# Copyright (c) 2020 PaddlePaddle Authors. 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 six import pickle import numpy as np import paddle from paddle import compat as cpt from paddle.fluid import core from paddle.fluid import framework from paddle.fluid import backward from paddle.fluid import unique_name from paddle.fluid.dygraph import layers from paddle.fluid.layers import nn from paddle.fluid.layers.utils import _hash_with_id from paddle.fluid.dygraph.base import switch_to_static_graph from paddle.fluid.framework import in_dygraph_mode __all__ = ['TranslatedLayer'] INFER_MODEL_SUFFIX = ".pdmodel" INFER_PARAMS_SUFFIX = ".pdiparams" INFER_PARAMS_INFO_SUFFIX = ".pdiparams.info" LOADED_VAR_SUFFIX = "load" PARAMETER_NAME_PREFIX = "param" BUFFER_NAME_PREFIX = "buffer" def _load_program_desc(model_file_path): # 1. parse program desc with open(model_file_path, "rb") as f: program_desc_str = f.read() program_desc = core.ProgramDesc(program_desc_str) if not core._is_program_version_supported(program_desc._version()): raise ValueError("Unsupported program version: %d\n" % program_desc._version()) return program_desc def _is_persistable(var_desc): if var_desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \ var_desc.type() == core.VarDesc.VarType.FETCH_LIST or \ var_desc.type() == core.VarDesc.VarType.READER or \ var_desc.type() == core.VarDesc.VarType.RAW: return False return var_desc.persistable() def _is_parameter(persistable_var_desc, program_desc): # 1. firstly, param should be input of op input_ops = [] # op can be repeated for block_idx in six.moves.range(program_desc.num_blocks()): block = program_desc.block(block_idx) for op_idx in six.moves.range(block.op_size()): op = block.op(op_idx) # NOTE: parameter is the input of a certain op if persistable_var_desc.name() in op.input_arg_names(): input_ops.append(op) # 2. secondly, param should not be output of op or be same op's output for block_idx in six.moves.range(program_desc.num_blocks()): block = program_desc.block(block_idx) for op_idx in six.moves.range(block.op_size()): op = block.op(op_idx) if persistable_var_desc.name() in op.output_arg_names(): # such as batch_norm_op if op in input_ops: continue else: return False return True def _get_persistable_vars(program_desc): persistable_vars = [] for i in six.moves.range(program_desc.num_blocks()): block = program_desc.block(i) persistable_vars.extend(list(filter(_is_persistable, block.all_vars()))) return persistable_vars def _get_persistable_var_names(program_desc): """ Get all persistable variable names in ProgramDesc. """ var_names = [] persistable_vars = _get_persistable_vars(program_desc) for var in persistable_vars: var_names.append(var.name()) return var_names def _get_all_var_names(program_desc): all_var_names = set() for i in six.moves.range(program_desc.num_blocks()): block = program_desc.block(i) for var in block.all_vars(): all_var_names.add(var.name()) return all_var_names @switch_to_static_graph def _append_loaded_suffix(name): """ Append loaded suffix to the given variable name e.g. x ==> x.load_0, x.load_0 ==> x.load_0.load_0 """ suffix = LOADED_VAR_SUFFIX name = cpt.to_text(name) new_name = unique_name.generate_with_ignorable_key('.'.join((name, suffix))) return new_name @switch_to_static_graph def _generate_unique_var_name(prefix): return unique_name.generate_with_ignorable_key(prefix) def _append_loaded_suffix_to_var(program_desc): suffix_varname_dict = dict() persistable_vars = _get_persistable_vars(program_desc) for var_desc in persistable_vars: old_name = var_desc.name() new_name = _append_loaded_suffix(var_desc.name()) suffix_varname_dict[new_name] = old_name var_desc.set_name(new_name) for block_idx in six.moves.range(program_desc.num_blocks()): block = program_desc.block(block_idx) block._rename_var(cpt.to_bytes(old_name), cpt.to_bytes(new_name)) for op_idx in six.moves.range(block.op_size()): op = block.op(op_idx) op._rename_input(old_name, new_name) op._rename_output(old_name, new_name) return suffix_varname_dict @switch_to_static_graph def _generate_unique_var_name_sync_with_main_program(prefix): return unique_name.generate(prefix) def _get_loaded_var_new_old(program_desc, all_new_old_dict_all): new_old_dict = dict() persistable_vars = _get_persistable_vars(program_desc) for var_desc in persistable_vars: name_new = var_desc.name() new_old_dict[name_new] = all_new_old_dict_all[name_new] return new_old_dict def _rename_var_program_desc(program_desc, include=None, exclude=None): """ Change the name of the loaded variables.Use 'unique_name.generate' to avoid duplication. It is used when loading multiple program during inference. e.g. linear_0.tmp_3 ==> linear_0.tmp_1, x ==> x_0. For double grad, x@GRAD ==> x_0@GRAD If 'include' is not `None`,variables in include and the corresponding double grad variables (if exist) are renamed. If 'exclude' is not `None`,variables that are in exclude and the corresponding double grad variables (if exist) are not renamed. Args: program_desc(ProgramDesc):the