# Copyright (c) 2021 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 import abc import paddle from paddle.distributed.fleet.meta_optimizers.common import OpRole, OP_ROLE_KEY, OP_ROLE_VAR_KEY from ..dist_attribute import OperatorDistributedAttribute from ..utils import _get_comm_group, _get_corresponding_rank, is_optimize_op from ..process_group import new_process_group _g_distributed_operator_impl_containers = {} _g_elementwise_ops = [ "elementwise", "gelu", "dropout", "cast", "gather", "concat", "fused_softmax_mask_upper_triangle" ] BACKWARD_ONLY_DIST_OPS = {'check_finite_and_unscale', 'update_loss_scaling'} class ParallelMode(): """ the parallel mode for communication or auxiliary operator """ DataParallel = "auto_parallel/data_parallel" ModelParallel = "auto_parallel/model_parallel" PipelineParalel = "auto_parallel/pipeline_paralel" MoEParallel = "auto_parallel/moe_parallel" def is_elementwise_op(op_type): if op_type in _g_elementwise_ops: return True if "elementwise" in op_type: return True return False class DistributedOperatorImplContainer: def __init__(self, op_type): self._type = op_type self._impls = [] @property def type(self): return self._type @type.setter def type(self, op_type): self._type = op_type @property def impls(self): return self._impls def register_impl(self, dist_impl): assert self.type == dist_impl.type, \ "Op type of container must be same as that of the implementation." impl_idx = len(self.impls) dist_impl.idx = impl_idx self._impls.append(dist_impl) def get_impl(self, impl_idx): return self._impls[impl_idx] def get_input_compatible_impls(self, dist_op): compatible_impls = [] for impl in self.impls: if impl.is_input_compatible(dist_op): compatible_impls.append(impl) return compatible_impls def get_output_compatible_impls(self, dist_op): compatible_impls = [] for impl in self.impls: if impl.is_output_compatible(dist_op): compatible_impls.append(impl) return compatible_impls def get_compatible_impls(self, dist_op): compatible_impls = [] for impl in self.impls: if impl.is_auto_compatible(dist_op): compatible_impls.append(impl) return compatible_impls class DistributedOperatorImpl(abc.ABC): def __init__(self, name): self._name = name self._type = None self._idx = None self._forward_implemented = False self._backward_implemented = False @property def name(self): return self._name @name.setter def name(self, name): self._name = name @property def type(self): return self._type @type.setter def type(self, op_type): self._type = op_type @property def idx(self): return self._idx @idx.setter def idx(self, impl_idx): self._idx = impl_idx @abc.abstractmethod def is_input_compatible(self, dist_op): raise NotImplementedError("Please Implement this method in Subclass.") @abc.abstractmethod def is_output_compatible(self, dist_op): raise NotImplementedError("Please Implement this method in Subclass.") @abc.abstractmethod def is_auto_compatible(self, dist_op): raise NotImplementedError("Please Implement this method in Subclass.") @staticmethod @abc.abstractmethod def forward(dist_ctx, *args, **kwargs): raise NotImplementedError("Please Implement this method in Subclass.") @staticmethod @abc.abstractmethod def backward(dist_ctx, *grad_outputs, **kwargs): raise NotImplementedError("Please Implement this method in Subclass.") def update_dims_mapping(self, dist_op): raise NotImplementedError("Please Implement this method in Subclass.") def register_distributed_operator_impl_container(container): global _g_distributed_operator_impl_containers _g_distributed_operator_impl_containers[container.type] = container def get_distributed_operator_impl_container(op_type): global _g_distributed_operator_impl_containers return _g_distributed_operator_impl_containers.get(op_type, None) def register_distributed_operator_impl(op_type, dist_impl): dist_op_impl_container = get_distributed_operator_impl_container(op_type) if dist_op_impl_container is not None: dist_impl.type = op_type dist_op_impl_container.register_impl(dist_impl) else: assert False, "Must register distributed operator registry first." def find_compatible_distributed_operator_impls(dist_op, fwd=True, partial=True): """ Here just return the first compatible implemention. This will be improved by cost model in the future. """ op_type = dist_op.serial_op.type dist_op_impl_container = get_distributed_operator_impl_container(op_type) dist_op_eltwise_impl_container = get_distributed_operator_impl_container( "elementwise") dist_op_default_impl_container = get_distributed_operator_impl_container( "default") compatible_impls = [] if partial: if fwd: # First, find impls in the corresponding container if dist_op_impl_container: