Source code for torch.onnx
import torch._C as _C
TensorProtoDataType = _C._onnx.TensorProtoDataType
OperatorExportTypes = _C._onnx.OperatorExportTypes
TrainingMode = _C._onnx.TrainingMode
PYTORCH_ONNX_CAFFE2_BUNDLE = _C._onnx.PYTORCH_ONNX_CAFFE2_BUNDLE
ONNX_ARCHIVE_MODEL_PROTO_NAME = "__MODEL_PROTO"
# TODO: Update these variables when there
# is a new ir_version and producer_version
# and use these values in the exporter
ir_version = _C._onnx.IR_VERSION
producer_name = "pytorch"
producer_version = _C._onnx.PRODUCER_VERSION
constant_folding_opset_versions = [9, 10, 11, 12]
class ExportTypes:
PROTOBUF_FILE = 1
ZIP_ARCHIVE = 2
COMPRESSED_ZIP_ARCHIVE = 3
DIRECTORY = 4
def _export(*args, **kwargs):
from torch.onnx import utils
result = utils._export(*args, **kwargs)
return result
[docs]def export(model, args, f, export_params=True, verbose=False, training=TrainingMode.EVAL,
input_names=None, output_names=None, aten=False, export_raw_ir=False,
operator_export_type=None, opset_version=None, _retain_param_name=True,
do_constant_folding=True, example_outputs=None, strip_doc_string=True,
dynamic_axes=None, keep_initializers_as_inputs=None, custom_opsets=None,
enable_onnx_checker=True, use_external_data_format=False):
r"""
Export a model into ONNX format. This exporter runs your model
once in order to get a trace of its execution to be exported;
at the moment, it supports a limited set of dynamic models (e.g., RNNs.)
Args:
model (torch.nn.Module): the model to be exported.
args (tuple of arguments or torch.Tensor, a dictionary consisting of named arguments (optional)):
a dictionary to specify the input to the corresponding named parameter:
- KEY: str, named parameter
- VALUE: corresponding input
args can be structured either as:
1. ONLY A TUPLE OF ARGUMENTS or torch.Tensor::
‘’args = (x, y, z)’'
The inputs to the model, e.g., such that ``model(*args)`` is a valid invocation
of the model. Any non-Tensor arguments will be hard-coded into the exported model;
any Tensor arguments will become inputs of the exported model, in the order they
occur in args. If args is a Tensor, this is equivalent to having
called it with a 1-ary tuple of that Tensor.
2. A TUPLE OF ARGUEMENTS WITH A DICTIONARY OF NAMED PARAMETERS::
‘’args = (x,
{
‘y’: input_y,
‘z’: input_z
}) ‘’
The inputs to the model are structured as a tuple consisting of
non-keyword arguments and the last value of this tuple being a dictionary
consisting of named parameters and the corresponding inputs as key-value pairs.
If certain named argument is not present in the dictionary, it is assigned
the default value, or None if default value is not provided.
Cases in which an dictionary input is the last input of the args tuple
would cause a conflict when a dictionary of named parameters is used.
The model below provides such an example.
class Model(torch.nn.Module):
def forward(self, k, x):
...
return x
m = Model()
k = torch.randn(2, 3)
x = {torch.tensor(1.): torch.randn(2, 3)}
In the previous iteration, the call to export API would look like
torch.onnx.export(model, (k, x), ‘test.onnx’)
This would work as intended. However, the export function
would now assume that the ‘x’ input is intended to represent the optional
dictionary consisting of named arguments. In order to prevent this from being
an issue a constraint is placed to provide an empty dictionary as the last
input in the tuple args in such cases. The new call would look like this.
torch.onnx.export(model, (k, x, {}), ‘test.onnx’)
f: a file-like object (has to implement fileno that returns a file descriptor)
or a string containing a file name. A binary Protobuf will be written
to this file.
export_params (bool, default True): if specified, all parameters will
be exported. Set this to False if you want to export an untrained model.
In this case, the exported model will first take all of its parameters
as arguments, the ordering as specified by ``model.state_dict().values()``
verbose (bool, default False): if specified, we will print out a debug
description of the trace being exported.
training (enum, default TrainingMode.EVAL):
TrainingMode.EVAL: export the model in inference mode.
TrainingMode.PRESERVE: export the model in inference mode if model.training is
False and to a training friendly mode if model.training is True.
