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| # Copyright (c) ONNX Project Contributors | |
| # SPDX-License-Identifier: Apache-2.0 | |
| import numpy as np | |
| from onnx.reference.custom_element_types import ( | |
| bfloat16, | |
| float8e4m3fn, | |
| float8e4m3fnuz, | |
| float8e5m2, | |
| float8e5m2fnuz, | |
| int4, | |
| uint4, | |
| ) | |
| from onnx.reference.op_run import OpRun, RefAttrName | |
| def _check_dtype(val): # type: ignore | |
| a = val.dtype | |
| if not isinstance(a, np.dtype) and a not in { | |
| bfloat16, | |
| float8e4m3fn, | |
| float8e4m3fnuz, | |
| float8e5m2, | |
| float8e5m2fnuz, | |
| uint4, | |
| int4, | |
| np.int8, | |
| np.uint8, | |
| np.float16, | |
| np.float32, | |
| np.float64, | |
| np.int32, | |
| np.int64, | |
| np.int16, | |
| np.uint16, | |
| np.uint32, | |
| np.bool_, | |
| np.str_, | |
| np.uint64, | |
| bool, | |
| str, | |
| }: | |
| raise TypeError( | |
| f"Type ({a}, {type(a)}) is not a numpy type (operator 'Constant')" | |
| ) | |
| class ConstantCommon(OpRun): | |
| def _check(self, cst): # type: ignore | |
| if isinstance(cst, tuple): | |
| raise TypeError(f"Unexpected type {type(cst)} for a constant.") | |
| return cst | |
| class Constant_1(ConstantCommon): | |
| def __init__(self, onnx_node, run_params): # type: ignore | |
| ConstantCommon.__init__(self, onnx_node, run_params) | |
| self.cst = self.value # type: ignore | |
| _check_dtype(self.cst) | |
| def _run(self, **overridden_attributes): # type: ignore | |
| if overridden_attributes and ( | |
| len(overridden_attributes) > 1 | |
| or "value" not in overridden_attributes | |
| or id(overridden_attributes["value"]) != id(self.value) | |
| ): | |
| raise RuntimeError( | |
| "Function attributes are not implemented for opset <= 11. Use opset > 12." | |
| ) | |
| return (self._check(self.cst),) | |
| class Constant_9(Constant_1): | |
| def __init__(self, onnx_node, run_params): # type: ignore | |
| Constant_1.__init__(self, onnx_node, run_params) | |
| class Constant_11(ConstantCommon): | |
| def __init__(self, onnx_node, run_params): # type: ignore | |
| ConstantCommon.__init__(self, onnx_node, run_params) | |
| if getattr(self, "sparse_value", None) is None: | |
| self.cst = self.value # type: ignore | |
| else: | |
| self.cst = self.sparse_value # type: ignore | |
| _check_dtype(self.cst) | |
| def _run(self, **overridden_attributes): # type: ignore | |
| if overridden_attributes and ( | |
| len(overridden_attributes) > 1 | |
| or "value" not in overridden_attributes | |
| or id(overridden_attributes["value"]) != id(self.value) | |
| ): | |
| raise RuntimeError( | |
| "Function attributes are not implemented for opset <= 11. Use opset > 12." | |
| ) | |
| return (self._check(self.cst),) | |
| class Constant_12(ConstantCommon): | |
| def __init__(self, onnx_node, run_params): # type: ignore | |
| ConstantCommon.__init__(self, onnx_node, run_params) | |
| if hasattr(self, "sparse_value") and self.sparse_value is not None: # type: ignore | |
| self.cst_name = "sparse_value" | |
| self.cst = self.sparse_value # type: ignore | |
| self.cst_convert = lambda v: v | |
| elif hasattr(self, "value") and self.value is not None: # type: ignore | |
| self.cst_name = "value" # type: ignore | |
| self.cst = self.value if isinstance(self.value, RefAttrName) else self.value # type: ignore | |
| self.cst_convert = lambda v: v | |
| else: | |
| for attr, np_dtype in { | |
| "value_float": np.float32, | |
| "value_floats": np.float32, | |
| "value_int": np.int64, | |
| "value_ints": np.int64, | |
| "value_string": np.str_, | |
| "value_strings": np.str_, | |
| }.items(): | |
| if hasattr(self, attr) and getattr(self, attr) is not None: # type: ignore | |
| self.cst_name = attr | |
| v = getattr(self, attr) | |
| self.cst = ( | |
| v # type: ignore | |
| if isinstance(v, RefAttrName) # type: ignore | |
| else np.array(v, dtype=np_dtype) # type: ignore | |
| ) | |
| self.cst_convert = lambda v, np_dtype=np_dtype: np.array( # type: ignore | |
| v, dtype=np_dtype | |
| ) | |
| break | |
| if not hasattr(self, "cst_name"): | |
| raise AttributeError( | |
| f"No constant is defined for operator 'Constant', outputs are {onnx_node.output}." | |
| ) | |
| def _run(self, **overridden_attributes): # type: ignore | |
| if self.has_linked_attribute: | |
| if overridden_attributes is None: | |
| raise RuntimeError( | |
| f"Attributes are empty, cannot retrieve value for {self.cst!r}." | |
| ) | |
| if self.cst_name not in overridden_attributes: | |
| raise RuntimeError( | |
| f"Cannot find attribute {self.cst_name!r} in {list(overridden_attributes)!r}." | |
| ) | |
| value = overridden_attributes[self.cst_name] | |
| if isinstance(value, np.ndarray): | |
| return (value,) | |
| return (self.cst_convert(value),) | |
| return (self._check(self.cst),) | |