# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2021 NVIDIA Corporation. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import os import pickle import warnings import functools from collections import OrderedDict import inspect import threading from typing import Text, Tuple, Any, List import six import paddle from paddle.fluid import core, dygraph from paddle.fluid.compiler import BuildStrategy, CompiledProgram, ExecutionStrategy from paddle.fluid.data_feeder import check_type from paddle.fluid.layers.utils import flatten, pack_sequence_as from paddle.fluid.dygraph.base import program_desc_tracing_guard, switch_to_static_graph from paddle.fluid.dygraph.dygraph_to_static import logging_utils from paddle.fluid.dygraph.dygraph_to_static.convert_call_func import ConversionOptions, CONVERSION_OPTIONS from paddle.fluid.dygraph.dygraph_to_static.logging_utils import set_code_level, set_verbosity from paddle.fluid.dygraph.dygraph_to_static.program_translator import ProgramTranslator, StaticFunction, unwrap_decorators from paddle.fluid.dygraph.io import TranslatedLayer, INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX, INFER_PARAMS_INFO_SUFFIX, INFER_PROPERTY_SUFFIX from paddle.fluid.dygraph.layers import Layer from paddle.fluid.executor import Executor, scope_guard from paddle.fluid.framework import Block, ParamBase, Program, Variable, Parameter, EagerParamBase from paddle.fluid.framework import _current_expected_place, _dygraph_guard, _dygraph_tracer from paddle.fluid.framework import dygraph_only, _non_static_mode from paddle.fluid.wrapped_decorator import wrap_decorator __all__ = [ 'TracedLayer', 'declarative', 'dygraph_to_static_func', 'set_code_level', 'set_verbosity', 'save', 'load', 'not_to_static' ] def create_program_from_desc(program_desc): program = Program() program.desc = program_desc program.blocks = [Block(program, 0)] program._sync_with_cpp() return program def _extract_vars(inputs, result_list, err_tag='inputs'): if isinstance(inputs, Variable): result_list.append(inputs) elif isinstance(inputs, (list, tuple)): for var in inputs: _extract_vars(var, result_list, err_tag) else: raise TypeError( "The type of 'each element of {}' in fluid.dygraph.jit.TracedLayer.trace must be fluid.Variable, but received {}." .format(err_tag, type(inputs))) def extract_vars(inputs, err_tag='inputs'): result_list = [] _extract_vars(inputs, result_list, err_tag) return result_list def _dygraph_to_static_func_(dygraph_func): """ Converts imperative dygraph APIs into declarative function APIs. Decorator @dygraph_to_static_func only converts imperative dygraph APIs into declarative net-building APIs, which means it doesn't return immediate digital result as imperative mode. Users should handle Program and Executor by themselves. Note: This decorator is NOT our recommended way to transform imperative function to declarative function. We will remove this decorator after we finalize cleaning up code. Args: dygraph_func (callable): callable imperative function. Returns: Callable: converting imperative dygraph APIs into declarative net-building APIs. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np from paddle.fluid.dygraph.jit import dygraph_to_static_func @dygraph_to_static_func def func(x): if paddle.mean(x) < 0: x_v = x - 1 else: x_v = x + 1 return x_v x = fluid.layers.fill_constant(shape=[3, 3], value=0, dtype='float64') x_v = func(x) exe = fluid.Executor(fluid.CPUPlace()) out = exe.run(fetch_list=[x_v]) print(out[0]) # [[1. 1. 1.] # [1. 1. 1.] # [1. 1. 1.]] """ # TODO: remove this decorator after we finalize training API def __impl__(*args, **kwargs): program_translator = ProgramTranslator() if _non_static_mode() or not program_translator.enable_to_static: logging_utils.warn( "The decorator 'dygraph_to_static_func' doesn't work in " "dygraph mode or set ProgramTranslator.enable to False. " "We will just return dygraph output.") return dygraph_func(*args, **kwargs) static_func = program_translator.get_func(dygraph_func) return static_func(*args, **kwargs) return __impl__ dygraph_to_static_func = wrap_decorator(_dygraph_to_static_func_) def copy_decorator_attrs(original_func, decorated_obj): """ Copies some necessary attributes from original function into decorated function. Args: original_func(callable): the original decorated function. decorated_obj(StaticFunction): the target decorated StaticFunction object. """ decorator_name = "declarative" decorated_obj.__name__ = original_func.__name__ decorated_obj._decorator_name = decorator_name decorated_obj.__wrapped__ = original_func decorated_obj.__doc__ = original_func.__doc__ if hasattr(original_func, "__module__"): decorated_obj.__module__ = original_func.__module__ return decorated_obj def declarative(function=None, input_spec=None, build_strategy=None, property=False): """ Converts imperative dygraph APIs into declarative function APIs. Decorator @declarative handles the Program and Executor of static mode and returns the result as dygraph Tensor(s). Users could use the returned dygraph Tensor(s) to do imperative training, inference, or other operations. If the decorated function calls other imperative function, the called one will be converted into declarative function as well. Args: function (callable): callable imperative function. input_spec(list[InputSpec]|tuple[InputSpec]): list/tuple of InputSpec to specific the shape/dtype/name information of each input Tensor. build_strategy(BuildStrategy|None): This argument is used to compile the converted program with the specified options, such as operators' fusion in the computational graph and memory optimization during the execution of the computational graph. For more information about build_strategy, please refer to :code:`paddle.static.BuildStrategy`. The default is None. property(bool, Optional): whether the fucntion is python property. The default is False. Returns: Tensor(s): containing the numerical result. Examples: .. code-block:: python import paddle from paddle.jit import to_static @to_static def func(x): if paddle.mean(x) < 0: x_v = x - 1 else: x_v = x + 1 return x_v x = paddle.ones([1, 2], dtype='float32') x_v = func(x) print(x_v) # [[2. 