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af3b15b74c
pytorch/torch/onnx/utils.py
Spandan Tiwari af3b15b74c Setting automatic default selection for ONNX IR v4 semantics in ONNX export API (#26146) 
Summary:
This is a follow-up PR for https://github.com/pytorch/pytorch/pull/23284. In that PR we had removed changing the default behavior for `keep_initializers_as_input` argument to the export API. With this PR we are enabling that change in that if `keep_initializers_as_input` is not specified then value/behavior for this argument is chosen automatically depending on whether the export type is ONNX or not.

This was part of the earlier PR was removed for further review. The test points have also been updated.

This change may fail some internal tests which may require explicitly setting `keep_initializers_as_input=True` to preserve old behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26146

Reviewed By: hl475

Differential Revision: D17369677

Pulled By: houseroad

fbshipit-source-id: 2aec2cff50d215714ee8769505ef24d2b7865a11
2019-09-24 10:02:31 -07:00

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from __future__ import absolute_import, division, print_function, unicode_literals
r"""
The torch.onnx module contains functions to export models into the ONNX
IR format. These models can be loaded with the ONNX library and then
converted to models which run on other deep learning frameworks.
"""
import torch
import torch.jit
import torch.autograd
import torch.serialization
import re
from torch._six import container_abcs
import contextlib
import numbers
import warnings
from torch._six import string_classes
from torch.jit import _unique_state_dict
from torch.onnx import ONNX_ARCHIVE_MODEL_PROTO_NAME, ExportTypes, OperatorExportTypes
from torch._C import ListType, _propagate_and_assign_input_shapes, _assign_output_shapes
# the flag to tell the user whether it's in the middle of ONNX export or not
__IN_ONNX_EXPORT = False
def is_in_onnx_export():
global __IN_ONNX_EXPORT
return __IN_ONNX_EXPORT
@contextlib.contextmanager
def set_training(model, mode):
if mode is None:
yield
return
old_mode = model.training
if old_mode != mode:
model.train(mode)
try:
yield
finally:
if old_mode != mode:
model.train(old_mode)
def export(model, args, f, export_params=True, verbose=False, training=False,
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=False, example_outputs=None, strip_doc_string=True,
dynamic_axes=None, keep_initializers_as_inputs=None):
if aten or export_raw_ir:
assert operator_export_type is None
assert aten ^ export_raw_ir
operator_export_type = OperatorExportTypes.ATEN if aten else OperatorExportTypes.RAW
elif operator_export_type is None:
if torch.onnx.PYTORCH_ONNX_CAFFE2_BUNDLE:
operator_export_type = OperatorExportTypes.ONNX_ATEN_FALLBACK
else:
operator_export_type = OperatorExportTypes.ONNX
_export(model, args, f, export_params, verbose, training, input_names, output_names,
operator_export_type=operator_export_type, opset_version=opset_version,
_retain_param_name=_retain_param_name, do_constant_folding=do_constant_folding,
example_outputs=example_outputs, strip_doc_string=strip_doc_string,
dynamic_axes=dynamic_axes, keep_initializers_as_inputs=keep_initializers_as_inputs)
# ONNX can't handle constants that are lists of tensors, which can
# get generated in constant prop. So we split them back into prim::ListConstructs
def _split_tensor_list_constants(g, block):
for node in block.nodes():
for subblock in node.blocks():
_split_tensor_list_constants(g, subblock)
if node.kind() == "prim::Constant":
output_type = node.output().type()
if output_type.isSubtypeOf(ListType.ofTensors()):
inputs = [g.create("prim::Constant").t_('value', t)
.insertBefore(node).output()
for t in node['value']]
lc = (g.create("prim::ListConstruct", inputs)
.insertBefore(node)
.output()
