.torch_dynamo

modules

• .torch_dynamo._dynamo_exporter
• .torch_dynamo.backend_helper
• .torch_dynamo.debug_backend
• .torch_dynamo.fast_backend
• experimental_experiment.torch_dynamo.partition

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, options: OptimizationOptions | None = None, export_options: str | ExportOptions | 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

• options – to define custom Optimization options, in that case, any other optimization parameter is ignored

• export_options – see ExportOptions

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.

The following example shows how to use the custom backend (based on onnxruntime).

<<<

import torch
from experimental_experiment.torch_dynamo import onnx_custom_backend


class MLP(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = torch.nn.Sequential(
            torch.nn.Linear(10, 32),
            torch.nn.Sigmoid(),
            torch.nn.Linear(32, 1),
        )

    def forward(self, x):
        return self.layers(x)


x = torch.randn(3, 10, dtype=torch.float32)

mlp = MLP()
expected = mlp(x)

compiled_model = torch.compile(
    mlp,
    backend=lambda *args, **kwargs: onnx_custom_backend(*args, verbose=1, **kwargs),
    dynamic=False,
    fullgraph=True,
)

try:
    got = compiled_model(x)
    diff = (expected - got).max()
    print(f"discrepancies: {diff}")
except (ImportError, AttributeError) as e:
    print("onnxruntime-training is not installed", e)

>>>

    [onnx_custom_backend] starts conversion to onnx.
    [to_onnx] build the graph module from <class 'torch.fx.graph_module.GraphModule.__new__.<locals>.GraphModuleImpl'>, type(args)=<class 'tuple'>
    [to_onnx] build the graph module with input_names=['input0', 'input1', 'input2', 'input3', 'input4']
    [_make_builder_interpreter] use existing <class 'torch.fx.graph_module.GraphModule.__new__.<locals>.GraphModuleImpl'>
    [to_onnx] graph module done in 0.0006636869984504301 s
    [to_onnx] start creating the onnx nodes
    [to_onnx] interpreter.function_options=FunctionOptions(export_as_function=True, name='*', domain='*', external_threshold=256, move_initializer_to_constant=True, return_initializer=True, merge_allowed=True, rename_allowed=True)
    [to_onnx] 13 onnx nodes done in 0.0017919490001077065 s
    [to_onnx] start conversion to onnx (before optimization) mask_outputs=None
    [GraphBuilder-UEI.optimize] start with 13 nodes
    [GraphBuilder-UEI.optimize] #patterns=50
    [GraphBuilder-UEI.optimize] start with subgraphs
    [GraphBuilder-UEI.optimize] done with subgraphs
    [GraphBuilderPatternOptimization-UEI.optimize] start with 7 nodes, 0 initializers, 50 patterns, priorities=[0, 1, 3]
    [GraphBuilderPatternOptimization-UEI.optimize] iteration 0: 7 nodes, priority=0
    [GraphBuilderPatternOptimization-UEI.optimize] increase priority to 1
    [GraphBuilderPatternOptimization-UEI.optimize] iteration 1: 7 nodes, priority=1
    [GraphBuilderPatternOptimization-UEI.optimize] applies 2 matches, 1*TransposeEqualReshapePattern, 1*TransposeMatMulPattern - time=0.001 | max_time=TransposeMatMulPattern:0.000
    [GraphBuilderPatternOptimization-UEI.optimize] iteration 2: 6 nodes, priority=1
    [GraphBuilderPatternOptimization-UEI.optimize] increase priority to 3
    [GraphBuilderPatternOptimization-UEI.optimize] iteration 3: 6 nodes, priority=3
    [GraphBuilderPatternOptimization-UEI.optimize] applies 2 matches, 2*MatMulAddPattern - time=0.001 | max_time=SlicesSplitPattern:0.000
    [GraphBuilderPatternOptimization-UEI.optimize] iteration 4: 4 nodes, priority=3
    [GraphBuilderPatternOptimization-UEI.optimize] stops current_priority_index=3, priorities=[0, 1, 3]
    [GraphBuilderPatternOptimization-UEI.optimize] done after 5 iterations with 4 nodes in 0.006
    [GraphBuilder-UEI.optimize] done with 4 nodes in 0.007
    [GraphBuilder-UEI.to_onnx] make_model 1 inits 0 params
    [GraphBuilder-UEI.time_evaluation_constants_] 0
    [GraphBuilder-UEI._build_initializers] start with 1 initializers, large_model=False, external_threshold=1024
    [GraphBuilder-UEI._build_initializers] switch low/high order
    [GraphBuilder-UEI._build_initializers] done in 2.1380001271609217e-06s with 1 initializers, 0 large initializers
    [GraphBuilder-UEI._add_shape_information] dynamic shapes replacements={}
    [to_onnx] to_onnx done in 0.007812095000190311s and 4 nodes, 1 initializers, 5 inputs, 1 outputs
    [onnx_custom_backend] to_onnx done in 0.011196019999260898 with 4 nodes and 0 local functions.
    [onnx_custom_backend] starts creating InferenceSession
    [onnx_custom_backend] InferenceSession done in 0.0038189269998838427
    discrepancies: 5.21540641784668e-08