2.]] """ def decorated(python_func): """ Decorates a python function into a StaticFunction object. """ # Step 1. unwrap the function if it is already decorated. _, python_func = unwrap_decorators(python_func) # Step 2. copy some attributes from original python function. static_layer = copy_decorator_attrs(original_func=python_func, decorated_obj=StaticFunction( function=python_func, input_spec=input_spec, build_strategy=build_strategy, property=property)) return static_layer build_strategy = build_strategy or BuildStrategy() if not isinstance(build_strategy, BuildStrategy): raise TypeError( "Required type(build_strategy) shall be `paddle.static.BuildStrategy`, but received {}" .format(type(build_strategy).__name__)) # for usage: `declarative(foo, ...)` if function is not None: if isinstance(function, Layer): if isinstance(function.forward, StaticFunction): class_name = function.__class__.__name__ logging_utils.warn( "`{}.forward` has already been decorated somewhere. It will be redecorated to replace previous one." .format(class_name)) function.forward = decorated(function.forward) return function else: return decorated(function) # for usage: `@declarative` return decorated def not_to_static(func=None): """ A Decorator to suppresses the convertion of a function. Args: func(callable): The function to decorate. Returns: callable: A function which won't be converted in Dynamic-to-Static. Examples: .. code-block:: python import paddle @paddle.jit.not_to_static def func_not_to_static(x): res = x - 1 return res @paddle.jit.to_static def func(x): if paddle.mean(x) < 0: out = func_not_to_static(x) else: out = x + 1 return out x = paddle.ones([1, 2], dtype='float32') out = func(x) print(out) # [[2. 2.]] """ if func is None: return not_to_static options = ConversionOptions(not_convert=True) setattr(func, CONVERSION_OPTIONS, options) return func class _SaveLoadConfig(object): def __init__(self): self._output_spec = None self._model_filename = None self._params_filename = None self._separate_params = False # used for `paddle.load` self._keep_name_table = False # NOTE: Users rarely use following configs, so these configs are not open to users, # reducing user learning costs, but we retain the configuration capabilities # If True, programs are modified to only support direct inference deployment. # Otherwise,more information will be stored for flexible optimization and re-training. # Currently, only True is supported self._export_for_deployment = True # If True, It will save inference program only, and do not save params of Program self._program_only = False self.with_hook = False # if True, multi `StaticFunction` will share params in one file. self.combine_params = False @property def output_spec(self): return self._output_spec @output_spec.setter def output_spec(self, spec): if spec is None: return if not isinstance(spec, list): raise TypeError( "The config `output_spec` should be 'list', but received input type is %s." % type(input)) for var in spec: if not isinstance(var, core.VarBase): raise TypeError( "The element in config `output_spec` list should be 'Variable', but received element's type is %s." % type(var)) self._output_spec = spec @property def model_filename(self): return self._model_filename @model_filename.setter def model_filename(self, filename): if filename is None: return if not isinstance(filename, six.string_types): raise TypeError( "The config `model_filename` should be str, but received input's type is %s." % type(filename)) if len(filename) == 0: raise ValueError("The config `model_filename` is empty string.") self._model_filename = filename @property def params_filename(self): return self._params_filename @params_filename.setter def params_filename(self, filename): if filename is None: return if not isinstance(filename, six.string_types): raise TypeError( "The config `params_filename` should be str, but received input's type is %s." % type(filename)) if len(filename) == 0: raise ValueError("The config `params_filename` is empty string.") self._params_filename = filename @property def keep_name_table(self): return self._keep_name_table @keep_name_table.setter def keep_name_table(self, value): if value is None: return if not isinstance(value, bool): raise TypeError( "The config `keep_name_table` should be bool value, but received input's type is %s." % type(value)) self._keep_name_table = value def _parse_save_configs(configs): supported_configs = [ 'output_spec', "with_hook", "combine_params", "clip_extra", "skip_forward" ] # input check for key in configs: if key not in supported_configs: raise ValueError( "The additional config (%s) of `paddle.jit.save` is not supported." % (key)) # construct inner config inner_config = _SaveLoadConfig() inner_config.output_spec = configs.get('output_spec', None) inner_config.with_hook = configs.get('with_hook', False) inner_config.combine_params = configs.get("combine_params", False) inner_config.clip_extra = configs.get("clip_extra", True) inner_config.skip_forward = configs.get("skip_forward", False) return inner_config def _parse_load_config(configs): supported_configs = ['model_filename', 'params_filename'] # input check for key in configs: if key not in supported_configs: raise ValueError( "The additional config (%s) of `paddle.jit.load` is not supported." % (key)) # construct inner config inner_config = _SaveLoadConfig() inner_config.model_filename = configs.get('model_filename', None) inner_config.params_filename = configs.get('params_filename', None) return inner_config def _get_input_var_names(inputs, input_spec): name_none_error = "The %s's name is None. " \ "When using jit.save, please set InputSepc's name in " \ "to_static(input_spec=[]) and jit.save(input_spec=[]) " \ "and make sure they are consistent." name_no_exists_error = "The tensor `%s` does not exists. " \ "Please make sure the name of InputSpec or example Tensor " \ "in input_spec is the same as the name of InputSpec in " \ "`to_static` decorated on the Layer.forward method." result_list = [] input_var_names = [ var.name for var in flatten(inputs) if isinstance(var, Variable) ] if input_spec is None: # no prune return input_var_names else: # fileter out non-tensor type spec infos. input_spec = [ spec for spec in input_spec if isinstance(spec, paddle.static.InputSpec) ] if len(input_spec) == len(input_var_names): # no prune result_list = input_var_names # if input spec name not in input_var_names, only raise warning for spec in input_spec: if spec.name is None: