torch_dynamo

dynger_backend

experimental_experiment.torch_dynamo.dynger_backend(graph_module: GraphModule, args: List[Tensor | SymInt], optimize: bool = True, verbose: int | Tuple[int, int] = 0) → Callable

Eager backend for dynamo.

Parameters:

• graph_module – graph to export

• args – arguments

• optimize – optimize or not, those optimization would be done on the graph module itself

• verbose – adjust verbosity, if tuple, if gives different verbosity level to the exporter and the runtime

Returns:

Callable

onnx_debug_backend

experimental_experiment.torch_dynamo.onnx_debug_backend(graph_module: torch.fx.GraphModule, args: List[torch.Tensor | torch.SymInt], target_opset: int | None = None, backend: str | Callable[[ModelProto, bool | None], Any] = 'ort', verbose: int | Tuple[int, int] = 0, dump_prefix: None = None, dump_patterns: str | None = None, providers: Tuple[str] | None = None, raise_exc: bool = True, storage: Dict[str, Any] | None = None, raise_list: Set[str] | None = None, enable_pattern: str | List[str | type] | None = 'default', disable_pattern: str | List[str | type] | None = None, pre_ort_model_transforms: Callable[[ModelProto], ModelProto] | List[Callable[[ModelProto], ModelProto]] | None = None, ort_optimization_level: str | None = None, dispatcher: Dispatcher | None = None, rename_inputs: bool = True, optimize: bool = True, processor: str = 'CPU', order_algorithm: str | None = None) → Callable

Custom backend to export torch models into onnx (see torch.compiler). This backend is not meant to be efficient, it is more to check the conversion is ok. It relies either on onnxruntime or the python reference implementation.

Parameters:

• graph_module – graph to export

• args – arguments

• target_opset – opset to use for the conversion

• backend – after the conversion, the model is executed with a runtime, onnxruntime or the reference implementation, it must be a value among ‘ort’, ‘ref’ or a class, it can be a function as well which returns an object behaving the same way

• verbose – adjust verbosity, if tuple, if gives different verbosity level to the exporter and the runtime

• dump_prefix – prefix used to dump the model generated by the backend

• dump_patterns – dump the patterns as well

• providers – where to run the model, by default

• raise_exc – raise an exception whenever something goes wrong

• storage – to store any interesting objects during the process

• raise_list – the builder stops any time a name falls into that list, this is a debbuging tool

• enable_pattern – optimization patterns to enable

• disable_pattern – optimization patterns to disable

• pre_ort_model_transforms – list of transformations applied on the final ModelProto

• ort_optimization_level – graph optimization level for onnxruntime, the default value is the same as what onnxruntime defines

• dispatcher – see experimental_experiment.torch_interpreter.Dispatcher

• rename_inputs – rename inputs into input_{i}

• optimize – enable or disable the optimization

• processor – specifies the processor it is optimized for

• order_algorithm – algorithm optimizing the order the onnx node, none by default

Returns:

Callable

See 301: Compares LLAMA exporters for onnxrt backend for an example. If not empty, storage keeps the memory of the data generated, onnx models, graph module as well the inputs and outputs when the model is run.

onnx_custom_backend

experimental_experiment.torch_dynamo.onnx_custom_backend(graph_module: torch.fx.GraphModule, args: List[torch.Tensor], target_opset: int | None = None, backend: str = 'ort', verbose: int | Tuple[int, int] = 0, dump_prefix: None = None, dump_patterns: str | None = None, providers: Tuple[str] | None = None, raise_exc: bool = True, storage: Dict[str, Any] | None = None, enable_pattern: str | List[str | type] | None = 'default', disable_pattern: str | List[str | type] | None = None, pre_ort_model_transforms: Callable[[ModelProto], ModelProto] | List[Callable[[ModelProto], ModelProto]] | None = None, ort_optimization_level: str | None = None, dispatcher: Dispatcher | None = None, rename_inputs: bool = True, optimize: bool = True, exporter: str | None = None, processor: str = 'CPU', order_algorithm: str | None = None) → Callable

Custom backend to export torch models into onnx (see torch.compiler). This backend relies on onnxruntime and tries to be as efficient as possible.

