Source code for torch.onnx

import functools
import types

import torch._C as _C

TensorProtoDataType = _C._onnx.TensorProtoDataType
OperatorExportTypes = _C._onnx.OperatorExportTypes


class ExportTypes:

def _export(*args, **kwargs):
    from torch.onnx import utils
    return utils._export(*args, **kwargs)

[docs]def export(*args, **kwargs): from torch.onnx import utils return utils.export(*args, **kwargs)
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(trace, operator_export_type): from torch.onnx import utils trace.set_graph(utils._optimize_graph(trace.graph(), operator_export_type)) def set_training(*args, **kwargs): from torch.onnx import utils return utils.set_training(*args, **kwargs) 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) def symbolic_override(symbolic_fn): r""" Decorator to override ONNX export of the a function with specified subgraph. Effectively allows to attach symbolic() implementation to an arbitrary python function or autograd.Function. Requirements for the decorated function: - being non-member function or autograd.Function - positional inputs are Tensors or (nested) lists or tuples of them (similar requirement to NestedIOFunction) - outputs are similarly Tensors or (nested) lists or tuples of them - non-tensor typed values should be keyword arguments both in definition and when called Example usage: ``` def symb(g, x, y): return g.op('Sum', x, y[0], y[1]) @symbolic_override(symb) def foo(x, y): return x + y[0] + y[1] ``` """ def decorator(fn): import torch from torch.autograd import function def wrapper(*args, **kwargs): tstate = torch._C._get_tracing_state() if not tstate: return fn(*args, **kwargs) flat_args = tuple(function._iter_tensors_permissive(args)) arg_values = [torch._C._get_value_trace(x) if isinstance(x, torch.Tensor) else x for x in flat_args] # This must come after the calls to get_value_trace, lest we # lose information due to in-place operations. # temporarily disable tracing so that we don't cause errors # for things inside of fn that may not be tracable with torch.jit._disable_tracing(): output_vars = fn(*args, **kwargs) symbolic_args = function._unflatten(arg_values, args) output_vals = symbolic_fn(tstate.graph(), *symbolic_args, **kwargs) for var, val in zip( function._iter_tensors(output_vars), function._iter_jit_values(output_vals)): val.inferTypeFrom( torch._C._set_value_trace(var, val) return output_vars # fn might be autograd.Function too, in this case wrapping doesn't work if isinstance(fn, types.FunctionType): wrapper = functools.wraps(fn)(wrapper) return wrapper return decorator


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