.setType(ListType.ofTensors()))
node.output().replaceAllUsesWith(lc)
def _optimize_graph(graph, operator_export_type, _disable_torch_constant_prop=False, fixed_batch_size=False):
# Inline everyting
torch._C._jit_pass_inline(graph)
# Remove fork/wait nodes
torch._C._jit_pass_inline_fork_wait(graph)
torch._C._jit_pass_dce(graph)
torch._C._jit_pass_lint(graph)
torch._C._jit_pass_remove_inplace_ops(graph)
# we record now record some ops like ones/zeros
# into a trace where we previously recorded constants
# use constant prop to maintain our current level of onnx support
# without implementing symbolics for all of them
if _disable_torch_constant_prop is False:
torch._C._jit_pass_constant_propagation(graph)
_split_tensor_list_constants(graph, graph)
# run dce to eliminate dead parts of the graph that might have been
# left behind by things like symbolic_override
torch._C._jit_pass_dce(graph)
torch._C._jit_pass_lint(graph)
torch._C._jit_pass_canonicalize_ops(graph)
torch._C._jit_pass_lint(graph)
torch._C._jit_pass_peephole(graph, True)
torch._C._jit_pass_lint(graph)
if operator_export_type != OperatorExportTypes.RAW:
# onnx does not support tuples, so try to remove them
torch._C._jit_pass_lower_all_tuples(graph)
torch._C._jit_pass_peephole(graph, True)
torch._C._jit_pass_lint(graph)
# onnx only supports tensors, but 1 / 2 = 0.5 and tensor(1) / tensor(2) = 0
torch._C._jit_pass_prepare_division_for_onnx(graph)
torch._C._jit_pass_onnx_remove_print(graph)
torch._C._jit_pass_onnx_preprocess_caffe2(graph)
# onnx only supports tensors, so we turn all out number types into tensors
torch._C._jit_pass_erase_number_types(graph)
graph = torch._C._jit_pass_onnx(graph, operator_export_type)
torch._C._jit_pass_lint(graph)
torch._C._jit_pass_onnx_scalar_type_analysis(graph)
torch._C._jit_pass_lint(graph)
from torch.onnx.symbolic_helper import _export_onnx_opset_version
torch._C._jit_pass_onnx_peephole(graph, _export_onnx_opset_version, fixed_batch_size)
torch._C._jit_pass_lint(graph)
# graph is not a valid jit graph anymore because types have been replaced
# (e.g. int with Tensor), so it now contains operators that don't actually
# exist. We can't run normal dead code elimination because it'd fail trying
# to look up if an operator has side effects, but we can run a dead code
# elimination variant that doesn't need to look up if an op has side effects.
torch._C._jit_pass_dce_allow_deleting_nodes_with_side_effects(graph)
torch._C._jit_pass_lint(graph)
torch._C._jit_pass_fixup_onnx_loops(graph)
torch._C._jit_pass_lint(graph)
graph = torch._C._jit_pass_canonicalize(graph)
torch._C._jit_pass_lint(graph)
return graph
def _trace(func, args, operator_export_type, return_outs=False):
# Special case for common case of passing a single Tensor
if isinstance(args, torch.Tensor):
args = (args, )
trace, torch_out = torch.jit.get_trace_graph(func, args, _force_outplace=True)
trace.set_graph(_optimize_graph(trace.graph(), operator_export_type))
if return_outs:
return trace, torch_out
return trace
def _trace_and_get_graph_from_model(model, args, training):
# A basic sanity check: make sure the state_dict keys are the same
# before and after running the model. Fail fast!
orig_state_dict_keys = _unique_state_dict(model).keys()
# By default, training=False, which is good because running a model in
# training mode could result in internal buffers getting updated, dropout
# getting applied, etc. If you really know what you're doing, you
# can turn training=True (or None, to preserve whatever the original
# training mode was.)
with set_training(model, training):
trace, torch_out = torch.jit.get_trace_graph(model, args, _force_outplace=True)
if orig_state_dict_keys != _unique_state_dict(model).keys():
raise RuntimeError("state_dict changed after running the tracer; "
"something weird is happening in your model!")
