# Copyright (c) 2018 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 from paddle.fluid import framework as framework from . import core import collections import copy import six import logging from .. import compat as cpt from . import unique_name from . import log_helper import paddle.fluid __all__ = [ 'append_backward', 'gradients', ] _logger = log_helper.get_logger( __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s') class ProgramStats(object): def __init__(self, block, ops): self.block = block self.ops = ops self.op_deps = {} # op-> in_ops, out_ops self.var_op_deps = {} # var as input op, var as output op def get_input_nodes(self): input_names = [] for name in self.var_op_deps: if len(self.var_op_deps[name]["var_as_output_ops"]) == 0 and \ len(self.var_op_deps[name]["var_as_input_ops"]) > 0: if self.block.var(name).persistable: continue input_names.append(name) for op in self.ops: if op.desc.type() == "read": input_names.extend(op.desc.output_arg_names()) return input_names def get_reserved_vars(self): var_name = [] for op in self.ops: if op.desc.type() == "seed": var_name.extend(op.desc.output_arg_names()) return var_name def get_out_of_subgraph_vars(self, begin_op_idx, end_op_idx): var_name = [] for i in range(begin_op_idx, end_op_idx, 1): for name in self.ops[i].desc.output_arg_names(): if name in self.var_op_deps: for idx in self.var_op_deps[name]["var_as_input_ops"]: if idx >= end_op_idx: var_name.append(name) return var_name def is_subgraph(self, var_group1, var_group2): # should traverse from var_group1 to var_group2 # max op idx in var_group2 # min op idx in var_group1 min_op_idx = len(self.ops) max_op_idx = -1 for name in var_group1: if name not in self.var_op_deps: return False, min_op_idx, max_op_idx for name in var_group2: if name not in self.var_op_deps: return False, min_op_idx, max_op_idx for name in var_group1: op_idx = self.var_op_deps[name]["var_as_input_ops"] for idx in op_idx: min_op_idx = min(min_op_idx, idx) for name in var_group2: op_idx = self.var_op_deps[name]["var_as_output_ops"] for idx in op_idx: max_op_idx = max(max_op_idx, idx) if min_op_idx >= max_op_idx: return False, min_op_idx, max_op_idx return True, min_op_idx, max_op_idx def build_stats(self): for i, op in enumerate(self.ops): self.op_deps[i] = {"in_ops": [], "out_ops": []} for j, name in enumerate(op.desc.input_arg_names()): if name in self.var_op_deps: self.op_deps[i]["in_ops"].extend(self.var_op_deps[name][ "var_as_output_ops"]) for j, name in enumerate(op.desc.input_arg_names()): if name in self.var_op_deps: self.var_op_deps[name]["var_as_input_ops"].extend([i]) else: self.var_op_deps[name] = {} self.var_op_deps[name]["var_as_input_ops"] = [i] self.var_op_deps[name]["var_as_output_ops"] = [] for j, name in enumerate(op.desc.output_arg_names()): if name in self.var_op_deps: self.var_op_deps[name]["var_as_output_ops"].extend([i]) else: self.var_op_deps[name] = {} self.var_op_deps[name]["var_as_input_ops"] = [] self.var_op_deps[name]["var_as_output_ops"] = [i] for op_idx in self.op_deps[i]["in_ops"]: self.op_deps[op_idx]["out_ops"].extend([i]) def sort_checkpoints(self, checkpoints_name): sorted_checkpoints = [] for name in checkpoints_name: if name not in self.var_op_deps: _logger.debug( "Recompute Optimizer: deleted %s from checkpoints, because it is not used in paddle program." % name) elif self.var_op_deps[name]["var_as_output_ops"] == []: # input nodes sorted_checkpoints.append((name, -1)) else: sorted_checkpoints.append( (name, max(self.var_op_deps[name]["var_as_output_ops"]))) sorted_checkpoints = sorted(sorted_checkpoints, key=lambda x: x[1]) return [x[0] for x in sorted_checkpoints] def modify_forward_desc_for_recompute(self): op_types = [op.desc.type() for op in self.ops] if "dropout" not in op_types: return op_idx = 0 while (op_idx < len(self.ops)): op = self.ops[op_idx] if op.desc.type() != "dropout": op_idx += 1 continue # add a seed op so that the two dropout op can generate same output op_unique_name = unique_name.generate("seed") var_unique_name = unique_name.generate_with_ignorable_key(".".join( [op_unique_name, 'tmp'])) added_var = self.block.create_var( name=var_unique_name, dtype='int32', type=core.VarDesc.VarType.LOD_TENSOR, persistable=False, stop_gradient=False) seed = 0 if op.attr("fix_seed") is False else int(op.attr("seed")) added_op = self.block._insert_op( index=op.idx, type='seed', inputs={}, outputs={'Out': [added_var]}, attrs={'seed': seed}) self.ops.insert(op_idx, added_op) # modify dropout op desc so that it accept a seed var as input op.desc.set_input("Seed", [var_unique_name]) op.desc.remove_attr("fix_seed") op.desc.remove_attr("seed") self.block._sync_with_cpp() op_idx += 2 def _pretty_op_desc_(op_desc, prefix): out_s = "%s\tname:[%s]\n%s \tinputs:[%s]\n%s \toutputs:[%s]" % \ (prefix + "_op", str(op_desc.type()), prefix + "_input", " ".join(op_desc.input_arg_names()), prefix + "_output", " ".join(op_desc.output_arg_names())) return out_s def _add_needed_descs_to_block(descs, block, main_block, in_memory_vars): if len(descs) == 0: return [] result_descs = [] op_role_attr_name = \ core.op_proto_and_checker_maker.kOpRoleAttrName() backward = core.op_proto_and_checker_maker.OpRole.Backward for desc in descs: if isinstance(desc, framework.Operator): desc = desc.desc if isinstance(desc, tuple): desc = desc[0] is_needed = False for name in desc.output_arg_names(): if main_block.has_var(name) and main_block.var(name).persistable: continue if name not in in_memory_vars: is_needed = True if is_needed: new_op_desc = block.desc.append_op() new_op_desc.copy_from(desc) new_op_desc._set_attr(op_role_attr_name, backward) result_descs.append(new_op_desc) return result_descs def _add_descs_to_block(descs, block): if len(descs) == 0: return [] result_descs = [] op_role_attr_name = \ core.op_proto_and_checker_maker.kOpRoleAttrName() backward = core.op_proto_and_checker_maker.OpRole.Backward for desc in descs: if isinstance(desc, framework.Operator): desc = desc.desc if isinstance(desc, tuple): desc = desc[0] new_op_desc = block.desc.append_op() new_op_desc.copy_from(desc) new_op_desc._set_attr(op_role_attr_name, backward) result_descs.append(new_op_desc) return result_descs def _find_loss_op_(loss): for op in reversed(loss.block.ops): assert isinstance(op, framework.Operator) if len(op.output_arg_names) == 1 and op.output_arg_names[ 0] == loss.name: loss.op = op break if loss.op is None: raise ValueError("loss.op is None. Should not happend") def _rename_arg_(op_descs, old_name, new_name, begin_idx=None, end_idx=None): """ Traverse all ops in op_descs[begin_idx : end_idx], if any op has inputs/outputs named "old_name", rename it as 'new_name' """ if