MLBEDSW-3711: Added operator checks for PAD.
Constraints and unit tests were added to check the new pad operator.
Change-Id: Id6d4cf2c4da486928c8f46ba1fa124eec66895a6
Signed-off-by: erik.andersson@arm.com <erik.andersson@arm.com>
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index f2c2eb9..2e35d77 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -82,6 +82,7 @@
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 @@
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 @@
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 @@
# 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 @@
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 @@
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)