TrainingMode.TRAINING: export the model in a training friendly mode.
input_names(list of strings, default empty list): names to assign to the
input nodes of the graph, in order
output_names(list of strings, default empty list): names to assign to the
output nodes of the graph, in order
aten (bool, default False): [DEPRECATED. use operator_export_type] export the
model in aten mode. If using aten mode, all the ops original exported
by the functions in symbolic_opset<version>.py are exported as ATen ops.
export_raw_ir (bool, default False): [DEPRECATED. use operator_export_type]
export the internal IR directly instead of converting it to ONNX ops.
operator_export_type (enum, default OperatorExportTypes.ONNX):
OperatorExportTypes.ONNX: All ops are exported as regular ONNX ops
(with ONNX namespace).
OperatorExportTypes.ONNX_ATEN: All ops are exported as ATen ops
(with aten namespace).
OperatorExportTypes.ONNX_ATEN_FALLBACK: If an ATen op is not supported
in ONNX or its symbolic is missing, fall back on ATen op. Registered ops
are exported to ONNX regularly.
Example graph::
graph(%0 : Float)::
%3 : int = prim::Constant[value=0]()
%4 : Float = aten::triu(%0, %3) # missing op
%5 : Float = aten::mul(%4, %0) # registered op
return (%5)
is exported as::
graph(%0 : Float)::
%1 : Long() = onnx::Constant[value={0}]()
%2 : Float = aten::ATen[operator="triu"](%0, %1) # missing op
%3 : Float = onnx::Mul(%2, %0) # registered op
return (%3)
In the above example, aten::triu is not supported in ONNX, hence
exporter falls back on this op.
OperatorExportTypes.RAW: Export raw ir.
OperatorExportTypes.ONNX_FALLTHROUGH: If an op is not supported
in ONNX, fall through and export the operator as is, as a custom
ONNX op. Using this mode, the op can be exported and implemented by
the user for their runtime backend.
Example graph::
graph(%x.1 : Long(1, strides=[1]))::
%1 : None = prim::Constant()
%2 : Tensor = aten::sum(%x.1, %1)
%y.1 : Tensor[] = prim::ListConstruct(%2)
return (%y.1)
is exported as::
graph(%x.1 : Long(1, strides=[1]))::
%1 : Tensor = onnx::ReduceSum[keepdims=0](%x.1)
%y.1 : Long() = prim::ListConstruct(%1)
return (%y.1)
In the above example, prim::ListConstruct is not supported, hence
exporter falls through.
opset_version (int, default is 9): by default we export the model to the
opset version of the onnx submodule. Since ONNX's latest opset may
evolve before next stable release, by default we export to one stable
opset version. Right now, supported stable opset version is 9.
The opset_version must be _onnx_main_opset or in _onnx_stable_opsets
which are defined in torch/onnx/symbolic_helper.py
do_constant_folding (bool, default False): If True, the constant-folding
optimization is applied to the model during export. Constant-folding
optimization will replace some of the ops that have all constant
inputs, with pre-computed constant nodes.
example_outputs (tuple of Tensors, default None): Model's example outputs being exported.
example_outputs must be provided when exporting a ScriptModule or TorchScript Function.
strip_doc_string (bool, default True): if True, strips the field
"doc_string" from the exported model, which information about the stack
trace.
dynamic_axes (dict<string, dict<int, string>> or dict<string, list(int)>, default empty dict):
a dictionary to specify dynamic axes of input/output, such that:
- KEY: input and/or output names
- VALUE: index of dynamic axes for given key and potentially the name to be used for
exported dynamic axes. In general the value is defined according to one of the following
ways or a combination of both:
(1). A list of integers specifying the dynamic axes of provided input. In this scenario
automated names will be generated and applied to dynamic axes of provided input/output
during export.
OR (2). An inner dictionary that specifies a mapping FROM the index of dynamic axis in
corresponding input/output TO the name that is desired to be applied on such axis of
such input/output during export.