onnx_debug_backend

experimental_experiment.torch_dynamo.onnx_debug_backend(graph_module: torch.fx.GraphModule, args: List[torch.Tensor | torch.SymInt | torch.SymFloat], 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.

The following example shows how to use the reference implementation (experimental_experiment.reference.ExtendedReferenceEvaluator) to run the onnx model and display the intermediate results.

<<<

import torch
from experimental_experiment.torch_dynamo import onnx_debug_backend


class MLP(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = torch.nn.Sequential(
            torch.nn.Linear(10, 32),
            torch.nn.Sigmoid(),
            torch.nn.Linear(32, 1),
        )

    def forward(self, x):
        return self.layers(x)


x = torch.randn(3, 10, dtype=torch.float32)

mlp = MLP()
expected = mlp(x)

compiled_model = torch.compile(
    mlp,
    backend=lambda *args, **kwargs: onnx_debug_backend(
        *args, verbose=(1, 10), backend="ref", **kwargs
    ),
    dynamic=False,
    fullgraph=True,
)

got = compiled_model(x)
diff = (expected - got).max()
print(f"discrepancies: {diff}")

>>>

    [to_onnx] build the graph module from <class 'torch.fx.graph_module.GraphModule.__new__.<locals>.GraphModuleImpl'>, type(args)=<class 'tuple'>
    [to_onnx] build the graph module with input_names=['input0', 'input1', 'input2', 'input3', 'input4']
    [_make_builder_interpreter] use existing <class 'torch.fx.graph_module.GraphModule.__new__.<locals>.GraphModuleImpl'>
    [to_onnx] graph module done in 0.0003567710009519942 s
    [to_onnx] start creating the onnx nodes
    [to_onnx] interpreter.function_options=FunctionOptions(export_as_function=True, name='*', domain='*', external_threshold=256, move_initializer_to_constant=True, return_initializer=True, merge_allowed=True, rename_allowed=True)
    [to_onnx] 13 onnx nodes done in 0.0011508319985296112 s
    [to_onnx] start conversion to onnx (before optimization) mask_outputs=None
    [GraphBuilder-SOE.optimize] start with 13 nodes
    [GraphBuilder-SOE.optimize] #patterns=50
    [GraphBuilder-SOE.optimize] start with subgraphs
    [GraphBuilder-SOE.optimize] done with subgraphs
    [GraphBuilderPatternOptimization-SOE.optimize] start with 7 nodes, 0 initializers, 50 patterns, priorities=[0, 1, 3]
    [GraphBuilderPatternOptimization-SOE.optimize] iteration 0: 7 nodes, priority=0
    [GraphBuilderPatternOptimization-SOE.optimize] increase priority to 1
    [GraphBuilderPatternOptimization-SOE.optimize] iteration 1: 7 nodes, priority=1
    [GraphBuilderPatternOptimization-SOE.optimize] applies 2 matches, 1*TransposeEqualReshapePattern, 1*TransposeMatMulPattern - time=0.001 | max_time=MatMulReshape2Of3Pattern:0.000
    [GraphBuilderPatternOptimization-SOE.optimize] iteration 2: 6 nodes, priority=1
    [GraphBuilderPatternOptimization-SOE.optimize] increase priority to 3
    [GraphBuilderPatternOptimization-SOE.optimize] iteration 3: 6 nodes, priority=3