begin_idx is None: begin_idx = 0 if end_idx is None: end_idx = len(op_descs) for i in range(begin_idx, end_idx): op_desc = op_descs[i] if isinstance(op_desc, tuple): op_desc = op_desc[0] op_desc._rename_input(old_name, new_name) op_desc._rename_output(old_name, new_name) def _create_op_desc_(op_type, inputs, outputs, attrs): """ Create a C++ OpDesc object with specified inputs, outputs and attributes. """ op_desc = core.OpDesc() op_desc.set_type(op_type) for para, args in six.iteritems(inputs): op_desc.set_input( para, list( map(lambda arg: arg.decode() if isinstance(arg, six.binary_type) else arg, args))) for para, args in six.iteritems(outputs): op_desc.set_output( para, list( map(lambda arg: arg.decode() if isinstance(arg, six.binary_type) else arg, args))) op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName() if op_role_attr_name not in attrs: attrs[ op_role_attr_name] = core.op_proto_and_checker_maker.OpRole.Backward for name, val in six.iteritems(attrs): if isinstance(val, framework.Block): op_desc.set_block_attr(name, val.desc) else: op_desc._set_attr(name, val) return op_desc def _create_loss_op_desc_(loss): op_desc = _create_op_desc_( "fill_constant", {}, {"Out": [_append_grad_suffix_(loss.name)]}, { "shape": [1], "value": 1.0, "dtype": loss.dtype, "force_cpu": False, core.op_proto_and_checker_maker.kOpRoleAttrName(): int(core.op_proto_and_checker_maker.OpRole.Backward) | int(core.op_proto_and_checker_maker.OpRole.Loss), }) return op_desc def _infer_var_data_type_shape_(grad_var_name, block): """ Infer the data type and shape of given grad variable """ grad_var = block.desc.find_var(cpt.to_bytes(grad_var_name)) fwd_name = _strip_grad_suffix_(grad_var_name) if block.desc.has_var_recursive(cpt.to_bytes(fwd_name)): fwd_var = block.desc.find_var_recursive(cpt.to_bytes(fwd_name)) grad_var.set_dtype(fwd_var.dtype()) grad_var.set_shape(fwd_var.shape()) else: grad_var.set_dtype(core.VarDesc.VarType.FP32) def _all_in_set_(cands, s): """ Test if all elements of 'cands' are in set 's' """ if len(cands) == 0: return False for c in cands: if not c in s: return False return True def _some_in_set_(cands, s): """ Test if some elements of 'cands' are in set 's' """ if len(cands) == 0: return False literal_set = cpt.to_text(s) literal_cands = cpt.to_text(cands) for c in literal_cands: if c in literal_set: return True return False def _strip_grad_suffix_(name): """ Strip the grad suffix from the given variable name e.g. x@GRAD ==> x y@GRAD@RENAME@1 ==> y """ name = cpt.to_text(name) pos = name.find(core.grad_var_suffix()) return name[:pos] if pos != -1 else name def _append_grad_suffix_(name): """ Append grad suffix to the given variable name e.g. x ==> x@GRAD """ return cpt.to_text(name) + core.grad_var_suffix() def _addup_repetitive_outputs_(op_descs, block_idx): """ In backward part, an variable may be the output of more than one ops. And one op may yield its multiple outputs to the same variable. In these cases, the variable should be the accumulation of all the outputs. `sum_op`s are added to implement the accumulate. """ pending_sum_ops = [] var_rename_count = collections.defaultdict(int) renamed_vars = collections.defaultdict(list) renamed_var_start_idx = collections.defaultdict(list) for idx, op_desc in enumerate(op_descs): for var_name in op_desc.input_arg_names(): if "@GRAD" not in var_name: continue if len(renamed_vars[var_name]) > 1: pending_sum_ops.append((_create_op_desc_( "sum", {"X": renamed_vars[var_name]}, {"Out": [var_name]}, {"use_mkldnn": False}), idx)) renamed_vars[var_name] = [var_name] for param_idx, param_name in enumerate(op_desc.output_names()): arg_names = op_desc.output(param_name) for arg_idx, var_name in enumerate(arg_names): if "@GRAD" not in var_name: continue #if "@RENAME@" in var_name: # continue if var_name == core.empty_var_name( ) or var_name in op_desc.input_arg_names(): # empty variable or inplace op continue if len(renamed_vars[var_name]) == 0: # it's the first time we get the variable renamed_vars[var_name] = [var_name] renamed_var_start_idx[var_name] = idx else: if len(renamed_vars[var_name]) == 1: new_name = var_name + "@RENAME@block" + str(block_idx) + "@" + \ str(var_rename_count[var_name]) var_rename_count[var_name] += 1 # rename original var_name renamed_vars[var_name][0] = new_name # before change: _rename_arg_(op_descs, var_name, # new_name, 0, idx) # rename arg from idx of the first appearance # in backward, not always from 0 _rename_arg_(op_descs, var_name, new_name, renamed_var_start_idx[var_name], idx) _rename_arg_(pending_sum_ops, var_name, new_name) for p in op_desc.output_names()[:param_idx]: p_arg_names = op_desc.output(p) if var_name in p_arg_names: op_desc.set_output(p, [ new_name if x == var_name else x for x in p_arg_names ]) arg_names = [ new_name if x == var_name else x for x in arg_names[:arg_idx] ] + arg_names[arg_idx:] new_name = var_name + "@RENAME@block" + str(block_idx) + "@" + \ str(var_rename_count[var_name]) var_rename_count[var_name] += 1 arg_names[arg_idx] = new_name op_desc.set_output(param_name, arg_names) renamed_vars[var_name].append(new_name) for var_name, inputs in six.iteritems(renamed_vars): if len(inputs) > 1: pending_sum_ops.append( (_create_op_desc_("sum", {"X": inputs}, {"Out": [var_name]}, {"use_mkldnn": False}), len(op_descs))) # sum_op descs are sorted according to their insert position for p in reversed(pending_sum_ops): op_descs.insert(p[1], p[0]) return op_descs def _remove_no_grad_branch_(op_descs, no_grad_set): """ Remove unnecessary grad ops A grad op can be removed in two cases: 1. all outputs of the grad op are in 'no_grad_set' 2. all grad inputs of the grad op are in 'no_grad_set' """ def _op_can_be_removed_(op_desc, no_grad_set): out_arg_names = op_desc.output_arg_names() if len(out_arg_names) == 0 or _all_in_set_(out_arg_names, no_grad_set): return True if _all_in_set_([ name for name in op_desc.input_arg_names() if name.find(core.grad_var_suffix()) != -1 ], no_grad_set): no_grad_set.update(out_arg_names) return True return False # Remove ops whose outputs are all in no_grad_dict op_descs = [ op_desc for op_desc in op_descs if not _op_can_be_removed_(op_desc, no_grad_set) ] # Insert fill_zeros_like_op to_insert = [] for idx, op_desc in enumerate(op_descs): for arg in op_desc.input_arg_names(): # arg is a gradient var name and arg should not have gradient if core.grad_var_suffix() in arg and arg in no_grad_set: x_in = _strip_grad_suffix_(arg) # the reason should be: arg can be input of another grad op # and the op is a not-to-remove op to_insert.append((_create_op_desc_( "fill_zeros_like", {"X": [x_in]}, {"Out": [arg]}, {}), idx)) list([op_descs.insert(p[1], p[0]) for p in