variables in it will be modified. include(List):list of names of variables. exclude(List):list of names of variables. Returns: tuple of (dict_rename_var_new_old, dict_rename_var_old_new) dict_rename_var_new_old is a dict mapping from new name to old name dict_rename_var_old_new is a dict mapping from old name to new name """ dict_rename_var_old_new = dict() dict_rename_var_new_old = dict() old_names = [] # Store all old names for b_idx in six.moves.range(program_desc.num_blocks()): cur_block = program_desc.block(b_idx) for var in cur_block.all_vars(): old_names.append(var.name()) # Create dict_rename_var_new_old and dict_rename_var_old_new for non double # grad variables has_double_grad = False for b_idx in six.moves.range(program_desc.num_blocks()): cur_block = program_desc.block(b_idx) for var_idx, var in enumerate(cur_block.all_vars()): name_old = var.name() is_double_grad_var = "@GRAD" in name_old has_double_grad = has_double_grad or is_double_grad_var should_rename = (include is None or name_old in include) and ( exclude is None or name_old not in exclude) and not is_double_grad_var if should_rename: temp_name = name_old.split('_') if len(temp_name) > 1 and temp_name[-1].isnumeric(): temp_name = "_".join(temp_name[:-1]) else: temp_name = name_old while True: name_new = _generate_unique_var_name_sync_with_main_program( temp_name) if name_new not in old_names[:var_idx] + old_names[var_idx + 1:]: break else: name_new = name_old if name_old != name_new: cur_block._rename_var( cpt.to_bytes(name_old), cpt.to_bytes(name_new)) if not is_double_grad_var: dict_rename_var_old_new[name_old] = name_new dict_rename_var_new_old[name_new] = name_old # Handle double grad names if has_double_grad: double_grad_rename_dict = {} for name_old in dict_rename_var_old_new: for b_idx in six.moves.range(program_desc.num_blocks()): cur_block = program_desc.block(b_idx) for var_idx, var in enumerate(cur_block.all_vars()): var_name = var.name() if "@GRAD" in var_name and name_old in var_name: new_var_name = var_name.replace( name_old, dict_rename_var_old_new[name_old]) double_grad_rename_dict[var_name] = new_var_name for var_name in double_grad_rename_dict: dict_rename_var_old_new[var_name] = double_grad_rename_dict[ var_name] dict_rename_var_new_old[double_grad_rename_dict[ var_name]] = var_name # Rename on program desc for b_idx in six.moves.range(program_desc.num_blocks()): cur_block = program_desc.block(b_idx) for op_idx in six.moves.range(cur_block.op_size()): op = cur_block.op(op_idx) for input_arg_name in op.input_arg_names(): if input_arg_name in dict_rename_var_old_new: if input_arg_name != dict_rename_var_old_new[ input_arg_name]: op._rename_input( input_arg_name, dict_rename_var_old_new[input_arg_name]) if cur_block.has_var(cpt.to_bytes(input_arg_name)): cur_block._rename_var( cpt.to_bytes(input_arg_name), cpt.to_bytes(dict_rename_var_old_new[ input_arg_name])) for output_arg_name in op.output_arg_names(): if output_arg_name in dict_rename_var_old_new: if output_arg_name != dict_rename_var_old_new[ output_arg_name]: op._rename_output( output_arg_name, dict_rename_var_old_new[output_arg_name]) if cur_block.has_var(cpt.to_bytes(output_arg_name)): cur_block._rename_var( cpt.to_bytes(output_arg_name), cpt.to_bytes(dict_rename_var_old_new[ output_arg_name])) program_desc.flush() return dict_rename_var_new_old, dict_rename_var_old_new @switch_to_static_graph def _build_program_by_desc(program_desc): prog = framework.Program() prog.desc = program_desc prog.blocks = [ framework.Block(prog, i) for i in six.moves.range(prog.desc.num_blocks()) ] prog._sync_with_cpp() return prog def _change_is_test_status(program_desc, is_test): # change all `is_test` attributes for i in six.moves.range(program_desc.num_blocks()): block = program_desc.block(i) for j in six.moves.range(block.op_size()): op = block.op(j) if op.has_attr('is_test'): op._set_attr('is_test', is_test) class _ProgramHolder(object): """ Holds the execution information of a Program. _ProgramHolder is the execution unit of TranslatedLayer, if TranslatedLayer contains multiple _ProgramHolder, it can execute multiple methods _ProgramHolder is an internal concept. """ def __init__(self, program_desc): super(_ProgramHolder, self).__init__() # input, output, persistable, double_grads var info self._input_descs = [] self._output_descs = [] self._double_grad_descs = [] self._persistable_names = [] # execution scope self._inner_scope = core.Scope() # append suffix var name dict self._suffix_varname_dict = None # forward program self._infer_program_desc = self._preprocess(program_desc) # forward + backward program self._train_program_desc = self._append_backward_desc( self._infer_program_desc) @property def infer_program(self): return self._infer_program_desc @property def train_program(self): return self._train_program_desc @property def input_descs(self): return self._input_descs @property def output_descs(self): return self._output_descs @property def persistable_names(self): return self._persistable_names @property def double_grad_descs(self): return self._double_grad_descs @property def scope(self): return self._inner_scope def _preprocess(self, program_desc): # rename persistable variables of 'program_desc' list_persistable_var = _get_persistable_var_names(program_desc) rename_new_old_dict, _ = _rename_var_program_desc(program_desc, list_persistable_var) # 1. Prune original program # remove feed, fetch and scale-1 op, remove op_callstack attr ops_to_remove = [] root_block = program_desc.block(0) for i in six.moves.range(root_block.op_size()): op = root_block.op(i) if op.type() == 'feed': ops_to_remove.append(i) feed_var_name = cpt.to_bytes(op.input('X')[0]) root_block._remove_var(feed_var_name) self._input_descs.append( root_block.find_var(cpt.to_bytes(op.output('Out')[0]))) elif op.type() == 'scale' and op.output('Out')[0].startswith( 'save_infer_model/scale_'): ops_to_remove.append(i) out_var_name = cpt.to_bytes(op.output('Out')[0]) root_block._remove_var(out_var_name) self._output_descs.append( root_block.find_var(cpt.to_bytes(op.input('X')[0]))) elif op.type() == 'fetch': ops_to_remove.append(i) fetch_var_name = cpt.to_bytes(op.output('Out')[0]) root_block._remove_var(fetch_var_name) # NOTE: some old pre-train models have no extra scale_op if not op.input('X')[0].startswith('save_infer_model/scale_'): self._output_descs.append( root_block.find_var(cpt.to_bytes(op.input('X')[0]))) else: if op.has_attr("op_callstack"): op.remove_attr("op_callstack") for op_idx in reversed(ops_to_remove): root_block._remove_op(op_idx, op_idx + 1) for i in range(program_desc.num_blocks()): block_desc = program_desc.block(i) for var_desc in block_desc.all_vars(): if "@GRAD" in var_desc.name(): self._double_grad_descs.append(var_desc) # 2. Input processing, reverse feed vars self._input_descs.reverse() # 3. Output processing, add scale for outputs tmp_program = _build_program_by_desc(program_desc) # NOTE: [why need append scale for outputs] # When dealing with some more complex pre-training models, there # will be situations where the pre-training model has multiple # fetch outputs. In the scenario of multiple fetch outputs, # there is a special case where multiple outputs of the model # may be on the same branch. According to the user's subsequent # use, multiple outputs may be associated with multiple branches. # These subsequent operations are added in TranslatedLayer is # agnostic during initialization, which results in subsequent # gradient accumulation operations that are required on the # output node in the middle of the branch will not be performed, # resulting in error, details see pull request: # [https://github.com/PaddlePaddle/Paddle/pull/24627] self._append_scale_to_output(tmp_program) # 4. Persistable vars processing # - append loaded suffix to persistable vars # NOTE: [why need to append suffix to persistable vars] # Dygraph and static graph mode use the same naming mechanism. # If users want to load the model fine-tune, it is possible # to add the existing Layer in the loaded model to enhance # the network. For example, the original saved model has linear, # and later after loading, a new linear is added. At this time, # there will be a problem of duplicate names, so here is unified # to add the LOADED suffix to the parameters of the model loaded self._suffix_varname_dict = _get_loaded_var_new_old(program_desc, rename_new_old_dict) # - get persistable var self._persistable_names = _get_persistable_var_names(program_desc) return program_desc @switch_to_static_graph def _append_scale_to_output(self, program): # 1. append scale & save var scale_output_vars = [] with framework.program_guard(program): for i, out in enumerate(self._output_descs): var = program.global_block().var(out.name()) var = nn.scale( var, 1., name="translated_layer/scale_{}".format(i)) scale_output_vars.append(var) # 2. update output names & descs for i, var in enumerate(scale_output_vars): self._output_descs[i] = var.desc @switch_to_static_graph def _append_backward_desc(self, infer_program_desc): program_desc_copy = core.ProgramDesc(infer_program_desc) # 1. set all `is_test` attributes to False _change_is_test_status(program_desc_copy, False) # 2. prepare program and related var # NOTE: To reuse backward interfaces, build Program firstly. # Originally, there is no need to build a program, but need to almost # rewrite a series of methods for append_backward for program_desc. # Therefore, in order to reuse the method of backward.py, build the program here. program = _build_program_by_desc(program_desc_copy) # 3. Add the outputs which is only used for training and not saved in # inference program. for block_idx in six.moves.range(program.num_blocks): block = program.block(block_idx) for op in block.ops: if op.type == "batch_norm": if "ReserveSpace" not in op.output_names or len( op.output("ReserveSpace")) == 0: reserve_space = block.create_var( name=unique_name.generate_with_ignorable_key( ".".join(["reserve_space", 