# 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 numpy as np import six from paddle.fluid import framework, backward, core from paddle.fluid.dygraph import layers from paddle.fluid.dygraph.base import switch_to_static_graph from paddle.fluid.dygraph.dygraph_to_static import logging_utils from paddle.fluid.dygraph.dygraph_to_static.return_transformer import RETURN_NO_VALUE_MAGIC_NUM from paddle.fluid.layers.utils import flatten from paddle.fluid.layers.utils import pack_sequence_as import paddle.compat as cpt class NestSequence(object): """ A wrapper class that easily to flatten and restore the nest structure of given sequence. """ def __init__(self, raw_input, need_check=False): self.__raw_input = raw_input self.__var_ids = self._get_var_ids() self._check_non_variable(need_check) def tolist(self): """ Flattens the nested sequences into single list. """ return flatten(self.__raw_input) def restore(self, value_list): """ Restores the nested sequence from value list. """ assert len(self.tolist()) == len(value_list) return pack_sequence_as(self.__raw_input, value_list) def _get_var_ids(self): var_ids = [] for idx, var in enumerate(self.tolist()): if isinstance(var, (framework.Variable, core.VarBase)): var_ids.append(idx) return var_ids def _check_non_variable(self, need_check): """ Raises warning if output of traced function contains non-tensor type values. """ if need_check: warning_types = set() for var in self.tolist(): if not isinstance(var, (framework.Variable, core.VarBase)): warning_types.add(type(var)) if warning_types: logging_utils.warn( "Output of traced function contains non-tensor type values: {}. " "Currently, We don't support to update them while training and will return " "what we first saw. Please try to return them as tensor.". format(list(warning_types))) @property def var_ids(self): return self.__var_ids def __getitem__(self, item): return self.tolist()[item] class LazyInitialized(object): """ Descriptor to implement lazy initialization of property. """ def __init__(self, function): self.function = function def __get__(self, instance, cls): val = self.function(instance) setattr(instance, self.function.__name__, val) return val def _change_is_test_status(program, is_test): # change all `is_test` attributes for block in program.blocks: for op in block.ops: if op.has_attr('is_test'): op._set_attr('is_test', is_test) return program class PartialProgramLayer(layers.Layer): """ PartialProgramLayer wraps all the ops from layers decorated by `@declarative` and execute them as a static subgraph. .. note:: **1. This is a very low level API. Users should not use this API directly. Please use `partial_program_from(concrete_program)` to create it. **2. LoDTensorArray is not currently supported in the output. Args: main_program(Program): The main program that contains ops need to be executed. inputs(list[Variable]): The input list of the decorated function by `@declarative`. outputs(list[Variable]): The output list of the decorated function by `@declarative`. parameters(list[VarBase]|None): All trainable parameters included in the program. Default None. Returns: Layer: A Layer object that run all ops internally in static mode. """ def __init__(self, main_program, inputs, outputs, parameters=None): super(PartialProgramLayer, self).__init__() self._inputs = NestSequence(inputs) self._outputs = NestSequence(outputs, need_check=True) self._params = parameters if parameters is not None else [] self._origin_main_program = self._verify_program(main_program) self._inner_scope = core.Scope() # Set default mode to train self.training = True @LazyInitialized def _infer_program(self): """ Lazy initialized property of infer_program. """ return self._clone_for_test(self._origin_main_program) @LazyInitialized def _train_program(self): """ Lazy initialized property of train_program. """ train_program = self._append_backward_desc(self._origin_main_program) # Note: Only set grad type once after initializing train program. So we # put it here. self._set_grad_type(self._params, train_program) return train_program def _verify_program(self, main_program): """ Verify that the program parameter is initialized, prune some unused params, and remove redundant op callstack. """ # 1. Check all params from main program can be found in self._params self._check_params_all_inited(main_program) # 2. Prune the parameters not used anywhere in the program. self._prune_unused_params(main_program) return main_program @switch_to_static_graph def _append_backward_desc(self, main_program): # make sure all status of is_test are False in train mode. program = _change_is_test_status(main_program.clone(), is_test=False) targets = [] for out in self._outputs.tolist(): if isinstance(out, framework.Variable): targets.append(program.global_block().var(out.name)) if targets and self._params: backward.gradients(targets=targets, inputs=[]) return program def _prune_unused_params(self, program): """ Prune the parameters not used anywhere in the program. The `@declarative` may only decorated a sub function which contains some unused parameters created in `__init__`. So prune these parameters to avoid unnecessary operations in `run_program_op`. """ required_params = [] for param in self._params: for block in program.blocks: if param.name in block.vars: required_params.append(param) break self._params = required_params def forward(self, inputs): in_vars, out_vars, tmp_scope_vec = self._prepare(inputs) framework._dygraph_tracer().trace_op( type='run_program', inputs={ 'X': valid_vars(in_vars), 