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, TrainingMode from torch._C import ListType, OptionalType, _propagate_and_assign_input_shapes, _assign_output_shapes, _check_onnx_proto # 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 select_model_mode_for_export(model, mode): if not isinstance(model, torch.jit.ScriptFunction): is_originally_training = model.training if mode is None: mode = TrainingMode.EVAL # if the model is in training mode but the user did not specify # to export the model in training mode, export the model in inference # mode (default) and warn them if is_originally_training: warnings.warn("You are exporting the model to ONNX while in training mode with " "'train' parameter not specified. The model will default to inference mode export. " "If you wish to export a training amenable ONNX model, specify training=TrainingMode.TRAINING or " "training=TrainingMode.PRESERVE (to preserve the original model state) in torch.onnx.export().") # if mode == TrainingMode.EVAL or (mode == TrainingMode.PRESERVE and not is_originally_training) => is_training = False is_export_training = False # ONNX opset 12 has better support for training amenable models, with updated # versions of the dropout and batch_norm operators if mode == TrainingMode.TRAINING or (mode == TrainingMode.PRESERVE and is_originally_training): from torch.onnx.symbolic_helper import _export_onnx_opset_version if _export_onnx_opset_version < 12: warnings.warn("You are exporting the model in training mode with onnx opset version {}. " "Opset versions lower than opset 12 will not be able to export nodes such as" "Dropout and BatchNorm correctly.".format(_export_onnx_opset_version)) is_export_training = True from torch.onnx.symbolic_helper import _set_training_mode _set_training_mode(is_export_training) model.train(is_export_training) try: yield finally: if not isinstance(model, torch.jit.ScriptFunction): model.train(is_originally_training) def export(model, args, f, export_params=True, verbose=False, training=None, 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): 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, custom_opsets=custom_opsets, enable_onnx_checker=enable_onnx_checker, use_external_data_format=use_external_data_format) def _is_constant_tensor_list(node): if node.kind() != "prim::Constant": return False output_type = node.output().type() if output_type.isSubtypeOf(ListType.ofTensors()): return True if output_type.isSubtypeOf(ListType(OptionalType.ofTensor())): return True # 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 _is_constant_tensor_list(node): inputs = [] for val in node.output().toIValue(): input = g.insertConstant(val) input.node().moveBefore(node) inputs.append(input) 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, params_dict=None): # Inline everyting torch._C._jit_pass_inline(graph) # Remove fork/wait nodes torch._C._jit_pass_inline_fork_wait(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_graph_fuser_ops(graph) torch._C._jit_pass_lint(graph) torch._C._jit_pass_peephole(graph, True) torch._C._jit_pass_fuse_addmm(graph) torch._C._jit_pass_lint(graph) if operator_export_type != OperatorExportTypes.RAW: torch._C._jit_pass_peephole(graph, True) torch._C._jit_pass_lower_all_tuples(graph) # in _jit_pass_onnx, symbolic functions are called for each node for conversion. # However, there are nodes that cannot be converted without additional context. # For example, the number of outputs from split (and whether it is static or dynamic) is unknown # until the point where it is unpacked by listUnpack node. # This pass does a preprocess, and prepares the nodes such that enough context can be received # by the symbolic function. torch._C._jit_pass_onnx_preprocess(graph) # _prepare_inplace_ops makes the IR invalid for JIT passes / alias db torch._C._jit_pass_onnx_prepare_inplace_ops_for_onnx(graph) # onnx does not support tuples, so try to remove them 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) if operator_export_type == OperatorExportTypes.ONNX_ATEN_FALLBACK: torch.onnx.symbolic_helper._quantized_ops.clear() # Unpack quantized weights for conv and linear ops and insert into graph. torch._C._jit_pass_onnx_unpack_quantized_weights(graph, params_dict) # Insert permutes before and after each conv op to ensure correct order. torch._C._jit_pass_onnx_quantization_insert_permutes(graph, params_dict) # Find consecutive permutes that are no-ops and remove them. torch._C._jit_pass_custom_pattern_based_rewrite_graph(""" graph(%Pi): %Pq = quantized::nhwc2nchw(%Pi) %Pr = quantized::nchw2nhwc(%Pq) return (%Pr)""", """ graph(%Ri): return (%Ri)""", 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) graph = torch._C._jit_pass_canonicalize(graph) torch._C._jit_pass_lint(graph) return graph # We accept dictionnaries and strings as ONNX inputs, # but they should be only for configuration use. # we detect here if these inputs are modified, and if so # we warn the user that the changes won't take effect in the # traced ONNX graph def warn_on_static_input_change(input_states): for input, traced_input in zip(input_states[0], input_states[1]): if