return trace.graph(), torch_out
def _model_to_graph(model, args, verbose=False, training=False,
input_names=None, output_names=None,
operator_export_type=OperatorExportTypes.ONNX,
example_outputs=None, propagate=False,
_retain_param_name=False, do_constant_folding=False,
_disable_torch_constant_prop=False, fixed_batch_size=False):
from torch.onnx.symbolic_helper import _export_onnx_opset_version
# Special case for common case of passing a single Tensor
if isinstance(args, torch.Tensor):
args = (args, )
if isinstance(example_outputs, torch.Tensor):
example_outputs = [example_outputs]
torch_out = None
if isinstance(model, torch.jit.ScriptModule):
assert example_outputs is not None, "example_outputs must be provided when exporting a ScriptModule"
try:
method_graph, params = model.forward._lowered_graph()
in_vars, in_desc = torch.jit._flatten(tuple(args) + tuple(params))
graph = _propagate_and_assign_input_shapes(
method_graph, tuple(in_vars), False, propagate)
except AttributeError:
raise RuntimeError('\'forward\' method must be a script method')
elif isinstance(model, torch.jit.Function):
assert example_outputs is not None, "example_outputs must be provided when exporting a TorchScript Function"
method = model
params = ()
in_vars, in_desc = torch.jit._flatten(tuple(args))
graph = _propagate_and_assign_input_shapes(
model.graph, tuple(in_vars), False, propagate)
else:
graph, torch_out = _trace_and_get_graph_from_model(model, args, training)
state_dict = _unique_state_dict(model)
params = list(state_dict.values())
if _retain_param_name:
graph_inputs = list(graph.inputs())
user_input_num = len(graph_inputs) - len(state_dict)
param_names = list(state_dict.keys())
for i, inp in enumerate(graph_inputs):
if i >= user_input_num:
inp.setDebugName(param_names[i - user_input_num])
graph = _optimize_graph(graph, operator_export_type,
_disable_torch_constant_prop=_disable_torch_constant_prop,
fixed_batch_size=fixed_batch_size)
if isinstance(model, torch.jit.ScriptModule) or isinstance(model, torch.jit.Function):
out_vars, _ = torch.jit._flatten(tuple(example_outputs))
graph = _assign_output_shapes(graph, out_vars)
# NB: ONNX requires complete information about output types, which might be
# erased by some optimizations, so we need to set it explicitly again.
if torch_out is not None:
output_tensors, _ = torch._C._jit_flatten(torch_out)
for output, tensor in zip(graph.outputs(), output_tensors):
output.inferTypeFrom(tensor)
_set_input_and_output_names(graph, input_names, output_names)
# make sure that the param dict and the graph match each other
flatten_args, _ = torch._C._jit_flatten(args)
assert len(params) + len(flatten_args) == sum(1 for _ in graph.inputs())
input_and_param_names = [val.debugName() for val in graph.inputs()]
param_names = input_and_param_names[len(input_and_param_names) - len(params):]
params_dict = dict(zip(param_names, params))
if do_constant_folding and _export_onnx_opset_version in [9, 10]:
params_dict = torch._C._jit_pass_onnx_constant_fold(graph, params_dict,
_export_onnx_opset_version)
torch._C._jit_pass_dce_allow_deleting_nodes_with_side_effects(graph)