compatible_impls.extend( dist_op_impl_container.get_input_compatible_impls(dist_op)) # Second, find impls in the elementwise container if dist_op_eltwise_impl_container and is_elementwise_op(op_type): compatible_impls.extend( dist_op_eltwise_impl_container.get_input_compatible_impls( dist_op)) # Third, find impls in the default container if dist_op_default_impl_container: compatible_impls.extend( dist_op_default_impl_container.get_input_compatible_impls( dist_op)) else: # First, find impls in the corresponding container if dist_op_impl_container: compatible_impls.extend( dist_op_impl_container.get_output_compatible_impls(dist_op)) # Second, find impls in the elementwise container if dist_op_eltwise_impl_container and is_elementwise_op(op_type): compatible_impls.extend( dist_op_eltwise_impl_container.get_output_compatible_impls( dist_op)) # Third, find impls in the default container if dist_op_default_impl_container: compatible_impls.extend( dist_op_default_impl_container.get_output_compatible_impls( dist_op)) else: # First, find impls in the corresponding container if dist_op_impl_container: compatible_impls.extend( dist_op_impl_container.get_compatible_impls(dist_op)) # Second, find impls in the elementwise container if dist_op_eltwise_impl_container and is_elementwise_op(op_type): compatible_impls.extend( dist_op_eltwise_impl_container.get_compatible_impls(dist_op)) # Third, find impls in the default container if dist_op_default_impl_container: compatible_impls.extend( dist_op_default_impl_container.get_compatible_impls(dist_op)) if compatible_impls: # For now, just return the first compatible impl # best_compatible_impl = compatible_impls[0] best_compatible_impl = compatible_impls else: best_compatible_impl = None return best_compatible_impl def is_parameter_related(varname, block): if ".subprog_" in varname: varname = varname[:varname.index(".subprog_")] if ".cast_fp" in varname: varname = varname[:varname.index(".cast_fp")] if ".quantized" in varname: varname = varname[:varname.index(".quantized")] assert block.has_var(varname) var = block.var(varname) return var.is_parameter def infer_shape(block, src_var, src_var_dist_attr, op_input_dist_attr): var_shape = block.var(src_var.name).shape var_topoloy = src_var_dist_attr.process_mesh.topology var_dims_mapping = src_var_dist_attr.dims_mapping complete_shape = [] for idx, shape in enumerate(var_shape): if var_dims_mapping[idx] == -1: complete_shape.append(shape) else: new_shape = shape * var_topoloy[var_dims_mapping[idx]] complete_shape.append(new_shape) exact_shape = [] input_topology = op_input_dist_attr.process_mesh.topology input_dims_mapping = op_input_dist_attr.dims_mapping for idx, shape in enumerate(complete_shape): if input_dims_mapping[idx] == -1: exact_shape.append(shape) else: new_shape = shape // input_topology[input_dims_mapping[idx]] exact_shape.append(new_shape) return exact_shape def set_comm_op_dist_attr_for_program(new_op, process_mesh, tensor_dist_attr, ctx): assert process_mesh is not None assert tensor_dist_attr is not None new_op_dist_attr = OperatorDistributedAttribute() new_op_dist_attr.process_mesh = process_mesh for input_varname in new_op.desc.input_arg_names(): new_op_dist_attr.set_input_dist_attr(input_varname, tensor_dist_attr) for output_varname in new_op.desc.output_arg_names(): new_op_dist_attr.set_output_dist_attr(output_varname, tensor_dist_attr) ctx.set_op_dist_attr_for_program(new_op, new_op_dist_attr) def naive_copy_op_dist_attr_for_program(new_op, ref_op, ctx): ref_dist_attr = ctx.get_op_dist_attr_for_program(ref_op) new_op_dist_attr = OperatorDistributedAttribute() new_op_dist_attr.process_mesh = ref_dist_attr.process_mesh for input_name in ref_op.input_names: assert input_name in new_op.input_names assert len(ref_op.input(input_name)) == 1 assert len(new_op.input(input_name)) == 1 ref_tensor_dist_attr = ref_dist_attr.get_input_dist_attr( ref_op.input(input_name)[0]) new_op_dist_attr.set_input_dist_attr( new_op.input(input_name)[0], ref_tensor_dist_attr) for output_name in ref_op.output_names: assert output_name in new_op.output_names assert len(ref_op.output(output_name)) == 1 assert len(new_op.output(output_name)) == 1 ref_tensor_dist_attr = ref_dist_attr.get_output_dist_attr( ref_op.output(output_name)[0]) new_op_dist_attr.set_output_dist_attr( new_op.output(output_name)[0], ref_tensor_dist_attr) ctx.set_op_dist_attr_for_program(new_op, new_op_dist_attr) def get_data_parallel_group(dist_ctx, op, act_grad_names, rank): """ deduce the data parallel communication group for current operator. Args: dist_ctx (DistributedContext): dist context. op (Operator): the current (backward) operator which