warnings.warn(name_none_error % spec) elif spec.name not in input_var_names: warnings.warn(name_no_exists_error % spec.name) else: # do nothing pass else: # prune for spec in input_spec: if spec.name is None: # name is None, the input_spec only can be InputSpec raise ValueError(name_none_error % spec) elif spec.name not in input_var_names: # the input_spec can be `InputSpec` or `VarBase` raise ValueError(name_no_exists_error % spec.name) else: result_list.append(spec.name) return result_list def _get_output_vars(outputs, output_spec, with_hook=False): name_no_exists_error = "The tensor `%s` does not exists. " \ "Please make sure the name of example Tensor " \ "in configs.output_spec is the output tensor of " \ "Layer.forward method." if output_spec and with_hook: raise RuntimeError( "Currently not support specify output_spec while founding pre/post hooks in your outermost layer." ) result_list = [] output_vars_dict = OrderedDict() for var in flatten(outputs): if isinstance(var, Variable): output_vars_dict[var.name] = var if output_spec is None: result_list = list(output_vars_dict.values()) elif output_spec is not None and len(output_spec) == len(output_vars_dict): result_list = list(output_vars_dict.values()) for var in output_spec: if var.name not in output_vars_dict: warnings.warn(name_no_exists_error % var.name) else: for var in output_spec: if var.name not in output_vars_dict: raise ValueError(name_no_exists_error % var.name) else: result_list.append(output_vars_dict[var.name]) return result_list # NOTE(chenweihang): [ Handling of use cases of API paddle.jit.load ] # `paddle.jit.load` may be used to load saved results of: # 1. Expected cases: # - paddle.jit.save # - paddle.static.save_inference_model # - paddle.fluid.io.save_inference_model # 2. Error cases: # - paddle.save: no .pdmodel for prefix # - paddle.static.save: no .pdiparams but .pdparams exists # - paddle.fluid.io.save_params/save_persistables: no __model__ # TODO(chenweihang): polish error message in above error cases def _build_load_path_and_config(path, config): # NOTE(chenweihang): If both [prefix save format] and [directory save format] exist, # raise error, avoid confusing behavior prefix_format_path = path + INFER_MODEL_SUFFIX prefix_format_exist = os.path.exists(prefix_format_path) directory_format_exist = os.path.isdir(path) if prefix_format_exist and directory_format_exist: raise ValueError( "The %s.pdmodel and %s directory exist at the same time, " "don't know which one to load, please make sure that the specified target " "of ``path`` is unique." % (path, path)) elif not prefix_format_exist and not directory_format_exist: raise ValueError("The ``path`` (%s) to load model not exists. " "Please make sure that *.pdmodel exists or " "don't using ``skip_forward=True`` to jit.save." % path) else: if prefix_format_exist: file_prefix = os.path.basename(path) model_path = os.path.dirname(path) if config.model_filename is not None: warnings.warn( "When loading the result saved with the " "specified file prefix, the ``model_filename`` config does " "not take effect.") config.model_filename = file_prefix + INFER_MODEL_SUFFIX if config.params_filename is not None: warnings.warn( "When loading the result saved with the " "specified file prefix, the ``params_filename`` config does " "not take effect.") config.params_filename = file_prefix + INFER_PARAMS_SUFFIX else: # Compatible with the old save_inference_model format model_path = path return model_path, config _save_pre_hooks_lock = threading.Lock() _save_pre_hooks = [] class HookRemoveHelper(object): """ A HookRemoveHelper that can be used to remove hook. """ def __init__(self, hook): self._hook = hook def remove(self): _remove_save_pre_hook(self._hook) def _register_save_pre_hook(hook): """ Register a save pre-hook for `paddle.jit.save`. This hook will be executed before `save` function has been invoked. hook(layer, input_spec, configs) -> None - layer (Layer|function): This argument is corresponding to `layer` in `paddle.jit.save`. - input_spec (list or tuple[InputSpec|Tensor|Python built-in variable]): This argument is corresponding to `input_spec` in `paddle.jit.save`. - configs (dict): This argument is corresponding to `configs` in `paddle.jit.save`. Args: hook(function): a function registered as a save pre-hook Returns: HookRemoveHelper: a HookRemoveHelper object that can be used to remove the added hook by calling `hook_remove_helper.remove()`. Examples: .. code-block:: python import numpy as np import paddle IMAGE_SIZE = 256 CLASS_NUM = 10 class LinearNet(paddle.nn.Layer): def __init__(self): super(LinearNet, self).__init__() self._linear = paddle.nn.Linear(IMAGE_SIZE, CLASS_NUM) def forward(self, x): return self._linear(x) saving_count = 0 def save_pre_hook(layer, input_spec, configs): global saving_count saving_count += 1 remove_handler = paddle.jit.register_save_pre_hook(save_pre_hook) layer = LinearNet() paddle.jit.save(layer, "/tmp", [paddle.static.InputSpec(shape=[-1, IMAGE_SIZE])]) # saving_count == 1 remove_handler.remove() paddle.jit.save(layer, "/tmp", [paddle.static.InputSpec(shape=[-1, IMAGE_SIZE])]) # saving_count == 1 """ global _save_pre_hooks_lock global _save_pre_hooks _save_pre_hooks_lock.acquire() if hook not in _save_pre_hooks: _save_pre_hooks.append(hook) _save_pre_hooks_lock.release() return HookRemoveHelper(hook) def _clear_save_pre_hooks(): global _save_pre_hooks_lock global _save_pre_hooks _save_pre_hooks_lock.acquire() _save_pre_hooks.clear() _save_pre_hooks_lock.release() def _remove_save_pre_hook(hook): global _save_pre_hooks_lock global _save_pre_hooks _save_pre_hooks_lock.acquire() if hook in _save_pre_hooks: _save_pre_hooks.remove(hook) _save_pre_hooks_lock.release() @wrap_decorator def _run_save_pre_hooks(func): def wrapper(layer, path, input_spec=None, **configs): global _save_pre_hooks for hook in _save_pre_hooks: hook(layer, input_spec, configs) func(layer, path, input_spec, **configs) return wrapper def _save_property(filename: Text, property_vals: List[Tuple[Any, Text]]): """class property serialization. Args: filename (Text): *.meta property_vals (List[Tuple): class property. """ def set_property(meta, key, val): if isinstance(val, float): meta.set_float(key, val) elif isinstance(val, int): meta.set_int(key, val) elif isinstance(val, str): meta.set_string(key, val) elif