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 import collections import contextlib import numbers import warnings import functools import types from torch._six import string_classes from torch.autograd import Function, function from torch.jit import _unique_state_dict from torch.onnx import ONNX_ARCHIVE_MODEL_PROTO_NAME, ExportTypes @contextlib.contextmanager def set_training(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. """ 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): 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.) See also: :ref:`onnx-export` Arguments: model (torch.nn.Module): the model to be exported. args (tuple of arguments): 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. (Note: passing keyword arguments to the model is not currently supported. Give us a shout if you need it.) 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 (bool, default False): export the model in training mode. At the moment, ONNX is oriented towards exporting models for inference only, so you will generally not need to set this to True. 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): export the model in aten mode. If using aten mode, all the ops original exported by the functions in symbolic.py are exported as ATen ops. """ _export(model, args, f, export_params, verbose, training, input_names, output_names, aten) def _optimize_graph(graph, aten): # run dce first 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_peephole(graph) torch._C._jit_pass_lint(graph) graph = torch._C._jit_pass_onnx(graph, aten) torch._C._jit_pass_lint(graph) torch._C._jit_pass_onnx_peephole(graph) torch._C._jit_pass_lint(graph) torch._C._jit_pass_dce(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, return_outs=False, aten=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) trace.set_graph(_optimize_graph(trace.graph(), aten)) 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) 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, f, verbose=False, training=False, input_names=None, output_names=None, aten=False): # Special case for common case of passing a single Variable if isinstance(args, torch.Tensor): args = (args, ) if isinstance(model, torch.jit.ScriptModule): torch_out = None try: method = model.__getattr__('forward') graph = method.propagate_shapes(args, False) params = method.params() except AttributeError: # TODO: just trace it raise RuntimeError('\'forward\' method must be a script method') else: graph, torch_out = _trace_and_get_graph_from_model(model, args, training) params = list(_unique_state_dict(model).values()) graph = _optimize_graph(graph, aten) _set_input_and_output_names(graph, input_names, output_names) if verbose: print(graph) return graph, params, 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_type=ExportTypes.PROTOBUF_FILE): graph, params, torch_out = _model_to_graph(model, args, f, verbose, training, input_names, output_names, aten) from torch.onnx.symbolic import _onnx_opset_version return graph.prettyPrintExport(params, _onnx_opset_version, False) # 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, aten=False, export_type=ExportTypes.PROTOBUF_FILE): graph, params, torch_out = _model_to_graph(model, args, f, verbose, training, input_names, output_names, aten) # TODO: Don't allocate a in-memory string for the protobuf from torch.onnx.symbolic import _onnx_opset_version defer_weight_export = export_type is not ExportTypes.PROTOBUF_FILE if export_params: proto, export_map = graph.export(params, _onnx_opset_version, defer_weight_export) else: proto, export_map = graph.export([], _onnx_opset_version, False) 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') 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) did not match number of %ss (%d)" % (descriptor, len(name_list), descriptor, len(node_list))) for name, node in zip(name_list, node_list): if node.uniqueName() != name: node.setUniqueName(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, collections.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, aten=False): # NB: Returning None means the node gets cloned as is into # the new graph try: import torch.onnx.symbolic # 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": if aten: # 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 hasattr(torch.onnx.symbolic, op_name): warnings.warn("ONNX export failed on ATen operator {} because torch.onnx.symbolic.{} does not exist" .format(op_name, op_name)) return None fn = getattr(torch.onnx.symbolic, op_name) return fn(g, *inputs, **attrs) elif ns == "prim": if op_name == "Constant": return g.op("Constant", value_t=n["value"]) elif op_name == "Undefined": # Undefined is not an ONNX operator; keep it as prim::Undefined # and let the exporter handle finally eliminating these 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, aten, env) return new_op_outputs else: warnings.warn("ONNX export failed on primitive operator {}; please report a bug".format(op_name)) return None else: warnings.warn("ONNX export failed on an operator with unrecognized namespace {}::{}; " "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) torch._C.Graph.op = _graph_op torch._C.Graph.at = _graph_at torch._C.Graph.constant = _graph_constant torch._C.Node.__getitem__ = _node_getitem