chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2020 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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import paddle
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from paddle.framework import core
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from paddle.utils import unique_name
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from ..base.private_helper_function import wait_server_ready
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__all__ = []
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OpRole = core.op_proto_and_checker_maker.OpRole
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OP_ROLE_KEY = core.op_proto_and_checker_maker.kOpRoleAttrName()
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OP_ROLE_VAR_KEY = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
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def is_update_op(op):
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return (
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'Param' in op.input_names
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and 'Grad' in op.input_names
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and "LearningRate" in op.input_names
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)
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def is_loss_grad_op(op):
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if OP_ROLE_KEY not in op.attr_names:
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return False
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op_role = int(op.all_attrs()[OP_ROLE_KEY])
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return op_role & int(OpRole.Backward) and op_role & int(OpRole.Loss)
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def is_backward_op(op):
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return OP_ROLE_KEY in op.attr_names and int(
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op.all_attrs()[OP_ROLE_KEY]
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) & int(OpRole.Backward)
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def is_optimizer_op(op):
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return OP_ROLE_KEY in op.attr_names and int(
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op.all_attrs()[OP_ROLE_KEY]
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) & int(OpRole.Optimize)
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class CollectiveHelper:
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def __init__(self, role_maker, nrings=1, wait_port=True):
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self.nrings = nrings
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self.wait_port = wait_port
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self.role_maker = role_maker
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def update_startup_program(self, startup_program=None):
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self.startup_program = startup_program
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if startup_program is None:
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self.startup_program = paddle.static.default_startup_program()
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endpoints = self.role_maker._get_trainer_endpoints()
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current_endpoint = endpoints[self.role_maker._worker_index()]
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for ring_id in range(self.nrings):
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self._init_communicator(
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self.startup_program,
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current_endpoint,
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endpoints,
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self.role_maker._worker_index(),
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ring_id,
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self.wait_port,
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)
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self._broadcast_params()
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def _init_communicator(
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self,
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program,
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current_endpoint,
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endpoints,
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rank,
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ring_id,
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wait_port,
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global_ring_id=None,
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sync=True,
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):
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# if current_endpoint is None, it means just for sync,
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# no group is created.
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endpoints_str = ",".join(endpoints)
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if current_endpoint:
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nranks = len(endpoints)
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other_endpoints = endpoints[:]
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other_endpoints.remove(current_endpoint)
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def _add_sync_by_allreduce(block):
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sync_var = block.create_var(
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name=unique_name.generate('sync_var'),
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dtype=core.VarDesc.VarType.INT32,
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persistable=False,
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stop_gradient=True,
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)
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block.append_op(
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type='fill_constant',
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inputs={},
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outputs={'Out': [sync_var]},
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attrs={
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'shape': [1],
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'dtype': sync_var.dtype,
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'value': 1,
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'force_cpu': False,
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OP_ROLE_KEY: OpRole.Forward,
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},
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)
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block.append_op(
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type='all_reduce',
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inputs={'x': [sync_var]},
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outputs={'out': [sync_var]},
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attrs={
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'ring_id': global_ring_id,
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'reduce_type': paddle.distributed.ReduceOp.SUM,
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OP_ROLE_KEY: OpRole.Forward,
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},
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)
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block.append_op(
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type='c_sync_calc_stream',
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inputs={'X': sync_var},
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outputs={'Out': sync_var},
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attrs={OP_ROLE_KEY: OpRole.Forward},
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)
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block = program.global_block()
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if current_endpoint is None:
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assert endpoints is None
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assert sync
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_add_sync_by_allreduce(block)
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return
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comm_id_var = block.create_var(
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name=unique_name.generate('comm_id'),
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persistable=True,
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type=core.VarDesc.VarType.RAW,
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)
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if core.is_compiled_with_cuda():
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block.append_op(
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type='c_gen_nccl_id',
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inputs={},
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outputs={'Out': comm_id_var},
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attrs={
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'rank': rank,
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'endpoint': current_endpoint,
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'other_endpoints': other_endpoints,
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'ring_id': ring_id,
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OP_ROLE_KEY: OpRole.Forward,
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},
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)
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block.append_op(
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type='c_comm_init',
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inputs={'X': comm_id_var},
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outputs={},
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attrs={
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'nranks': nranks,
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'rank': rank,
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'ring_id': ring_id,
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'endpoints': endpoints_str,
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OP_ROLE_KEY: OpRole.Forward,
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},
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)
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elif core.is_compiled_with_xpu():
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block.append_op(
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type='c_gen_bkcl_id',
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inputs={},
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outputs={'Out': comm_id_var},
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attrs={
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'rank': rank,
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'endpoint': current_endpoint,
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'other_endpoints': other_endpoints,
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'ring_id': ring_id,
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OP_ROLE_KEY: OpRole.Forward,
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},
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)
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block.append_op(
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type='c_comm_init',
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inputs={'X': comm_id_var},
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outputs={},
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attrs={
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'nranks': nranks,
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'rank': rank,
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'ring_id': ring_id,
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'endpoints': endpoints_str,
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OP_ROLE_KEY: OpRole.Forward,
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},
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)
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else:
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raise ValueError(
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"comm_id must be generated in paddlepaddle-xpu or paddlepaddle-xpu."
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)
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if sync:
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_add_sync_by_allreduce(block)
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def _wait(self, current_endpoint, endpoints):
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assert self.wait_port
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other_endpoints = endpoints[:]
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other_endpoints.remove(current_endpoint)
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wait_server_ready(other_endpoints)
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def _broadcast_params(self):
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block = self.startup_program.global_block()
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ring_id = -1
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for param in block.iter_parameters():
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if param.is_distributed:
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continue
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ring_id = (ring_id + 1) % self.nrings
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block.append_op(
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type='broadcast',
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inputs={'x': param},
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outputs={'out': param},
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attrs={
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'ring_id': ring_id,
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'root': 0,
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OP_ROLE_KEY: OpRole.Forward,
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},
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)
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for ring_id in range(self.nrings):
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block.append_op(
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type='c_sync_comm_stream',
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inputs={'X': param},
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outputs={'Out': param},
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attrs={'ring_id': ring_id, OP_ROLE_KEY: OpRole.Forward},
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)
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