isinstance(val, (tuple, list)): if isinstance(val[0], float): meta.set_floats(key, val) elif isinstance(val[0], int): meta.set_ints(key, val) elif isinstance(val[0], str): meta.set_strings(key, val) else: raise ValueError(f"Note support val type: {type(val)}") return with open(filename, 'wb') as f: meta = paddle.framework.core.Property() for item in property_vals: val, key = item[0], item[1] set_property(meta, key, val) f.write(meta.serialize_to_string()) @_run_save_pre_hooks @switch_to_static_graph def save(layer, path, input_spec=None, **configs): """ Saves input Layer or function as ``paddle.jit.TranslatedLayer`` format model, which can be used for inference or fine-tuning after loading. It will save the translated program and all related persistable variables of input Layer to given ``path`` . ``path`` is the prefix of saved objects, and the saved translated program file suffix is ``.pdmodel`` , the saved persistable variables file suffix is ``.pdiparams`` , and here also saved some additional variable description information to a file, its suffix is ``.pdiparams.info``, these additional information is used in fine-tuning. The saved model can be loaded by follow APIs: - ``paddle.jit.load`` - ``paddle.static.load_inference_model`` - Other C++ inference APIs .. note:: When using ``paddle.jit.save`` to save a function, parameters will not be saved. If you have to save the parameter, please pass the Layer containing function and parameter to ``paddle.jit.save``. Args: layer (Layer|function): The Layer or function to be saved. path (str): The path prefix to save model. The format is ``dirname/file_prefix`` or ``file_prefix``. input_spec (list or tuple[InputSpec|Tensor|Python built-in variable], optional): Describes the input of the saved model's forward method, which can be described by InputSpec or example Tensor. Moreover, we support to specify non-tensor type argument, such as int, float, string, or list/dict of them.If None, all input variables of the original Layer's forward method would be the inputs of the saved model. Default None. **configs (dict, optional): Other save configuration options for compatibility. We do not recommend using these configurations, they may be removed in the future. If not necessary, DO NOT use them. Default None. The following options are currently supported: (1) output_spec (list[Tensor]): Selects the output targets of the saved model. By default, all return variables of original Layer's forward method are kept as the output of the saved model. If the provided ``output_spec`` list is not all output variables, the saved model will be pruned according to the given ``output_spec`` list. Returns: None Examples: .. code-block:: python # example 1: save layer import numpy as np import paddle import paddle.nn as nn import paddle.optimizer as opt BATCH_SIZE = 16 BATCH_NUM = 4 EPOCH_NUM = 4 IMAGE_SIZE = 784 CLASS_NUM = 10 # define a random dataset class RandomDataset(paddle.io.Dataset): def __init__(self, num_samples): self.num_samples = num_samples def __getitem__(self, idx): image = np.random.random([IMAGE_SIZE]).astype('float32') label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64') return image, label def __len__(self): return self.num_samples class LinearNet(nn.Layer): def __init__(self): super(LinearNet, self).__init__() self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM) @paddle.jit.to_static def forward(self, x): return self._linear(x) def train(layer, loader, loss_fn, opt): for epoch_id in range(EPOCH_NUM): for batch_id, (image, label) in enumerate(loader()): out = layer(image) loss = loss_fn(out, label) loss.backward() opt.step() opt.clear_grad() print("Epoch {} batch {}: loss = {}".format( epoch_id, batch_id, np.mean(loss.numpy()))) # 1. train & save model. # create network layer = LinearNet() loss_fn = nn.CrossEntropyLoss() adam = opt.Adam(learning_rate=0.001, parameters=layer.parameters()) # create data loader dataset = RandomDataset(BATCH_NUM * BATCH_SIZE) loader = paddle.io.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, num_workers=2) # train train(layer, loader, loss_fn, adam) # save path = "example_model/linear" paddle.jit.save(layer, path) # example 2: save function import paddle from paddle.static import InputSpec def save_function(): @paddle.jit.to_static def fun(inputs): return paddle.tanh(inputs) path = 'test_jit_save_load_function_1/func' inps = paddle.rand([3, 6]) origin = fun(inps) paddle.jit.save(fun, path) load_func = paddle.jit.load(path) load_result = load_func(inps) print((load_result - origin).abs().max() < 1e-10) save_function() """ # 1. input build & check prog_translator = ProgramTranslator() if not prog_translator.enable_to_static: raise RuntimeError( "The paddle.jit.save doesn't work when setting ProgramTranslator.enable to False." ) if not (isinstance(layer, Layer) or inspect.isfunction(layer) or isinstance(layer, StaticFunction)): raise TypeError( "The input of paddle.jit.save should be 'Layer' or 'Function', but received input type is %s." % type(layer)) elif inspect.isfunction(layer) or isinstance(layer, StaticFunction): warnings.warn( 'What you save is a function, and `jit.save` will generate the name of the model file according to `path` you specify. When loading these files with `jit.load`, you get a `TranslatedLayer` whose inference result is the same as the inference result of the function you saved.' ) # NOTE(chenweihang): If the input layer be wrapped by DataParallel, # the args and kwargs of forward method will can't be parsed by # function_spec, so here we save DataParallel._layers instead # DataParallel it self # NOTE(chenweihang): using inner_layer, do not change input layer if isinstance(layer, paddle.DataParallel): inner_layer = layer._layers else: inner_layer = layer # path check file_prefix = os.path.basename(path) if file_prefix == "": raise ValueError( "The input path MUST be format of dirname/file_prefix " "[dirname\\file_prefix in Windows system], but received " "file_prefix is empty string.") dirname = os.path.dirname(path) if dirname and not os.path.exists(dirname): os.makedirs(dirname) # avoid change user given input_spec inner_input_spec = None if input_spec is not None: if isinstance(layer, Layer): for attr_func in dir(inner_layer): static_func = getattr(inner_layer, attr_func, None) if isinstance(static_func, StaticFunction) and 