chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,538 @@
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from functools import reduce
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import numpy as np
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import paddle
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import paddle.distributed as dist
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from paddle.distributed.fleet.meta_optimizers.common import OP_ROLE_KEY, OpRole
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from ..auto_parallel.process_mesh import ProcessMesh
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from ..auto_parallel.static.dist_attribute import (
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OperatorDistAttr,
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TensorDistAttr,
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)
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from ..auto_parallel.static.operators.common import (
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SyncMode,
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is_data_parallel_reduce_op,
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)
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from ..auto_parallel.static.process_group import (
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get_all_process_groups,
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get_world_process_group,
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)
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from ..auto_parallel.static.reshard import Resharder
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from ..auto_parallel.static.utils import (
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_get_comm_group,
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insert_dependencies_for_vars,
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is_gradient_clip_op,
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is_optimize_op,
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is_reshard_op,
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)
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from .auto_parallel_sharding import ShardingPass
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from .pass_base import PassBase, register_pass
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def _get_params_grads(block):
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params_grads = []
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for op in reversed(block.ops):
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if not is_optimize_op(op):
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break
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if "Param" in op.input_names and "Grad" in op.input_names:
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param_name = op.input("Param")[0]
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grad_name = op.input("Grad")[0]
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param = block.var(param_name)
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grad = block.var(grad_name)
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params_grads.append((param, grad))
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return params_grads
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def _get_dpmp_topology(origin_topology, sharding_group):
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"""
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Get dpmp topology from origin_topology
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Example:
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the parallel strategy: dp4-mp2-sharding2
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the complete process_mesh:
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topology: [4, 2]
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processes: [0, 1, 2, 3, 4, 5, 6, 7]
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the dpmp topology: [2, 2]
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the sharding axis: 1
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"""
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sharding_axis = 1
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dp_sharding_topology = [
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origin_topology[0] // sharding_group.nranks,
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sharding_group.nranks,
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]
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if dp_sharding_topology[0] == 1:
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sharding_axis = 0
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dp_sharding_topology = dp_sharding_topology[1:]
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product_dp_sharding = reduce(lambda x, y: x * y, dp_sharding_topology, 1)
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product_topology = reduce(lambda x, y: x * y, origin_topology, 1)
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if product_topology == product_dp_sharding:
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dpmp_topology = dp_sharding_topology
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else:
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assert product_topology % product_dp_sharding == 0
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mp_degree = product_topology // product_dp_sharding
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dpmp_topology = [*dp_sharding_topology, mp_degree]
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return dpmp_topology, sharding_axis
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def _get_dpmp_process_mesh(rank_id, topology, processes, sharding_group):
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"""
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Get dpmp process_mesh from the complete process_mesh which apply sharding.
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Example:
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the parallel strategy: dp4-mp2-sharding2
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the complete process_mesh:
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topology: [4, 2]
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processes: [0, 1, 2, 3, 4, 5, 6, 7]
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the dpmp process_mesh is:
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1) topology: [2, 2], processes: [0, 1, 4, 5]
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2) topology: [2, 2], processes: [2, 3, 6, 7]
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"""
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if sharding_group is None:
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return topology, processes
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# get dpmp_topology
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dpmp_topology, sharding_axis = _get_dpmp_topology(topology, sharding_group)
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# get all sharding_groups of ranks
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sharding_groups = []
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for rank in processes:
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group = _get_comm_group(processes, dpmp_topology, sharding_axis, rank)
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if group not in sharding_groups:
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sharding_groups.append(group)
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# get dpmp_processes
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sharding_groups = np.array(sharding_groups)
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dpmp_processes_in_sharding = None
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for i in range(sharding_groups.shape[-1]):
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if rank_id in sharding_groups[:, i]:
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dpmp_processes_in_sharding = sharding_groups[:, i]
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assert dpmp_processes_in_sharding is not None
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return dpmp_topology, list(dpmp_processes_in_sharding)
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def _is_about_global_norm(
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rank_id, tensor_shape, topology, processes, dims_mapping, sharding_group
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):
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# get current process_mesh where the parameter exist.