might need. act_grad_names (list): list of input activation grads variable name to the current operator. out_grad_names (list): list of the output parameter's grads variable name of the current operator. rank (int): global ranks index for current process. """ dp_group = None op_dist_attr = dist_ctx.get_op_dist_attr_for_program(op) process_mesh = op_dist_attr.process_mesh mesh_shape = process_mesh.topology # FIXME Hack for Pipeline Parallelism where the current operator # not belong to the mesh the current rank belong to. if rank not in process_mesh.processes: rank = _get_corresponding_rank(dist_ctx, process_mesh, rank) for var_name in act_grad_names: var_dim_mapping = op_dist_attr.get_input_dims_mapping(var_name) # consider that the variable's shape is None # TODO utilize the batch_dim attr instead of "0" in future batch_size_axis = var_dim_mapping[0] if len(var_dim_mapping) > 0 else -1 if batch_size_axis > -1 and mesh_shape[batch_size_axis] > 1: group_ranks = _get_comm_group(process_mesh.processes, process_mesh.topology, batch_size_axis, rank) dp_group = new_process_group(group_ranks) break return dp_group def sync_and_scale_gradients(dist_ctx, op, dp_group, allreduce_var_names): """ insert the allreudce and scale ops for gradients of model parameters for operator in data parallelism. Args: dist_ctx (DistributedContext): dist context. op (Operator): the current (backward) operator which might need. allreduce_var_names (list): list of the parameter's grads variable name in the current operator output. """ op_dist_attr = dist_ctx.get_op_dist_attr_for_program(op) process_mesh = op_dist_attr.process_mesh dist_op_context = dist_ctx.dist_op_context main_block = dist_op_context.work_block dp_degree = len(dp_group.ranks) for var_name in allreduce_var_names: added_ops = [] grad_var = main_block.var(var_name) allreduce_op = main_block.append_op(type='c_allreduce_sum', inputs={'X': [grad_var]}, outputs={'Out': [grad_var]}, attrs={ 'ring_id': dp_group.id, 'use_calc_stream': True, OP_ROLE_KEY: OpRole.Backward }) allreduce_op._set_attr('op_namescope', str('/') + ParallelMode.DataParallel) added_ops.append(allreduce_op) if dist_ctx.gradient_scale: scale_op = main_block.append_op(type='scale', inputs={'X': grad_var}, outputs={'Out': grad_var}, attrs={ 'scale': 1.0 / dp_degree, OP_ROLE_KEY: OpRole.Backward }) scale_op._set_attr('op_namescope', str('/') + ParallelMode.DataParallel) added_ops.append(scale_op) dims_mapping = op_dist_attr.get_output_dims_mapping(grad_var.name) assert dims_mapping is not None, "Unexception: dims_mapping of output [{}] of op [{}] is None".format( grad_var.name, op_dist_attr.op_type) # NOTE auxiliary op's dist attr should follow dist_op not dist_tensor for new_op in added_ops: new_op_attr = OperatorDistributedAttribute() new_op_attr.process_mesh = process_mesh new_op_attr.set_output_dims_mapping(grad_var.name, dims_mapping) new_op_attr.set_input_dims_mapping(grad_var.name, dims_mapping) dist_ctx.set_op_dist_attr_for_program(new_op, new_op_attr) def gradient_synchronization(dist_ctx, op, act_grad_names, out_grad_names, rank): """ conduct the allreudce and scaling（dp size）for gradients of model parameters for operator in data parallelism. Args: dist_ctx (DistributedContext): dist context. op (Operator): the current (backward) operator which might need. act_grad_names (list): list of input activation grads variable name to the current operator. out_grad_names (list): list of the output parameter's grads variable name of the current operator. rank (int): global ranks index for current process. """ if not is_in_backward_phase(dist_ctx): return if is_optimize_op(op) or len(act_grad_names) == 0 or len( out_grad_names) == 0: return dp_group = get_data_parallel_group(dist_ctx, op, act_grad_names, rank) if not dp_group: return sync_and_scale_gradients(dist_ctx, op, dp_group, out_grad_names) def is_data_parallel_scale_op(op): return op.type == "scale" and op.desc.has_attr("op_namescope") \ and ParallelMode.DataParallel in op.desc.attr("op_namescope") def is_data_parallel_reduce_op(op): return op.type in ["c_reduce_sum", "c_allreduce_sum"] and op.desc.has_attr("op_namescope") \ and ParallelMode.DataParallel in op.desc.attr("op_namescope") def is_in_backward_phase(dist_ctx): # NOTE currently high-order differential in Paddle dose NOT distinguish gradient computation operators # in Forward phase and operators in Backward phase (both with op_role=1), which will mislead # auto parallel to add gradient synchronization for gradient computation operators in Forward phase. # we use this FLAG to distinguish these two phases temporarily. return dist_ctx.dist_op_context.in_backward_phase()