# Copyright (c) 2020 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 numpy as np import os from ..fluid.layer_helper import LayerHelper from ..fluid.framework import Variable from ..fluid.framework import OpProtoHolder from ..fluid.framework import in_dygraph_mode from ..fluid.framework import convert_np_dtype_to_dtype_ from ..fluid.framework import _varbase_creator from ..fluid.data_feeder import convert_dtype from ..fluid.data_feeder import check_variable_and_dtype from ..fluid.data_feeder import check_type from ..fluid.data_feeder import check_dtype from ..fluid.layers.tensor import fill_constant from ..fluid.layers import utils from ..fluid.dygraph import layers from ..fluid.dygraph.parallel import prepare_context import paddle import paddle.fluid as fluid import paddle.fluid.core as core from paddle import _C_ops import paddle.fluid.dygraph_utils as dygraph_utils __all__ = [] class ReduceOp: """ Specify the type of operation used for element-wise reductions. It should be one of the following values: ReduceOp.SUM ReduceOp.MAX ReduceOp.MIN ReduceOp.PROD Examples: .. code-block:: python import numpy as np import paddle from paddle.distributed import ReduceOp from paddle.distributed import init_parallel_env paddle.set_device('gpu:%d'%paddle.distributed.ParallelEnv().dev_id) init_parallel_env() if paddle.distributed.ParallelEnv().local_rank == 0: np_data = np.array([[4, 5, 6], [4, 5, 6]]) else: np_data = np.array([[1, 2, 3], [1, 2, 3]]) data = paddle.to_tensor(np_data) paddle.distributed.all_reduce(data, op=ReduceOp.SUM) out = data.numpy() # [[5, 7, 9], [5, 7, 9]] """ SUM = 0 MAX = 1 MIN = 2 PROD = 3 class Group(): """ The abstract representation of group. """ def __init__(self, rank, rank_num, id=0, ranks=[]): self.rank = rank self.nranks = rank_num self.id = id self.ranks = ranks def is_member(self): if self.rank < 0: return False if self.nranks < 2: return False return True def get_group_rank(self, rank): if self.is_member() and rank in self.ranks: return self.ranks.index(rank) else: return -1 def __repr__(self): debug_str = "rank: {}, nranks: {}, id: {}, ranks: ".format( self.rank, self.nranks, self.id) debug_str += ", ".join(map(str, self.ranks)) debug_str += ". " return debug_str _global_env = None def _get_global_env(): global _global_env if not _global_env: _global_env = paddle.distributed.ParallelEnv() return _global_env # group map : the map of all group, 0 for GlobalGroup # Dict[int, Group] _group_map = {} def _get_group_map(): global _group_map if not _group_map: genv = _get_global_env() _group_map[0] = Group( genv.rank, genv.world_size, ranks=list(range(genv.world_size))) return _group_map def _get_global_group(): return _get_group_map()[0] def _new_ring_id(): return len(_get_group_map()) + max(_get_global_env().nrings, 9) def get_group(id=0): """ Get group instance by group id. Args: id (int): the group id. Default value is 0. Returns: Group: the group instance. Examples: .. code-block:: python ... gid = paddle.distributed.new_group([2,4,6]) paddle.distributed.get_group(gid.id) """ gm = _get_group_map() return gm[id] if id in gm else None def barrier(group=None): """ Barrier among all participators in the group. Args: group (Group): The group instance return by new_group or None for global default group. Returns: None. Examples: .. code-block:: python import paddle from paddle.distributed import init_parallel_env paddle.set_device('gpu:%d'%paddle.distributed.ParallelEnv().dev_id) init_parallel_env() paddle.distributed.barrier() """ if group is not None and not group.is_member(): return ring_id = 0 if group is None else group.id temp = fill_constant([1], dtype="int32", value="1") if in_dygraph_mode(): return _C_ops.barrier(temp, temp, 'ring_id', ring_id) op_type = 'barrier' if not isinstance(ring_id, int): raise ValueError("The type of 'group' for barrier must be int.") helper = LayerHelper(op_type, **locals()) helper.append_op( type=op_type, inputs={'X': [temp]}, outputs={'Out': [temp]}, attrs={'ring_id': ring_id}) def new_group(ranks=None, backend=None): """ Creates a new distributed communication group. Args: ranks (list): The global ranks of group members. backend (str): The backend used to create group, only nccl is supported now. Returns: Group: The group instance. Examples: .. code-block:: python import paddle paddle.distributed.init_parallel_env() tindata = paddle.randn(shape=[2, 3]) gp = paddle.distributed.new_group([2,4,6]) paddle.distributed.all_reduce(tindata, group=gp, use_calc_stream=False) """ if not backend: backend = 'nccl' assert backend == 'nccl', ("backend other than nccl is not supported yet") genv = _get_global_env() global_rank = genv.rank ring_id = _new_ring_id() global _group_map if global_rank not in ranks: gp = Group(-1, -1, ring_id, ranks) _group_map[ring_id] = gp else: ranks = sorted(ranks) group_rank = ranks.index(global_rank) group_size = len(ranks) gp = Group(group_rank, group_size, ring_id, ranks) _group_map[ring_id] = gp if group_size >= 2: strategy = core.ParallelStrategy() strategy.nranks = group_size strategy.local_rank = group_rank