# For ONNX opset < 9, constants only have three data types: float16, float, double.
# In this pass transform constants of other data types to float/double + cast operator.
if _export_onnx_opset_version < 9:
torch._C._jit_pass_onnx_cast_all_constant_to_floating(graph)
if verbose:
print(graph)
return graph, params_dict, torch_out
def export_to_pretty_string(model, args, f, export_params=True, verbose=False, training=False,
input_names=None, output_names=None, aten=False, export_raw_ir=False,
operator_export_type=None, export_type=ExportTypes.PROTOBUF_FILE,
example_outputs=None, propagate=False, google_printer=False,
opset_version=None, _retain_param_name=True,
keep_initializers_as_inputs=None):
if aten or export_raw_ir:
assert operator_export_type is None
assert aten ^ export_raw_ir
operator_export_type = OperatorExportTypes.ATEN if aten else OperatorExportTypes.RAW
elif operator_export_type is None:
operator_export_type = OperatorExportTypes.ONNX
return _export_to_pretty_string(model, args, f, export_params, verbose, training,
input_names, output_names, operator_export_type,
export_type, example_outputs, propagate, google_printer,
opset_version, _retain_param_name,
keep_initializers_as_inputs=keep_initializers_as_inputs)
def _export_to_pretty_string(model, args, f, export_params=True, verbose=False, training=False,
input_names=None, output_names=None, operator_export_type=OperatorExportTypes.ONNX,
export_type=ExportTypes.PROTOBUF_FILE, example_outputs=None, propagate=False,
google_printer=False, opset_version=None, _retain_param_name=False,
do_constant_folding=False, keep_initializers_as_inputs=None, fixed_batch_size=False):
from torch.onnx.symbolic_helper import _default_onnx_opset_version, _set_opset_version
from torch.onnx.symbolic_helper import _set_operator_export_type
if opset_version is None:
opset_version = _default_onnx_opset_version
_set_opset_version(opset_version)
_set_operator_export_type(operator_export_type)
val_keep_init_as_ip = True if keep_initializers_as_inputs is None else keep_initializers_as_inputs
if keep_initializers_as_inputs is None and operator_export_type is OperatorExportTypes.ONNX:
val_keep_init_as_ip = False
graph, params_dict, torch_out = _model_to_graph(model, args, verbose,
training, input_names,
output_names, operator_export_type,
example_outputs, propagate, _retain_param_name,
do_constant_folding, fixed_batch_size=fixed_batch_size)
return graph._pretty_print_onnx(params_dict, opset_version, False,
operator_export_type, google_printer,
val_keep_init_as_ip)
# NOTE: the output `torch_out` will contain the output tensors resulting from
# the trace of a Module. In the case that a torch.nn.ScriptModule is passed in,
# this output will be None, since we are not doing any tracing but rather
# directly extracting the graph.
def _export(model, args, f, export_params=True, verbose=False, training=False,
input_names=None, output_names=None, operator_export_type=OperatorExportTypes.ONNX,
export_type=ExportTypes.PROTOBUF_FILE, example_outputs=None, propagate=False,
opset_version=None, _retain_param_name=False, do_constant_folding=False,
strip_doc_string=True, dynamic_axes=None, keep_initializers_as_inputs=None, fixed_batch_size=False):
if isinstance(model, torch.nn.DataParallel):
raise ValueError('torch.nn.DataParallel is not supported by ONNX '
'exporter, please use \'attribute\' module to '
'unwrap model from torch.nn.DataParallel. Try '
'torch.onnx.export(model.module, ...)')
