# 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 from .common import DistributedOperatorImplContainer from .common import DistributedOperatorImpl from .common import register_distributed_operator_impl_container from .common import register_distributed_operator_impl from .common import is_parameter_related from ..utils import is_dim_shard from ..utils import is_dim_replicate from ..utils import is_valid_list_index from ..utils import compute_compatible_dim_mapping from ..utils import compute_compatible_dims_mapping from ..utils import compute_compatible_and_update_dim_mapping from .dist_default import DistributedDefaultImpl0 from ..cost import _g_op_cost_factory from ..cost import build_comp_desc_from_dist_op, build_dp_costs from ..cost import build_comp_costs_from_descs from ..cost import SoftmaxOpCost, SoftmaxGradOpCost from paddle.distributed.fleet.meta_optimizers.common import OpRole from paddle.distributed.auto_parallel.cost.comm_op_cost import AllreduceSumOpCost class DistributedSoftmax(DistributedOperatorImplContainer): def __init__(self, op_type): super(DistributedSoftmax, self).__init__(op_type) register_distributed_operator_impl_container(DistributedSoftmax("softmax")) class DistributedSoftmaxImpl(DistributedOperatorImpl): def __init__(self, name): super(DistributedSoftmaxImpl, self).__init__(name) self._forward_implemented = False self._backward_implemented = False def calc_cost(self, op_role, dist_op, ctx, cluster): cost = None if int(op_role) == int(OpRole.Backward): cost = self.calc_bwd_cost(dist_op, ctx, cluster) else: cost = self.calc_fwd_cost(dist_op, ctx, cluster) assert cost is not None return cost def calc_fwd_cost(self, dist_op, ctx, cluster): # calc comp op cost desc_mapping = build_comp_desc_from_dist_op(dist_op=dist_op, dist_context=ctx) processes = dist_op.dist_attr.process_mesh.processes cost_mapping = build_comp_costs_from_descs(SoftmaxOpCost, ctx, processes, desc_mapping, cluster) res_cost = [cost_mapping] return res_cost def calc_bwd_cost(self, dist_op, ctx, cluster): # calc comp op cost res = [] desc_mapping = build_comp_desc_from_dist_op(dist_op=dist_op, dist_context=ctx) dist_attr = dist_op.dist_attr process_mesh = dist_attr.process_mesh processes = process_mesh.processes cost_mapping = build_comp_costs_from_descs(SoftmaxGradOpCost, ctx, processes, desc_mapping, cluster) res.append(cost_mapping) backward_op = dist_op.serial_op main_block = backward_op.block need_gradient_allreduce = False vars = main_block.vars for input_name in backward_op.desc.input_names(): for varname in backward_op.desc.input(input_name): if "@GRAD" not in varname and is_parameter_related( varname, main_block): # NOTE input var's dim_mapping of backward op should be the same with input var instead of corresponding varname of forward op var_dim_mapping = dist_attr.get_input_dims_mapping(varname) mesh_shape = process_mesh.topology batch_size_axis = var_dim_mapping[0] if batch_size_axis > -1 and mesh_shape[batch_size_axis] > 1: parallel_axis = batch_size_axis attrs = {"use_calc_stream": True} var_names = [varname + "@GRAD"] build_dp_costs(res, dist_op, ctx, var_names, attrs, parallel_axis, cluster) return res def is_input_compatible(self, dist_op): op_desc = dist_op.serial_op.desc op_dist_attr = dist_op.dist_attr x_name = op_desc.input('X')[0] axis = op_desc.attr('axis') x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name) # if axis != -1 and axis != len(x_dims_mapping) - 1: # return False if is_dim_shard(x_dims_mapping[axis]): return False return True def is_output_compatible(self, dist_op): op_desc = dist_op.serial_op.desc op_dist_attr = dist_op.dist_attr out_name = op_desc.output('Out')[0] axis = op_desc.attr('axis') out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name) # if axis != -1 and axis != len(out_dims_mapping) - 1: # return False if is_dim_shard(out_dims_mapping[axis]): return False return True def is_auto_compatible(self, dist_op): if (not self.is_input_compatible(dist_op)) or \ (not self.is_output_compatible(dist_op)): return False op_desc = dist_op.serial_op.desc op_dist_attr = dist_op.dist_attr x_name = op_desc.input('X')[0] axis = op_desc.attr('axis') out_name = op_desc.output('Out')[0] x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name) out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name) # if axis != -1 and axis != len(x_dims_mapping) - 1: # return False if x_dims_mapping != out_dims_mapping: return False return True def update_dims_mapping(self, dist_op): changed = False op_desc = dist_op.serial_op.desc op_dist_attr = dist_op.dist_attr x_name = op_desc.input('X')[0] out_name = op_desc.output('Out')[0] x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name) out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name) for i in range(len(x_dims_mapping)): dim_changed = compute_compatible_and_update_dim_mapping( [x_dims_mapping, out_dims_mapping], [i, i]) if dim_changed: changed = True return changed @staticmethod def forward(ctx, *args, **kwargs): DistributedDefaultImpl0.forward(ctx, *args, **kwargs) @staticmethod def backward(ctx, *args, **kwargs): DistributedDefaultImpl0.backward(ctx, *args, **kwargs) register_distributed_operator_impl( "softmax", DistributedSoftmaxImpl("replicate_last_axis"))