Parameters:

• graph_module – graph to export

• args – arguments

• target_opset – opset to use for the conversion

• backend – only ‘ort’ is allowed

• verbose – adjust verbosity, if tuple, if gives different verbosity level to the exporter and the runtime

• dump_prefix – to dump the models and the inputs

• dump_patterns – dump the patterns as well

• providers – where to run the model, by default

• raise_exc – raise an exception whenever something goes wrong

• storage – to store any interesting objects during the process

• enable_pattern – optimization patterns to enable

• disable_pattern – optimization patterns to disable

• pre_ort_model_transforms – list of transformations applied on the final ModelProto

• ort_optimization_level – graph optimization level for onnxruntime, the default value is the same as what onnxruntime defines

• dispatcher – see experimental_experiment.torch_interpreter.Dispatcher

• rename_inputs – rename the inputs

• optimize – enable or disable the optimization

• exporter – use a different exporter

• processor – optimization should be made for this processor or this list of processors (comma separated value)

• order_algorithm – algorithm optimizing the order the onnx node, none by default

Returns:

Callable

See 301: Compares LLAMA exporters for onnxrt backend or 101: A custom backend for torch for examples. If not empty, storage keeps the memory of the data generated, onnx models, graph module as well the inputs and outputs when the model is run.

filter_decomposition_table

experimental_experiment.torch_dynamo.filter_decomposition_table(existing_table: Dict | None = None) → Dict

Returns the decomposition table when some conversions because their translation in ONNX is less efficient.

Parameters:

existing_table – dictionary of decompositions, by default, it is torch._decomp.decomposition_table.

Returns:

new table

import torch
from torch._dynamo.backends.common import aot_autograd
from experimental_experiment.torch_dynamo import filter_decomposition_table

aot_compiler = aot_autograd(fw_compiler=backend_debug, decompositions=filter_decomposition_table())

compiled_model = torch.compile(
    model,
    backend=aot_compiler,
    dynamic=dynamic,
    fullgraph=fullgraph,
)

The value is:

<<<

import pprint
from experimental_experiment.torch_dynamo import filter_decomposition_table

pprint.pprint(filter_decomposition_table())

>>>

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get_decomposition_table

experimental_experiment.torch_dynamo.get_decomposition_table()

Returns the decomposition table needed to translate backward graph into onnx. It should used as follows:

import torch
from torch._dynamo.backends.common import aot_autograd
from experimental_experiment.torch_dynamo import get_decomposition_table

aot_compiler = aot_autograd(fw_compiler=backend_debug, decompositions=get_decomposition_table())

compiled_model = torch.compile(
    model,
    backend=aot_compiler,
    dynamic=dynamic,
    fullgraph=fullgraph,
)

The value is:

<<<

import pprint
from experimental_experiment.torch_dynamo import get_decomposition_table

pprint.pprint(get_decomposition_table())

>>>

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get_decomposition_table_dynamo

experimental_experiment.torch_dynamo.get_decomposition_table_dynamo(onnx_registry=None)

Returns the decomposition table needed for the dynamo exporter.

Parameters:

onnx_registry – instance of class torch.onnx._internal.exporter.OnnxRegistry

The value is:

<<<

import pprint
from experimental_experiment.torch_dynamo import get_decomposition_table_dynamo

pprint.pprint(get_decomposition_table_dynamo())

>>>

    /home/xadupre/.local/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py:137: UserWarning: torch.onnx.dynamo_export only implements opset version 18 for now. If you need to use a different opset version, please register them with register_custom_op.
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OrtBackend

experimental_experiment.torch_dynamo.fast_backend.OrtBackend(sess: onnxruntime.InferenceSession, run_options: onnxruntime.RunOptions | None = None, devices: Dict[int, Any] | None = None, input_names: List[str] | None = None, output_names: List[str] | None = None, is_dimension_in: List[Tuple[bool, int, str]] | None = None, is_dimension_out: List[Tuple[bool, int, str | None]] | None = None, dump_first_inputs: str | None = None, stor: Dict[str, Any] | None = None, onnx_model: ModelProto | None = None)

Wraps method run_with_ortvaluevector from InferenceSession to implement a backend for torch.dynamo.