global __IN_ONNX_EXPORT
assert __IN_ONNX_EXPORT is False
__IN_ONNX_EXPORT = True
try:
from torch.onnx.symbolic_helper import _default_onnx_opset_version, _set_opset_version
from torch.onnx.symbolic_helper import _set_operator_export_type
if opset_version is None:
opset_version = _default_onnx_opset_version
_set_opset_version(opset_version)
_set_operator_export_type(operator_export_type)
val_keep_init_as_ip = True if keep_initializers_as_inputs is None else keep_initializers_as_inputs
if keep_initializers_as_inputs is None and operator_export_type is OperatorExportTypes.ONNX:
val_keep_init_as_ip = False
graph, params_dict, torch_out = _model_to_graph(model, args, verbose,
training, input_names,
output_names, operator_export_type,
example_outputs, propagate,
_retain_param_name, do_constant_folding,
fixed_batch_size=fixed_batch_size)
# TODO: Don't allocate a in-memory string for the protobuf
defer_weight_export = export_type is not ExportTypes.PROTOBUF_FILE
if dynamic_axes is None:
dynamic_axes = {}
_validate_dynamic_axes(dynamic_axes, model, input_names, output_names)
if export_params:
proto, export_map = graph._export_onnx(
params_dict, opset_version, dynamic_axes, defer_weight_export,
operator_export_type, strip_doc_string, val_keep_init_as_ip)
else:
proto, export_map = graph._export_onnx(
{}, opset_version, dynamic_axes, False, operator_export_type,
strip_doc_string, val_keep_init_as_ip)
if export_type == ExportTypes.PROTOBUF_FILE:
assert(len(export_map) == 0)
torch.serialization._with_file_like(f, "wb", lambda f: f.write(proto))
elif export_type in [ExportTypes.ZIP_ARCHIVE, ExportTypes.COMPRESSED_ZIP_ARCHIVE]:
import zipfile
compression = zipfile.ZIP_DEFLATED \
if export_type == ExportTypes.COMPRESSED_ZIP_ARCHIVE \
else zipfile.ZIP_STORED
with zipfile.ZipFile(f, 'w', compression=compression) as z:
z.writestr(ONNX_ARCHIVE_MODEL_PROTO_NAME, proto)
for k, v in export_map.items():
z.writestr(k, v)
elif export_type == ExportTypes.DIRECTORY:
import os
if os.path.exists(f):
assert(os.path.isdir(f))
else:
os.makedirs(f)
model_proto_file = os.path.join(f, ONNX_ARCHIVE_MODEL_PROTO_NAME)
torch.serialization._with_file_like(
model_proto_file, "wb", lambda f: f.write(proto))
for k, v in export_map.items():
weight_proto_file = os.path.join(f, k)
torch.serialization._with_file_like(
weight_proto_file, "wb", lambda f: f.write(v))
else:
raise RuntimeError('Unknown export type')
finally:
assert __IN_ONNX_EXPORT
__IN_ONNX_EXPORT = False
return torch_out
def _set_input_and_output_names(graph, input_names, output_names):
def set_names(node_list, name_list, descriptor):
if name_list is None:
return
if len(name_list) > len(node_list):
raise RuntimeError(
"number of %s names provided (%d) exceeded number of %ss (%d)"
% (descriptor, len(name_list), descriptor, len(node_list)))
for name, node in zip(name_list, node_list):
if node.debugName() != name:
node.setDebugName(name)
set_names(list(graph.inputs()), input_names, 'input')
set_names(list(graph.outputs()), output_names, 'output')
attr_pattern = re.compile("^(.+)_([ifstgz])$")
def _run_symbolic_method(op_name, symbolic_fn, args):
r"""
This trampoline function gets invoked for every symbolic method
call from C++.
"""
try:
return symbolic_fn(*args)
except TypeError as e:
# Handle the specific case where we didn't successfully dispatch
# to symbolic_fn. Otherwise, the backtrace will have the clues
# you need.
e.args = ("{} (occurred when translating {})".format(e.args[0], op_name), )
raise
def _is_onnx_list(value):
if not isinstance(value, string_classes) and \
not isinstance(value, torch.Tensor) and \
isinstance(value, container_abcs.Iterable):
return True
return False
def _add_attribute(node, key, value, aten):
r""" initializes the right attribute based on type of value """
m = attr_pattern.match(key)
if m is None:
raise IndexError((
"Invalid attribute specifier '{}' names " +
" must be suffixed with type, e.g. 'dim_i' or 'dims_i'").format(key))
name, kind = m.group(1), m.group(2)
if _is_onnx_list(value):
kind += "s"
if aten:
if isinstance(value, torch.Tensor):
# Caffe2 proto does not support tensor attribute.
if value.numel() > 1:
raise ValueError("Should not pass tensor attribute")
value = _scalar(value)
if isinstance(value, float):
kind = "f"
else:
kind = "i"
return getattr(node, kind + "_")(name, value)
def _scalar(x):
"""Convert a scalar tensor into a Python value."""