'forward' != attr_func: raise ValueError( "If there are static functions other than 'forward' that need to be saved, the input 'input_spec' should be None, but received the type of 'input_spec' is %s." % type(input_spec)) if not isinstance(input_spec, (list, tuple)): raise TypeError( "The input input_spec should be 'list', but received input_spec's type is %s." % type(input_spec)) inner_input_spec = [] for var in flatten(input_spec): if isinstance(var, paddle.static.InputSpec): inner_input_spec.append(var) elif isinstance(var, (core.VarBase, core.eager.Tensor, Variable)): inner_input_spec.append( paddle.static.InputSpec.from_tensor(var)) else: # NOTE(Aurelius84): Support non-Tensor type in `input_spec`. inner_input_spec.append(var) # parse configs configs = _parse_save_configs(configs) # whether outermost layer has pre/post hook, if does, we need also save # these operators in program. with_hook = configs.with_hook combine_params = configs.combine_params if combine_params: configs._program_only = True scope = core.Scope() extra_var_info = dict() if isinstance(layer, Layer): functions = dir(inner_layer) if inner_layer._forward_pre_hooks or inner_layer._forward_post_hooks: with_hook = True else: # layer is function functions = [ layer, ] combine_vars = {} property_vals = [] # (value, key) concrete_program = None for attr_func in functions: if isinstance(layer, Layer): static_func = getattr(inner_layer, attr_func, None) if isinstance(static_func, StaticFunction): if static_func.is_property: # property method to be exported immediate_val = static_func() property_vals.append( (immediate_val, layer.__class__.__name__ + '.' + attr_func)) continue concrete_program = static_func.concrete_program_specify_input_spec( inner_input_spec, with_hook=with_hook) elif 'forward' == attr_func: if configs.skip_forward: # do not jit.save forward function continue # transform in jit.save, if input_spec is incomplete, declarative will throw error # inner_input_spec is list[InputSpec], it should be packed with same structure # as original input_spec here. if inner_input_spec: inner_input_spec = pack_sequence_as(input_spec, inner_input_spec) static_forward = declarative(inner_layer.forward, input_spec=inner_input_spec) concrete_program = static_forward.concrete_program_specify_input_spec( with_hook=with_hook) # the input_spec has been used in declarative, which is equal to # @declarative with input_spec and jit.save without input_spec, # avoid needless warning inner_input_spec = None else: continue else: # When layer is a function if isinstance(attr_func, StaticFunction): if attr_func.is_property: # property method to be exported immediate_val = attr_func() property_vals.append((immediate_val, attr_func)) continue concrete_program = attr_func.concrete_program_specify_input_spec( inner_input_spec) else: if inner_input_spec: inner_input_spec = pack_sequence_as(input_spec, inner_input_spec) static_function = declarative(attr_func, input_spec=inner_input_spec) concrete_program = static_function.concrete_program if static_function._class_instance is None: warnings.warn( '`jit.save` will only save the `Program`, not the parameters. If you have to save the parameters, please make sure that {} is a member function of `paddle.nn.Layer` and the saved parameters are in `state_dict`' .format(layer)) # when save multi `StaticFunction`, all `StaticFunction` share params. dygraph_state_dict = None if isinstance(inner_layer, Layer): dygraph_state_dict = inner_layer.to_static_state_dict() elif isinstance(attr_func, StaticFunction): if attr_func._class_instance: dygraph_state_dict = attr_func._class_instance.to_static_state_dict( ) if dygraph_state_dict: # NOTE(chenweihang): we maintain the mapping of variable name to # structured name, the buffer variable (non-persistable) # saved to inference program may not need by dygraph Layer, # we only record the state_dict variable's structured name state_names_dict = dict() state_var_dict = dict() for structured_name, var in six.iteritems(dygraph_state_dict): state_names_dict[var.name] = structured_name state_var_dict[var.name] = var # 3. share parameters from Layer to scope & record var info with dygraph.guard(): for param_or_buffer in concrete_program.parameters: # share to scope if param_or_buffer.type == core.VarDesc.VarType.VOCAB: scr_tensor = param_or_buffer.value().get_map_tensor() tgt_var = scope.var(param_or_buffer.name) tgt_var.set_vocab(scr_tensor) else: param_or_buffer_tensor = scope.var( param_or_buffer.name).get_tensor() #src_tensor = param_or_buffer.value().get_tensor() src_tensor = state_var_dict[ param_or_buffer.name].value().get_tensor() param_or_buffer_tensor._share_data_with(src_tensor) # record var info if param_or_buffer.name not in extra_var_info: extra_info_dict = dict() if param_or_buffer.name in state_names_dict: extra_info_dict['structured_name'] = state_names_dict[ param_or_buffer.name] extra_info_dict[ 'stop_gradient'] = param_or_buffer.stop_gradient if isinstance(param_or_buffer, (ParamBase, EagerParamBase)): extra_info_dict['trainable'] = param_or_buffer.trainable extra_var_info[param_or_buffer.name] = extra_info_dict # 4. build input & output of save_infernece_model # NOTE(chenweihang): [ Get input variables name ] # There are two cases, whether to prune the inputs or not # - not prune inputs (recommend): # - the len(input_spec) == len((concrete_program.inputs) - 1 # - here can use concrete_program.inputs directly # - prune inputs: # - the input_spec length < len((concrete_program.inputs) - 1 # - the input_spec's name should be in concrete_program.inputs input_var_names = _get_input_var_names(concrete_program.inputs, inner_input_spec) # NOTE(chenweihang): [ Get output variables ] # the rule is like [ Get input variables name ]. For output var, # we only support VarBase spec, and actually, we only need the # var name of output, and we don't recommended to use output_spec # print(concrete_program.main_program) # print(concrete_program.outputs, configs.output_spec) output_vars = _get_output_vars(concrete_program.outputs, configs.output_spec, with_hook) # 5. save inference model from paddle.fluid.io import save_inference_model # construct new save_inference_model arguments model_path = dirname # NOTE(chenweihang): because prefix contains model and params filename, # so we don't support