'tmp'])), dtype=block.var(op.input("X")[0]).dtype, type=core.VarDesc.VarType.LOD_TENSOR, persistable=False, stop_gradient=True) op.desc.set_output("ReserveSpace", [reserve_space.name]) targets = [] for out in self._output_descs: targets.append(program.global_block().var(out.name())) # 3. append backward backward.gradients(targets=targets, inputs=[]) return program.desc # [ TranslatedLayer : Run program in imperative mode ] # # DESIGN IDEA: using an special operator `RunProgram`, execute program inside operator. # # Op's Inputs: # - the input variable of the user feed # - the necessary parameters of the network # Op's Outputs: # - the output variable of fetch # # This op receives a complete program desc, internally creates scope # and executor, executes this program. Key points: # # 1. Data Sharing: # The varBase of the dynamic graph is not in the scope, so before the op # executes the program internally, create persistent variables with the # same name as feed, parameters, and fetch in the scope, and share the # LoDTensor of the op input. # # 2. Forward and Backward Separation: # Because the dynamic graph op performs the forward and backward separately, # in the forward op RunProgram, we only execute the forward part of whole program, # and in the backward op RunProgramGrad, we execute the backward part of program. # We can not separate the program into forward and backward part, which will # make some control flow execution logic wrong. # NOTE: [compatible] deal with model saved by save_inference_model, # which need get var info from program desc def _load_persistable_vars_by_program(model_path, program_holder, params_filename=None): # make sure the path has been checked persistable_vars = _get_persistable_vars(program_holder.infer_program) load_var_dict = {} for each_var in persistable_vars: orig_each_name = program_holder._suffix_varname_dict[each_var.name()] if _is_parameter(each_var, program_holder.infer_program): # create output varbase new_var = framework.ParamBase( shape=each_var.shape(), dtype=each_var.dtype(), name=each_var.name(), type=each_var.type(), persistable=True) else: new_var = framework._varbase_creator( type=each_var.type(), name=each_var.name(), shape=each_var.shape(), dtype=each_var.dtype(), persistable=True) if params_filename is None: framework._dygraph_tracer().trace_op( type='load', inputs={}, outputs={'Out': new_var}, attrs={'file_path': os.path.join(model_path, orig_each_name)}) new_var.stop_gradient = False load_var_dict[each_var.name()] = new_var if params_filename is not None: load_var_list = [] dict_name_old_new = { v: k for k, v in program_holder._suffix_varname_dict.items() } for name in sorted(dict_name_old_new.keys()): load_var_list.append(load_var_dict[dict_name_old_new[name]]) framework._dygraph_tracer().trace_op( type='load_combine', inputs={}, outputs={'Out': load_var_list}, attrs={'file_path': os.path.join(model_path, params_filename)}) for each_var in persistable_vars: if not _is_parameter(each_var, program_holder.infer_program): continue param = load_var_dict[each_var.name()] param.stop_gradient = False # NOTE: [Recovery stop gradient information based on the program] # After loading the model, the stop_gradient information # of the original variable is lost, but if a parameter does not # have a corresponding @GRAD variable in the backward program, # it can be said that it is also stop_gradient all_var_names = _get_all_var_names(program_holder.train_program) for var_name in load_var_dict: grad_var_name = var_name + core.grad_var_suffix() if grad_var_name not in all_var_names: load_var_dict[var_name].stop_gradient = True return load_var_dict def _load_persistable_vars(model_path, var_info_path, program_holder, params_filename): # 1. load extra var info with open(var_info_path, 'rb') as f: extra_var_info = pickle.load(f) # 2. construct var dict load_var_dict = dict() load_var_list = [] inv_suffix_varname_dict = { value: key for key, value in program_holder._suffix_varname_dict.items() } # NOTE(chenweihang): we need load persistable vars based the program, # because the program may be pruned when `save_inference_model`, some # var in `extra_var_info` may have been pruned for name in sorted(inv_suffix_varname_dict): if name not in extra_var_info: raise RuntimeError( "The model to be loaded is not complete." "The variable `%s` of program cannot be found in loaded model.", name) # get suffix var name, see [why need to append suffix to persistable vars] new_name = inv_suffix_varname_dict[name] # create output varbase if extra_var_info[name].get('trainable', None) is not None: # use default shape and dtype new_var = framework.ParamBase( shape=[1], # only to pass check, this shape is not meaningful dtype=core.VarDesc.VarType.FP32, name=new_name, persistable=True) else: new_var = framework._varbase_creator( name=new_name, persistable=True) new_var.stop_gradient = extra_var_info[name]['stop_gradient'] load_var_dict[new_name] = new_var load_var_list.append(new_var) # 3. load all vars assert params_filename is not None, "params_filename should not be