diff options
Diffstat (limited to 'ethosu/vela/supported_operators.py')
| -rw-r--r-- | ethosu/vela/supported_operators.py | 25 |
1 files changed, 12 insertions, 13 deletions
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py index 6bbb04b9..deae75a2 100644 --- a/ethosu/vela/supported_operators.py +++ b/ethosu/vela/supported_operators.py @@ -38,13 +38,17 @@ def docstring_format_args(args): 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 @@ class SupportedOperators: 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 @@ class SupportedOperators: 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 @@ class SupportedOperators: 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 @@ class SupportedOperators: 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 @@ class SupportedOperators: 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 @@ class SupportedOperators: 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 @@ class SupportedOperators: 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 |