strategy.trainer_endpoints = [ genv.trainer_endpoints[i] for i in ranks ] strategy.current_endpoint = genv.current_endpoint strategy.nrings = 1 if core.is_compiled_with_cuda(): place = core.CUDAPlace(genv.device_id) core.NCCLParallelContext(strategy, place).init_with_ring_id(ring_id) elif core.is_compiled_with_npu(): place = core.NPUPlace(genv.device_id) core.HCCLParallelContext(strategy, place).init_with_ring_id(ring_id) else: assert False, ("no cuda device found") else: return gp # TODO(shenliang03): This is a temporary solution to solve the problem of # hang caused by cross-creation of new_group tmp = paddle.to_tensor( [1], dtype="int32") if in_dygraph_mode() else fill_constant( [0], dtype="int32", value="1") paddle.distributed.all_reduce(tmp, use_calc_stream=True) paddle.distributed.wait(tmp) return gp def wait(tensor, group=None, use_calc_stream=True): """ wait to sync stream for group. Args: tensor (Tensor): The Tensor used before sync. group (Group): The Group instance to perform sync. use_calc_stream (bool): Wether to use calculation stream (True) or communication stream (False). Default to True. Returns: None. Examples: .. code-block:: python import paddle paddle.distributed.init_parallel_env() tindata = paddle.randn(shape=[2, 3]) paddle.distributed.all_reduce(tindata, use_calc_stream=True) paddle.distributed.wait(tindata) """ if group is not None and not group.is_member(): return ring_id = 0 if group is None else group.id if use_calc_stream: _sync_calc_stream(tensor) else: _sync_comm_stream(tensor, ring_id) def _sync_calc_stream(tensor): if in_dygraph_mode(): return _C_ops.c_sync_calc_stream(tensor, tensor) op_type = 'c_sync_calc_stream' helper = LayerHelper(op_type, **locals()) helper.append_op( type=op_type, inputs={'X': [tensor]}, outputs={'Out': [tensor]}, ) def _sync_comm_stream(tensor, ring_id=0): if in_dygraph_mode(): return _C_ops.c_sync_comm_stream([tensor], [tensor], 'ring_id', ring_id) op_type = 'c_sync_comm_stream' helper = LayerHelper(op_type, **locals()) helper.append_op( type=op_type, inputs={'X': [tensor]}, outputs={'Out': [tensor]}, attrs={'ring_id': ring_id}, ) def broadcast(tensor, src, group=None, use_calc_stream=True): """ Broadcast a tensor from the source to all others. As shown below, 4 GPUs each start 4 processes and GPU0 owns data 0. Through broadcast operator, the data 0 will be sent to all GPUs from GPU0. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/broadcast.png :width: 800 :alt: broadcast :align: center Args: tensor (Tensor): The Tensor to send if current rank is the source, or the tensor to receive otherwise. Its data type should be float16, float32, float64, int32 or int64. src (int): The source rank. group (Group): The group instance return by new_group or None for global default group. use_calc_stream (bool): Wether to use calculation stream (True) or communication stream (False). Default to True. Returns: None. Examples: .. code-block:: python # required: distributed import numpy as np import paddle from paddle.distributed import init_parallel_env paddle.set_device('gpu:%d'%paddle.distributed.ParallelEnv().dev_id) init_parallel_env() if paddle.distributed.ParallelEnv().local_rank == 0: np_data = np.array([[4, 5, 6], [4, 5, 6]]) else: np_data = np.array([[1, 2, 3], [1, 2, 3]]) data = paddle.to_tensor(np_data) paddle.distributed.broadcast(data, 1) out = data.numpy() # [[1, 2, 3], [1, 2, 3]] """ if group is not None and not group.is_member(): return if not isinstance(src, int): raise ValueError("src should be int.") ring_id = 0 if group is None else group.id gsrc = src if group is None else group.get_group_rank(src) assert gsrc >= 0, ("src rank out of group, need global rank") if in_dygraph_mode(): return _C_ops.c_broadcast(tensor, tensor, 'root', gsrc, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id) op_type = 'c_broadcast' check_variable_and_dtype( tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'], 'broadcast') helper = LayerHelper(op_type, **locals()) helper.append_op( type=op_type, inputs={'X': [tensor]}, outputs={'Out': [tensor]}, attrs={ 'root': gsrc, 'use_calc_stream': use_calc_stream, 'ring_id': ring_id, }) def all_reduce(tensor, op=ReduceOp.SUM, group=None, use_calc_stream=True): """ Reduce a tensor over all ranks so that all get the result. As shown below, 4 GPUs each start 4 processes and the data on each GPU is represnted by the GPU number. The reduce operator is sum. Through all_reduce operator, each GPU will have the sum of the data from all GPUs. