.attrs["padding"] == Padding.VALID: + consumers_to_pad += 1 + valid = len(consumers) > 0 and len(consumers) == consumers_to_pad + return ( + valid, + f"Operator is followed by {consumers_to_pad} consumers with " + f"padding set to VALID, out of {len(consumers)} consumers", + ) + + @staticmethod def constraint_stridedslice_inputs_const(op): "Begin, End and Stride Input tensors must be constant" valid = True @@ -870,7 +935,7 @@ class SupportedOperators: if not check_quantized_tens_scaling_equal(op.ofm, op.ifm): valid = False extra.append(op.ifm.name) - if not check_quantized_tens_scaling_equal(op.ofm, op.ifm2): + if op.ifm2 is not None and not check_quantized_tens_scaling_equal(op.ofm, op.ifm2): valid = False extra.append(op.ifm2.name) extra = ", ".join(extra) |