.torch_dynamo¶
modules
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[source]¶
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.Dispatcherrename_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 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 0.018390917997749057 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 0.0019064779990003444 s [to_onnx] start conversion to onnx (before optimization) mask_outputs=None [GraphBuilder-NEU._add_shape_information] dynamic shapes replacements={} [GraphBuilder-NEU.optimize] start with 13 nodes [GraphBuilder-NEU.optimize] #patterns=85 [GraphBuilder-NEU.optimize] start with subgraphs [GraphBuilder-NEU.optimize] done with subgraphs [GraphBuilderPatternOptimization-NEU.optimize] start with 7 nodes, 0 initializers, 85 patterns, priorities=[0, 1, 3], max_iter=30 [GraphBuilderPatternOptimization-NEU.optimize] same children={'SameChildrenPattern', 'SameChildrenFromInputPattern'} [GraphBuilderPatternOptimization-NEU.optimize] iteration 0: 7 nodes, priority=0 [GraphBuilderPatternOptimization-NEU.optimize] increase priority to 1 [GraphBuilderPatternOptimization-NEU.optimize] iteration 1: 7 nodes, priority=1 [GraphBuilderPatternOptimization-NEU.optimize] applies 2 matches, 1*TransposeEqualReshapePattern, 1*TransposeMatMulPattern - time=0.001 | max_time=TransposeMatMulPattern:0.000 [GraphBuilderPatternOptimization-NEU.optimize] iteration 2: 6 nodes, priority=1 [GraphBuilderPatternOptimization-NEU.optimize] increase priority to 3 [GraphBuilderPatternOptimization-NEU.optimize] iteration 3: 6 nodes, priority=3 [GraphBuilderPatternOptimization-NEU.optimize] applies 2 matches, 2*MatMulAddPattern - time=0.001 | max_time=MatMulAddPattern:0.000 [GraphBuilderPatternOptimization-NEU.optimize] iteration 4: 4 nodes, priority=3 [GraphBuilderPatternOptimization-NEU.optimize] stops current_priority_index=3, priorities=[0, 1, 3] [GraphBuilderPatternOptimization-NEU.optimize] done after 5 iterations with 4 nodes in 0.008 [GraphBuilder-NEU.optimize] done with 4 nodes in 0.009 [GraphBuilder-NEU.to_onnx] make_model 1 inits 0 params [GraphBuilder-NEU.time_evaluation_constants_] 0 [GraphBuilder-NEU._build_initializers] start with 1 initializers, large_model=False, external_threshold=1024 [GraphBuilder-NEU._build_initializers] switch low/high order [GraphBuilder-NEU._build_initializers] done in 9.969953680410981e-07s with 1 initializers, 0 large initializers [GraphBuilder-NEU._add_shape_information] dynamic shapes replacements={} [to_onnx] to_onnx done in 0.023284736002096906s and 4 nodes, 1 initializers, 5 inputs, 1 outputs [onnx_custom_backend] to_onnx done in 0.0472444600018207 with 4 nodes and 0 local functions. [onnx_custom_backend] starts creating InferenceSession [onnx_custom_backend] InferenceSession done in 0.059248491001199 discrepancies: 2.9802322387695312e-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[source]¶
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.Dispatcherrename_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
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 0.00041006099490914494 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 0.001484738000726793 s [to_onnx] start conversion to onnx (before optimization) mask_outputs=None [GraphBuilder-NAU._add_shape_information] dynamic shapes replacements={} [GraphBuilder-NAU.optimize] start with 13 nodes [GraphBuilder-NAU.optimize] #patterns=85 [GraphBuilder-NAU.optimize] start with subgraphs [GraphBuilder-NAU.optimize] done with subgraphs [GraphBuilderPatternOptimization-NAU.optimize] start with 7 nodes, 0 initializers, 85 patterns, priorities=[0, 1, 3], max_iter=30 [GraphBuilderPatternOptimization-NAU.optimize] same children={'SameChildrenPattern', 'SameChildrenFromInputPattern'} [GraphBuilderPatternOptimization-NAU.optimize] iteration 0: 7 nodes, priority=0 [GraphBuilderPatternOptimization-NAU.optimize] increase priority to 1 [GraphBuilderPatternOptimization-NAU.optimize] iteration 1: 7 nodes, priority=1 [GraphBuilderPatternOptimization-NAU.optimize] applies 2 matches, 1*TransposeEqualReshapePattern, 1*TransposeMatMulPattern - time=0.001 | max_time=TransposeMatMulPattern:0.000 [GraphBuilderPatternOptimization-NAU.optimize] iteration 2: 6 nodes, priority=1 [GraphBuilderPatternOptimization-NAU.optimize] increase priority to 3 [GraphBuilderPatternOptimization-NAU.optimize] iteration 3: 6 nodes, priority=3 [GraphBuilderPatternOptimization-NAU.optimize] applies 2 matches, 2*MatMulAddPattern - time=0.001 | max_time=ShapeBasedEditDistanceReshapePattern:0.000 [GraphBuilderPatternOptimization-NAU.optimize] iteration 4: 4 nodes, priority=3 [GraphBuilderPatternOptimization-NAU.optimize] stops current_priority_index=3, priorities=[0, 1, 3] [GraphBuilderPatternOptimization-NAU.optimize] done after 5 iterations with 4 nodes in 0.007 [GraphBuilder-NAU.optimize] done with 4 nodes in 0.008 [GraphBuilder-NAU.to_onnx] make_model 1 inits 0 params [GraphBuilder-NAU.time_evaluation_constants_] 0 [GraphBuilder-NAU._build_initializers] start with 1 initializers, large_model=False, external_threshold=1024 [GraphBuilder-NAU._build_initializers] switch low/high order [GraphBuilder-NAU._build_initializers] done in 8.189963409677148e-07s with 1 initializers, 0 large initializers [GraphBuilder-NAU._add_shape_information] dynamic shapes replacements={} [to_onnx] to_onnx done in 0.008945138004492037s and 4 nodes, 1 initializers, 5 inputs, 1 outputs +C init7_s2_-1_1: int64:(2,):[-1, 1] +I input0: float32:(32, 10):0.27241018414497375,-0.03815949335694313,0.10592873394489288,-0.22973860800266266,0.037709012627601624... +I input1: float32:(32,):0.1299392729997635,0.2340960055589676,0.054008450359106064,-0.06522972881793976,-0.01168148498982191... +I input2: float32:(3, 10):-0.5710235834121704,1.2187507152557373,-0.9150499701499939,-0.8638495802879333,-1.2209982872009277... +I input3: float32:(1, 32):0.15309809148311615,-0.02297319285571575,0.12369871884584427,0.1560295969247818,0.0856046974658966... +I input4: float32:(1,):[-0.08546961098909378] Gemm(input2, input0, input1) -> input_1 + input_1: float32:(3, 32):-0.0701008290052414,-0.5499999523162842,0.45859789848327637,0.359585165977478,-0.046965863555669785... Sigmoid(input_1) -> input_2 + input_2: float32:(3, 32):0.4824819564819336,0.36586442589759827,0.61268150806427,0.5889400243759155,0.4882606863975525... 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):0.15309809148311615,-0.02297319285571575,0.12369871884584427,0.1560295969247818,0.0856046974658966... Gemm(input_2, l_self_modules_layers_modules_2_parameters_weight_::T10, input4) -> output_0 + output_0: float32:(3, 1):[0.15709257125854492, 0.14745518565177917, -0.050584908574819565] discrepancies: 2.9802322387695312e-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[source]¶
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]):0.24609524011611938,-1.8116490840911865,-0.19826310873031616,0.09716463088989258,0.09442870318889618... <built-in method sigmoid of type object at 0x71d26b538100>((input_1,)) -> input_2 + input_2: torch.float32:torch.Size([3, 32]):0.5612151622772217,0.14043892920017242,0.4505959451198578,0.524272084236145,0.5235896706581116... <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]):-0.039011768996715546,0.4181481599807739,0.3182689845561981 [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[source]¶
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())
>>>
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- experimental_experiment.torch_dynamo.get_decomposition_table()[source]¶
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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- experimental_experiment.torch_dynamo.get_decomposition_table_by_name(name: str)[source]¶
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)[source]¶
Returns the decomposition table needed for the dynamo exporter.
- Parameters:
onnx_registry – sent to
create_onnx_friendly_decomposition_table
The value is:
<<<
import pprint from experimental_experiment.torch_dynamo import get_decomposition_table_dynamo pprint.pprint(get_decomposition_table_dynamo())
>>>
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- experimental_experiment.torch_dynamo.get_decomposition_table_onnxscript()[source]¶
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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