isinstance(input, dict): if list(input.keys()) != list(traced_input.keys()): warning = "We detected that you are modifying a dictionnary that is an input to your " \ "model. " \ "Note that dictionaries are allowed as inputs in ONNX but they should be " \ "handled with care. " \ "Usages of dictionaries is not recommended, and should not be used except " \ "for configuration use. " \ "Also note that the order and values of the keys must remain the same. " warnings.warn(warning) elif isinstance(input, str): if input != traced_input: warning = "The model seems to have string inputs/outputs. " \ "Note that strings will not appear as inputs/outputs of the ONNX graph. " warnings.warn(warning) def _resolve_args_by_export_type(arg_name, arg_value, operator_export_type): # This helper method resolves the arguments that are ignored when export_type != operator_export_type.ONNX if operator_export_type is not operator_export_type.ONNX: if arg_value is True: warnings.warn("`{}' can be set to True only when 'operator_export_type' is " "`ONNX`. Since 'operator_export_type' is not set to 'ONNX', " "`{}` argument will be ignored.".format(arg_name, arg_name)) arg_value = False return arg_value def _decide_keep_init_as_input(keep_initializers_as_inputs, operator_export_type, opset_version): # This method encapsulates the logic to decide whether the initializers in the graph # should be listed as ONNX graph inputs (i.e., whether to choose ONNX IR v3 or v4). # If keep_initializers_as_inputs is not specified (None), then we decide whether to keep # intializers as graph inputs (val_keep_init_as_ip) based on export type. If export type # is ONNX, then do not keep initializers as input (val_keep_init_as_ip=False). For all other # export types keep initializers as input (val_keep_init_as_ip=True). # If keep_initializers_as_inputs is specified, then respect it. Unless opset version <= 8, # in which case it must be ignored because for opset version <= 8, all initializers MUST be # part of graph input (only ONNX IR v3 is allowed), i.e. val_keep_init_as_ip=True. # Special handling is needed for opset version 8 or lower, because irrespective # of user input for keep_initializers_as_inputs, the graph must follow ONNX IR v3 # semantics, i.e. all intializers must be listed as ONNX graph input. if opset_version < 9: if keep_initializers_as_inputs is False: warnings.warn("Setting 'keep_initializers_as_inputs=False' for opset version" "8 or lower would lead to an invalid ONNX graph. Therefore, " "'keep_initializers_as_inputs=False' is ignored during export." "Exported model will have initialiers as graph inputs (compliant " " to ONNX IR v3).") return True # i.e. True == initializers are part of graph input (ONNX IR v3) 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 return val_keep_init_as_ip def _decide_add_node_names(add_node_names, operator_export_type): return _resolve_args_by_export_type("add_node_names", add_node_names, operator_export_type) def _decide_constant_folding(do_constant_folding, operator_export_type, training): do_constant_folding = _resolve_args_by_export_type("do_constant_folding", do_constant_folding, operator_export_type) if do_constant_folding and (training is not None and training is not TrainingMode.EVAL): warnings.warn("It is recommended that constant folding be turned off ('do_constant_folding=False') " "when exporting the model in training-amenable mode, i.e. with 'training=TrainingMode.TRAIN' " "or 'training=TrainingMode.PRESERVE' (when model is in training mode). Otherwise, some " "learnable model parameters may not translate correctly in the exported ONNX model " "because constant folding mutates model parameters. Please consider " "turning off constant folding or setting the training=TrainingMode.EVAL.") return do_constant_folding def _decide_external_data_format(use_external_data_format, operator_export_type, f): val_use_external_data_format = _resolve_args_by_export_type("use_external_data_format", use_external_data_format, operator_export_type) # f can be a non-string in regular-sized model export case, but for large model export, f must be a non-empty # string specifying the location of the model. For large model cases, if f is not a non-empty string, # then this method returns an empty string, which is an error condition for the large model export code # path later (but not for regular model export code path). model_file_location = f if val_use_external_data_format and isinstance(f, str) else str() return val_use_external_data_format, model_file_location 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_graph, torch_out, inputs_states = \ torch.jit._get_trace_graph(func, args, strict=False, _force_outplace=False, _return_inputs_states=True) warn_on_static_input_change(inputs_states) trace_graph = _optimize_graph(trace_graph, operator_export_type) if return_outs: return trace_graph, torch_out return trace_graph def _trace_and_get_graph_from_model(model, args): # 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() trace_graph, torch_out, inputs_states = \ torch.jit._get_trace_graph(model, args, strict=False, _force_outplace=False, _return_inputs_states=True) warn_on_static_input_change(inputs_states) 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, input_names=None, output_names=None, operator_export_type=OperatorExportTypes.ONNX, example_outputs=None, propagate=False, _retain_param_name=False, do_constant_folding=True, _disable_torch_constant_prop=False, fixed_batch_size=False, training=None): 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: graph = model.forward.graph torch._C._jit_pass_onnx_function_substitution(graph) method_graph, params = torch._C._jit_pass_lower_graph(graph, model._c) 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.ScriptFunction): assert example_outputs is not None, "example_outputs must be provided when exporting a TorchScript ScriptFunction" method = model params = () in_vars, in_desc = torch.jit._flatten(tuple(args)) graph = model.graph torch._C._jit_pass_onnx_function_substitution(graph) graph = _propagate_and_assign_input_shapes( graph, tuple(in_vars), False, propagate) else: graph, torch_out = _trace_and_get_graph_from_model(model, args) 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]) torch._C._jit_pass_onnx_function_substitution(graph) 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)) graph = _optimize_graph(graph, operator_export_type, _disable_torch_constant_prop=_disable_torch_constant_prop, fixed_batch_size=fixed_batch_size, params_dict=params_dict) if isinstance(model, torch.jit.ScriptModule) or isinstance(model, torch.jit.ScriptFunction): 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 training is None or training == TrainingMode.EVAL or (training == TrainingMode.PRESERVE and not is_originally_training): params_dict = torch._C._jit_pass_onnx_eval_peephole(graph, params_dict) if do_constant_folding and _export_onnx_opset_version in torch.onnx.constant_folding_opset_versions: 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) params_dict = torch._C._jit_pass_onnx_eliminate_unused_items(graph, params_dict) # 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) params_dict = torch._C._jit_pass_filter_non_tensor_arguments(params_dict) torch._C._jit_decay_packed_param_input_types(graph) return graph, params_dict, torch_out def export_to_pretty_string(model, args, f, export_params=True, verbose=False, training=None, 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, custom_opsets=None, add_node_names=True, do_constant_folding=True): 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, do_constant_folding=do_constant_folding, add_node_names=add_node_names, keep_initializers_as_inputs=keep_initializers_as_inputs, custom_opsets=custom_opsets) def _export_to_pretty_string(model, args, f, export_params=True, verbose=False, training=None, 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=True, keep_initializers_as_inputs=None, fixed_batch_size=False, custom_opsets=None, add_node_names=True): 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 if custom_opsets is None: custom_opsets = {} _set_opset_version(opset_version) _set_operator_export_type(operator_export_type) with select_model_mode_for_export(model, training): val_keep_init_as_ip = _decide_keep_init_as_input(keep_initializers_as_inputs, operator_export_type, opset_version) val_add_node_names = _decide_add_node_names(add_node_names, operator_export_type) val_do_constant_folding = _decide_constant_folding(do_constant_folding, operator_export_type, training) graph, params_dict, torch_out = _model_to_graph(model, args, verbose, input_names, output_names, operator_export_type, example_outputs, propagate, _retain_param_name, val_do_constant_folding, fixed_batch_size=fixed_batch_size, training=training) return graph._pretty_print_onnx(params_dict, opset_version, False, operator_export_type, google_printer, val_keep_init_as_ip, custom_opsets, val_add_node_names) # 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=None, input_names=None, output_names=None, operator_export_type=None, export_type=ExportTypes.PROTOBUF_FILE, example_outputs=None, propagate=False, opset_version=None, _retain_param_name=False, do_constant_folding=True, strip_doc_string=True, dynamic_axes=None, keep_initializers_as_inputs=None, fixed_batch_size=False, custom_opsets=None, add_node_names=True, enable_onnx_checker=True, use_external_data_format=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 if not operator_export_type: if torch.onnx.PYTORCH_ONNX_CAFFE2_BUNDLE: operator_export_type = OperatorExportTypes.ONNX_ATEN_FALLBACK else: operator_export_type = OperatorExportTypes.ONNX # By default, training=None, (which defaults to TrainingMode.EVAL), # 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=TrainingMode.TRAINING or training=TrainingMode.PRESERVE, # (to preserve whatever the original training mode was.) _set_opset_version(opset_version) _set_operator_export_type(operator_export_type) with select_model_mode_for_export(model, training): val_keep_init_as_ip = _decide_keep_init_as_input(keep_initializers_as_inputs, operator_export_type, opset_version) val_add_node_names = _decide_add_node_names(add_node_names, operator_export_type) val_do_constant_folding = _decide_constant_folding(do_constant_folding, operator_export_type, training) val_use_external_data_format, model_file_location = _decide_external_data_format(use_external_data_format, operator_export_type, f) graph, params_dict, torch_out = _model_to_graph(model, args, verbose, input_names, output_names, operator_export_type, example_outputs, propagate, _retain_param_name, val_do_constant_folding, fixed_batch_size=fixed_batch_size, training=training) # 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 = {} if custom_opsets is None: custom_opsets = {} _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, custom_opsets, val_add_node_names, val_use_external_data_format, model_file_location) else: proto, export_map = graph._export_onnx( {}, opset_version, dynamic_axes, False, operator_export_type, strip_doc_string, val_keep_init_as_ip, custom_opsets, val_add_node_names, val_use_external_data_format, model_file_location) if enable_onnx_checker and \ operator_export_type is OperatorExportTypes.ONNX and \ not val_use_external_data_format: # Only run checker if enabled and we are using ONNX export type and # large model format export in not enabled. _check_onnx_proto(proto) if export_type == ExportTypes.PROTOBUF_FILE: assert(len(export_map) == 0) with torch.serialization._open_file_like(f, 'wb') as opened_file: opened_file.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) with torch.serialization._open_file_like(model_proto_file, 'wb') as opened_file: opened_file.write(proto) for k, v in export_map.items(): weight_proto_file = os.path.join(f, k) with torch.serialization._open_file_like(weight_proto_file, 'wb') as opened_file: opened_file.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 _find_symbolic_in_registry(domain, op_name, opset_version, operator_export_type): import torch.onnx.symbolic_registry as sym_registry if not sym_registry.is_registered_op(op_name, domain, opset_version): if operator_export_type == OperatorExportTypes.ONNX_FALLTHROUGH: # Use the original node directly return None return sym_registry.get_registered_op(op_name, domain, opset_version) 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: import torch 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) # Quantized op symbolics are registered for opset 9 only. if operator_export_type == OperatorExportTypes.ONNX_ATEN_FALLBACK and opset_version == 9: import torch.onnx.symbolic_caffe2 torch.onnx.symbolic_caffe2.register_quantized_ops('caffe2', 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 domain = '' symbolic_fn = _find_symbolic_in_registry(domain, op_name, opset_version, operator_export_type) if symbolic_fn is None: return None attrs = {k: n[k] for k in n.attributeNames()} return symbolic_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"]) if n.kindOf("value") == "s": return g.op("Constant", value_s=n["value"]) elif n.output().type().isSubtypeOf(ListType.ofInts()) or n.output().type().isSubtypeOf(ListType.ofFloats()): vals = n.output().toIValue() value = torch.stack([torch.tensor(v) for v in vals]) if len(vals) 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 and finally eliminate these ops # ListConstruct and ListUnpack 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) new_op_outputs = torch._C._jit_pass_fixup_onnx_controlflow_node(new_node, opset_version) return new_op_outputs else: symbolic_name = 'prim_' + op_name domain = '' symbolic_fn = _find_symbolic_in_registry(domain, symbolic_name, opset_version, operator_export_type) if symbolic_fn is None: return None attrs = {k: n[k] for k in n.attributeNames()} return symbolic_fn(g, *inputs, **attrs) elif ns == "quantized": domain = '' if operator_export_type == OperatorExportTypes.ONNX_ATEN_FALLBACK: domain = 'caffe2' symbolic_fn = _find_symbolic_in_registry(domain, op_name, opset_version, operator_export_type) if symbolic_fn is None: return None 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): domain = ns symbolic_fn = _find_symbolic_in_registry(domain, op_name, opset_version, operator_export_type) if symbolic_fn is None: return None attrs = {k: n[k] for k in n.attributeNames()} return symbolic_fn(g, *inputs, **attrs) else: raise RuntimeError("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)) except RuntimeError: if operator_export_type == OperatorExportTypes.ONNX_FALLTHROUGH: return None raise 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 = ("{} \n(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 should 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 from torch.onnx.symbolic_helper import _onnx_stable_opsets for version in _onnx_stable_opsets: if version >= opset_version: sym_registry.register_op(op_name, symbolic_fn, ns, 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 def _add_block(node, input_node, op_name, **kwargs): new_block = node.addBlock() new_node = new_block.addNode(input_node, op_name) for k, v in kwargs.items(): _add_attribute(new_node, k, v, False) torch._C.Graph.op = _graph_op torch._C.Graph.at = _graph_at torch._C.Graph.constant = _graph_constant torch._C.Node.__getitem__ = _node_getitem