set model_filename & params_filename if 'forward' == attr_func or not isinstance(layer, Layer): model_filename = file_prefix + INFER_MODEL_SUFFIX params_filename = file_prefix + INFER_PARAMS_SUFFIX else: model_filename = file_prefix + '.' + attr_func + INFER_MODEL_SUFFIX params_filename = file_prefix + '.' + attr_func + INFER_PARAMS_SUFFIX with scope_guard(scope): save_inference_model( dirname=model_path, feeded_var_names=input_var_names, target_vars=output_vars, executor=Executor(_current_expected_place()), main_program=concrete_program.main_program.clone(), model_filename=model_filename, params_filename=params_filename, export_for_deployment=configs._export_for_deployment, program_only=configs._program_only, clip_extra=configs.clip_extra) if combine_params: clone_main_program = concrete_program.main_program.clone() clone_main_program = clone_main_program._prune_with_input( input_var_names, output_vars) for block in clone_main_program.blocks: combine_vars.update(block.vars) # save shared params if combine_params: # sort vars by name combine_vars = sorted(combine_vars.items(), key=lambda item: item[0]) ordered_vars = [] for name, var in combine_vars: ordered_vars.append(var) params_filename = file_prefix + INFER_PARAMS_SUFFIX with scope_guard(scope): paddle.static.save_vars(Executor(_current_expected_place()), dirname=model_path, vars=list( filter(paddle.fluid.io.is_persistable, ordered_vars)), filename=params_filename) # save property property_save_path = os.path.join(os.path.normpath(model_path), file_prefix + INFER_PROPERTY_SUFFIX) _save_property(property_save_path, property_vals) # NOTE(chenweihang): [ Save extra variable info ] # save_inference_model will lose some important variable information, including: # - Variable name and correspondence (when saved variables as one file) # - Variable.stop_gradient information # - Which persistent variable are parameter and which are not # - Parameter.trainable information # # The lost information cannot be recovered when it is loaded again, # so if we want to perform fine-tune after loading, we may need to # configure redundant information to proceed. # # Due to compatibility issues, we cannot change the original storage structure, # but we can save these information in `jit.save` without changing the original # storage to improve user experience. So we save extra information into # file `***.pdiparams.info` # "layer" can only be Layer or function or StaticFunction. contain_parameter = False if concrete_program is not None: for var in concrete_program.main_program.list_vars(): contain_parameter |= isinstance(var, Parameter) if (isinstance(layer, Layer) or contain_parameter) and extra_var_info: with scope_guard(scope): extra_var_info_path = path + INFER_PARAMS_INFO_SUFFIX with open(extra_var_info_path, 'wb') as f: pickle.dump(extra_var_info, f, protocol=2) @dygraph_only def load(path, **configs): """ :api_attr: imperative Load model saved by ``paddle.jit.save`` or ``paddle.static.save_inference_model`` or paddle 1.x API ``paddle.fluid.io.save_inference_model`` as ``paddle.jit.TranslatedLayer``, then performing inference or fine-tune training. .. note:: If you load model saved by ``paddle.static.save_inference_model`` , there will be the following limitations when using it in fine-tuning: 1. Imperative mode do not support LoDTensor. All original model's feed targets or parametars that depend on LoD are temporarily unavailable. 2. All saved model's feed targets need to be passed into TranslatedLayer's forward function. 3. The variable's ``stop_gradient`` information is lost and can not be recovered. 4. The parameter's ``trainable`` information is lost and can not be recovered. Args: path (str): The path prefix to load model. The format is ``dirname/file_prefix`` or ``file_prefix`` . **configs (dict, optional): Other load configuration options for compatibility. We do not recommend using these configurations, they may be removed in the future. If not necessary, DO NOT use them. Default None. The following options are currently supported: (1) model_filename (str): The inference model file name of the paddle 1.x ``save_inference_model`` save format. Default file name is :code:`__model__` . (2) params_filename (str): The persistable variables file name of the paddle 1.x ``save_inference_model`` save format. No default file name, save variables separately by default. Returns: TranslatedLayer: A Layer object can run saved translated model. Examples: 1. Load model saved by ``paddle.jit.save`` then performing inference and fine-tune training. .. code-block:: python import numpy as np import paddle import paddle.nn as nn import paddle.optimizer as opt BATCH_SIZE = 16 BATCH_NUM = 4 EPOCH_NUM = 4 IMAGE_SIZE = 784 CLASS_NUM = 10 # define a random dataset class RandomDataset(paddle.io.Dataset): def __init__(self, num_samples): self.num_samples = num_samples def __getitem__(self, idx): image = np.random.random([IMAGE_SIZE]).astype('float32') label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64') return image, label def __len__(self): return self.num_samples class LinearNet(nn.Layer): def __init__(self): super(LinearNet, self).__init__() self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM) @paddle.jit.to_static def forward(self, x): return self._linear(x) def train(layer, loader, loss_fn, opt): for epoch_id in range(EPOCH_NUM): for batch_id, (image, label) in enumerate(loader()): out = layer(image) loss = loss_fn(out, label) loss.backward() opt.step() opt.clear_grad() print("Epoch {} batch {}: loss = {}".format( epoch_id, batch_id, np.mean(loss.numpy()))) # 1. train & save model. # create network layer = LinearNet() loss_fn = nn.CrossEntropyLoss() adam = opt.Adam(learning_rate=0.001, parameters=layer.parameters()) # create data loader dataset = RandomDataset(BATCH_NUM * BATCH_SIZE) loader = paddle.io.