'Params': valid_vars(self._params) }, outputs={'Out': valid_vars(out_vars), 'OutScope': tmp_scope_vec}, attrs={ 'global_block': self.program.desc.block(0), 'start_op_index': 0, 'end_op_index': self._infer_program.desc.block(0).op_size(), 'is_test': not self.training }) restored_nest_out = self._restore_out(out_vars) return self._remove_no_value(restored_nest_out) @property def program(self): return self._train_program if self.training else self._infer_program def _prepare(self, inputs): """ Prepare inputs, outputs, attrs. """ assert isinstance(inputs, (tuple, list)) # Flatten inputs with nested structure into single list. flatten_inputs = flatten(inputs) # Convert variable into VarBase and feed in training data. input_vars = [] for i, value in enumerate(flatten_inputs): if isinstance(value, np.ndarray): var = core.VarBase( value=value, name=self._inputs[i].desc.name(), persistable=False, place=framework._current_expected_place(), zero_copy=True) elif isinstance(value, core.VarBase): var = value var.name = self._inputs[i].desc.name() else: continue input_vars.append(var) # Create VarBase to receive output data. out_vars = [] for idx in self._outputs.var_ids: var = self._outputs[idx] assert isinstance(var, framework.Variable) var_desc = var.desc var_base = core.VarBase(var_desc.dtype(), var_desc.shape(), var_desc.name(), var_desc.type(), False) out_vars.append(var_base) # 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(self._inner_scope) return input_vars, out_vars, tmp_scope_vec def _restore_out(self, out_vars): """ Restores same nested outputs by only replacing the Variable with VarBase. """ flatten_outputs = self._outputs.tolist() for i, idx in enumerate(self._outputs.var_ids): flatten_outputs[idx] = out_vars[i] outs = self._outputs.restore(flatten_outputs) if outs is not None and len(outs) == 1: outs = outs[0] return outs @switch_to_static_graph def _clone_for_test(self, main_program): return main_program.clone(for_test=True) def _is_no_value(self, var): if isinstance(var, core.VarBase): if var.shape == [1] and var.numpy()[0] == RETURN_NO_VALUE_MAGIC_NUM: return True return False def _remove_no_value(self, out_vars): """ Removes invalid value for various-length return statement """ if isinstance(out_vars, core.VarBase): if self._is_no_value(out_vars): return None return out_vars elif isinstance(out_vars, (tuple, list)): if isinstance(out_vars, tuple): res = tuple( var for var in out_vars if not self._is_no_value(var)) else: # isinstance(out_vars, list) res = [var for var in out_vars if not self._is_no_value(var)] has_removed = (len(out_vars) > len(res)) # len(out_vars) > len(res) means we have removed var. This is # preventing out_vars is empty or just one element at the beginning if len(res) == 0 and has_removed: return None elif len(res) == 1 and has_removed: return res[0] return res return out_vars def _set_grad_type(self, params, train_program): # NOTE: 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 param in params: grad_name = param.name + core.grad_var_suffix() grad_var = train_program.desc.block(0).find_var( cpt.to_bytes(grad_name)) # NOTE: cannot find var desc maybe no problem, such as in batch_norm if grad_var is None: continue param._set_grad_type(grad_var.type()) def _remove_op_call_stack(self, main_program): """ Remove op's python call stack with redundant low-level error messages related to transforamtions to avoid confusing users. """ assert isinstance(main_program, framework.Program) for block in main_program.blocks: for op in block.ops: if op.has_attr("op_callstack"): op._remove_attr("op_callstack") return main_program def _check_params_all_inited(self, main_program): """ Check all params from main program are already initialized, see details as follows: 1. all parameters in self._params should be type `framework.ParamBase` which are created in dygraph. 2. all parameters from transformed program can be found in self._params. Because they share same data with ParamBase of original dygraph. """ if not isinstance(self._params, (list, tuple)): raise TypeError( "Type of self._params in PartialProgramLayer should be list or tuple, but received %s." % type(self._params)) param_and_buffer_names_set = set() for i, var in enumerate(self._params): # self._params constains parameters and buffers with persistable=True. if not isinstance(var, core.VarBase): raise TypeError( 'Type of self._params[{}] in PartialProgramLayer should be Parameter or Variable, but received {}.'. format(i, type(var))) param_and_buffer_names_set.add(var.name) for block in main_program.blocks: for name, var in six.iteritems(block.vars): if isinstance(var, framework.Parameter): if name not in param_and_buffer_names_set: raise ValueError( "\n\tWe don't support to define layer with parameters in the function " "decorated by `@declarative`.\n\tBecause that will re-defined parameters " "every time when you run the function.\n\t" "But we found parameter(%s) was created in the decorated function.\n\t" "Please define the layer with parameters in `__init__` function." % name) def valid_vars(vars): """ Note: run_program_op.InferShape requires `X`/'Out' not be null. But it's common in dy2static, fake varBase is created to handle the problem. """ if vars: return vars return [ core.VarBase( value=[1], name='Fake_var', place=framework._current_expected_place()) ] def partial_program_from(concrete_program): inputs = concrete_program.inputs if inputs and isinstance(inputs[0], layers.Layer): inputs = inputs[1:] return PartialProgramLayer(concrete_program.main_program, inputs, concrete_program.outputs, concrete_program.parameters)