onnx_diagnostic.reference.torch_ops¶
modules
- onnx_diagnostic.reference.torch_ops.access_ops
- onnx_diagnostic.reference.torch_ops.binary_ops
- onnx_diagnostic.reference.torch_ops.controlflow_ops
- onnx_diagnostic.reference.torch_ops.generator_ops
- onnx_diagnostic.reference.torch_ops.nn_ops
- onnx_diagnostic.reference.torch_ops.other_ops
- onnx_diagnostic.reference.torch_ops.reduce_ops
- onnx_diagnostic.reference.torch_ops.sequence_ops
- onnx_diagnostic.reference.torch_ops.shape_ops
- onnx_diagnostic.reference.torch_ops.unary_ops
OpRunKernel¶
- class onnx_diagnostic.reference.torch_ops.OpRunKernel(node: NodeProto, version: int | None = None, verbose: int = 0, custom_kernels: Dict[Tuple[str, str], type] | None = None)[source][source]¶
- Main class. Every kernel should inherit from it. It does not copy the proto. - classmethod device_dependent() bool[source][source]¶
- Returns True if the kernel needs a device to be efficiently initialized. 
 - get_attribute_float(node: NodeProto, name: str, default_value: float | None = None) float | None[source][source]¶
- Returns an attribute as an int. - Parameters:
- node – NodeProto 
- name – name 
- default_value – default_value 
 
- Returns:
- value 
 
 - get_attribute_int(node: NodeProto, name: str, default_value: int | None = None) int | None[source][source]¶
- Returns an attribute as an int. - Parameters:
- node – NodeProto 
- name – name 
- default_value – default_value 
 
- Returns:
- value 
 
 - get_attribute_ints(node: NodeProto, name: str, default_value: Tuple[int, ...] | None = None) Tuple[int, ...] | None[source][source]¶
- Returns an attribute as a tuple of ints. - Parameters:
- node – NodeProto 
- name – name 
- default_value – default_value 
 
- Returns:
- value 
 
 - get_attribute_string(node: NodeProto, name: str, default_value: str | None = None) str | None[source][source]¶
- Returns an attribute as a tuple of ints. - Parameters:
- node – NodeProto 
- name – name 
- default_value – default_value 
 
- Returns:
- value 
 
 - get_attribute_tensor(node: NodeProto, name: str) Tensor | None[source][source]¶
- Returns an attribute as a torch tensor. - Parameters:
- node – NodeProto 
- name – name 
- default_value – default_value 
 
- Returns:
- value 
 
 - run(*args: OpRunValue | None) OpRunValue | Tuple[OpRunValue | None, ...][source][source]¶
- Kernel implementation. 
 
OpRunTensor¶
- class onnx_diagnostic.reference.torch_ops.OpRunTensor(tensor, is_constant: bool = False, may_cpu: bool = False)[source][source]¶
- Wrapper around a tensor. - Parameters:
- tensor – torch.Tensor 
- is_constant – is it a constant 
- may_cpu – change the device the tensor is if more appropriate 
 
 - copy() OpRunTensor[source][source]¶
- Shallow copy. 
 - property device¶
- Returns the device. 
 - property dtype¶
 - property shape¶
 - to(to: Any) OpRunTensor[source][source]¶
- Changes the device. 
 
OpRunValue¶
OpRunSequence¶
- class onnx_diagnostic.reference.torch_ops.OpRunSequence(sequence: List[Tensor] | None = None, dtype: dtype = torch.float32)[source][source]¶
- Defines a sequence. - copy() OpRunSequence[source][source]¶
- Shallow copy. 
 - property dtype¶
 - insert_at(tensor: Tensor, position: OpRunTensor | None = None) OpRunSequence[source][source]¶
- Inserts a value at a given position. 
 
OpRunFunction¶
- class onnx_diagnostic.reference.torch_ops.OpRunFunction(runtime: onnx_diagnostic.reference.TorchOnnxEvaluator, node: NodeProto, version: int | None = None, verbose: int = 0)[source][source]¶
- Defines a kernel based on a local functions. - run(*args: OpRunValue | None) OpRunValue | Tuple[OpRunValue | None, ...][source][source]¶
- Kernel implementation.