reversed(to_insert)]) return op_descs def _find_not_need_ops(grad_op_descs, forward_ops, input_grad_names_set): """ Pruning Program with Structural Analysis Method of Computational Graph. The nodes of the computational graph composed of backward OPS should be interconnected. If there are unconnected sub-graphs in the computational graph, these sub-graphs should be cut off. Args: grad_op_descs(list[core.OpDesc]): The candidate backward OpDescs. forward_ops(list[Operator]): The forward ops. input_grad_names_set(set): this set is used to store the gradients' name which is generated by backward ops, and input_grad_names_set can help to prune the unnecessary backward ops. Return: (set[core.OpDesc]): A set of OpDescs which should be pruned. """ class Var(object): def __init__(self, var_name): self.var_name = var_name self.gen_op = None self.pendding_ops = [] def set_gen_op(self, gen_op): assert isinstance(gen_op, Op) assert self.gen_op is None self.gen_op = gen_op def add_pending_op(self, op): assert isinstance(op, Op) self.pendding_ops.append(op) class Op(object): def __init__(self, op_desc): self.op_desc = op_desc self.inputs = [] self.outputs = [] def insert_input(self, var): assert isinstance(var, Var) self.inputs.append(var) def insert_output(self, var): assert isinstance(var, Var) self.outputs.append(var) var_versions = dict() def _create_node(name): if name not in var_versions.keys(): var_versions[name] = [Var(name)] else: var_versions[name].append(Var(name)) return var_versions[name][-1] def _create_or_get_last_version_node(name): if name not in var_versions.keys(): var_versions[name] = [Var(name)] return var_versions[name][-1] def _create_op_node(op_desc): op_node = Op(op_desc) for input in op_desc.input_arg_names(): var = _create_or_get_last_version_node(name=input) var.add_pending_op(op_node) op_node.insert_input(var) for output in op_desc.output_arg_names(): var = _create_node(name=output) var.set_gen_op(op_node) op_node.insert_output(var) return op_node # Record the forward vars forward_vars_set = set() if input_grad_names_set is None else set( input_grad_names_set) for op in forward_ops: forward_vars_set.update(op.desc.input_arg_names()) forward_vars_set.update(op.desc.output_arg_names()) # Record the vars which are created during backward and is not generated by op. backward_vars_set = set() # special_op_nodes is the candidate sub-graph head node. special_op_nodes = set() for op_desc in grad_op_descs: input_set = set(op_desc.input_arg_names()) # The new_vars are created during backward and is not generated by op. new_vars = input_set - forward_vars_set - backward_vars_set backward_vars_set.update(op_desc.output_arg_names()) op_node = _create_op_node(op_desc) if len(new_vars) == len(input_set): special_op_nodes.add(op_node) not_need_op_descs = [] # Start traversing all candidate sub-graph headers to check whether # they are connected to backward computational graphs, and if they are # not, list them in not_need_op_descs for special_op_node in special_op_nodes: op_list = [special_op_node] ready_vars = set(special_op_node.inputs) remove_ops = True candidate_ops = [special_op_node] while len(candidate_ops) > 0: op_node = candidate_ops.pop(0) if _all_in_set_(op_node.inputs, ready_vars): for out_var in op_node.outputs: candidate_ops.extend(out_var.pendding_ops) op_list.extend(out_var.pendding_ops) ready_vars.update(op_node.outputs) else: remove_ops = False break if remove_ops: not_need_op_descs.extend([node.op_desc for node in op_list]) not_need_op_descs_set = set(not_need_op_descs) grad_op_descs_set = set(grad_op_descs) # If a backward computational graph is simply one sub-graph header, the # not_need_op_descs will be whole graph, this IF clause avoids it. if grad_op_descs_set == not_need_op_descs_set: return set() return not_need_op_descs_set from .proto import framework_pb2 def serialize_op_decs(op_desc): protostr = op_desc.serialize_to_string() proto = framework_pb2.OpDesc.FromString(six.binary_type(protostr)) return proto.__str__() def _append_backward_ops_with_checkpoints_( block, ops, target_block, no_grad_dict, grad_to_var, checkpoints): """ Create grad ops with forward ops, and insert them into given block Args: block(Block): the block where forward ops are ops(Op): the forward operators whose forward recomputation backward ops need to be added target_block(Block): the block which is going to hold new generated grad ops no_grad_dict(dict): key(int) block index val(str): corresponding forward variable name checkpoints: variables that a user defined as checkpoint for forward recomputation Algorithms: 0) deal with forward recomputing program descs 1) find ops between checkpoints, i.e. recompute_segments 2) go through all forward ops and induct all variables that will be hold in memory a. variables that are used across segments will be held in memory b. output of dropout op will be held in memory c. input variables will be held in memory 3) go through each recompute_segments, add backward ops with forward recomputation a. add ops in current recompute_segment as forward recomputation ops b. rename all non-checkpoint variables in recomputation ops c. add backward ops of current recomputation ops d. add sum op for repetitive_outputs 4) remove no grad branch as it is in _remove_no_grad_branch_ 5) Note1: all appended ops' OpRole are Backward 6) Note2: all variables with new name should be returned so that _append_backward_vars_ can be called 7) Note3: current forward recomputation backpropagation does not handle programs with subblock """ checkpoints_name = [x.name for x in checkpoints] checkpoints_name = list(set(checkpoints_name)) local_block = block.program._create_block() buffer_block = block.program._create_block() # 0) deal with forward recomputing program descs program_stat = ProgramStats(block, ops) program_stat.modify_forward_desc_for_recompute() program_stat.build_stats() # 1) find ops between checkpoints, i.e. recompute_segments checkpoints_name = program_stat.sort_checkpoints(checkpoints_name) segments = [] if len(checkpoints_name) == 1: # only one checkpoint max_op_idx = -1 var_group = [checkpoints_name[0]] for name in var_group: if name not in program_stat.var_op_deps: break op_idx = program_stat.var_op_deps[name]["var_as_output_ops"] for idx in op_idx: max_op_idx = max(max_op_idx, idx) if max_op_idx > 0: segments.append([0, max_op_idx + 1]) else: start_idx = 0 while True: if start_idx >= len(checkpoints_name) - 1: break flag, min_idx, max_idx = program_stat.is_subgraph( [checkpoints_name[start_idx]], [checkpoints_name[start_idx + 1]]) if flag: segments.append([min_idx, max_idx + 1]) start_idx += 1 if segments != [] and segments[0][0] != 0: recompute_segments = [[0, segments[0][0]]] + segments else: recompute_segments = segments # 2) go through all forward ops and induct all variables that will be hold in memory vars_should_be_hold = [] # a. variables that are used across segments will be held in memory for segment in recompute_segments: vars_should_be_hold.extend( program_stat.get_out_of_subgraph_vars(segment[0], segment[1])) # b. output of dropout op will be held in memory vars_should_be_hold.extend(program_stat.get_reserved_vars()) # c. input variables are checkpoints vars_should_be_hold.extend(program_stat.get_input_nodes()) vars_should_be_hold = list(set(vars_should_be_hold)) # 3) go through each recompute_segments, add backward ops with forward recomputation grad_op_descs = [] var_name_dict = {} vars_in_memory = vars_should_be_hold + checkpoints_name max_calculated_op_position = len(ops) if recompute_segments == []: # if there is no recompute segment, add backward ops like # _append_backward_ops_ function gap_ops = ops[0:max_calculated_op_position] for op in reversed(gap_ops): if op.has_attr("sub_block"): raise Exception("Recompute don't support ops with sub_block" "invoke op: %s" % _pretty_op_desc_(op.desc, "with_sub_block")) grad_op_desc, op_grad_to_var = core.get_grad_op_desc( op.desc, cpt.to_text(no_grad_dict[block.idx]), []) added_descs = _add_descs_to_block(grad_op_desc, local_block) grad_op_descs.extend(added_descs) grad_to_var.update(op_grad_to_var) for i, segment in enumerate(recompute_segments[::-1]): # add grad op for ops not in any segments gap_ops = ops[segment[1]:max_calculated_op_position] max_calculated_op_position = segment[0] for op in reversed(gap_ops): if op.has_attr("sub_block"): raise Exception("Recompute don't support ops with sub_block" "invoke op: %s" % _pretty_op_desc_(op.desc, "with_sub_block")) grad_op_desc, op_grad_to_var = core.get_grad_op_desc( op.desc, cpt.to_text(no_grad_dict[block.idx]), []) added_descs = _add_descs_to_block(grad_op_desc, local_block) grad_op_descs.extend(added_descs) grad_to_var.update(op_grad_to_var) ff_ops = ops[segment[0]:segment[1]] var_suffix = ".subprog_%d" % i for op in ff_ops: if op.has_attr("sub_block"): raise Exception("Recompute don't support ops with sub_block" "invoke op: %s" % _pretty_op_desc_(op.desc, "with_sub_block")) input_and_output_names = [] input_and_output_names.extend(op.desc.input_arg_names()) input_and_output_names.extend(op.desc.output_arg_names()) for name in input_and_output_names: if block.var(name).persistable or name in checkpoints_name: continue if name in vars_should_be_hold: continue if name not in var_name_dict: var_name_dict[name] = name + var_suffix # 3.a. add ops in current recompute_segment as forward recomputation ops buffer_descs = _add_needed_descs_to_block(ff_ops, buffer_block, block, vars_in_memory) added_descs = _add_descs_to_block(ff_ops, local_block) # 3.b. rename all non-checkpoint variables in recomputation ops for key in var_name_dict: _rename_arg_(buffer_descs, key, var_name_dict[key]) # added_descs should be in grad_op_descs because it is backward op desc grad_op_descs.extend(buffer_descs) # 3.c. add backward ops of current recomputation ops for op_desc in reversed(added_descs): grad_op_desc, op_grad_to_var = core.get_grad_op_desc( op_desc, cpt.to_text(no_grad_dict[block.idx]), []) for key in var_name_dict: _rename_arg_(grad_op_desc, key, var_name_dict[key]) grad_op_descs.extend(grad_op_desc) grad_to_var.update(op_grad_to_var) # 3.d. add sum op for repetitive_outputs grad_op_descs = _addup_repetitive_outputs_(grad_op_descs, block.idx) # 4) remove no grad branch as it is in _remove_no_grad_branch_ grad_op_descs = _remove_no_grad_branch_(grad_op_descs, no_grad_dict[block.idx]) added_descs = _add_descs_to_block(grad_op_descs, target_block) return program_stat, checkpoints_name, vars_should_be_hold, recompute_segments def _get_sub_block_path(sub_block, sub_block_op_desc, no_grad_set): """ Get output vars in subblock which will be assigned to parent block. It is used to find the grad path in subblock """ assert sub_block_op_desc.has_attr( "sub_block") and sub_block.idx == sub_block_op_desc._block_attr_id( "sub_block") # TODO(huihuangzheng): add support for recurrent op and while op if sub_block_op_desc.type == "conditional_block": sub_outputs = [] sub_assign_to_out_ops = [] for var in sub_block_op_desc.output_arg_names: for op_desc in sub_block.ops: if op_desc.type == "assign" and var in op_desc.output_arg_names: sub_assign_to_out_ops.append(op_desc) for name in op_desc.input_arg_names: if sub_block.has_var(name): sub_outputs.append(sub_block.var(name)) sub_block_op_path = _find_op_path_(sub_block, sub_outputs, [], no_grad_set) # TODO better way than finding in list for op_desc in sub_assign_to_out_ops: if op_desc not in sub_block_op_path: sub_block_op_path.append(op_desc) return sub_block_op_path return sub_block.ops def _append_backward_ops_(block, ops, target_block, no_grad_dict, grad_to_var, callbacks=None, input_grad_names_set=None): """ Create all grad ops, and insert them into given block Args: block(Block): the block where forward ops are ops(Op): the forward operators whose backward ops need to be added target_block(Block): the block which is going to hold new generated grad ops no_grad_dict(dict): key(int) block index val(set) a set of variable names. These variables have no gradient grad_to_var(dict)(output argument): key(str): grad variable name val(str): corresponding forward variable name callbacks(callable object): a callable object used to decorate new generated grad ops input_grad_names_set(set): this set is used to store the gradients' name which is generated by backward ops, and input_grad_names_set can help to prune the unnecessary backward ops. """ if callbacks is not None: assert (isinstance(callbacks, list)) for cb in callbacks: if not hasattr(cb, '__call__'): raise ValueError("'callback' must be a callable object.") # grad_op_descs holds created grad_op, and will be appended to target_block grad_op_descs = [] program = block.program # add grad_op_desc by reversed ops for op in reversed(ops): grad_sub_block_list = [] # If the op has its own sub-block, deal with the sub-block first if op.has_attr("sub_block"): sub_block = program.block(op._block_attr_id("sub_block")) grad_sub_block = program._create_block() grad_sub_block._set_forward_block_idx(sub_block.idx) # see follwing comments for why set None here. pre_input_grad_names_set = copy.copy(input_grad_names_set) input_grad_names_set = None sub_block_path = _get_sub_block_path(sub_block, op, no_grad_dict[sub_block.idx]) _append_backward_ops_(sub_block, sub_block_path, grad_sub_block, no_grad_dict, grad_to_var, callbacks, input_grad_names_set) input_grad_names_set = pre_input_grad_names_set program._rollback() grad_sub_block_list.append(grad_sub_block.desc) # Getting op's corresponding grad_op grad_op_desc, op_grad_to_var = core.get_grad_op_desc( op.desc, cpt.to_text(no_grad_dict[block.idx]), grad_sub_block_list) # If input_grad_names_set is not None, extend grad_op_descs only when # any input grad in outputs of previous grad ops. # But this strategy is not suited for while op for some control flow, # for example, for while op, the grads maybe generated in next loop. if input_grad_names_set is not None: is_append_grad = False for op_desc in grad_op_desc: input_grad_names = [ name for name in op_desc.input_arg_names() if name.find(core.grad_var_suffix()) != -1 ] # some code of gradient ops, like increment, are not very # standard, there is no @GRAD in these ops' inputs. if len(input_grad_names) == 0: is_append_grad = True break if _some_in_set_(input_grad_names, input_grad_names_set): grad_op_descs.append(op_desc) is_append_grad = True for name in op_desc.output_arg_names(): input_grad_names_set.add(name) if is_append_grad: grad_to_var.update(op_grad_to_var) else: grad_op_descs.extend(grad_op_desc) grad_to_var.update(op_grad_to_var) # sum parameter's gradients' var given multiple var gradient grad_op_descs = _addup_repetitive_outputs_(grad_op_descs, block.idx) # if all outputs of the grad op are in no_grad_set, then just remove and fill zero # if all inputs of the grad op are in no_grad_set, just remove this op grad_op_descs = _remove_no_grad_branch_(grad_op_descs, no_grad_dict[block.idx]) # remove some backward ops not_need_ops = _find_not_need_ops(grad_op_descs, ops, input_grad_names_set) grad_op_descs = [ op_desc for op_desc in grad_op_descs if op_desc not in not_need_ops ] # append op_desc in grad_op_descs to target_block op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName() backward = core.op_proto_and_checker_maker.OpRole.Backward for op_desc in grad_op_descs: new_op_desc = target_block.desc.append_op() new_op_desc.copy_from(op_desc) new_op_desc._set_attr(op_role_attr_name, backward) grad_to_var["__current_op_desc__"] = new_op_desc if callbacks is not None: assert (isinstance(callbacks, list)) for cb in callbacks: cb(block=target_block, context=grad_to_var) def _is_grad_var_(var_name): return core.grad_var_suffix() in var_name # Find the op who holds the sub_block as its "sub_block" attr def _find_parent_op_(sub_block): sub_block_id = sub_block.idx if sub_block_id == 0: return None program = sub_block.program for block_id in six.moves.range(program.num_blocks): block_desc = program.block(block_id).desc for op_idx in six.moves.range(block_desc.op_size()): op = block_desc.op(op_idx) if op.has_attr("sub_block") and op._block_attr_id( "sub_block") == sub_block_id: return op # NOTE(paddle-dev): When optimizer is added in conditional block, # sub_block may not be found. return None def _append_backward_vars_(block, start_op_idx, grad_to_var, grad_info_map): """ Create new variables required by backward pass. Args: block(Block): the block where new variables will be created start_op_idx(int): Only variables required by ops in block.ops[start_op_idx : ] will be created grad_to_var(dict): key(str): grad variable name val(str): corresponding forward variable name In most cases, this dict is generated by _append_backward_ops_() grad_info_map(dict)(output argument): key(str): forward variable name val(tuple): a tuple of (str, Block), str is the corresponding grad name, Block is the block containing grad variable """ ops_to_remove = [] ''' NOTE(paddle-dev): while_grad op may hold some inputs which are not found in the parent/forward block, and they are also the outputs of while_grad op. These kinds of inputs are the recursive outputs inside while_grad op. They should be considered as "already created" when scanning the inner ops of while_grad ops. ''' parent_op = _find_parent_op_(block) parent_op_vars = [] if parent_op is not None: input_args = parent_op.input_arg_names() output_args = parent_op.output_arg_names() for in_arg in input_args: if in_arg in output_args: parent_op_vars.append(in_arg) for op_idx in range(start_op_idx, block.desc.op_size()): op_desc = block.desc.op(op_idx) if op_desc.has_attr("sub_block"): sub_block = block.program.block(op_desc._block_attr_id("sub_block")) _append_backward_vars_(sub_block, 0, grad_to_var, grad_info_map) grad_var_ins = [ var for var in op_desc.input_arg_names() if _is_grad_var_(var) ] grad_var_outs = [ var for var in op_desc.output_arg_names() if _is_grad_var_(var) ] inputs = [ var for var in op_desc.input_arg_names() if var != core.empty_var_name() ] outputs = [ var for var in op_desc.output_arg_names() if var != core.empty_var_name() ] # If the outputs of grad op is empty, just remove it if not outputs: ops_to_remove.append(op_idx) continue else: ''' If the output is not empty and there is any grad input, find whether there is any existing input. If not, just remove it. ''' if grad_var_ins: existing_grad_var_ins = [ var for var in grad_var_ins if block.desc.has_var_recursive(cpt.to_bytes(var)) or var in parent_op_vars ] if not existing_grad_var_ins: ''' FIXME(paddle-dev, zengjinle): rnn_memory_helper_grad is used in recurrent op. The input of this op does not even exist in the program! Therefore, any dependency analysis would not work to this op! If I do not add the following code, this op would be pruned, and the calculation result would be wrong. Maybe we should re-design this op later... ''' if op_desc.type() not in ['rnn_memory_helper_grad']: ops_to_remove.append(op_idx) continue new_vars = set() # create new gradient variables for grad_var_name in op_desc.output_arg_names(): if block.desc.has_var_recursive(cpt.to_bytes( grad_var_name)) or grad_var_name == core.empty_var_name(): continue block.desc.var(cpt.to_bytes(grad_var_name)) new_vars.add(grad_var_name) if grad_var_name not in grad_to_var: continue grad_info_map[grad_to_var[grad_var_name]] = (grad_var_name, block) # infer_shape and infer_type op_desc.infer_var_type(block.desc) op_desc.infer_shape(block.desc) for arg in op_desc.output_arg_names(): if arg in new_vars: _infer_var_data_type_shape_(arg, block) for op_idx in reversed(ops_to_remove): block.desc._remove_op(op_idx, op_idx + 1) def _rename_grad_(block, start_op_idx, grad_to_var, target_grad_map): var_map = copy.copy(target_grad_map) for op_idx in range(start_op_idx, block.desc.op_size()): op_desc = block.desc.op(op_idx) for name in op_desc.input_arg_names(): if name in var_map: op_desc._rename_input(name, var_map[name]) for name in op_desc.output_arg_names(): if "@GRAD" not in name: continue if block.desc.find_var(name.encode("ascii")): new_name = unique_name.generate(name) op_desc._rename_output(name, new_name) var_map[name] = new_name for g, ng in six.iteritems(var_map): if g in grad_to_var: grad_to_var[ng] = grad_to_var[g] grad_to_var.pop(g) def _get_stop_gradients_(program): no_grad_dict = dict() assert isinstance(program, framework.Program) for block in program.blocks: assert isinstance(block, framework.Block) block_no_grad_set = set() for var in list(block.vars.values()): assert isinstance(var, framework.Variable) if var.stop_gradient: block_no_grad_set.add(_append_grad_suffix_(var.name)) no_grad_dict[block.idx] = block_no_grad_set return no_grad_dict def _get_son_parent_block_idx_dict(program, current_block_idx): son_parent_block_idx_dict = collections.OrderedDict() while current_block_idx >= 0: parent_block_idx = program.block(current_block_idx).parent_idx son_parent_block_idx_dict[current_block_idx] = parent_block_idx current_block_idx = parent_block_idx return son_parent_block_idx_dict def _get_no_grad_set_name(no_grad_set): no_grad_set_name = set() if no_grad_set is not None: if isinstance(no_grad_set, (set, list, tuple)): for i, no_grad_var in enumerate(no_grad_set): if isinstance(no_grad_var, framework.Variable): no_grad_set_name.add(no_grad_var.name) elif isinstance(no_grad_var, six.string_types): no_grad_set_name.add(no_grad_var) else: raise TypeError( "The type of no_grad_set's member must be paddle.fluid.Variable or str, but received %s." % (type(no_grad_var))) else: raise TypeError( "The type of no_grad_set should be set or list or tuple, but received {}". format(type(no_grad_set))) return no_grad_set_name def append_backward(loss, parameter_list=None, no_grad_set=None, callbacks=None, checkpoints=None): """ This function appends backward part to main_program. A complete neural network training is made up of forward and backward propagation. However, when we configure a network, we only need to specify its forward part. This function uses the chain rule to automatically generate the backward part according to the forward part. In most cases, users do not need to invoke this function manually. It will be automatically invoked by the optimizer's `minimize` function. Parameters: loss( :ref:`api_guide_Variable_en` ): The loss variable of the network. parameter_list(list[Variable|str], optional): List of Parameters or Parameter.names that need to be updated by optimizers. If it is None, all parameters will be updated. Default: None. no_grad_set(set[Variable|str], optional): Set of Variables or Variable.names in the :ref:`api_guide_Block_en` 0 whose gradients should be ignored. All variables with `stop_gradient=True` from all blocks will be automatically added into this set. If this parameter is not None, the Variables or Variable.names in this set will be added to the default set. Default: None. callbacks(list[callable object], optional): List of callback functions. The callbacks are used for doing some custom jobs during backward part building. All callable objects in it will be invoked once each time a new gradient operator is added into the program. The callable object must have two input parameters: 'block' and 'context'. The 'block' is the :ref:`api_guide_Block_en` which the new gradient operator will be added to. The 'context' is a map, whose keys are gradient variable names and values are corresponding original :ref:`api_guide_Variable_en` . In addition to this, the 'context' has another special key-value pair: the key is string '__current_op_desc__' and the value is the op_desc of the gradient operator who has just triggered the callable object. Default: None. Returns: list of tuple ( :ref:`api_guide_Variable_en` , :ref:`api_guide_Variable_en` ): Pairs of parameter and its corresponding gradients. The key is the parameter and the value is gradient variable. Raises: AssertionError: If `loss` is not an instance of Variable. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name='x', shape=[None, 13], dtype='int64') y = fluid.data(name='y', shape=[None, 1], dtype='float32') x_emb = fluid.embedding(x, size=[100, 256]) y_predict = fluid.layers.fc(input=x_emb, size=1, act=None, name='my_fc') loss = fluid.layers.square_error_cost(input=y_predict, label=y) avg_loss = fluid.layers.mean(loss) # Get all weights in main_program, not include bias. all_weights = [param for param in fluid.default_main_program().block(0).all_parameters() if 'w_' in param.name] all_weights_name = [w.name for w in all_weights] # return all param_grads needed to be updated if parameter_list set default None. p_g_list1 = fluid.backward.append_backward(loss=avg_loss) # output: [(embedding_0.w_0, embedding_0.w_0@GRAD), (my_fc.w_0, my_fc.w_0@GRAD), (my_fc.b_0, my_fc.b_0@GRAD)] # return the param_grads corresponding to parameter_list that can be list of param (Variable). p_g_list2 = fluid.backward.append_backward(loss=avg_loss, parameter_list=all_weights) # output: [(embedding_0.w_0, embedding_0.w_0@GRAD), (my_fc.w_0, my_fc.w_0@GRAD)] # parameter_list can be list of param.name (str). p_g_list3 = fluid.backward.append_backward(loss=avg_loss, parameter_list=all_weights_name) # output: [(embedding_0.w_0, embedding_0.w_0@GRAD), (my_fc.w_0, my_fc.w_0@GRAD)] # no_grad_set can be set of Variables that means grad will be cut off from these Variables. p_g_list4 = fluid.backward.append_backward(loss=avg_loss, no_grad_set=set([x_emb])) # output: [(my_fc.w_0, my_fc.w_0@GRAD), (my_fc.b_0, my_fc.b_0@GRAD)] # no_grad_set can be set of Variable.name when the Variable is created inside layers and can't be specified explicitly. p_g_list5 = fluid.backward.append_backward(loss=avg_loss, no_grad_set=set(['my_fc.b_0'])) # output: [(embedding_0.w_0, embedding_0.w_0@GRAD), (my_fc.w_0, my_fc.w_0@GRAD)] # return [] because all param_grads are filtered by no_grad_set. p_g_list6 = fluid.backward.append_backward(loss=avg_loss, parameter_list=all_weights, no_grad_set=set(all_weights)) """ assert isinstance(loss, framework.Variable) if loss.op is None: # the loss is from a cloned program. Find loss op manually. _find_loss_op_(loss) loss.op._set_attr(core.op_proto_and_checker_maker.kOpRoleAttrName(), int(core.op_proto_and_checker_maker.OpRole.Forward) | int(core.op_proto_and_checker_maker.OpRole.Loss)) if callbacks is not None: isinstance(callbacks, list) program = loss.block.program root_block = program.block(0) current_block_idx = program.current_block_idx current_block = program.block(current_block_idx) is_in_control_flow = current_block_idx != 0 # Double grad is not supported in sub-block (control flow) if not is_in_control_flow: # _appending_grad_times used for double grad program._appending_grad_times += 1 if no_grad_set is None: no_grad_set = set() else: no_grad_set = _get_no_grad_set_name(copy.copy(no_grad_set)) no_grad_dict = _get_stop_gradients_(program) # no_grad_set only contains vars in block 0 # Todo(liym27): support vars in sub block no_grad_dict[0].update(list(map(_append_grad_suffix_, no_grad_set))) # Currently it is only to support the optimizer.minimize # in a switch branch, which can append_backward in a sub_block. # Note: while_loop is in control flow, but it makes no sense to call optimizer in while. # Todo: report error when it is in while_loop if is_in_control_flow: # create grad block if in switch control flow. target_grad_block = program._create_block( parent_idx=current_block.parent_idx) target_grad_block._set_forward_block_idx(current_block_idx) # after _create_block, program.current_block changes else: target_grad_block = root_block son_parent_block_idx_dict = _get_son_parent_block_idx_dict( program, current_block_idx) block_fwd_op_num_dict = {} # block_id: fwd_op_num for idx in son_parent_block_idx_dict: block_fwd_op_num_dict[idx] = program.block(idx).desc.op_size() grad_to_var = dict() op_desc = _create_loss_op_desc_(loss) target_grad_block.desc.append_op().copy_from(op_desc) for block_idx in son_parent_block_idx_dict: block = program.block(block_idx) block_no_grad_set = set( map(_strip_grad_suffix_, no_grad_dict[block_idx])) op_path = _find_op_path_(block, [loss], [], block_no_grad_set) no_grad_vars = _find_no_grad_vars(block, op_path, [loss], block_no_grad_set) block_no_grad_set.update(no_grad_vars) no_grad_dict[block_idx].update( list(map(_append_grad_suffix_, block_no_grad_set))) input_grad_names_set = None # For double backward, input_grad_names is used for filtering # some non-used gradients op(s). # Todo(liym27): need a better design. # not support double grad in control flow sub-block now. if not is_in_control_flow: if program._appending_grad_times > 1: input_grad_names_set = set([_append_grad_suffix_(loss.name)]) # Todo: support _append_backward_ops_with_checkpoints_ in # sub-block (control flow) if checkpoints != None and \ isinstance(checkpoints, list) and \ len(checkpoints) > 0: program_stat, checkpoint_names, \ vars_should_be_hold, \ recompute_segments = \ _append_backward_ops_with_checkpoints_( root_block, op_path, root_block, no_grad_dict, grad_to_var, checkpoints) else: _append_backward_ops_( block, # the block where forward ops are in op_path, target_grad_block, no_grad_dict, grad_to_var, callbacks, input_grad_names_set=input_grad_names_set) grad_info_map = dict() # if in control flow, target_grad_block is a created new block which only contains grad ops, # so fwd_op_num is set to 0. fwd_op_num = block_fwd_op_num_dict[ current_block_idx] if not is_in_control_flow else 0 # Because append_backward may be called multiple times, # we need rename the internal gradient variables so that they have # different names. _rename_grad_(target_grad_block, fwd_op_num, grad_to_var, {}) _append_backward_vars_(target_grad_block, fwd_op_num, grad_to_var, grad_info_map) program.current_block_idx = current_block_idx program._sync_with_cpp() if parameter_list is not None: if not isinstance(parameter_list, (list, tuple, set)): raise TypeError( "The type of parameter_list argument must be list or tuple or set, but received %s." % (type(parameter_list))) parameters = [] for i, param in enumerate(parameter_list): if isinstance(param, framework.Variable): parameters.append(param.name) elif isinstance(param, six.string_types): parameters.append(param) else: raise TypeError( "The type of parameter_list's member must be paddle.fluid.Variable or str, but received %s." % (type(param))) else: params = program.global_block().all_parameters() parameters = [param.name for param in params if param.trainable] params_and_grads = [] op_role_var_attr_name = core.op_proto_and_checker_maker.kOpRoleVarAttrName() for param in parameters: if cpt.to_text(param) not in grad_info_map: continue grad_info = grad_info_map[param] grad_block = grad_info[1] if not grad_block.has_var(grad_info[0]): raise ValueError("grad block[{0}] did not have grad var {1}".format( grad_info[1], grad_info[0])) # Get the param var from the global block param_var = program.global_block().var(param) grad_var = grad_block.var(grad_info[0]) if not is_in_control_flow: if loss.block.has_var(grad_info[0]): params_and_grads.append((param_var, grad_var)) else: params_and_grads.append((param_var, None)) else: params_and_grads.append((param_var, grad_var)) for p, g in params_and_grads: if g is None: continue ops = grad_block.ops if is_in_control_flow else program.global_block( ).ops for op in reversed(ops): assert isinstance(op, framework.Operator) if g.name in op.output_arg_names: g.op = op break if g.op is None: raise ValueError("Unexpected branch") attr_val = [p.name, g.name] if g.op.has_attr(op_role_var_attr_name): attr_val.extend(g.op.attr(op_role_var_attr_name)) g.op._set_attr(op_role_var_attr_name, attr_val) return params_and_grads def _as_list(x): if x is None: return [] return list(x) if isinstance(x, collections.Sequence) else [x] def _is_ancestor_block(ancestor_block, block): prog = block.program ancestor_idx = ancestor_block.idx parent_idx = block.parent_idx while parent_idx != -1: if parent_idx == ancestor_idx: return True parent_idx = prog.block(parent_idx).parent_idx return False def _get_output_names(cur_block, targets): """ In `cur_block`, get output names those linked to targets. NOTE: 1. `targets` can be in `cur_block`; Usually, `targets` is in `cur_block`. However, considering control flow, 2. `targets` may be in sub-block but `cur_block` is an ancestor of `targets[0].block`; 3. `targets` may be in the block which is ancestor of `cur_block`. """ block = targets[0].block if targets else cur_block prog = cur_block.program if _is_ancestor_block(block, cur_block): return set() current_output_names = set([out.name for out in targets]) # if `cur_block` is an ancestor of `targets[0].block`, run while loop while block.idx != cur_block.idx: assert block.parent_idx != -1 parent_block = prog.block(block.parent_idx) parent_block_output_names = set() for op in reversed(block.ops): if _some_in_set_(op.desc.output_arg_names(), current_output_names): for name in op.desc.input_arg_names(): current_output_names.add(name) if not block.desc.find_var(cpt.to_bytes(name)) \ and parent_block.desc.find_var(cpt.to_bytes(name)): parent_block_output_names.add(name) block = parent_block current_output_names = parent_block_output_names return current_output_names def _find_no_grad_vars(block, op_path, targets, no_grad_set): """ Find the vars which is not used in the program, and those vars belong to no_grad_var. """ output_names = _get_output_names(block, targets) no_grad_var = [] for i, op