Example. if we have the following shape for inputs and outputs:
.. code-block:: none
shape(input_1) = ('b', 3, 'w', 'h')
and shape(input_2) = ('b', 4)
and shape(output) = ('b', 'd', 5)
Then `dynamic axes` can be defined either as:
1. ONLY INDICES::
``dynamic_axes = {'input_1':[0, 2, 3],
'input_2':[0],
'output':[0, 1]}``
where automatic names will be generated for exported dynamic axes
2. INDICES WITH CORRESPONDING NAMES::
``dynamic_axes = {'input_1':{0:'batch',
1:'width',
2:'height'},
'input_2':{0:'batch'},
'output':{0:'batch',
1:'detections'}}``
where provided names will be applied to exported dynamic axes
3. MIXED MODE OF (1) and (2)::
``dynamic_axes = {'input_1':[0, 2, 3],
'input_2':{0:'batch'},
'output':[0,1]}``
keep_initializers_as_inputs (bool, default None): If True, all the
initializers (typically corresponding to parameters) in the
exported graph will also be added as inputs to the graph. If False,
then initializers are not added as inputs to the graph, and only
the non-parameter inputs are added as inputs.
This may allow for better optimizations (such as constant folding
etc.) by backends/runtimes that execute these graphs. If
unspecified (default None), then the behavior is chosen
automatically as follows. If operator_export_type is
OperatorExportTypes.ONNX, the behavior is equivalent to setting
this argument to False. For other values of operator_export_type,
the behavior is equivalent to setting this argument to True. Note
that for ONNX opset version < 9, initializers MUST be part of graph
inputs. Therefore, if opset_version argument is set to a 8 or
lower, this argument will be ignored.
custom_opsets (dict<string, int>, default empty dict): A dictionary to indicate
custom opset domain and version at export. If model contains a custom opset,
it is optional to specify the domain and opset version in the dictionary:
- KEY: opset domain name
- VALUE: opset version
If the custom opset is not provided in this dictionary, opset version is set
to 1 by default.
enable_onnx_checker (bool, default True): If True the onnx model checker will be run
as part of the export, to ensure the exported model is a valid ONNX model.
external_data_format (bool, default False): If True, then the model is exported
in ONNX external data format, in which case some of the model parameters are stored
in external binary files and not in the ONNX model file itself. See link for format
details:
https://github.com/onnx/onnx/blob/8b3f7e2e7a0f2aba0e629e23d89f07c7fc0e6a5e/onnx/onnx.proto#L423
Also, in this case, argument 'f' must be a string specifying the location of the model.
The external binary files will be stored in the same location specified by the model
location 'f'. If False, then the model is stored in regular format, i.e. model and
parameters are all in one file. This argument is ignored for all export types other
than ONNX.
"""
from torch.onnx import utils
return utils.export(model, args, f, export_params, verbose, training,
input_names, output_names, aten, export_raw_ir,
operator_export_type, opset_version, _retain_param_name,
do_constant_folding, example_outputs,
strip_doc_string, dynamic_axes, keep_initializers_as_inputs,
custom_opsets, enable_onnx_checker, use_external_data_format)
[docs]def export_to_pretty_string(*args, **kwargs):
from torch.onnx import utils
return utils.export_to_pretty_string(*args, **kwargs)
def _export_to_pretty_string(*args, **kwargs):
from torch.onnx import utils
return utils._export_to_pretty_string(*args, **kwargs)
def _optimize_trace(graph, operator_export_type):
from torch.onnx import utils
return utils._optimize_graph(graph, operator_export_type)
[docs]def select_model_mode_for_export(model, mode):
r"""
A context manager to temporarily set the training mode of 'model'
to 'mode', resetting it when we exit the with-block. A no-op if
mode is None.
In version 1.6 changed to this from set_training
"""
from torch.onnx import utils
return utils.select_model_mode_for_export(model, mode)
def _run_symbolic_function(*args, **kwargs):
from torch.onnx import utils
return utils._run_symbolic_function(*args, **kwargs)
def _run_symbolic_method(*args, **kwargs):
from torch.onnx import utils
return utils._run_symbolic_method(*args, **kwargs)
[docs]def is_in_onnx_export():
r"""
Check whether it's in the middle of the ONNX export.
This function returns True in the middle of torch.onnx.export().
torch.onnx.export should be executed with single thread.
"""
from torch.onnx import utils
return utils.is_in_onnx_export()
[docs]def register_custom_op_symbolic(symbolic_name, symbolic_fn, opset_version):
from torch.onnx import utils
return utils.register_custom_op_symbolic(symbolic_name, symbolic_fn, opset_version)