assert x.numel() == 1
return x[0]
def _newNode(g, opname, outputs, *args, **kwargs):
if "::" in opname:
aten = False
ns_opname = opname
else:
aten = kwargs.pop("aten", False)
ns = "aten" if aten else "onnx"
ns_opname = ns + "::" + opname
n = g.create(ns_opname, args, outputs)
for k, v in sorted(kwargs.items()):
# TODO: enable inplace in aten exporting mode.
if k == "inplace":
continue
_add_attribute(n, k, v, aten=aten)
return n
def _graph_op(g, opname, *raw_args, **kwargs):
r"""
Create an ONNX operator 'opname', taking 'args' as inputs and attributes
'kwargs'; returning the node representing the single output of this operator
(see the `outputs` keyword argument for multi-return nodes).
The set of operators and the inputs/attributes they take
is documented at https://github.com/onnx/onnx/blob/master/docs/Operators.md
This function is monkey-patched onto Graph.
Arguments:
opname (string): The ONNX operator name, e.g., `Abs` or `Add`.
args (Node...): The inputs to the operator; usually provided
as arguments to the `symbolic` definition.
kwargs: The attributes of the ONNX operator, with keys named
according to the following convention: `alpha_f` indicates
the `alpha` attribute with type `f`. The valid type specifiers are
`f` (float), `i` (int), `s` (string) or `t` (Tensor). An attribute
specified with type float accepts either a single float, or a
list of floats (e.g., you would say `dims_i` for a `dims` attribute
that takes a list of integers).
outputs (int, optional): The number of outputs this operator returns;
by default an operator is assumed to return a single output.
If `outputs` is greater than one, this functions returns a tuple
of output `Node`, representing each output of the ONNX operator
in positional.
"""
outputs = kwargs.pop('outputs', 1)
# Filter out None attributes, this can be convenient client side because
# now they can pass through None attributes, and have them not show up
kwargs = dict((k, v) for k, v in kwargs.items() if v is not None)
def const_if_tensor(arg):
if arg is None:
return arg
elif isinstance(arg, torch._C.Value):
return arg
else:
return g.op("Constant", value_z=arg)
args = list(const_if_tensor(arg) for arg in raw_args)
n = g.insertNode(_newNode(g, opname, outputs, *args, **kwargs))
if outputs == 1:
return n.output()
return tuple(o for o in n.outputs())