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/allreduce.png :width: 800 :alt: all_reduce :align: center Args: tensor (Tensor): The input Tensor. It also works as the output Tensor. Its data type should be float16, float32, float64, int32 or int64. op (ReduceOp.SUM|ReduceOp.MAX|ReduceOp.Min|ReduceOp.PROD): Optional. The operation used. Default value is ReduceOp.SUM. group (Group): The group instance return by new_group or None for global default group. use_calc_stream (bool): Wether to use calculation stream (True) or communication stream (False). Default to True. Returns: None. Examples: .. code-block:: python # required: distributed import numpy as np import paddle from paddle.distributed import ReduceOp from paddle.distributed import init_parallel_env paddle.set_device('gpu:%d'%paddle.distributed.ParallelEnv().dev_id) init_parallel_env() if paddle.distributed.ParallelEnv().local_rank == 0: np_data = np.array([[4, 5, 6], [4, 5, 6]]) else: np_data = np.array([[1, 2, 3], [1, 2, 3]]) data = paddle.to_tensor(np_data) paddle.distributed.all_reduce(data) out = data.numpy() # [[5, 7, 9], [5, 7, 9]] """ if group is not None and not group.is_member(): return ring_id = 0 if group is None else group.id if in_dygraph_mode(): if op == ReduceOp.SUM: return _C_ops.c_allreduce_sum_(tensor, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id) elif op == ReduceOp.MAX: return _C_ops.c_allreduce_max_(tensor, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id) elif op == ReduceOp.MIN: return _C_ops.c_allreduce_min_(tensor, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id) elif op == ReduceOp.PROD: return _C_ops.c_allreduce_prod_(tensor, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id) else: raise ValueError("Unknown parameter: {}.".format(op)) check_variable_and_dtype( tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'], 'all_reduce') if not op in [ReduceOp.SUM, ReduceOp.MAX, ReduceOp.MIN, ReduceOp.PROD]: raise ValueError("The op for all_reduce must be one of educeOp.PROD, " "ReduceOp.SUM, ReduceOp.MAX, ReduceOp.MIN.") if op == ReduceOp.SUM: op_type = 'c_allreduce_sum' elif op == ReduceOp.MAX: op_type = 'c_allreduce_max' elif op == ReduceOp.MIN: op_type = 'c_allreduce_min' elif op == ReduceOp.PROD: op_type = 'c_allreduce_prod' if not isinstance(ring_id, int): raise ValueError("The type of 'ring_id' for all_reduce should be int.") helper = LayerHelper(op_type, **locals()) helper.append_op( type=op_type, inputs={'X': [tensor]}, outputs={'Out': [tensor]}, attrs={'ring_id': ring_id, 'use_calc_stream': use_calc_stream}) def reduce(tensor, dst, op=ReduceOp.SUM, group=None, use_calc_stream=True): """ Reduce a tensor to the destination from all others. As shown below, 4 GPUs each start 4 processes and the data on each GPU is respresnted by the GPU number. The destination of the reduce operator is GPU0 and the process is sum. Through reduce operator, the GPU0 will owns the sum of all data from all GPUs. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/reduce.png :width: 800 :alt: reduce :align: center Args: tensor (Tensor): The output Tensor for the destination and the input Tensor otherwise. Its data type should be float16, float32, float64, int32 or int64. dst (int): The destination rank id. op (ReduceOp.SUM|ReduceOp.MAX|ReduceOp.Min|ReduceOp.PROD): Optional. The operation used. Default value is ReduceOp.SUM. group (Group): The group instance return by new_group or None for global default group. use_calc_stream (bool): Wether to use calculation stream (True) or communication stream (False). Default to True. Returns: None. Examples: .. code-block:: python # required: distributed import numpy as np import paddle from paddle.distributed import init_parallel_env paddle.set_device('gpu:%d'%paddle.distributed.ParallelEnv().dev_id) init_parallel_env() if paddle.distributed.ParallelEnv().local_rank == 0: np_data = np.array([[4, 5, 6], [4, 5, 6]]) else: np_data = np.array([[1, 2, 3], [1, 2, 3]]) data = paddle.to_tensor(np_data) paddle.distributed.reduce(data, 0) out = data.numpy() # [[5, 7, 9], [5, 7, 9]] """ if group is not None and not group.is_member(): return if not isinstance(dst, int): raise ValueError("dst should be int.") ring_id = 0 if group is None else group.id gdst = dst if group is None else group.get_group_rank(dst) assert gdst >= 0, ("dst rank out of group, need global rank") if in_dygraph_mode(): if op == ReduceOp.SUM: return _C_ops.c_reduce_sum(tensor, tensor, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id, 'root_id', gdst) elif op == ReduceOp.MAX: return _C_ops.c_reduce_max(tensor, tensor, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id, 'root_id', gdst) elif op == ReduceOp.MIN: return _C_ops.c_reduce_min(tensor, tensor, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id, 'root_id', gdst) elif op == ReduceOp.PROD: return _C_ops.c_reduce_prod(tensor, tensor, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id, 'root_id', gdst) else: raise ValueError("Unknown parameter: {}.".format(op)) op_type = 'c_reduce' check_variable_and_dtype( tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'], 'all_reduce') if not op in [ReduceOp.SUM, ReduceOp.MAX, ReduceOp.MIN, ReduceOp.PROD]: raise ValueError("The op for reduce must be one of educeOp.PROD, " "ReduceOp.SUM, ReduceOp.MAX, ReduceOp.MIN.") if op == ReduceOp.SUM: op_type = 'c_reduce_sum' elif op == ReduceOp.MAX: op_type = 'c_reduce_max' elif op == ReduceOp.MIN: op_type = 'c_reduce_min' elif op == ReduceOp.PROD: op_type = 'c_reduce_prod' helper = LayerHelper(op_type, **locals()) helper.append_op( type=op_type, inputs={'X': [tensor]}, outputs={'Out': [tensor]}, attrs={ 'ring_id': ring_id, 'use_calc_stream': use_calc_stream, 'root_id': gdst, }) def all_gather(tensor_list, tensor, group=None, use_calc_stream=True): """ Gather tensors from all participators and all get the result. As shown below, 4 GPUs each start 4 processes and the data on each GPU is represnted by the GPU number. Through the all_gather operator, each GPU will have data from all GPUs. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/allgather.png :width: 800 :alt: all_gather :align: center Args: tensor_list (list): A list of output Tensors. Every element in the list must be a Tensor whose data type should be float16, float32, float64, int32 or int64. tensor (Tensor): The Tensor to send. Its data type should be float16, float32, float64, int32 or int64. group (Group): The group instance return by new_group or None for global default group. use_calc_stream (bool): Wether to use calculation stream (True) or communication stream (False). Default to True. Returns: None. Examples: .. code-block:: python # required: distributed import numpy as np import paddle from paddle.distributed import init_parallel_env paddle.set_device('gpu:%d'%paddle.distributed.ParallelEnv().dev_id) init_parallel_env() tensor_list = [] if paddle.distributed.ParallelEnv().local_rank == 0: np_data1 = np.array([[4, 5, 6], [4, 5, 6]]) np_data2 = np.array([[4, 5, 6], [4, 5, 6]]) data1 = paddle.to_tensor(np_data1) data2 = paddle.to_tensor(np_data2) paddle.distributed.all_gather(tensor_list, data1) else: np_data1 = np.array([[1, 2, 3], [1, 2, 3]]) np_data2 = np.array([[1, 2, 3], [1, 2, 3]]) data1 = paddle.to_tensor(np_data1) data2 = paddle.to_tensor(np_data2) paddle.distributed.all_gather(tensor_list, data2) """ if group is not None and not group.is_member(): return ring_id = 0 if group is None else group.id nranks = _get_global_group().nranks if group is None else group.nranks if in_dygraph_mode(): out = _C_ops.c_allgather(tensor, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id, 'nranks', nranks) else: op_type = 'c_allgather' helper = LayerHelper(op_type, **locals()) out = helper.create_variable_for_type_inference(dtype=tensor.dtype) if not isinstance(tensor_list, list): raise ValueError("The type of 'tensor_list' for all_gather " "should be list.") for elem in tensor_list: check_variable_and_dtype( elem, 'tensor_list', ['float16', 'float32', 'float64', 'int32', 'int64'], 'all_gather') check_variable_and_dtype( tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'], 'all_gather') helper.append_op( type=op_type, inputs={'X': [tensor]}, outputs={'Out': [out]}, attrs={ 'ring_id': ring_id, 'use_calc_stream': use_calc_stream, 'nranks': nranks }) tensor_list.extend(paddle.split(out, nranks, 0)) def scatter(tensor, tensor_list=None, src=0, group=None, use_calc_stream=True): """ Scatter a tensor to all participators. As shown below, 4 GPUs each start 4 processes and the source of the scatter is GPU0. Through scatter operator, the data in GPU0 will be sent to all GPUs averagely. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/scatter.png :width: 800 :alt: scatter :align: center Args: tensor (Tensor): The output Tensor. Its data type should be float16, float32, float64, int32 or int64. tensor_list (list|tuple): A list/tuple of Tensors to scatter. Every element in the list must be a Tensor whose data type should be float16, float32, float64, int32 or int64. Default value is None. src (int): The source rank id. Default value is 0. group (Group): The group instance return by new_group or None for global default group. use_calc_stream (bool): Wether to use calculation stream (True) or communication stream (False). Default to True. Returns: None. Examples: .. code-block:: python # required: distributed import numpy as np import paddle from paddle.distributed import init_parallel_env paddle.set_device('gpu:%d'%paddle.distributed.ParallelEnv().dev_id) init_parallel_env() if paddle.distributed.ParallelEnv().local_rank == 0: np_data1 = np.array([7, 8, 9]) np_data2 = np.array([10, 11, 12]) else: np_data1 = np.array([1, 2, 3]) np_data2 = np.array([4, 5, 6]) data1 = paddle.to_tensor(np_data1) data2 = paddle.to_tensor(np_data2) if paddle.distributed.ParallelEnv().local_rank == 0: paddle.distributed.scatter(data1, src=1) else: paddle.distributed.scatter(data1, tensor_list=[data1, data2], src=1) out = data1.numpy() """ if group is not None and not group.is_member(): return if not isinstance(src, int): raise ValueError("src should be int.") ring_id = 0 if group is None else group.id gsrc = src if group is None else group.get_group_rank(src) assert gsrc >= 0, ("src rank out of group, need global rank") rank = _get_global_group().rank if group is None else group.rank nranks = _get_global_group().nranks if group is None else group.nranks if rank != gsrc: tensor_list = [] for _ in range(nranks): tensor_list.append(tensor) temp = paddle.concat(tensor_list, axis=0) if in_dygraph_mode(): return _C_ops.c_scatter(temp, tensor, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id, 'nranks', nranks, 'root', gsrc) op_type = 'c_scatter' check_variable_and_dtype( tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'], 