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dpmp_topology, dpmp_processes = _get_dpmp_process_mesh(
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rank_id, topology, processes, sharding_group
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)
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complete_shape = Resharder.compute_complete_shape(
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tensor_shape, dpmp_topology, dims_mapping
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)
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complete_partitions = []
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complete_param_ranks = []
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for process in dpmp_processes:
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partition_index = Resharder.compute_partition_index(
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process, complete_shape, dims_mapping, dpmp_topology, dpmp_processes
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)
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if partition_index not in complete_partitions:
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complete_partitions.append(partition_index)
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complete_param_ranks.append(process)
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return rank_id in complete_param_ranks
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class ClipHelper:
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def __init__(
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self, params_grads, rank_id, block, dist_context, pass_context
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):
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params, _ = zip(*params_grads)
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self.params = list(params)
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self.params_name = [p.name for p in self.params]
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self.rank_id = rank_id
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self.block = block
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self.dist_context = dist_context
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self.pass_context = pass_context
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self.sharding_group = None
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self.world_ranks = get_world_process_group().ranks
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if hasattr(dist_context, '_sharding_group'):
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self.sharding_group = dist_context._sharding_group
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self.world_nranks = len(self.world_ranks)
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self.pure_data_parallel = self._is_pure_data_parallel()
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self.rank_to_params = self._partition_parameters(params)
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def is_calculate_norm(self, name):
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"""
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whether the param_name@GRAD participate in the calculation of global_norm
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"""
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if not self.is_local_param(name):
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return False
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param = self.params[self.params_name.index(name)]
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if not self.pure_data_parallel:
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dist_attr = self._get_dist_attr(name)
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topology = dist_attr.process_mesh.shape
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processes = dist_attr.process_mesh.process_ids
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dims_mapping = dist_attr.dims_mapping
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return _is_about_global_norm(
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self.rank_id,
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param.shape,
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topology,
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processes,
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dims_mapping,
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self.sharding_group,
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)
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else:
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return param.name in self.rank_to_params[self.rank_id]
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def is_local_param(self, name):
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"""
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whether the param_name is updated with opt in cur_rank
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"""
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if name not in self.params_name:
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return False
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return True
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def _get_dist_attr(self, name):
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var = self.block.vars[name]
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return self.dist_context.get_tensor_dist_attr_for_program(var)
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def is_local_var_with_dist_attr(self, name):
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"""
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whether the var_name is belong to cur_rank
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"""
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dist_attr = self._get_dist_attr(name)
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assert dist_attr is not None
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return self.rank_id in dist_attr.process_mesh.process_ids
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def _init_dist_attr(self, op):
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op_dist_attr = OperatorDistAttr()
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op_dist_attr.process_mesh = ProcessMesh(self.world_ranks)
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for in_name in op.input_arg_names:
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in_var = self.block.vars[in_name]
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in_dist_attr = TensorDistAttr()
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in_dist_attr.process_mesh = ProcessMesh(self.world_ranks)
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in_dist_attr.dims_mapping = [-1 for i in in_var.shape]
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self.dist_context.set_tensor_dist_attr_for_program(
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in_var, in_dist_attr
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)
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op_dist_attr.set_input_dist_attr(in_name, in_dist_attr)
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for out_name in op.output_arg_names:
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out_var = self.block.vars[out_name]
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out_dist_attr = TensorDistAttr()
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out_dist_attr.process_mesh = ProcessMesh(self.world_ranks)
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out_dist_attr.dims_mapping = [-1 for i in out_var.shape]
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self.dist_context.set_tensor_dist_attr_for_program(
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out_var, out_dist_attr
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)
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op_dist_attr.set_output_dist_attr(out_name, out_dist_attr)
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self.dist_context.set_op_dist_attr_for_program(op, op_dist_attr)
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def _is_pure_data_parallel(self):
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for applied_pass in self.pass_context.passes:
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if isinstance(applied_pass, ShardingPass):
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return False
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groups = get_all_process_groups()
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for g in groups:
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if g.nranks != self.world_nranks:
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return False
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for op in self.block.ops:
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if (
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(
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op.type == "reduce"
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and op.desc.attr("reduce_type") == dist.ReduceOp.SUM
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)
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or (
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op.type == "all_reduce"
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and op.desc.attr("reduce_type") == dist.ReduceOp.SUM
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)
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and not is_data_parallel_reduce_op(op)
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):
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return False
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if op.type in ["send_v2", "recv_v2"]:
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return False
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return True
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def _partition_parameters(self, params):
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"""
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build rank_id_to_params by the param's numel
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to guarantee params in every rank of dp_group as even as possible.
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"""
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mapping = {}
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if not self.pure_data_parallel:
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for rank_ in range(self.world_nranks):
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mapping[rank_] = [p.name for p in params]
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else:
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for rank_ in range(self.world_nranks):
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mapping[rank_] = []
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sizes = [0] * self.world_nranks
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for param in params:
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rank = sizes.index(min(sizes))
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mapping[rank].append(param.name)
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numel = reduce(lambda x, y: x * y, param.shape, 1)
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assert numel > 0, (
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f"param [{param.name}] should larger than 0, but it is [{numel}]"
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)
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sizes[rank] += numel
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return mapping
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@register_pass("auto_parallel_grad_clip")
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class ClipGradByGlobalNormPass(PassBase):
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"""
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1. Remove norm-compute op and grad-scale op when the grad is not in current rank
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or is independent of the calculation of norm.