    [GraphBuilderPatternOptimization-SOE.optimize] applies 2 matches, 2*MatMulAddPattern - time=0.000 | max_time=MatMulAddPattern:0.000
    [GraphBuilderPatternOptimization-SOE.optimize] iteration 4: 4 nodes, priority=3
    [GraphBuilderPatternOptimization-SOE.optimize] stops current_priority_index=3, priorities=[0, 1, 3]
    [GraphBuilderPatternOptimization-SOE.optimize] done after 5 iterations with 4 nodes in 0.005
    [GraphBuilder-SOE.optimize] done with 4 nodes in 0.005
    [GraphBuilder-SOE.to_onnx] make_model 1 inits 0 params
    [GraphBuilder-SOE.time_evaluation_constants_] 0
    [GraphBuilder-SOE._build_initializers] start with 1 initializers, large_model=False, external_threshold=1024
    [GraphBuilder-SOE._build_initializers] switch low/high order
    [GraphBuilder-SOE._build_initializers] done in 1.0949988791253418e-06s with 1 initializers, 0 large initializers
    [GraphBuilder-SOE._add_shape_information] dynamic shapes replacements={}
    [to_onnx] to_onnx done in 0.006358175000059418s and 4 nodes, 1 initializers, 5 inputs, 1 outputs
     +C init7_s2_-1_1: int64:(2,):[-1, 1]
     +I input0: float32:(32, 10):0.28775644302368164,0.26809242367744446,0.19505134224891663,-0.3084428906440735,0.030534425750374794...
     +I input1: float32:(32,):0.12526658177375793,0.11417041718959808,-0.1301477700471878,0.10220877081155777,-0.24447889626026154...
     +I input2: float32:(3, 10):0.0814129188656807,-1.196628451347351,-0.4546431303024292,2.6977527141571045,1.1262048482894897...
     +I input3: float32:(1, 32):-0.11466863006353378,-0.11456090956926346,-0.023113815113902092,0.05341425910592079,0.14014112949371338...
     +I input4: float32:(1,):[0.01023565698415041]
    Gemm(input2, input0, input1) -> input_1
     + input_1: float32:(3, 32):-0.9519858360290527,-0.006021060049533844,1.0189129114151,0.4728514850139618,0.10596977174282074...
    Sigmoid(input_1) -> input_2
     + input_2: float32:(3, 32):0.27848562598228455,0.4984947443008423,0.7347608208656311,0.6160584688186646,0.5264676809310913...
    Reshape(input3, init7_s2_-1_1) -> l_self_modules_layers_modules_2_parameters_weight_::T10
     + l_self_modules_layers_modules_2_parameters_weight_::T10: float32:(32, 1):-0.11466863006353378,-0.11456090956926346,-0.023113815113902092,0.05341425910592079,0.14014112949371338...
    Gemm(input_2, l_self_modules_layers_modules_2_parameters_weight_::T10, input4) -> output_0
     + output_0: float32:(3, 1):[-0.15895725786685944, -0.27114176750183105, -0.24197576940059662]
    discrepancies: 5.960464477539063e-08

dynger_backend

experimental_experiment.torch_dynamo.dynger_backend(graph_module: GraphModule, args: List[Tensor | SymInt | SymFloat], dynamic_shapes: Dict[str, Any] | Tuple[Any] | None = None, 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

Next examples shows how to display intermediate results while executing the graph produced by torch dynamo.