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, num_workers=2) # train train(layer, loader, loss_fn, adam) # save path = "example_model/linear" paddle.jit.save(layer, path) # 2. load model # load loaded_layer = paddle.jit.load(path) # inference loaded_layer.eval() x = paddle.randn([1, IMAGE_SIZE], 'float32') pred = loaded_layer(x) # fine-tune loaded_layer.train() adam = opt.Adam(learning_rate=0.001, parameters=loaded_layer.parameters()) train(loaded_layer, loader, loss_fn, adam) 2. Load model saved by ``paddle.fluid.io.save_inference_model`` then performing and fine-tune training. .. code-block:: python import numpy as np import paddle import paddle.static as static import paddle.nn as nn import paddle.optimizer as opt import paddle.nn.functional as F BATCH_SIZE = 16 BATCH_NUM = 4 EPOCH_NUM = 4 IMAGE_SIZE = 784 CLASS_NUM = 10 # define a random dataset class RandomDataset(paddle.io.Dataset): def __init__(self, num_samples): self.num_samples = num_samples def __getitem__(self, idx): image = np.random.random([IMAGE_SIZE]).astype('float32') label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64') return image, label def __len__(self): return self.num_samples paddle.enable_static() image = static.data(name='image', shape=[None, 784], dtype='float32') label = static.data(name='label', shape=[None, 1], dtype='int64') pred = static.nn.fc(x=image, size=10, activation='softmax') loss = F.cross_entropy(input=pred, label=label) avg_loss = paddle.mean(loss) optimizer = paddle.optimizer.SGD(learning_rate=0.001) optimizer.minimize(avg_loss) place = paddle.CPUPlace() exe = static.Executor(place) exe.run(static.default_startup_program()) # create data loader dataset = RandomDataset(BATCH_NUM * BATCH_SIZE) loader = paddle.io.DataLoader(dataset, feed_list=[image, label], places=place, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, return_list=False, num_workers=2) # 1. train and save inference model for data in loader(): exe.run( static.default_main_program(), feed=data, fetch_list=[avg_loss]) model_path = "fc.example.model" paddle.fluid.io.save_inference_model( model_path, ["image"], [pred], exe) # 2. load model # enable dygraph mode paddle.disable_static(place) # load fc = paddle.jit.load(model_path) # inference fc.eval() x = paddle.randn([1, IMAGE_SIZE], 'float32') pred = fc(x) # fine-tune fc.train() loss_fn = nn.CrossEntropyLoss() adam = opt.Adam(learning_rate=0.001, parameters=fc.parameters()) loader = paddle.io.DataLoader(dataset, places=place, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, num_workers=2) for epoch_id in range(EPOCH_NUM): for batch_id, (image, label) in enumerate(loader()): out = fc(image) loss = loss_fn(out, label) loss.backward() adam.step() adam.clear_grad() print("Epoch {} batch {}: loss = {}".format( epoch_id, batch_id, np.mean(loss.numpy()))) """ # 1. construct correct config config = _parse_load_config(configs) model_path, config = _build_load_path_and_config(path, config) return TranslatedLayer._construct(model_path, config) @dygraph_only def _trace(layer, inputs, feed_prefix='feed_', fetch_prefix='fetch_', tmp_prefix='t_'): assert isinstance(layer, Layer) if not isinstance(inputs, (list, tuple)): inputs = [inputs] tracer = _dygraph_tracer()._get_program_desc_tracer() var_list = extract_vars(inputs) with program_desc_tracing_guard(True): original_outputs = layer(*inputs) if not isinstance(original_outputs, (list, tuple)): outputs = [original_outputs] else: outputs = original_outputs out_vars = extract_vars(outputs, err_tag='outputs') program_desc, feed_names, fetch_names, parameters = tracer.create_program_desc( var_list, feed_prefix, out_vars, fetch_prefix, tmp_prefix) tracer.reset() with _dygraph_guard(None): program = create_program_from_desc(program_desc) return original_outputs, program, feed_names, fetch_names, parameters class TracedLayer(object): """ :api_attr: imperative TracedLayer is used to convert a forward dygraph model to a static graph model. This is mainly used to save the dygraph model for online inference using C++. Besides, users can also do inference in Python using the converted static graph model, which usually has better performance than the original dygraph model. TracedLayer would run the static graph model using :code:`Executor` and :code:`CompiledProgram` . The static graph model would share parameters with the dygraph model. All TracedLayer objects should not be created by constructor and should be created by static method :code:`TracedLayer.trace(layer, inputs)` . The TracedLayer can only be used to convert the data-independent dygraph model into the static graph model, which means the dygraph model should be independent with the tensor data and shape. """ def __init__(self, program, parameters, feed_names, fetch_names): self._program = program self._feed_names = feed_names self._fetch_names = fetch_names self._params = parameters self._place = _current_expected_place() self._scope = core.Scope() for p in parameters: src_tensor = p.value().get_tensor() dst_tensor = self._scope.var(p.name).get_tensor() dst_tensor._share_data_with(src_tensor) self._exe = Executor(self._place) self._compiled_program = None self._build_strategy = None self._exec_strategy = None @property def program(self): return self._program def _switch(self, is_test=True): for block_id in range(self._program.num_blocks): block = self._program.block(block_id) for op in block.ops: if op.has_attr("is_test"): op._set_attr("is_test", is_test) @staticmethod @dygraph_only def trace(layer, inputs): """ This method is the only allowed method to create TracedLayer object. It would call the :code:`layer(*inputs)` method to run the dygraph model and convert it into a static graph model. Args: layer (paddle.nn.Layer): the layer object to be traced. inputs (list(Tensor)|tuple(Tensor)|Tensor): the input tensors of the layer object. Returns: tuple: A tuple of 2 items, whose the first item is the output of :code:`layer(*inputs)` , and the second item is the created TracedLayer object. Examples: .. code-block:: python: import paddle class ExampleLayer(paddle.nn.Layer): def __init__(self): super(ExampleLayer, self).__init__() self._fc = paddle.nn.Linear(3, 10) def forward(self, input): return self._fc(input) layer = ExampleLayer() in_var = paddle.uniform(shape=[2, 3], dtype='float32') out_dygraph, static_layer = paddle.jit.TracedLayer.trace(layer, inputs=[in_var]) # run the static graph model using Executor inside out_static_graph = static_layer([in_var]) print(len(out_static_graph)) # 1 print(out_static_graph[0].shape) # (2, 10) # save the static graph model for inference static_layer.save_inference_model(dirname='./saved_infer_model') """ assert isinstance( layer, Layer ), "The type of 'layer' in fluid.dygraph.jit.TracedLayer.trace must be fluid.dygraph.Layer, but received {}.".format( type(layer)) outs, prog, feed, fetch, parameters = _trace(layer, inputs) traced = TracedLayer(prog, parameters, feed, fetch) return outs, traced def set_strategy(self, build_strategy=None, exec_strategy=None): """ Set the strategies when running static graph model. Args: build_strategy (BuildStrategy, optional): build strategy of :code:`CompiledProgram` inside TracedLayer. Default None. exec_strategy (ExecutionStrategy, optional): execution strategy of :code:`CompiledProgram` inside TracedLayer. Default None. Returns: None Examples: .. code-block:: python: import paddle class ExampleLayer(paddle.nn.Layer): def __init__(self): super(ExampleLayer, self).