# Note [Export inplace]
# ~~~~~~~~~~~~~~~~~~~~~
# In abstract, it would be better for us to export inplace annotations,
# than to not export them, since it is useful information that can
# help the target of an ONNX export export more efficiently. However,
# ONNX doesn't currently formalize inplace. Fortunately, it's sound to drop
# inplace annotations, but we are losing information this way.
def _run_symbolic_function(g, n, inputs, env, operator_export_type=OperatorExportTypes.ONNX):
# NB: Returning None means the node gets cloned as is into
# the new graph
try:
from torch.onnx.symbolic_helper import _export_onnx_opset_version as opset_version
import torch.onnx.symbolic_registry as sym_registry
sym_registry.register_version('', opset_version)
# See Note [Export inplace]
# TODO: I think this is not necessary anymore
if n.kind().endswith('_'):
ns_op_name = n.kind()[:-1]
else:
ns_op_name = n.kind()
ns, op_name = ns_op_name.split("::")
if ns == "onnx":
# Use the original node directly
return None
elif ns == "aten":
is_exportable_aten_op = sym_registry.is_registered_op(op_name, '', opset_version)
is_onnx_aten_export = operator_export_type == OperatorExportTypes.ONNX_ATEN
is_aten_fallback_export = operator_export_type == OperatorExportTypes.ONNX_ATEN_FALLBACK
if is_onnx_aten_export or (not is_exportable_aten_op and is_aten_fallback_export):
# Direct ATen export requested
attrs = {k + "_" + n.kindOf(k)[0]: n[k] for k in n.attributeNames()}
outputs = n.outputsSize()
attrs["outputs"] = outputs
return _graph_at(g, op_name, *inputs, aten=True, **attrs)
else:
# Export it regularly
attrs = {k: n[k] for k in n.attributeNames()}
if not is_exportable_aten_op:
warnings.warn("ONNX export failed on ATen operator {} because "
"torch.onnx.symbolic_opset{}.{} does not exist"
.format(op_name, opset_version, op_name))
op_fn = sym_registry.get_registered_op(op_name, '', opset_version)
return op_fn(g, *inputs, **attrs)
elif ns == "prim":
if op_name == "Constant" and not n.mustBeNone():
if n.kindOf("value") == "t":
return g.op("Constant", value_t=n["value"])
elif n.kindOf("value") == "is":
value = torch.stack([torch.tensor(v) for v in n["value"]]) if n["value"] else []
return g.op("Constant", value_t=value)
elif n.output().type().kind() == "DeviceObjType":
return None
else:
raise RuntimeError("Unsupported prim::Constant kind: `{}`. Send a bug report.".format(
n.kindOf("value")))
elif n.mustBeNone() or op_name == "ListConstruct" or op_name == "ListUnpack":
# None is not an ONNX operator; keep it as None
# let the exporter handle finally eliminating these
# For ListConstruct/ListUnpack, it will be erased in the ONNX peephole pass
return None
elif op_name == 'Loop' or op_name == 'If':
new_op_outputs = g.op(op_name, *inputs, outputs=n.outputsSize())
new_node = new_op_outputs[0].node() if n.outputsSize() > 1 else new_op_outputs.node()
for b in n.blocks():
new_block = new_node.addBlock()
torch._C._jit_pass_onnx_block(b, new_block, operator_export_type, env)
return new_op_outputs
else:
# TODO: we sould lift prim's symbolic out
symbolic_name = 'prim_' + op_name
is_exportable = sym_registry.is_registered_op(symbolic_name, '', opset_version)
if not is_exportable:
warnings.warn("ONNX export failed on primitive operator {}; please report a bug".format(op_name))
symbolic_fn = sym_registry.get_registered_op(symbolic_name, '', opset_version)
attrs = {k: n[k] for k in n.attributeNames()}
return symbolic_fn(g, *inputs, **attrs)
# custom ops
elif sym_registry.is_registered_version(ns, opset_version):
if not sym_registry.is_registered_op(op_name, ns, opset_version):
warnings.warn("ONNX export failed on custom operator {}::{} because "
"torch.onnx.symbolic_opset{}.{} does not exist. "
"Have you registered your symbolic function with "
"torch.onnx.register_custom_op_symbolic(symbolic_name, symbolic_fn)?"
.format(ns, op_name, opset_version, op_name))
symbolic_fn = sym_registry.get_registered_op(op_name, ns, opset_version)
attrs = {k: n[k] for k in n.attributeNames()}
return symbolic_fn(g, *inputs, **attrs)
else:
warnings.warn("ONNX export failed on an operator with unrecognized namespace {}::{}; "
"If you are trying to export a custom operator, make sure you registered "
"it with the right domain and version."