<<<

import torch
from experimental_experiment.torch_dynamo import dynger_backend


class MLP(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = torch.nn.Sequential(
            torch.nn.Linear(10, 32),
            torch.nn.Sigmoid(),
            torch.nn.Linear(32, 1),
        )

    def forward(self, x):
        return self.layers(x)


x = torch.randn(3, 10, dtype=torch.float32)

mlp = MLP()
expected = mlp(x)

compiled_model = torch.compile(
    mlp,
    backend=lambda *args, **kwargs: dynger_backend(*args, verbose=10, **kwargs),
    dynamic=False,
    fullgraph=True,
)

got = compiled_model(x)
diff = (expected - got).max()
print(f"discrepancies: {diff}")

>>>

    [dynger_backend] use existing <class 'torch.fx.graph_module.GraphModule.__new__.<locals>.GraphModuleImpl'>
    [dynger_backend] begin execution with 9 nodes
    <built-in function linear>((l_x_, l_self_modules_layers_modules_0_parameters_weight_, l_self_modules_layers_modules_0_parameters_bias_)) -> input_1
      + input_1: torch.float32:torch.Size([3, 32]):0.15862350165843964,-0.07750317454338074,0.5649838447570801,0.5340415835380554,-0.34463751316070557...
    <built-in method sigmoid of type object at 0x7b969bd7d320>((input_1,)) -> input_2
      + input_2: torch.float32:torch.Size([3, 32]):0.5395729541778564,0.4806338846683502,0.6376048922538757,0.6304252743721008,0.41468343138694763...
    <built-in function linear>((input_2, l_self_modules_layers_modules_2_parameters_weight_, l_self_modules_layers_modules_2_parameters_bias_)) -> input_3
      + input_3: torch.float32:torch.Size([3, 1]):-0.11279261112213135,-0.20974323153495789,0.09444430470466614
    [dynger_backend] done
    discrepancies: 0.0

Other functions

experimental_experiment.torch_dynamo.filter_decomposition_table(existing_table: Dict | None = None, filter_fct: Callable[[Any], bool] | 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.

• filter_fct – if specified, a decomposition function is remove if the function returns false

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())

>>>

    {<torch._higher_order_ops.out_dtype.OutDtypeOperator object at 0x7b959de97860>: <function out_dtype_decomp at 0x7b959d757420>,
     <OpOverload(op='aten.hardtanh', overload='default')>: <function hardtanh at 0x7b959d5c32e0>,
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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())

>>>

    {<OpOverload(op='aten.embedding_dense_backward', overload='default')>: <function embedding_dense_backward at 0x7b959d6e1f80>,
     <OpOverload(op='aten.native_layer_norm_backward', overload='default')>: <function native_layer_norm_backward at 0x7b959d6e3740>}

experimental_experiment.torch_dynamo.get_decomposition_table_by_name(name: str)

Returns a predefined decomposition table.

Parameters:

name – name see below

Returns:

decomposition table

• ‘none’: do not decompose

• ‘default’: get_decomposition_table()

• ‘onnxscript’: get_decomposition_table_onnxscript()

• ‘dynamo’: 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/github/experimental-experiment/experimental_experiment/torch_dynamo/__init__.py:140: DeprecationWarning: torch.onnx.dynamo_export is deprecated since 2.7.0. Please use torch.onnx.export(..., dynamo=True) instead.
      return create_onnx_friendly_decomposition_table(onnx_registry or OnnxRegistry())
    /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/onnx/_internal/_exporter_legacy.py:91: 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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experimental_experiment.torch_dynamo.get_decomposition_table_onnxscript()

Returns the decomposition table used by torch.onnx.export().

The value is:

<<<

import pprint
from experimental_experiment.torch_dynamo import get_decomposition_table_onnxscript

pprint.pprint(get_decomposition_table_onnxscript())

>>>

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experimental_experiment.torch_dynamo.pprint_storage(storage: Any, indent: int = 0, as_list: bool = False) → List[str] | str

Pretty print for the storage.

Parameters:

• storage – any object

• indent – indentation

• as_list – return list or string

Returns:

list or string