diff options
| -rw-r--r-- | SUPPORTED_OPS.md | 10 | ||||
| -rw-r--r-- | ethosu/vela/supported_operators.py | 25 |
2 files changed, 17 insertions, 18 deletions
diff --git a/SUPPORTED_OPS.md b/SUPPORTED_OPS.md index fdda1570..bc1f0aae 100644 --- a/SUPPORTED_OPS.md +++ b/SUPPORTED_OPS.md @@ -55,7 +55,7 @@ This is a list of constraints that all NPU operators must satisfy in order to be - Output tensors cannot be scalar - Scalar Input tensors are only valid for op type: ADD, MAXIMUM, MINIMUM, MUL, SPLIT, SPLIT_V, SUB - Input(s) and Output tensors must not be greater than 4D -- Tensors must be of type: {int8, int32, int16, uint8} +- Tensors must be of type: int16, int32, int8, uint8 - Tensors which are int32 are only valid when op type is: ADD, MUL, SUB - Tensor dimensions must be in the range [1, 65535] - Input(s), Output and Weight tensors must have quantization parameters @@ -117,7 +117,7 @@ This is a list of constraints that the CONV_2D operator must satisfy in order to - Weight tensor must be 8-bit - Weight tensor must be constant - The sum of the weights cannot exceed 8323072 -- Optional Bias tensor must be of type: {int32, int64} +- Optional Bias tensor must be of type: int32, int64 - Optional Bias tensor values must fit within 40-bits - IFM Tensor batch size must be 1 @@ -134,7 +134,7 @@ This is a list of constraints that the DEPTHWISE_CONV_2D operator must satisfy i - Weight tensor must be 8-bit - Weight tensor must be constant - The sum of the weights cannot exceed 8323072 -- Optional Bias tensor must be of type: {int32, int64} +- Optional Bias tensor must be of type: int32, int64 - Optional Bias tensor values must fit within 40-bits - IFM Tensor batch size must be 1 - For depth multipliers > 1, IFM channels must be 1 and OFM channels must be equal to the depth multiplier @@ -145,7 +145,7 @@ This is a list of constraints that the FULLY_CONNECTED operator must satisfy in - Weight tensor must be 8-bit - Weight tensor must be constant -- Optional Bias tensor must be of type: {int32, int64} +- Optional Bias tensor must be of type: int32, int64 - Optional Bias tensor values must fit within 40-bits ## LEAKY_RELU Constraints @@ -277,7 +277,7 @@ This is a list of constraints that the TRANSPOSE_CONV operator must satisfy in o - Weight tensor must be 8-bit - Weight tensor must be constant - The sum of the weights cannot exceed 8323072 -- Optional Bias tensor must be of type: {int32, int64} +- Optional Bias tensor must be of type: int32, int64 - Optional Bias tensor values must fit within 40-bits - IFM Tensor batch size must be 1 - Stride values for both width and height must be 2 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 |