None." var_file_path = os.path.join(model_path, params_filename) if not os.path.exists(var_file_path): if len(extra_var_info) != 0: raise ValueError("The model to be loaded is incomplete.") else: framework._dygraph_tracer().trace_op( type='load_combine', inputs={}, outputs={'Out': load_var_list}, attrs={'file_path': var_file_path}) return load_var_dict # NOTE(chenweihang): to adapt paddle.load to get state_dict def _remove_varname_suffix(var_dict, program_holder): no_suffix_var_dict = dict() for var_name in var_dict: no_suffix_name = program_holder._suffix_varname_dict[var_name] no_suffix_var_dict[no_suffix_name] = var_dict[var_name] return no_suffix_var_dict def _construct_program_holders(model_path, model_filename=None): # make sure the path has been checked program_holder_dict = dict() if model_filename is not None: # [compatible] if assign model_filename, only can load one program as Layer.forward model_filename = os.path.basename(model_filename) model_file_path = os.path.join(model_path, model_filename) model_name = model_filename[:-len(INFER_MODEL_SUFFIX)] #Load every file that meets the requirements in the directory model_path. for filename in os.listdir(model_path): if model_filename == filename: func_name = 'forward' model_file_path = os.path.join(model_path, model_filename) elif filename.endswith(INFER_MODEL_SUFFIX) and filename.startswith( model_name): parsing_names = filename[len(model_name):-len( INFER_MODEL_SUFFIX) + 1].split('.') if len(parsing_names) == 3 and len(parsing_names[1]) > 0: func_name = parsing_names[1] model_file_path = os.path.join(model_path, filename) else: continue else: continue program_holder_dict[func_name] = _ProgramHolder( _load_program_desc(model_file_path)) else: for _, _, file_names in os.walk(model_path): for name in file_names: if 'model' in name: model_file_path = os.path.join(model_path, name) method_name = name.strip('_') if method_name == 'model': method_name = 'forward' else: method_name.replace('model', '') program_holder_dict[method_name] = _ProgramHolder( _load_program_desc(model_file_path)) return program_holder_dict def _construct_params_and_buffers(model_path, programs, params_filename=None, append_suffix=True): var_info_filename = str(params_filename) + ".info" var_info_path = os.path.join(model_path, var_info_filename) params_path = os.path.join(model_path, str(params_filename)) if os.path.exists(var_info_path): var_dict = _load_persistable_vars(model_path, var_info_path, programs['forward'], params_filename) model_name = params_filename[:-len(INFER_PARAMS_SUFFIX)] #Load every file that meets the requirements in the directory model_path. for file_name in os.listdir(model_path): if file_name.startswith(model_name) and file_name.endswith( INFER_PARAMS_SUFFIX): parsing_names = file_name[len(model_name):-len( INFER_PARAMS_SUFFIX) + 1].split('.') if len(parsing_names) == 3 and len(parsing_names[1]) > 0: func_name = parsing_names[1] else: continue else: continue var_info_path = os.path.join(model_path, var_info_filename) var_dict.update( _load_persistable_vars(model_path, var_info_path, programs[ func_name], file_name)) elif params_filename is not None and not os.path.exists(params_path): # When saving XX, there is only '*.pdmodel' return dict() else: var_dict = _load_persistable_vars_by_program( model_path, programs['forward'], params_filename) if not append_suffix: var_dict = _remove_varname_suffix(var_dict, programs['forward']) return var_dict def _run_dygraph(instance, input, program_holder): # 1. prepare inputs, outputs, attrs input_vars = [] for i, value in enumerate(input): if not isinstance(value, (np.ndarray, core.VarBase)): raise TypeError( "The type of input in TranslatedLayer must be numpy array or Variable(VarBase), but received %s." % type(value)) # NOTE: In order to unify the API, firstly convert the input to VarBase if isinstance(value, np.ndarray): var = core.VarBase( value=value, name=program_holder.input_descs[i].name(), persistable=False, place=framework._current_expected_place(), zero_copy=True) else: var = value # NOTE: we changed var name here, # but it may be an important name set by user var.name = program_holder.input_descs[i].name() input_vars.append(var) if instance._input_args_names is None: instance._input_args_names = [ ins.name() for ins in program_holder.input_descs ] persistable_vars = [] for var_name in program_holder.persistable_names: dy_var_name = instance._persistable_var_name_dict[var_name] if dy_var_name in instance._parameters: persistable_vars.append(instance._parameters[dy_var_name]) elif dy_var_name in instance._buffers: persistable_vars.append(instance._buffers[dy_var_name]) else: raise ValueError( "The persistable variable %s does not exist in current TranslatedLayer." % var_name) output_vars = [] for var_desc in program_holder.output_descs: var = core.VarBase(var_desc.dtype(), var_desc.shape(), var_desc.name(), var_desc.type(), False) output_vars.append(var) # hold forward variables tmp_scope_vec = core.VarBase(core.VarDesc.VarType.FP32, [], "program_out_scope", core.VarDesc.VarType.STEP_SCOPES, True) tmp_scope_vec.value().set_scope(program_holder.scope) double_grad_vars = [] for var_desc in program_holder.double_grad_descs: var = core.VarBase(var_desc.dtype(), var_desc.shape(), var_desc.name(), var_desc.type(), False) double_grad_vars.append(var) if len(double_grad_vars) == 0: double_grad_vars = [ core.VarBase( value=[1], name='Fake_var', place=framework._current_expected_place()) ] # 2. run program by op trace_program = program_holder.infer_program if instance._is_test else program_holder.train_program end_op_index = program_holder.infer_program.block(0).op_size() framework._dygraph_tracer().trace_op( type='run_program', inputs={'X': input_vars, 'Params': persistable_vars}, outputs={ 'Out': output_vars, 'OutScope': tmp_scope_vec, 'DOut': double_grad_vars }, attrs={ 'global_block': trace_program.block(0), 'start_op_index': 0, 'end_op_index': end_op_index, 'is_test': instance._is_test, 'program_id': _hash_with_id(trace_program) }) # NOTE: [ why need set param's gradient type here ] # if user set sparse gradient mode, the param's gradient # will be SelectedRows, not LoDTensor. But tracer will just # set param grad VarBase by forward VarBase(LoDTensor) # If we don't change grad_var type here, RunProgramOp need # transform SelectedRows to LoDTensor forcibly, it may not # be user wanted result. for persistable_var in persistable_vars: grad_var_name = var.name + core.grad_var_suffix() grad_var = trace_program.block(0).find_var(cpt.to_bytes(grad_var_name)) # NOTE: cannot find var desc maybe not problem, # such as in batch_norm if grad_var is None: continue persistable_var._set_grad_type(grad_var.type()) drop_scope_if_no_grad(instance, tmp_scope_vec) # 3. prepare output, keep same form with inputs outs = output_vars if len(output_vars) == 1: outs = output_vars[0] return outs def drop_scope_if_no_grad(instance, scope_vec): tracer = framework._dygraph_tracer() if (not instance._is_test) and (not tracer._has_grad): scope_vec.value().get_scope().drop_kids() def _run_static_graph(input, program_holder, trace_program): main_program = framework.default_main_program() param_var_names = _get_persistable_var_names(trace_program) _, dict_rename_var_old_new = _rename_var_program_desc( trace_program, exclude=param_var_names) trace_program.flush() output_names = [var.name() for var in program_holder.output_descs] # append blocks from 'trace_program' _append_block(main_program, trace_program, program_holder, input, dict_rename_var_old_new) main_program._sync_with_cpp() outs = _get_output_from_program(main_program, program_holder, dict_rename_var_old_new) if len(outs) == 1: outs = outs[0] return outs def _collect_current_and_parent_var(program, block_idx): ''' Get variables in current block and its parent block. Args: program(Program): The program containing the current block. block_idx(int): index of current block. Returns: List: list of variables. ''' vars = [] if block_idx < 0: return vars for var in program.block(block_idx).vars: vars.append(var) parent_idx = program.block(block_idx).parent_idx if parent_idx > -1: vars += _collect_current_and_parent_var(program, parent_idx) return vars def _append_block(dest_program, src_program_desc, program_holder, input_variables, dict_rename_var_old_new=None): ''' Append Variables and Operators in 'src_program_desc' to dest_program. Args: dest_program(Program): Variables and Operators are appended to it. src_program_desc(ProgramDesc): Variables in it will be appended to 'dest_program'. program_holder(_ProgramHolder): program_holder of TranslatedLayer input_variables(list): list of input variables dict_rename_var_old_new(None|dict): When using '_rename_var_program_desc', use it to map the name of the variable before it was modified and the new name. ''' origin_block_idx = dest_program.current_block_idx param_var_names = _collect_current_and_parent_var(dest_program, origin_block_idx) append_var_from_block_desc_static( dest_program.block(origin_block_idx), src_program_desc.block(0), exclude=param_var_names) name_inp_desc = [inp.name() for inp in program_holder.input_descs] input_names = [inp.name for inp in input_variables] if len(name_inp_desc) != len(input_names): raise ValueError( "The number of input is invalid, expected {}, but received {}.". format(len(name_inp_desc), len(input_names))) for i, out_name in enumerate(name_inp_desc): if dict_rename_var_old_new: out_name = dict_rename_var_old_new[out_name] dest_program.block(origin_block_idx).append_op( type='assign', inputs={'X': [input_names[i]]}, outputs={'Out': [out_name]}) append_ops = append_op_from_block_desc_static( dest_program.block(origin_block_idx), src_program_desc.block(0)) dest_program._sync_with_cpp() offset_block_idx = dest_program.num_blocks - 1 if src_program_desc.num_blocks() > 1: for src_block_idx in range(1, src_program_desc.num_blocks()): src_block = src_program_desc.block(src_block_idx) src_parent_idx = src_block.parent if src_parent_idx > 0: parent_idx = offset_block_idx + parent_idx