"Otherwise please report a bug".format(ns, op_name))
return None
except TypeError as e:
# Handle the specific case where we didn't successfully dispatch.
# Otherwise, the backtrace will have the clues you need.
e.args = ("{} (occurred when translating {})".format(e.args[0], op_name), )
raise
# Generate an ONNX ATen op node.
def _graph_at(g, opname, *args, **kwargs):
return g.op("ATen", *args, operator_s=opname, **kwargs)
# This helper function can create either constant tensor or constant scalar.
# If dims is None or 0 or [0], generate a 0-d tensor (scalar).
#
# TODO: We might not need this anymore, since most scalars now show up
# as tensors
def _graph_constant(g, value, dims, type, *args, **kwargs):
assert isinstance(value, numbers.Number)
assert type is not None
isscalar = False
if dims is None or dims == 0 or set(dims) == set([0]):
dims = [1]
isscalar = True
type = type.lower()
if type == "char":
tensor = torch.CharTensor(*dims)
elif type == "short":
tensor = torch.ShortTensor(*dims)
elif type == "int":
tensor = torch.IntTensor(*dims)
elif type == "long":
tensor = torch.LongTensor(*dims)
elif type == "half":
tensor = torch.HalfTensor(*dims)
elif type == "float":
tensor = torch.FloatTensor(*dims)
elif type == "double":
tensor = torch.DoubleTensor(*dims)
else:
raise ValueError("Unknown type, type should be one of the following strings: "
"char, short, int, long, half, float, double")
tensor.fill_(value)
if isscalar:
return g.op("Constant", *args, value_z=tensor, **kwargs)
return g.op("Constant", *args, value_t=tensor, **kwargs)
def _node_getitem(self, k):
r"""
Accessor for attributes of a node which is polymorphic over
return type.
NB: This is monkey-patched onto Node.
"""
sel = self.kindOf(k)
return getattr(self, sel)(k)
def register_custom_op_symbolic(symbolic_name, symbolic_fn, opset_version):
if not bool(re.match(r"^[a-zA-Z0-9-_]*::[a-zA-Z]+[a-zA-Z0-9-_]*$", symbolic_name)):
raise RuntimeError("Failed to register operator {}. \
The symbolic name must match the format Domain::Name, \
and sould start with a letter and contain only \
alphanumerical characters"
.format(symbolic_name))
ns, op_name = symbolic_name.split('::')
unaccepted_domain_names = ["onnx", "aten", "prim"]
if ns in unaccepted_domain_names:
raise RuntimeError("Failed to register operator {}. The domain {} is already a used domain."
.format(symbolic_name, ns))
import torch.onnx.symbolic_registry as sym_registry
sym_registry.register_op(op_name, symbolic_fn, ns, opset_version)
# This helper function ensures dynamic axes argument is following the expected format
def _validate_dynamic_axes(dynamic_axes, model, input_names, output_names):
if len(dynamic_axes) == 0:
return
if(hasattr(model, 'graph')):
# Extracting set of valid input/output names that shall be used for dynamic_axes
if (input_names is None) or len(input_names) == 0:
input_names = [x.debugName() for x in model.graph.inputs()]
if (output_names is None) or len(output_names) == 0:
output_names = [y.debugName() for y in model.graph.outputs()]
valid_names = set((input_names or []) + (output_names or []))
# If dynamic axes are provided as a list rather than dictionary, they should
# first get converted to a dictionary in expected format. If desired axes names
# are not provided for dynamic axes, automatic names shall be generated for
# provided dynamic axes of specified input/output
for key, value in dynamic_axes.items():
if key not in valid_names:
warnings.warn("Provided key {} for dynamic axes is not a valid input/output name".format(key))
if isinstance(value, list):
warnings.warn('No names were found for specified dynamic axes of provided input.'
'Automatically generated names will be applied to each dynamic axes of input {}'.format(key))
value_dict = {}
for i, x in enumerate(value):
if not isinstance(x, int):
raise ValueError("The type of axis index is expected to be an integer")
if x in value_dict:
warnings.warn('Duplicate dynamic axis index {} was provided for input {}.'
.format(x, key))
else:
value_dict[x] = str(key) + '_dynamic_axes_' + str(i + 1)
dynamic_axes[key] = value_dict
torch._C.Graph.op = _graph_op
torch._C.Graph.at = _graph_at
torch._C.Graph.constant = _graph_constant
torch._C.Node.__getitem__ = _node_getitem
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