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2. Each rank computes its own norm value, then gets global_norm by allreduce_sum only once.
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"""
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def __init__(self):
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super().__init__()
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self.set_attr("rank_id", None)
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self.set_attr("dist_context", None)
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self.set_attr("params_grads", None)
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def _check_self(self):
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if self.get_attr("dist_context") is None:
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return False
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dist_context = self.get_attr("dist_context")
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if dist_context._serial_optimizer._grad_clip is None:
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return False
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if self.get_attr("params_grads") is None:
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return False
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return True
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def _check_conflict(self, other_pass):
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return True
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def _apply_single_impl(self, main_program, startup_program, context):
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dist_context = self.get_attr("dist_context", None)
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rank_id = self.get_attr("rank_id", None)
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block = main_program.global_block()
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dist_params_grads = self.get_attr("params_grads", None)
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# dist_params_grads = _get_params_grads(block)
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self.clip_helper = ClipHelper(
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dist_params_grads, rank_id, block, dist_context, context
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)
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self._remove_no_need_ops_vars(block)
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def _remove_no_need_ops_vars(self, block):
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removed_op_out_type = [
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'squared_l2_norm',
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'square',
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'reduce_sum',
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]
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removed_op_idx = set()
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removed_tmp_var = set()
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for idx, op in enumerate(block.ops):
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if not is_gradient_clip_op(op):
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continue
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if op.type == 'clip_by_norm':
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# remove 'clip_by_norm' op if the param is not updated with opt in current rank
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input_name = op.input("X")[0]
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if input_name.find("@GRAD") != -1:
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param_name = input_name[: input_name.find("@GRAD")]
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is_local = self.clip_helper.is_local_param(param_name)
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if not is_local:
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removed_op_idx.add(idx)
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removed_tmp_var.update(set(op.output_arg_names))
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elif op.type in removed_op_out_type:
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input_name = op.input("X")[0]
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if input_name.find("@GRAD") != -1:
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# remove 'squared_l2_norm' and 'square' ops,
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# if the param@GRAD in cur_rank does not participate in the calculation of global_norm
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param_name = input_name[: input_name.find("@GRAD")]
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is_local = self.clip_helper.is_local_param(param_name)
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is_calculate = self.clip_helper.is_calculate_norm(
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param_name
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)
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if not is_local or not is_calculate:
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removed_op_idx.add(idx)
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removed_tmp_var.update(set(op.output_arg_names))
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else:
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# 'reduce_sum' must be behind 'square'
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if idx - 1 in removed_op_idx:
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removed_op_idx.add(idx)
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removed_tmp_var.update(set(op.output_arg_names))
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elif op.type == 'elementwise_mul':
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# 'elementwise_mul' scale the param@GRAD with global_norm
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# remove 'elementwise_mul' op if the param is not updated with opt in current rank
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input_name = op.input("X")[0]
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if input_name.find("@GRAD") != -1:
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param_name = input_name[: input_name.find("@GRAD")]
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is_local = self.clip_helper.is_local_param(param_name)
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if not is_local:
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removed_op_idx.add(idx)
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if block.ops[idx - 1].type == 'cast':
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removed_op_idx.add(idx - 1)
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removed_tmp_var.update(
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set(block.ops[idx - 1].output_arg_names)
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)
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elif op.type == 'sum':