else: parent_idx = origin_block_idx dest_block = dest_program._create_block(parent_idx=parent_idx) append_var_from_block_desc_static( dest_block, src_block, exclude=param_var_names) append_ops += append_op_from_block_desc_static(dest_block, src_block) dest_program._sync_with_cpp() for op in append_ops: if op.has_attr('sub_block'): sub = op.attr('sub_block') if isinstance(sub, framework.core.BlockDesc): origin_id = sub.id if isinstance(sub, framework.Block): origin_id = sub.idx op._set_attr('sub_block', dest_program.block(offset_block_idx + origin_id)) dest_program._sync_with_cpp() dest_program.current_block_idx = origin_block_idx def _get_output_from_program(program, program_holder, dict_rename_var_old_new=None): """ Get output name of 'program' according to program_holder """ outs = list() for var in program_holder.output_descs: for idx in range(program.num_blocks): vars = program.block(idx).vars var_name = var.name() if dict_rename_var_old_new: var_name = dict_rename_var_old_new[var_name] if var_name in vars: out = vars[var_name] if out not in outs: outs.append(out) return outs def append_op_from_block_desc_static(block, src_block_desc): """ Append Operators of 'src_block_desc' to current block. Args: block(Block): append OP of 'src_block_desc' to it. src_block_desc(BlockDesc): append var of 'src_block_desc' Returns: List: list of the OP that are append to current block. """ ops = [] for i in range(src_block_desc.op_size()): ops.append(append_op_from_desc_static(block, src_block_desc.op(i))) return ops def append_op_from_desc_static(block, op_desc): """ Append Operators to 'block' according to 'op_desc'. Args: block(Block): append OP of 'src_block_desc' to it. op_desc(OpDesc): create OP according to it. Returns: Operator: OP appended to 'block'. """ op_type = op_desc.type() op_append = block.desc.append_op() op_append.copy_from(op_desc) op = framework.Operator( block=block, desc=op_append, type=op_type, inputs=None, outputs=None, attrs=None) block.ops.append(op) return op def append_var_from_block_desc_static(block, src_block_desc, include=None, exclude=None): """ Append Variables of 'src_block_desc' to current block. If 'include' is not `None`,variables that are not in include are not append. If 'exclude' is not `None`,variables that are in exclude will are not append. Args: block(Block): append Variables of 'src_block_desc' to it. src_block_desc(BlockDesc): append var of 'src_block_desc' include(List):list of names of variables exclude(List):list of names of variables Returns: List: list of the variables that are append to current block. """ vars_append = [] for var_desc in src_block_desc.all_vars(): var_desc_name = var_desc.name() should_append = (include is None or var_desc_name in include) and ( exclude is None or var_desc_name not in exclude) if not block.has_var(var_desc_name) and should_append: var_type = var_desc.type() if var_type in [ core.VarDesc.VarType.SELECTED_ROWS, core.VarDesc.VarType.LOD_TENSOR, core.VarDesc.VarType.LOD_TENSOR_ARRAY ]: data_type = var_desc.dtype() var_shape = var_desc.shape() else: data_type = None var_shape = None if var_type in [ core.VarDesc.VarType.LOD_TENSOR, core.VarDesc.VarType.LOD_TENSOR_ARRAY ]: lod_level = var_desc.lod_level() else: lod_level = None if var_desc.persistable(): current_block = block.program.global_block() else: current_block = block vars_append.append( current_block.create_var( name=var_desc.name(), dtype=data_type, type=var_type, shape=var_shape, lod_level=lod_level, persistable=var_desc.persistable(), set_need_check_feed=var_desc.need_check_feed())) return vars_append class TranslatedLayer(layers.Layer): """ TranslatedLayer is a ``paddle.nn.Layer`` for holding the model loaded by :ref:`api_paddle_jit_load` . It can be used like a general Layer object in eval or train mode. .. note: The TranslatedLayer objects should not be created by constructor, it only can be loaded and constructed by :ref:`api_paddle_jit_load` . Examples: .. 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 model_path = "linear.example.model" paddle.jit.save(layer, model_path) # 2. load model as TranslatedLayer # load translated_layer = paddle.jit.load(model_path) # inference translated_layer.eval() x = paddle.randn([1, IMAGE_SIZE], 'float32') pred = translated_layer(x) # fine-tune translated_layer.train() adam = opt.Adam(learning_rate=0.001, parameters=translated_layer.parameters()) train(translated_layer, loader, loss_fn, adam) """ def __init__(self, programs, persistable_vars): super(TranslatedLayer, self).__init__() if not isinstance(programs, dict): raise TypeError( "TranslatedLayer need to use _ProgramHolder's dict for initialization." ) if not isinstance(persistable_vars, dict): raise TypeError( "TranslatedLayer need to use persistable variable dict for initialization." ) self._program_holder_dict = programs # NOTE(chenweihang): [ why not use var name directly? ] # When add parameter or buffer to Layer by follow apis, # the variable name can't contain `.