__init__() self._fc = paddle.nn.Linear(3, 10) def forward(self, input): return self._fc(input) layer = ExampleLayer() in_var = paddle.uniform(shape=[2, 3], dtype='float32') out_dygraph, static_layer = paddle.jit.TracedLayer.trace(layer, inputs=[in_var]) build_strategy = paddle.static.BuildStrategy() build_strategy.enable_inplace = True exec_strategy = paddle.static.ExecutionStrategy() exec_strategy.num_threads = 2 static_layer.set_strategy(build_strategy=build_strategy, exec_strategy=exec_strategy) out_static_graph = static_layer([in_var]) """ assert self._compiled_program is None, "Cannot set strategy after run" assert isinstance( build_strategy, (type(None), BuildStrategy) ), "The type of 'build_strategy' in fluid.dygraph.jit.TracedLayer.set_strategy must be fluid.BuildStrategy, but received {}.".format( type(build_strategy)) assert isinstance( exec_strategy, (type(None), ExecutionStrategy) ), "The type of 'exec_strategy' in fluid.dygraph.jit.TracedLayer.set_strategy must be fluid.ExecutionStrategy, but received {}.".format( type(exec_strategy)) self._build_strategy = build_strategy self._exec_strategy = exec_strategy @switch_to_static_graph def _compile(self): self._compiled_program = CompiledProgram( self._program).with_data_parallel( build_strategy=self._build_strategy, exec_strategy=self._exec_strategy, places=self._place) def _build_feed(self, inputs): assert isinstance(inputs, (list, tuple)), \ "Inputs should be a list or tuple of variables" assert len(inputs) == len(self._feed_names) feed_dict = {} if _non_static_mode(): for x, name in zip(inputs, self._feed_names): feed_dict[name] = x.value().get_tensor() else: for x, name in zip(inputs, self._feed_names): feed_dict[name] = x return feed_dict @switch_to_static_graph def _run(self, feed): return self._exe.run(self._compiled_program, feed=feed, fetch_list=self._fetch_names) def __call__(self, inputs): with scope_guard(self._scope): if self._compiled_program is None: self._compile() return self._run(self._build_feed(inputs)) @switch_to_static_graph def save_inference_model(self, path, feed=None, fetch=None, **kwargs): """ Save the TracedLayer to a model for inference. The saved inference model can be loaded by C++ inference APIs. ``path`` is the prefix of saved objects, and the saved translated program file suffix is ``.pdmodel`` , the saved persistable variables file suffix is ``.pdiparams`` . Args: path(str): The path prefix to save model. The format is ``dirname/file_prefix`` or ``file_prefix``. feed (list[int], optional): the input variable indices of the saved inference model. If None, all input variables of the TracedLayer object would be the inputs of the saved inference model. Default None. fetch (list[int], optional): the output variable indices of the saved inference model. If None, all output variables of the TracedLayer object would be the outputs of the saved inference model. Default None. kwargs: Supported keys including 'clip_extra'.set to True if you want to clip extra information for every operator. Returns: None Examples: .. code-block:: python: import numpy as np import paddle class ExampleLayer(paddle.nn.Layer): def __init__(self): super(ExampleLayer, self).__init__() self._fc = paddle.nn.Linear(3, 10) def forward(self, input): return self._fc(input) save_dirname = './saved_infer_model' in_np = np.random.random([2, 3]).astype('float32') in_var = paddle.to_tensor(in_np) layer = ExampleLayer() out_dygraph, static_layer = paddle.jit.TracedLayer.trace(layer, inputs=[in_var]) static_layer.save_inference_model(save_dirname, feed=[0], fetch=[0]) paddle.enable_static() place = paddle.CPUPlace() exe = paddle.static.Executor(place) program, feed_vars, fetch_vars = paddle.static.load_inference_model(save_dirname, exe) fetch, = exe.run(program, feed={feed_vars[0]: in_np}, fetch_list=fetch_vars) print(fetch.shape) # (2, 10) """ check_type(path, "path", str, "fluid.dygraph.jit.TracedLayer.save_inference_model") check_type(feed, "feed", (type(None), list), "fluid.dygraph.jit.TracedLayer.save_inference_model") if isinstance(feed, list): for f in feed: check_type( f, "each element of feed", int, "fluid.dygraph.jit.TracedLayer.save_inference_model") check_type(fetch, "fetch", (type(None), list), "fluid.dygraph.jit.TracedLayer.save_inference_model") if isinstance(fetch, list): for f in fetch: check_type( f, "each element of fetch", int, "fluid.dygraph.jit.TracedLayer.save_inference_model") clip_extra = kwargs.get('clip_extra', True) # path check file_prefix = os.path.basename(path) if file_prefix == "": raise ValueError( "The input path MUST be format of dirname/file_prefix " "[dirname\\file_prefix in Windows system], but received " "file_prefix is empty string.") dirname = os.path.dirname(path) if dirname and not os.path.exists(dirname): os.makedirs(dirname) from paddle.fluid.io import save_inference_model def get_feed_fetch(all_vars, partial_vars): if partial_vars is None: return all_vars return [all_vars[idx] for idx in partial_vars] with scope_guard(self._scope): feeded_var_names = get_feed_fetch(self._feed_names, feed) target_var_names = get_feed_fetch(self._fetch_names, fetch) target_vars = [] for name in target_var_names: target_var = self._program.global_block().vars.get(name, None) assert target_var is not None, "{} cannot be found".format(name) target_vars.append(target_var) model_filename = file_prefix + INFER_MODEL_SUFFIX params_filename = file_prefix + INFER_PARAMS_SUFFIX save_inference_model(dirname=dirname, feeded_var_names=feeded_var_names, target_vars=target_vars, executor=self._exe, main_program=self._program.clone(), model_filename=model_filename, params_filename=params_filename, clip_extra=clip_extra)