'scatter') helper = LayerHelper(op_type, **locals()) helper.append_op( type=op_type, inputs={'X': [temp]}, outputs={'Out': [tensor]}, attrs={ 'ring_id': ring_id, 'root': gsrc, 'use_calc_stream': use_calc_stream, 'nranks': nranks, }) def _c_identity(tensor, group=None): """ Return a copy of the tensor, mainly used with model parallel. Args: tensor (Tensor): The input Tensor. Its data type should be float16, float32, float64, int32 or int64. group (int): The id of the process group to work on. Returns: Tensor. """ if group is not None and not group.is_member(): return ring_id = 0 if group is None else group.id if in_dygraph_mode(): return _C_ops.c_identity(tensor, 'use_calc_stream', True, 'ring_id', ring_id, 'use_model_parallel', True) op_type = 'c_identity' helper = LayerHelper(op_type, **locals()) out = helper.create_variable_for_type_inference(dtype=tensor.dtype) check_variable_and_dtype( tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'], '_c_identity') helper.append_op( type=op_type, inputs={'X': tensor}, outputs={'Out': out}, attrs={ 'ring_id': ring_id, 'use_calc_stream': True, 'use_model_parallel': True, }) return out def _c_concat(tensor, group=None): """ Return allgather of the tensor, mainly used with model parallel. Args: tensor (Tensor): The input Tensor. Its data type should be float16, float32, float64, int32 or int64. group (int): The id of the process group to work on. Returns: Tensor. """ if group is not None and not group.is_member(): return ring_id = 0 if group is None else group.id global_rank = _get_global_env().rank rank = global_rank if group is None else group.get_group_rank(global_rank) nranks = _get_global_env().world_size if group is None else group.nranks if in_dygraph_mode(): return _C_ops.c_concat(tensor, 'ring_id', ring_id, 'use_calc_stream', True, 'rank', rank, 'nranks', nranks, 'use_model_parallel', True) op_type = 'c_concat' helper = LayerHelper(op_type, **locals()) out = helper.create_variable_for_type_inference(dtype=tensor.dtype) check_variable_and_dtype( tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'], '_c_concat') helper.append_op( type=op_type, inputs={'X': tensor}, outputs={'Out': out}, attrs={ 'ring_id': ring_id, 'use_calc_stream': True, 'use_model_parallel': True, 'nranks': nranks, 'rank': rank }) return out def _c_split(tensor, group=None): """ Split tensor evenly among all members, mainly used with model parallel. Args: tensor (Tensor): The input Tensor. Its data type should be float16, float32, float64, int32 or int64. rank (int): The rank of the current process. group (int): The id of the process group to work on. Returns: Tensor. """ if group is not None and not group.is_member(): return ring_id = 0 if group is None else group.id global_rank = _get_global_env().rank rank = global_rank if group is None else group.get_group_rank(global_rank) nranks = _get_global_env().world_size if group is None else group.nranks if in_dygraph_mode(): return _C_ops.c_split(tensor, 'use_calc_stream', True, 'ring_id', ring_id, 'rank', rank, 'nranks', nranks, 'use_model_parallel', True) op_type = 'c_split' helper = LayerHelper(op_type, **locals()) out = helper.create_variable_for_type_inference(dtype=tensor.dtype) check_variable_and_dtype( tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'], '_c_split') helper.append_op( type=op_type, inputs={'X': tensor}, outputs={'Out': out}, attrs={ 'ring_id': ring_id, 'use_calc_stream': True, 'rank': rank, 'nranks': nranks, 'use_model_parallel': True, }) return out def _mp_allreduce(tensor, op=ReduceOp.SUM, group=None, use_calc_stream=True, use_model_parallel=True): """[it is same as allreduce above, but it suuports model parallel. And it support inplace startegy] """ if group is not None and not group.is_member(): return ring_id = 0 if group is None else group.id if in_dygraph_mode(): if op == ReduceOp.SUM: return _C_ops.c_allreduce_sum_( tensor, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id, "use_model_parallel", use_model_parallel) else: raise ValueError("Unknown parameter: {}.".format(op)) op_type = 'c_allreduce_sum' helper = LayerHelper(op_type, **locals()) out = helper.create_variable_for_type_inference(dtype=tensor.dtype) check_variable_and_dtype( tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'], op_type) helper.append_op( type=op_type, inputs={'X': tensor}, outputs={'Out': out}, attrs={ 'ring_id': ring_id, 'use_calc_stream': use_calc_stream, 'use_model_parallel': use_model_parallel, }) return out def _c_lookup_table(table, index, start_index=0, name=None): """ Lookup table according to index. Args: table (Tensor): The input Tensor. Its data type should be float16, float32, float64. index (Tensor): The index to lookup table. start_index (int): The initial index for table range. name (string): The name of the api Returns: Tensor. """ if in_dygraph_mode(): return _C_ops.c_embedding(table, index, "start_index", start_index) op_type = 'c_embedding' helper = LayerHelper(op_type, **locals()) dtype = helper.input_dtype(input_param_name='table') check_variable_and_dtype(index, 'input', ['int32', 'int64'], op_type) tmp = helper.create_variable_for_type_inference(dtype) helper.append_op( type='c_embedding', inputs={'Ids': index, 'W': table}, outputs={'Out': tmp}, attrs={"start_index": start_index}) return tmp class _Linear(layers.Layer): """ Linear """ def __init__(self, in_features, out_features, weight_attr=None, bias_attr=None, name=None): super(_Linear, self).