# 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 paddle.fluid import framework as framework from . import core import collections import copy import unique_name __all__ = [ 'append_backward', 'calc_gradient', ] 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 inputs.iteritems(): op_desc.set_input(para, args) for para, args in outputs.iteritems(): op_desc.set_output(para, 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 attrs.iteritems(): if isinstance(val, framework.Block): op_desc.set_block_attr(name, val.desc) else: op_desc.set_attr(name, val) return op_desc def _infer_var_data_type_(grad_var_name, block): """ Infer the data type of given grad variable """ grad_var = block.desc.find_var(grad_var_name.encode("ascii")) fwd_name = _strip_grad_suffix_(grad_var_name.encode("ascii")) if block.desc.has_var_recursive(fwd_name): fwd_var = block.desc.find_var_recursive(fwd_name.encode("ascii")) grad_var.set_dtype(fwd_var.dtype()) 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 for c in cands: if c in s: return True return False def _strip_grad_suffix_(name): """ Strip the grad suffix from the given varibale name e.g. x@GRAD ==> x y@GRAD@RENAME@1 ==> y """ 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 name + core.grad_var_suffix() def _addup_repetitive_outputs_(op_descs): """ In backward part, an variable may be the output of more than one ops. In this case, 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) for idx, op_desc in enumerate(op_descs): for var_name in op_desc.input_arg_names(): if len(renamed_vars[var_name]) > 1: pending_sum_ops.append( (_create_op_desc_("sum", {"X": renamed_vars[var_name]}, {"Out": [var_name]}, {}), idx)) renamed_vars[var_name] = [var_name] for var_name in op_desc.output_arg_names(): 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] else: if len(renamed_vars[var_name]) == 1: new_name = var_name + "@RENAME@" + \ str(var_rename_count[var_name]) var_rename_count[var_name] += 1 # rename original var_name renamed_vars[var_name][0] = new_name _rename_arg_(op_descs, var_name, new_name, 0, idx) _rename_arg_(pending_sum_ops, var_name, new_name) new_name = var_name + "@RENAME@" + \ str(var_rename_count[var_name]) var_rename_count[var_name] += 1 op_desc.rename_output(var_name, new_name) renamed_vars[var_name].append(new_name) for var_name, inputs in renamed_vars.iteritems(): if len(inputs) > 1: pending_sum_ops.append((_create_op_desc_( "sum", {"X": inputs}, {"Out": [var_name]}, {}), 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_( filter(lambda name: name.find(core.grad_var_suffix()) != -1, op_desc.input_arg_names()), 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 = filter( lambda op_desc: not _op_can_be_removed_(op_desc, no_grad_set), op_descs) # Insert fill_zeros_like_op to_insert = [] for idx, op_desc in enumerate(op_descs): for arg in op_desc.input_arg_names(): if core.grad_var_suffix() in arg and arg in no_grad_set: to_insert.append((_create_op_desc_("fill_zeros_like", { "X": [_strip_grad_suffix_(arg)] }, {"Out": [arg]}, {}), idx)) map(lambda p: op_descs.insert(p[1], p[0]), reversed(to_insert)) return op_descs import proto.framework_pb2 as framework_pb2 def serialize_op_decs(op_desc): protostr = op_desc.serialize_to_string() proto = framework_pb2.OpDesc.FromString(str(protostr)) return proto.__str__() def _callback_lookup_(op): """ Only used in _append_backward_ops_ Build and returns a callback function for certain op. For example parallel_do: AllReduce :param op: :return: callback function """ if op.type == 'parallel_do' and op.attr('use_nccl'): all_vars = op.block.vars param_names = set(op.input('parameters')) param_names = filter(lambda name: all_vars[name].stop_gradient is False, param_names) param_grad_names = [n + "@GRAD" for n in param_names] class ParallelDoCallBack(object): def __init__(self, param_grad_names, parallel_scopes_name): self.has_inserted_nccl_init = False self.param_grad_names = param_grad_names self.parallel_scopes_name = parallel_scopes_name def __call__(self, block, context): if not self.has_inserted_nccl_init: op_desc = _create_op_desc_( "ncclInit", {"parallel_scopes": self.parallel_scopes_name}, {"Communicator": ['nccl_com__do_not_change_']}, {}) block.program.global_block().desc.append_op().copy_from( op_desc) self.has_inserted_nccl_init = True current_op_desc = context["__current_op_desc__"] for o_param in current_op_desc.output_names(): for o_argu in current_op_desc.output(o_param): if o_argu in self.param_grad_names: allreduce_out_name = o_argu + "__nccl_all_reduce__" op_desc = _create_op_desc_( "ncclReduce", { "X": [o_argu], "Communicator": ['nccl_com__do_not_change_'] }, {"Out": [allreduce_out_name]}, {"reduction": "ncclSum", "root": 0}, ) block.desc.append_op().copy_from(op_desc) op_desc = _create_op_desc_( "assign", {"X": [allreduce_out_name]}, {"Out": [o_argu]}, {}) block.desc.append_op().copy_from(op_desc) return ParallelDoCallBack(param_grad_names, op.output("parallel_scopes")) else: return None def _append_backward_ops_(block, ops, target_block, no_grad_dict, grad_to_var, callbacks=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 varibale names. These varibales have no gradient grad_to_var(dict)(output argument): key(str): grad variable name val(str): corresponding forward variable name callback(callable object): a callable object used to decorate new generated grad 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 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("sub_block")) grad_sub_block = program.create_block() grad_sub_block.set_forward_block_idx(sub_block.idx) cb = _callback_lookup_(op) if cb is not None: if callbacks is None: new_callbacks = [cb] else: new_callbacks = callbacks + [_callback_lookup_(op)] _append_backward_ops_(sub_block, sub_block.ops, grad_sub_block, no_grad_dict, grad_to_var, new_callbacks) else: _append_backward_ops_(sub_block, sub_block.ops, grad_sub_block, no_grad_dict, grad_to_var, callbacks) 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, no_grad_dict[block.idx], grad_sub_block_list) grad_op_descs.extend(grad_op_desc) grad_to_var.update(op_grad_to_var) grad_op_descs = _addup_repetitive_outputs_(grad_op_descs) grad_op_descs = _remove_no_grad_branch_(grad_op_descs, no_grad_dict[block.idx]) # 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 _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 """ 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("sub_block")) _append_backward_vars_(sub_block, 0, grad_to_var, grad_info_map) new_vars = set() # create new gradient variables for grad_var_name in op_desc.output_arg_names(): grad_var_name = grad_var_name.encode("ascii") if block.desc.has_var_recursive( grad_var_name) or grad_var_name == core.empty_var_name(): continue block.desc.var(grad_var_name) new_vars.add(grad_var_name) if not grad_to_var.has_key(grad_var_name): 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) # ncclInit dones't need to set data_type if op_desc.type() == 'ncclInit': continue for arg in op_desc.output_arg_names(): if arg in new_vars: _infer_var_data_type_(arg, block) 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 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 var_map.iteritems(): 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 block.vars.itervalues(): 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 append_backward(loss, parameter_list=None, no_grad_set=None, callbacks=None): """ Append backward part to main_program Args: loss(Variable): The variable generated by cost function. parameter_list(list[string]): Parameters that need to be updated by optimizer. If None, it means all parameters need to be updated. no_grad_set(set): Variables that have no gradients in Block 0. All variables with `step_gradient=True` from all blocks will be automatically added. Return: (list[(Variable,Variable)]): list of (parameter, gradient) pair. """ assert isinstance(loss, framework.Variable) 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 if no_grad_set is None: no_grad_set = set() no_grad_set = copy.copy(no_grad_set) no_grad_dict = _get_stop_gradients_(program) no_grad_dict[0].update(map(_append_grad_suffix_, no_grad_set)) grad_info_map = dict() root_block = program.block(0) fwd_op_num = root_block.desc.op_size() current_block_idx = program.current_block_idx grad_to_var = dict() 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), }) root_block.desc.append_op().copy_from(op_desc) block_no_grad_set = set(map(_strip_grad_suffix_, no_grad_dict[0])) op_path = _find_op_path_(root_block, [loss], [], block_no_grad_set) no_grad_dict[0].update(map(_append_grad_suffix_, block_no_grad_set)) _append_backward_ops_(root_block, op_path, root_block, no_grad_dict, grad_to_var, callbacks) # Because calc_gradient may be called multiple times, # we need rename the internal gradient variables so that they have # different names. _rename_grad_(root_block, fwd_op_num, grad_to_var, {}) _append_backward_vars_(root_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: parameters = parameter_list else: params = program.global_block().all_parameters() parameters = [param.name for param in params] params_and_grads = [] for param in parameters: if 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 loss.block.has_var(grad_info[0]): params_and_grads.append((param_var, grad_var)) else: params_and_grads.append((param_var, None)) op_role_var_attr_name = core.op_proto_and_checker_maker.kOpRoleVarAttrName() for p, g in params_and_grads: if g is None: continue for op in reversed(program.global_block().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") g.op.set_attr(op_role_var_attr_name, p.name) return params_and_grads def _as_list(x): if x is None: return [] return list(x) if isinstance(x, collections.Sequence) else [x] 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 = set([out.name for out in 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): 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): 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: no_grad_set.add(name) return op_path def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None): """ Backpropagate the graidents of targets to inputs. Args: targets(Variable|list[Variable]): The target variables inputs(Variable|list[Variable]): The input variables no_grad_set(set[string]): The names of variables that have no gradients in Block 0. All variables with `stop_gradient=True` from all blocks will be automatically added. Return: (list[Variable]): 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 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() no_grad_set = copy.copy(no_grad_set) no_grad_dict = _get_stop_gradients_(prog) no_grad_dict[0].update(map(_append_grad_suffix_, no_grad_set)) fwd_op_num = block.desc.op_size() target_grad_map = {} for i, grad in enumerate(target_gradients): target = targets[i] if grad is None: grad_name = _append_grad_suffix_(target.name) op_desc = _create_op_desc_("fill_constant_batch_size_like", {"Input": [target.name]}, {"Out": [grad_name]}, { "shape": target.shape, "value": 1.0, "dtype": target.dtype, 'input_dim_idx': 0, 'output_dim_idx': 0 }) block.desc.append_op().copy_from(op_desc) 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 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(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) # 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