MLBEDSW-4223: Full support for PAD operator
- Added full support for PAD operator
- Hardware padding is still used whenever possible
- Bug fix Pad followed by max pool if IFM contains negative values
Change-Id: Ifc64d1943737d94466f5e2821009dab12a49a965
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index a82f812..2319706 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -259,15 +259,11 @@
self.specific_constraints[Op.FullyConnected].append(SupportedOperators.constraint_keep_dim_ifm_ofm)
# 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)
- self.specific_constraints[Op.Pad].append(SupportedOperators.constraint_pad_size)
# HardSwish specific checks:
self.specific_constraints[Op.HardSwish].append(SupportedOperators.constraint_input_8bit)
@@ -830,8 +826,8 @@
@staticmethod
def constraint_pad_shape(op):
- "The padding tensor must have the shape [4,2]"
- valid = op.inputs[1].shape == [4, 2]
+ "The padding tensor must have the shape [3,2] or [4,2]"
+ valid = op.inputs[1].shape in ([3, 2], [4, 2])
return valid, f"The pad tensor has the shape: {op.inputs[1].shape}"
@classmethod
@@ -846,7 +842,10 @@
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
+
+ valid = sum(pad_tensor[-1, :]) == 0
+ if valid and len(pad_tensor) > 3:
+ valid = sum(pad_tensor[0, :]) == 0
return valid, f"First dimension padding: {pad_tensor[0,:]}, last dimension padding: {pad_tensor[-1,:]}"
@staticmethod
@@ -856,65 +855,6 @@
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()
- unsupported_consumers = [
- cons.type
- for cons in consumers
- if cons is not None
- if cons.type not in cls.supported_pad_consumers or cons.attrs["padding"] != Padding.VALID
- ] + [None for cons in consumers if cons is None]
- none_string = ", ".join(["NoneType" for cons in consumers if cons is None])
- valid = len(unsupported_consumers) == 0
- return valid, f"PAD operator is followed by: {_optype_formatter(unsupported_consumers)+none_string}"
-
- @staticmethod
- def __leading_pad_ok(leading_pad, stride, kernel_size):
- # If kernel size // 2 > stride, then (left, top) padding must be a multiple of stride,
- # otherwise replacing PAD by hardware padding would iterate the wrong IFM rows/columns
- max_size = kernel_size // 2
- return leading_pad == max_size or max_size <= stride or leading_pad % stride == 0
-
- @staticmethod
- def constraint_pad_size(op):
- "Padding must be at most kernel size divided by 2"
- if SupportedOperators.constraint_pad_ofm(op)[0]:
- padding = op.inputs[1].values # 4x2 tensor, first dimension is N, H, W, C
- top, left, bottom, right = (padding[1][0], padding[2][0], padding[1][1], padding[2][1])
- for cons in op.ofm.consumers():
- if cons is not None:
- # Note: pre-order graph traversal removes inputs of operators that are in traversal,
- # which makes it impossible to calculate kernel size, hence use cached _kernel for those operators
- k = cons.kernel if cons.inputs else cons._kernel
- k_w, k_h = k.dilated_wh()
- if cons.type.is_avgpool_op():
- # For average pool, padding works different on the NPU; more restrictions apply
- for name, pad, k_size in (
- ("Left", left, k_w),
- ("Right", right, k_w),
- ("Top", top, k_h),
- ("Bottom", bottom, k_h),
- ):
- if pad not in (0, k_size // 2):
- return False, f"{name} padding is {pad}, only 0 or {k_size // 2} are supported"
- else:
- if left > k_w // 2:
- return False, f"Left padding is {left}, kernel width is {k_w}"
- if right > k_w // 2:
- return False, f"Right padding is {right}, kernel width is {k_w}"
- if top > k_h // 2:
- return False, f"Top padding is {top}, kernel height is {k_h}"
- if bottom > k_h // 2:
- return False, f"Bottom padding is {bottom}, kernel height is {k_h}"
- if not SupportedOperators.__leading_pad_ok(top, k.stride.y, k_h):
- return False, f"Top padding is {top}, must be {k_h // 2} or multiple of {k.stride.y}"
- if not SupportedOperators.__leading_pad_ok(left, k.stride.x, k_w):
- return False, f"Left padding is {left}, must be {k_w // 2} or multiple of {k.stride.x}"
- return True, "Pad size is ok"
-
@staticmethod
def constraint_stridedslice_inputs_const(op):
"Begin, End and Stride Input tensors must be constant"