diff options
Diffstat (limited to 'ethosu/vela/supported_operators.py')
| -rw-r--r-- | ethosu/vela/supported_operators.py | 137 |
1 files changed, 82 insertions, 55 deletions
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py index 88e10835..4e989124 100644 --- a/ethosu/vela/supported_operators.py +++ b/ethosu/vela/supported_operators.py @@ -122,16 +122,18 @@ class SupportedOperators: self.supported_operator_restrictions.update( {op: self.check_activation_ops for op in SupportedOperators.activation_ops} ) - # Setup the generic constraints + # Setup the generic constraints. Note: the order matters self.generic_constraints = [] self.generic_constraints.append(SupportedOperators.constraint_tens_defined_shape) - self.generic_constraints.append(SupportedOperators.constraint_tens_shapeless) + self.generic_constraints.append(SupportedOperators.constraint_tens_output_shapeless) + self.generic_constraints.append(SupportedOperators.constraint_tens_input_shapeless) self.generic_constraints.append(SupportedOperators.constraint_tens_shape_size) self.generic_constraints.append(SupportedOperators.constraint_tens_dtype) + self.generic_constraints.append(SupportedOperators.constraint_tens_int32_ops) self.generic_constraints.append(SupportedOperators.constraint_tens_dimension) - self.generic_constraints.append(SupportedOperators.constraint_faf) - self.generic_constraints.append(SupportedOperators.constraint_tens_quant_scale) self.generic_constraints.append(SupportedOperators.constraint_tens_quant_none_check) + self.generic_constraints.append(SupportedOperators.constraint_tens_quant_scale) + self.generic_constraints.append(SupportedOperators.constraint_faf) def is_operator_supported(self, op): if op.type not in SupportedOperators.supported_operators: @@ -140,7 +142,7 @@ class SupportedOperators: for constraint in self.generic_constraints: valid, extra = constraint(op) if not valid: - print('Warning: "{}" is not supported on the NPU. Placing on CPU instead'.format(op.type)) + print("Warning: {} '{}' is not supported on the NPU. Placing on CPU instead".format(op.type, op.name)) print(" - {}".format(constraint.__doc__)) if extra: print(" {}".format(extra)) @@ -154,89 +156,93 @@ class SupportedOperators: "Input(s) and Output Tensors must have a defined shape" valid = True extra = [] - for tens in op.inputs + op.outputs: - if tens: - valid &= tens.has_fully_defined_shape() - extra.append("shape={}".format(tens.shape)) - return valid, " ".join(extra) + tensors = [tens for tens in op.inputs + op.outputs if tens] + for tens in tensors: + if not tens.has_fully_defined_shape(): + valid = False + extra.append("Tensor '{}' has shape: {}".format(tens.name, tens.shape)) + return valid, ", ".join(extra) - @classmethod - @docstring_format_args([shapeless_input_ops]) - def constraint_tens_shapeless(cls, op): - "Scalar or Broadcasting Tensors are only valid for Input Tensors, and when op type is: {}" + @staticmethod + def constraint_tens_output_shapeless(op): + "Scalar or Broadcasting Tensors are only valid for Input Tensors" valid = True extra = [] - for tens in op.inputs: - if tens and tens.shape == []: - valid &= op.type in cls.shapeless_input_ops - extra.append("shape={}".format(tens.shape)) for tens in op.outputs: if tens.shape == []: valid = False - extra.append("shape={}".format(tens.shape)) - return valid, " ".join(extra) + extra.append("Output Tensor '{}' is shapeless".format(tens.name)) + return valid, ", ".join(extra) + + @classmethod + @docstring_format_args([shapeless_input_ops]) + def constraint_tens_input_shapeless(cls, op): + "Scalar or Broadcasting Input Tensors are only valid for op type: {}" + valid = True + extra = [] + tensors = [tens for tens in op.inputs if tens] + for tens in tensors: + if (tens.shape == []) and (op.type not in cls.shapeless_input_ops): + valid = False + extra.append(tens.name) + extra = "Op '{}' has shapeless input tensor(s): {}".format(op.name, ", ".join(extra)) + return valid, extra @staticmethod def constraint_tens_shape_size(op): "Input(s) and Output Tensors must not be greater than 4D" valid = True extra = [] - for tens in op.inputs + op.outputs: - if tens: - valid &= len(tens.shape) <= 4 - extra.append("shape={}".format(tens.shape)) - return valid, " ".join(extra) + tensors = [tens for tens in op.inputs + op.outputs if tens] + for tens in tensors: + if len(tens.shape) > 4: + valid = False + extra.append("Tensor '{}' has shape: {}".format(tens.name, tens.shape)) + return valid, ", ".join(extra) @classmethod - @docstring_format_args([supported_dtypes, supported_int32_tensor_ops]) + @docstring_format_args([supported_dtypes]) def constraint_tens_dtype(cls, op): - "Tensors must be of type: {}. Tensors which are int32 are only valid when op type is: {}" + "Tensors must be of type: {}" valid = True extra = [] tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] tensors = tensors if tensors else op.inputs for tens in tensors: - if tens.dtype == DataType.int32: - valid &= op.type in cls.supported_int32_tensor_ops - else: - valid &= tens.dtype in cls.supported_dtypes - extra.append("dtype={}".format(tens.dtype)) - return valid, " ".join(extra) + if tens.dtype not in cls.supported_dtypes: + valid = False + extra.append("Tensor '{}' has data type: {}".format(tens.name, tens.dtype)) + return valid, ", ".join(extra) @classmethod - @docstring_format_args(tens_dim_range) - def constraint_tens_dimension(cls, op): - "Tensor dimensions must be in the range {}-{} (inclusive)" - tens_min, tens_max = cls.tens_dim_range + @docstring_format_args([supported_int32_tensor_ops]) + def constraint_tens_int32_ops(cls, op): + "Tensors which are int32 are only valid when op type is: {}" valid = True extra = [] tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] tensors = tensors if tensors else op.inputs for tens in tensors: - valid &= all(tens_min <= dim <= tens_max for dim in tens.shape) - extra.append("shape={}".format(tens.shape)) - return valid, " ".join(extra) - - @classmethod - @docstring_format_args([supported_fused_activations]) - def constraint_faf(cls, op): - "The fused activation function (if present) must be one of type: {}" - faf = op.activation - valid = (faf is None) or (faf in cls.supported_fused_activations) - extra = "fused_activation_function={}".format(faf) + if (tens.dtype == DataType.int32) and (op.type not in cls.supported_int32_tensor_ops): + valid = False + extra.append(tens.name) + extra = "Op '{}' has int32 tensor(s): {}".format(op.name, ", ".join(extra)) return valid, extra - @staticmethod - def constraint_tens_quant_scale(op): - "Tensors with quantization scales must be finite" + @classmethod + @docstring_format_args(tens_dim_range) + def constraint_tens_dimension(cls, op): + "Tensor dimensions must be in the range {}-{} (inclusive)" + tens_min, tens_max = cls.tens_dim_range valid = True extra = [] tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] + tensors = tensors if tensors else op.inputs for tens in tensors: - if tens.quantization is not None and tens.quantization.scale_f32 is not None: - valid &= not np.isinf(tens.quantization.scale_f32).any() - extra.append("quantization.scale_f32={}".format(tens.quantization.scale_f32)) - return valid, " ".join(extra) + if not all(tens_min <= dim <= tens_max for dim in tens.shape): + valid = False + extra.append("Tensor '{}' has shape: {}".format(tens.name, tens.shape)) + return valid, ", ".join(extra) @staticmethod def constraint_tens_quant_none_check(op): @@ -250,6 +256,27 @@ class SupportedOperators: extra.append("Tensor '{}' has no quantization parameters".format(tens.name)) return valid, ", ".join(extra) + @staticmethod + def constraint_tens_quant_scale(op): + "Tensors with quantization scales must be finite" + valid = True + extra = [] + tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens] + for tens in tensors: + if (tens.quantization.scale_f32 is not None) and np.isinf(tens.quantization.scale_f32).any(): + valid = False + extra.append("Tensor '{}' has quantization scale: {}".format(tens.name, tens.quantization.scale_f32)) + return valid, ", ".join(extra) + + @classmethod + @docstring_format_args([supported_fused_activations]) + def constraint_faf(cls, op): + "The fused activation function (if present) must be one of type: {}" + faf = op.activation + valid = (faf is None) or (faf in cls.supported_fused_activations) + extra = "Op '{}' has its fused activation function as: {}".format(op.name, faf) + return valid, extra + @classmethod def check_convolution_restrictions(cls, op): # check stride |