TOSA: Add support for PAD
Added support for TOSA PAD operator
in line with legacy support
Limitations:
-Rank <= 4
-N = 1 if Rank = 4 for ifms/ofm
-only padding in W and H dimensions
-bool_t not supported
Signed-off-by: Patrik Gustavsson <patrik.gustavsson@arm.com>
Change-Id: I511608202b4c9bf6d86285b559c517fb41741fdf
diff --git a/ethosu/vela/tosa_supported_operators.py b/ethosu/vela/tosa_supported_operators.py
index d368616..a4f822e 100644
--- a/ethosu/vela/tosa_supported_operators.py
+++ b/ethosu/vela/tosa_supported_operators.py
@@ -39,16 +39,15 @@
mac_main_ops = convolution_like_ops | pooling_ops | fc_vector_products
memory_only_ops = set((Op.Reshape, Op.Transpose, Op.Concat, Op.SplitSliceRead,))
-
binary_elem_wise_add_mul_sub = set((Op.Add, Op.Mul, Op.RescaleMul, Op.Sub,))
-
type_conversion_ops = set((Op.Rescale,))
relu_ops = set((Op.Clamp, Op.ReluN,))
activation_ops = relu_ops
+ pad_ops = set((Op.Pad,))
npu_post_ops = activation_ops
supported_operators = (
- mac_main_ops | type_conversion_ops | npu_post_ops | memory_only_ops | binary_elem_wise_add_mul_sub
+ mac_main_ops | type_conversion_ops | npu_post_ops | memory_only_ops | binary_elem_wise_add_mul_sub | pad_ops
)
# Supported data types
@@ -60,12 +59,15 @@
# 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)
+ self.generic_constraints.append(TosaSupportedOperators.constraint_tens_dimension) # TODO as not supported yet
+ self.generic_constraints.append(TosaSupportedOperators.constraint_rank) # TODO as not supported yet
+ self.generic_constraints.append(TosaSupportedOperators.constraint_batch) # TODO as not supported yet
# Setup specific constraints. Note: the order matters
self.specific_constraints = defaultdict(list)
self.specific_constraints[Op.Transpose].append(TosaSupportedOperators.constraint_ifm_producer)
+ self.specific_constraints[Op.Pad].append(TosaSupportedOperators.constraint_padding_producer)
# Depthwise Conv specific checks:
for op_type in TosaSupportedOperators.depthwise_convolution_ops:
@@ -127,6 +129,38 @@
extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}")
return valid, ", ".join(extra)
+ # TODO This is for a HW limitation, that is to be resolved in SW later on
+ @staticmethod
+ def constraint_rank(op):
+ "Tensor rank must be <= 4"
+ 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:
+ rank = len(tens.shape)
+ if not rank <= 4:
+ valid = False
+ extra.append(f"Tensor '{tens.name}' has rank: {rank}")
+ return valid, ", ".join(extra)
+
+ # TODO This is for a HW limitation, that is to be resolved in SW later on
+ @staticmethod
+ def constraint_batch(op):
+ "If Tensor rank is 4 batch of ifms/ofm must be 1"
+ valid = True
+ extra = []
+ tensors = [tens for tens in op.get_ifm_ifm2_ofm() if tens]
+ if not tensors:
+ tensors = [tens for tens in op.inputs if tens]
+ for tens in tensors:
+ rank = len(tens.shape)
+ if rank == 4 and tens.shape[0] != 1:
+ valid = False
+ extra.append(f"Tensor '{tens.name}' has rank: 4 and N: {tens.shape[0]}")
+ return valid, ", ".join(extra)
+
@staticmethod
def constraint_ifm_producer(cls, op):
"Input must be constant data"
@@ -143,6 +177,14 @@
return valid, "Avgpool with pad_top {top} and pad_left {left}"
+ # TODO limit padding to be const data for now.
+ # For TFLite it is assumed to be constant.
+ @staticmethod
+ def constraint_padding_producer(op):
+ "Input must be constant data"
+ valid = op.inputs[1].ops and op.inputs[1].ops[0].type == Op.Const
+ return valid, "PAD Op with non-constant data padding"
+
# TODO duplicates tflite_supported operators, but support for depth multiplier should be added at a later stage
@staticmethod
def constraint_depth_multiplier(op):