vela: Improve printing of sets
When printing a set in the docstrings for the SUPPORTED_OPS.md file, the
order is random.
Reuse existing sorted string repr for the operator list and apply to
other printed sets (data types)
Signed-off-by: Michael McGeagh <michael.mcgeagh@arm.com>
Change-Id: I2ac12ea91c2637219e5c24f9a863aa0fc2086e77
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index 6bbb04b..deae75a 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -38,13 +38,17 @@
return docstring
+def _list_formatter(arg):
+ # Order and join into a string representation
+ return ", ".join(sorted(map(str, arg)))
+
+
def _optype_formatter(op_list):
# Convert internal op types to external names
output = map(optype_to_builtintype, op_list)
# Remove UNKNOWNs
output = (x for x in output if x is not BUILTIN_OPERATOR_UNKNOWN)
- # Order alphabetically and join into a string representation
- return ", ".join(str(op) for op in sorted(output))
+ return _list_formatter(output)
class SupportedOperators:
@@ -110,11 +114,6 @@
filter_range = (1, 8)
filter_height_range = (1, 256)
filter_product_range = (1, 256 * 256)
- # Ordered, external names of op types for the constraint reasons
- docstring_shapeless_input_ops = _optype_formatter(shapeless_input_ops)
- docstring_supported_int32_tensor_ops = _optype_formatter(supported_int32_tensor_ops)
- docstring_supported_fused_activations = _optype_formatter(supported_fused_activations)
- docstring_per_axis_quant_ops = _optype_formatter(per_axis_quant_ops)
def __init__(self):
# Setup the generic constraints. Note: the order matters
@@ -299,7 +298,7 @@
return valid, f"Output Tensor '{ofm.name}' is scalar"
@classmethod
- @docstring_format_args([docstring_shapeless_input_ops])
+ @docstring_format_args([_optype_formatter(shapeless_input_ops)])
def constraint_tens_input_scalar(cls, op):
"Scalar Input tensors are only valid for op type: {}"
valid = True
@@ -325,7 +324,7 @@
return valid, ", ".join(extra)
@classmethod
- @docstring_format_args([supported_op_dtypes])
+ @docstring_format_args([_list_formatter(supported_op_dtypes)])
def constraint_tens_dtype(cls, op):
"Tensors must be of type: {}"
valid = True
@@ -340,7 +339,7 @@
return valid, ", ".join(extra)
@classmethod
- @docstring_format_args([docstring_supported_int32_tensor_ops])
+ @docstring_format_args([_optype_formatter(supported_int32_tensor_ops)])
def constraint_tens_int32_ops(cls, op):
"Tensors which are int32 are only valid when op type is: {}"
valid = True
@@ -397,7 +396,7 @@
return valid, ", ".join(extra)
@classmethod
- @docstring_format_args([docstring_per_axis_quant_ops])
+ @docstring_format_args([_optype_formatter(per_axis_quant_ops)])
def constraint_tens_quant_per_axis(cls, op):
"Per-axis quantization is only supported for the following op types: {}"
valid = True
@@ -411,7 +410,7 @@
return valid, "The following tensor(s) have per-axis quantization parameters: " + ", ".join(extra)
@classmethod
- @docstring_format_args([docstring_supported_fused_activations])
+ @docstring_format_args([_optype_formatter(supported_fused_activations)])
def constraint_faf(cls, op):
"The fused activation function (if present) must be one of type: {}"
if op.activation is None:
@@ -497,7 +496,7 @@
return valid, f"Tensor '{weights.name}' has the sum of weights: {limit}"
@classmethod
- @docstring_format_args([supported_bias_dtypes])
+ @docstring_format_args([_list_formatter(supported_bias_dtypes)])
def constraint_bias_type(cls, op):
"Optional Bias tensor must be of type: {}"
bias = op.bias