in reversed(list(enumerate(op_path))): # If the op has sub_block, it is too complicated to find the correct no_grad_var. if not op.has_attr("sub_block"): for out_var in op.desc.output_arg_names(): if out_var not in output_names and out_var not in op.desc.input_arg_names( ) and not block.vars[out_var].stop_gradient: no_grad_var.append(out_var) for name in op.desc.input_arg_names(): if name not in no_grad_set: output_names.add(name) return set(no_grad_var) def _find_op_path_(block, outputs, inputs, no_grad_set): """ no_grad_set will also be changed """ input_names = set([inp.name for inp in inputs]) output_names = _get_output_names(block, outputs) relevant_op_flags = [True] * len(block.ops) # All the inputs of the block are used if inputs is empty, if inputs: for i, op in enumerate(block.ops): if _some_in_set_( op.desc.input_arg_names(), input_names) and core.has_non_empty_grad_op_maker(op.type): for name in op.desc.output_arg_names(): if name not in no_grad_set: input_names.add(name) else: relevant_op_flags[i] = False for i, op in reversed(list(enumerate(block.ops))): if _some_in_set_( op.desc.output_arg_names(), output_names) and core.has_non_empty_grad_op_maker(op.type): for name in op.desc.input_arg_names(): if name not in no_grad_set: output_names.add(name) else: relevant_op_flags[i] = False op_path = [ block.ops[i] for i in range(len(block.ops)) if relevant_op_flags[i] ] if inputs: for op in op_path: for name in op.desc.input_arg_names(): if name not in input_names and block.vars[name].stop_gradient: no_grad_set.add(name) return op_path def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None): """ Backpropagate the gradients of targets to inputs. Args: targets(Variable|list[Variable]): The target variables inputs(Variable|list[Variable]): The input variables target_gradients (Variable|list[Variable], optional): The gradient variables of targets which has the same shape with targets, If None, ones will be created for them. no_grad_set(set[Variable|str], optional): Set of Variables or Variable.names in the :ref:`api_guide_Block_en` 0 whose gradients should be ignored. All variables with `stop_gradient=True` from all blocks will be automatically added into this set. If this parameter is not None, the Variables or Variable.names in this set will be added to the default set. Default: None. Return: (list[Variable]): A list of gradients for inputs If an input does not affect targets, the corresponding gradient variable will be None """ targets = _as_list(targets) inputs = _as_list(inputs) target_gradients = _as_list(target_gradients) block = targets[0].block prog = block.program # increase appending gradients times prog._appending_grad_times += 1 block_idx = block.idx if not target_gradients: target_gradients = [None] * len(targets) if len(targets) != len(target_gradients): raise ValueError( "Should have the same number of target_gradients as targets") if no_grad_set is None: no_grad_set = set() else: no_grad_set = _get_no_grad_set_name(copy.copy(no_grad_set)) no_grad_dict = _get_stop_gradients_(prog) no_grad_dict[0].update(list(map(_append_grad_suffix_, no_grad_set))) fwd_op_num = block.desc.op_size() input_grad_names_set = set() target_grad_map = {} for i, grad in enumerate(target_gradients): target = targets[i] if grad is None: grad_name = _append_grad_suffix_(target.name) target_shape = target.name + '_shape' block.desc.append_op().copy_from( _create_op_desc_("shape", {'Input': [target.name]}, {"Out": [target_shape]}, {})) input_grad_names_set.add(target_shape) op_desc = _create_op_desc_("fill_constant", {"ShapeTensor": [target_shape]}, {"Out": [grad_name]}, { "shape": target.shape, "value": 1.0, "dtype": target.dtype, }) block.desc.append_op().copy_from(op_desc) input_grad_names_set.add(grad_name) else: if target.block.idx != block_idx or target.block.program != prog: raise ValueError("all targets must be in the same block") if target.shape != grad.shape: raise ValueError( "The shapes of target and grad are different: %s %s" % ( target.name, grad.name)) target_grad_map[_append_grad_suffix_(target.name)] = grad.name input_grad_names_set.add(grad.name) # For double backward, input_grad_names is used for filter # some non-used gradients op. if prog._appending_grad_times == 1: input_grad_names_set = None for input in inputs: if input.block.program != prog: raise "input must be in the same program as targets" block_no_grad_set = set(map(_strip_grad_suffix_, no_grad_dict[0])) op_path = _find_op_path_(block, targets, inputs, block_no_grad_set) no_grad_dict[0].update(list(map(_append_grad_suffix_, block_no_grad_set))) grad_to_var = dict() grad_info_map = dict() _append_backward_ops_( block, op_path, block, no_grad_dict, grad_to_var, input_grad_names_set=input_grad_names_set) # Because calc_gradient may be called multiple times, # we need rename the internal gradient variables so that they have # different names. _rename_grad_(block, fwd_op_num, grad_to_var, target_grad_map) _append_backward_vars_(block, fwd_op_num, grad_to_var, grad_info_map) prog._sync_with_cpp() grad_vars = [] for input_var in inputs: if input_var.name not in grad_info_map: grad_vars.append(None) else: grad_info = grad_info_map[input_var.name] grad_block = grad_info[1] grad_var = grad_block.var(grad_info[0]) grad_vars.append(grad_var) if len(grad_vars) == 1: return grad_vars[0] else: return grad_vars def gradients(targets, inputs, target_gradients=None, no_grad_set=None): """ Backpropagate the gradients of targets to inputs. Args: targets (Variable|list[Variable]): The target variables. inputs (Variable|list[Variable]): The input variables. target_gradients (Variable|list[Variable], optional): The gradient variables of targets which has the same shape with targets, If None, ones will be created for them. no_grad_set (set[Variable|str], optional): Set of Variables or Variable.names in the :ref:`api_guide_Block_en` 0 whose gradients should be ignored. All variables with `stop_gradient=True` from all blocks will be automatically added into this set. If this parameter is not None, the Variables or Variable.names in this set will be added to the default set. Default: None. Return: (list[Variable]): A list of gradients for inputs If an input does not affect targets, the corresponding gradient variable will be None. Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name='x', shape=[None,2,8,8], dtype='float32') x.stop_gradient=False y = fluid.layers.conv2d(x, 4, 1, bias_attr=False) y = fluid.layers.relu(y) y = fluid.layers.conv2d(y, 4, 1, bias_attr=False) y = fluid.layers.relu(y) z = fluid.gradients([y], x) print(z) """ outs = calc_gradient(targets, inputs, target_gradients, no_grad_set) return _as_list(outs)