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# 'sum' op is used to calculate global_norm, and need to filter inputs which is not in cur_rank
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reserved_vars = []
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for input_name in op.input_arg_names:
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if (
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input_name not in removed_tmp_var
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and self.clip_helper.is_local_var_with_dist_attr(
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input_name
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)
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):
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reserved_vars.append(input_name)
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if not reserved_vars:
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removed_op_idx.add(idx)
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removed_tmp_var.update(set(op.output_arg_names))
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if block.ops[idx + 1].type == 'cast':
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removed_op_idx.add(idx + 1)
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removed_tmp_var.update(
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set(block.ops[idx + 1].output_arg_names)
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)
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else:
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op.desc.set_input("X", reserved_vars)
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elif op.type == 'stack':
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# 'stack' op is also used to calculate global_norm ('stack' + 'reduce_sum'), and need to filter inputs which is not in cur_rank
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reserved_vars = []
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for input_name in op.input_arg_names:
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if (
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input_name not in removed_tmp_var
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and self.clip_helper.is_local_var_with_dist_attr(
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input_name
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)
|
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):
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reserved_vars.append(input_name)
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if not reserved_vars:
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removed_op_idx.add(idx)
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removed_tmp_var.update(set(op.output_arg_names))
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if block.ops[idx + 1].type == 'reduce_sum':
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removed_op_idx.add(idx + 1)
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removed_tmp_var.update(
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set(block.ops[idx + 1].output_arg_names)
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)
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if block.ops[idx + 2].type == 'cast':
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removed_op_idx.add(idx + 2)
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removed_tmp_var.update(
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set(block.ops[idx + 2].output_arg_names)
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)
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else:
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op.desc.set_input("X", reserved_vars)
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||||
for idx, op in reversed(list(enumerate(block.ops))):
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if not (is_optimize_op(op) or is_reshard_op(op)):
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||||
break
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||||
if not is_gradient_clip_op(op):
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||||
continue
|
||||
if idx in removed_op_idx:
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||||
block._remove_op(idx, sync=False)
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||||
|
||||
for idx, op in reversed(list(enumerate(block.ops))):
|
||||
if not (is_optimize_op(op) or is_reshard_op(op)):
|
||||
break
|
||||
if not is_gradient_clip_op(op):
|
||||
continue
|
||||
if op.type == 'sqrt':
|
||||
input_name = op.input("X")[0]
|
||||
input_var = block.vars[input_name]
|
||||
insert_leaf_fill_constant_node = False
|
||||
if paddle.distributed.get_world_size() > 1:
|
||||
offset = 0
|
||||
if input_name in removed_tmp_var:
|
||||
removed_tmp_var.remove(input_name)
|
||||
fill_constant_op = block._insert_op(
|
||||
idx,
|
||||
type='fill_constant',
|
||||
inputs={},
|
||||
outputs={'Out': [input_var]},
|
||||
attrs={
|
||||
'shape': [],
|
||||
'dtype': input_var.dtype,
|
||||
'value': 0,
|
||||
'force_cpu': False,
|
||||
OP_ROLE_KEY: OpRole.Optimize,
|
||||
},
|
||||
)
|
||||
fill_constant_op._set_attr(
|
||||
'op_namescope', "/gradient_clip_pass"
|
||||
)
|
||||
offset += 1
|
||||
self.clip_helper._init_dist_attr(fill_constant_op)
|
||||
insert_leaf_fill_constant_node = True
|
||||
|
||||
allreduce_op = block._insert_op(
|
||||
idx + offset,
|
||||
type='all_reduce',
|
||||
inputs={'x': [input_var]},
|
||||
outputs={'out': [input_var]},
|
||||
attrs={
|
||||
'ring_id': 0,
|
||||
'reduce_type': paddle.distributed.ReduceOp.SUM,
|
||||
OP_ROLE_KEY: OpRole.Optimize,
|
||||
},
|
||||
)
|
||||
# TODO better regular the usage of op namescope
|
||||
allreduce_op._set_attr(
|
||||
'op_namescope', '/' + SyncMode.GlobalNormSync
|
||||
)
|
||||
self.clip_helper._init_dist_attr(allreduce_op)
|
||||
|
||||
if insert_leaf_fill_constant_node:
|
||||
# NOTE add naive deps for global norm sync in graph exe
|
||||
j = idx - 1
|
||||
prior_op = None
|
||||
while j > 0:
|
||||
op_type = block.ops[j].type
|
||||
if op_type in [
|
||||
'update_loss_scaling',
|
||||
'check_finite_and_unscale',
|
||||
] or op_type.endswith("_grad"):
|
||||
prior_op = block.ops[j]
|
||||
break
|
||||
j -= 1
|
||||
assert prior_op is not None, (
|
||||
"Unexpected: ClipByGlobalNorm could not find priory depend op"
|
||||
)
|
||||
prior_var = block.vars[prior_op.output_arg_names[0]]
|
||||
assert prior_var is not None, (
|
||||
"Unexpected: ClipByGlobalNorm could not find priory depend var"
|
||||
)
|
||||
insert_dependencies_for_vars(
|
||||
block,
|
||||
idx,
|
||||
prior_var,
|
||||
input_var,
|
||||
self.clip_helper.dist_context,
|
||||
OpRole.Optimize,
|
||||
process_mesh=[
|
||||
-1
|
||||
], # hack to avoid initialize the dist attr for coalesce var
|
||||
is_recompute=False,
|
||||
sync=False,
|
||||
op_namescope="grad_clip_fill_constant_dep",
|
||||
)
|
||||
|
||||
for varname in removed_tmp_var:
|
||||
block._remove_var(varname, sync=False)
|
||||
|
||||
block._sync_with_cpp()
|
||||
Reference in New Issue
Block a user