`, beccause which may cause # AttributeError when access the newly added parameter or buffer # in the form of `self.**.**``, but the ParamBase or BarBase # name contains `.` originally, such as `linear_0.w_0`, so here # need to generate new var name for each var self._persistable_var_name_dict = dict() # the TranslatedLayer object holded var names count started from 0 with unique_name.guard(): for name, var in persistable_vars.items(): if isinstance(var, framework.ParamBase): dy_name = _generate_unique_var_name(PARAMETER_NAME_PREFIX) self._persistable_var_name_dict[name] = dy_name self.add_parameter(dy_name, var) elif isinstance(var, core.VarBase): dy_name = _generate_unique_var_name(BUFFER_NAME_PREFIX) self._persistable_var_name_dict[name] = dy_name self.register_buffer(dy_name, var) else: raise TypeError( "Adding persistent variable which to layer is not supported now" ) self._is_test = True self._input_args_names = None @staticmethod @framework.dygraph_only def _construct(model_path, configs=None): # 0. dir and filename check model_path = os.path.normpath(model_path) if not os.path.isdir(model_path): raise ValueError("There is no directory named '%s'" % model_path) model_filename = None params_filename = None if configs is not None: model_filename = configs.model_filename params_filename = configs.params_filename # 1. load program desc & construct _ProgramHolder programs = _construct_program_holders(model_path, model_filename) # 2. load layer parameters & buffers persistable_vars = _construct_params_and_buffers(model_path, programs, params_filename) # 3. construct TranslatedLayer object translated_layer = TranslatedLayer(programs, persistable_vars) # 4. create TranslatedLayer's execution method for method_name, program_holder in programs.items(): if translated_layer._input_args_names is None: translated_layer._input_args_names = [ ins.name() for ins in program_holder.input_descs ] setattr(TranslatedLayer, method_name, TranslatedLayer._execution_method_creator(method_name, program_holder)) # 5. set TranslatedLayer's default mode to eval translated_layer.eval() return translated_layer @staticmethod def _execution_method_creator(method_name, program_holder): def __i_m_p_l__(self, *input): program_holder = self._program_holder_dict[__i_m_p_l__.__name__] # When using jit.save, it runs in static graph mode. # Run in dynamic graph mode when the model is inferring. if in_dygraph_mode(): return _run_dygraph(self, input, program_holder) else: # NOTE(weixin): [ why not use 'program_holder.infer_program' directly? ] # When use '_run_static_graph(input, program_holder, program_holder.infer_program)', # because '_run_static_graph' modifies 'ProgramDesc', 'OpDesc.op_size()' will return a very large wrong number. # A Segmentation fault error may occur if used 'p=ProgramDesc(program_holder.infer_program)'. p = framework.Program._construct_from_desc( core.ProgramDesc(program_holder.infer_program)) return _run_static_graph(input, program_holder, p.desc) __i_m_p_l__.__name__ = method_name return __i_m_p_l__ def train(self): self._is_test = False def eval(self): self._is_test = True def program(self, method_name='forward'): """ Gets translated program of specified method. Args: - method_name (string): mehtod name corresponding to the program to be obtained. Default: 'forward'. Returns: Program Examples: .. 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()))) # 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 model_path = "linear.example.model" paddle.jit.save(layer, model_path) # load translated_layer = paddle.jit.load(model_path) # get program program = translated_layer.program() """ # 1. get program holder program_holder = self._get_program_holder(method_name) # 2. get inference program desc program_desc = program_holder.infer_program # 3. construct program program = _build_program_by_desc(program_desc) return program def _get_program_holder(self, method_name='forward'): program_holder = self._program_holder_dict.get(method_name, None) if program_holder is None: raise ValueError( "The method `%s` does not exist in loaded TranslatedLayer." % method_name) return program_holder def _input_spec(self, method_name='forward'): # 1. get program holder program_holder = self._get_program_holder(method_name) # 2. build input spec by input desc input_spec = [] for var_desc in program_holder.input_descs: spec = paddle.static.InputSpec( shape=var_desc.shape(), dtype=var_desc.dtype(), name=var_desc.name()) input_spec.append(spec) return input_spec def _output_spec(self, method_name='forward'): # 1. get program holder program_holder = self._get_program_holder(method_name) # 2. build output spec by output desc output_spec = [] for var_desc in program_holder.output_descs: # NOTE(chenweihang): InputSpec describes a tensor, not just input. # Maybe the name is not good enough. Here we use InputSpec to # construct the description of Output tensor spec = paddle.static.InputSpec( shape=var_desc.shape(), dtype=var_desc.dtype(), name=var_desc.name()) output_spec.append(spec) return output_spec