__init__() self._dtype = self._helper.get_default_dtype() self._weight_attr = weight_attr self._bias_attr = bias_attr self.weight = self.create_parameter( shape=[in_features, out_features], attr=self._weight_attr, dtype=self._dtype, is_bias=False) self.bias = self.create_parameter( shape=[out_features], attr=self._bias_attr, dtype=self._dtype, is_bias=True) self.name = name def forward(self, input): out = _linear( x=input, weight=self.weight, bias=self.bias, name=self.name) return out def extra_repr(self): name_str = ', name={}'.format(self.name) if self.name else '' return 'in_features={}, out_features={}, dtype={}{}'.format( self.weight.shape[0], self.weight.shape[1], self._dtype, name_str) def _c_softmax_with_cross_entropy(logits, label, group=None, return_softmax=False): if group is not None and not group.is_member(): return ring_id = 0 if group is None else group.id global_rank = _get_global_env().rank rank = global_rank if group is None else group.get_group_rank(global_rank) nranks = _get_global_env().world_size if group is None else group.nranks input_dims = len(list(logits.shape)) label_dims = len(list(label.shape)) if input_dims - 1 != label_dims and input_dims != label_dims: raise ValueError( 'Expected nput_dims - 1 = label_dims or input_dims == label_dims\ (got nput_dims{}, label_dims{})'.format(input_dims, label_dims)) if input_dims - 1 == label_dims: label = paddle.unsqueeze(label, axis=-1) if in_dygraph_mode(): softmax, loss = _C_ops.c_softmax_with_cross_entropy( logits, label, 'ring_id', ring_id, 'rank', rank, 'nranks', nranks) if not return_softmax: return loss else: return loss, softmax attrs = { 'ring_id': ring_id, 'rank': rank, 'nranks': nranks, } helper = LayerHelper('c_softmax_with_cross_entropy', **locals()) softmax = helper.create_variable_for_type_inference(dtype=logits.dtype) loss = helper.create_variable_for_type_inference(dtype=logits.dtype) helper.append_op( type='c_softmax_with_cross_entropy', inputs={'Logits': logits, 'Label': label}, outputs={'Softmax': softmax, 'Loss': loss}, attrs=attrs) if return_softmax: return loss, softmax return loss def _linear(x, weight, bias=None, name=None): """ Fuction Linear """ if in_dygraph_mode(): pre_bias = _varbase_creator(dtype=x.dtype) _C_ops.matmul(x, weight, pre_bias, 'transpose_X', False, 'transpose_Y', False, "alpha", 1) return dygraph_utils._append_bias_in_dygraph( pre_bias, bias, axis=len(x.shape) - 1) else: helper = LayerHelper('linear', **locals()) dtype = x.dtype assert len( x.shape) < 4, "X latitude is not supported greater than 3 now." check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'linear') check_dtype(dtype, 'dtype', ['float16', 'float32', 'float64'], 'linear') inputs = {'X': [x], 'Y': [weight]} attrs = { 'transpose_X': False, 'transpose_Y': False, 'alpha': 1, } tmp = helper.create_variable_for_type_inference(dtype) helper.append_op( type='matmul_v2', inputs=inputs, outputs={'Out': tmp}, attrs=attrs) if bias is not None: res = helper.create_variable_for_type_inference(dtype) helper.append_op( type='elementwise_add', inputs={'X': [tmp], 'Y': [bias]}, outputs={'Out': [res]}, attrs={'axis': len(x.shape) - 1}) else: res = tmp return res def _set_var_distributed(var): if var is None: return var.is_distributed = True # NOTE: use current_block and find_var_recursive to support while_loop startup_block = paddle.static.default_startup_program().current_block() main_block = paddle.static.default_main_program().current_block() startup_block._find_var_recursive(var.name).is_distributed = True main_block._find_var_recursive(var.name).is_distributed = True def _parallel_linear(x, num_rows, num_cols, axis, param_attr, bias_attr, gather_out, inner_rank, nranks, split_tensor, name, group=None): """ Parallel Linear axis the dimension of the parameter of linear layer. axis = 0: the row dimension axis = 1: the col dimension """ if group is not None and not group.is_member(): return ring_id = 0 if group is None else group.id if axis == 0: if split_tensor: x = _c_split(x, group=group) else: x = _c_identity(x, group=group) linear = paddle.nn.Linear( num_rows, num_cols, weight_attr=param_attr, bias_attr=bias_attr, name=name) # NOTE: npu linear function use matmul_v2 but linear use matmul linear_function = _linear if core.is_compiled_with_npu()\ else paddle.nn.functional.linear linear_out = linear_function( x, linear.weight, # NOTE(wangxi): row split, bias need add after allreduce None if axis == 0 else linear.bias, linear.name) _set_var_distributed(linear.weight) # set is_distributed for splited bias # if a linear layer is splited by row, each rank would hold a complete bias and they should be the same in each rank. # if a linear layer is splited by col, the bias would also be split into each rank as its weight if axis == 1 and linear._bias_attr != False: _set_var_distributed(linear.bias) if not gather_out: return linear_out out_shape = list(linear_out.shape) out_shape[0] *= 1 if axis == 0 else nranks main_block = paddle.static.default_main_program().current_block() out = main_block.create_var( shape=out_shape, dtype=linear_out.dtype, type=linear_out.type, lod_level=linear_out.lod_level, persistable=False, is_data=False, need_check_feed=linear_out.desc.need_check_feed()) if axis == 0: main_block.append_op( type='c_allreduce_sum', inputs={'X': linear_out}, outputs={'Out': out}, attrs={ 'ring_id': ring_id, 'use_calc_stream': True, 'use_model_parallel': True }) if linear.bias is not None: out = out + linear.bias else: main_block.append_op( type='c_concat', inputs={'X': linear_out}, outputs={'Out': out}, attrs={ 'rank': inner_rank, 'ring_id': ring_id, 'nranks': nranks, 'use_calc_stream': True, 'use_model_parallel': True }) return out def _parallel_embedding(x, per_part_embeddings, origin_size, param_attr, inner_rank, num_partitions, name, group=None): """ Parallel Embedding """ if group is not None and not group.is_member(): return ring_id = 0 if group is None else group.id helper = LayerHelper("_parallel_embedding", **locals()) per_part_size = per_part_embeddings rank = inner_rank vocab_start_index = rank * per_part_size dtype = helper.get_default_dtype() size = [per_part_size, origin_size[1]] weight = helper.create_parameter( attr=param_attr, shape=size, dtype=dtype, is_bias=False) if num_partitions == 1: return paddle.nn.functional.embedding( x, weight=weight, padding_idx=None, sparse=False, name=name) startup_block = paddle.static.default_startup_program().global_block() main_block = paddle.static.default_main_program().global_block() startup_block.vars[weight.name].is_distributed = True main_block.vars[weight.name].is_distributed = True output_parallel = paddle.distributed.collective._c_lookup_table( weight, x, start_index=vocab_start_index, name=name) out = paddle.distributed.collective._mp_allreduce( output_parallel, group=group, use_calc_stream=True, use_model_parallel=True) return out def split(x, size, operation, axis=0, num_partitions=1, gather_out=True, weight_attr=None, bias_attr=None, name=None): """ Split the weight of the specified operation into multiple devices and do the computation in parallel. Now the following three cases are supported. Case 1: Parallel Embedding The weight of the embedding operation is a NxM matrix with N rows and M columns. With parallel embedding, the weight is split into num_partitions partitions, each of which is a matrix with (N/num_partitions + 1) rows and M column where the last row as the padding idx. Suppose we split the NxM weight into two partitons on device_0 and device_1 respectively. Then, one each device, the final weight has (N/2 + 1) rows with the index range from 0 to N/2. On device_0, all values in the input within [0, N/2 -1] keep unchanged and all other values are changed to N/2 which is the padding index and are mapped to all zeros after embedding. In the same way, on device_1, the value V in the input within [N/2, N-1] will be changed to (V - N/2), and all other values are changed to N/2 and are mapped to all zeros after embedding. Finally, the results on the two devices are sum-reduced. The Embedding put on single card is as shown below: .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_embedding_single.png :width: 800 :height: 350 :alt: single_embedding :align: center Parallel Embedding is shown as below: .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_embedding_split.png :width: 800 :alt: split_embedding :align: center Case 2: Row Parallel Linear The weight of the linear operation is a NxM matrix with N rows and M columns. With row parallel linear, the weight is split into num_partitions partitions, each of which is a matrix with N/num_partitions rows and M column. The linear layer put on single card is shown as below, the input variable is represented by X, the weight matrix is represented by W and the output vaiable is O. The linear layer on single card is simple matrix multiplication operation, O = X * W. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_single.png :width: 800 :alt: single_linear :align: center Row Parallel Linear is shown as below. As the name suggests, Row Parallel Linear splits the weight matrix W into [[W_row1], [W_row2]] along the row. And accordingly the input is splitted along the column into [X_col1, X_col2] and multiply their respective weight matrices. Finally apply AllReduce on the output from each card to get the final output. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_row.png :width: 800 :alt: split_row :align: center Case 3: Column Parallel Linear The weight of the linear operation is a NxM matrix with N rows and M columns. With column parallel linear, the weight is split into num_paratitions partitions, each of which is a matrix with N rows and M/num_partitions column. The linear layer put on single card has been illustrated on case 2 and Column Parallel Linear is shown as below. The Column Parallel Linear splits the weight matrix W into [W_col1, W_col2] along the column and these splitted matrices respectively multiply the input. Finally apply AllGather on the output from each card to get the final output. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_col.png :width: 800 :alt: split_col :align: center As observed, the column parallel linear and row parallel linear can be combined to skip one ALLGATHER communication operator. Furthermore the Attention and MLP can be combined to imporve the performance as shown below. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_col_row.png :width: 800 :alt: split_col_row :align: center Args: x (Tensor): Input tensor. It's data type should be float16, float32, float64, int32 or int64. size (list|tuple): A list or tuple with two elements indicating the shape of the weight. operation (str): The name of the operation. The supported operations are 'linear' and 'embedding'. axis (int, Optional): Indicate along which axis to split the weight. Default: 0. num_partitions (int, Optional): How many parts the weight is partitioned. Default: 1. gather_out (bool, Optional): Whether to gather the output after computation. By default, the output on each partitions will be gathered after computation. Default: True. weight_attr (ParamAttr, Optional): The parameter attribute for the learnable weights(Parameter) of the specified operation. Default: None. bias_attr (ParamAttr, Optional): The parameter attribute for the bias of the specified operation. Default: None. name (str, Optional): The default value is None. Normally there is no need for user to set this property. Default: None. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. Examples: .. code-block:: python # required: distributed import paddle import paddle.distributed.fleet as fleet paddle.enable_static() paddle.set_device('gpu:%d'%paddle.distributed.ParallelEnv().dev_id) fleet.init(is_collective=True) data = paddle.randint(0, 8, shape=[10,4]) emb_out = paddle.distributed.split( data, (8, 8), operation="embedding", num_partitions=2) """ assert isinstance(size, (list, tuple)), ( "The type of size for " "paddle.distributed.split must be list or tuple.") assert len(size) == 2, ("Number of elements in size of " "paddle.distributed.split must be two.") assert isinstance(operation, str), ("The type of operation for " "paddle.distributed.split must be str.") supported_operations = [ 'linear', 'embedding', ] assert operation in supported_operations, ( "The operation for " "paddle.distributed.split must be one of {}.".format( supported_operations)) if in_dygraph_mode(): raise ValueError( "paddle.distributed.split cannot be used in dynamic " "graph mode, plese use ParallelEmbedding, ParallelRowLinear, " "ParallelColumnLinear instead.") else: from .fleet import fleet assert fleet._role_maker, ("To use paddle.distributed.split, " "you must call fleet.init() firstly.") rank = fleet.worker_index() nranks = fleet.worker_num() # rank within a model parallel group inner_rank = rank % num_partitions if operation == "embedding": assert axis == 0, ("We only support to split the weight of embedding " "along the first axis now.") assert size[0] % num_partitions == 0, \ "The length of the vocabulary must be divisible by num_partitions " \ "but received vocabulary={} num_partitions={}".format(size[0], num_partitions) per_part_size = size[0] // num_partitions emb_out = _parallel_embedding( x, per_part_size, size, weight_attr, inner_rank, num_partitions, name, group=None) return emb_out else: should_split = False if axis == 0: assert size[0] % num_partitions == 0, ( "Number of rows of the weight for linear ({}) must be" " divisible by num_partitions ({})".format(size[0], num_partitions)) per_part_size = size[0] // num_partitions linear_size = (per_part_size, size[1]) if x.shape[-1] == size[0]: should_split = True elif axis == 1: assert size[1] % num_partitions == 0, ( "Number of column of the weight for linear ({}) must be" " divisible by num_partitions ({})".format(size[1], num_partitions)) per_part_size = size[1] // num_partitions linear_size = (size[0], per_part_size) else: raise ValueError("The value of axis must be 0 or 1, but the value " "given is {}.".format(axis)) linear_out = _parallel_linear( x, linear_size[0], linear_size[1], axis, weight_attr, bias_attr, gather_out, inner_rank, num_partitions, should_split, name=name, group=None) return linear_out def alltoall(in_tensor_list, out_tensor_list, group=None, use_calc_stream=True): """ Scatter tensors in in_tensor_list to all participators averagely and gather the result tensors in out_tensor_list. As shown below, the in_tensor_list in GPU0 includes 0_0 and 0_1, and GPU1 includes 1_0 and 1_1. Through alltoall operator, the 0_0 in GPU0 will be sent to GPU0 and 0_1 to GPU1, 1_0 in GPU1 sent to GPU0 and 1_1 to GPU1. Finally the out_tensor_list in GPU0 includes 0_0 and 1_0, and GPU1 includes 0_1 and 1_1. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/alltoall.png :width: 800 :alt: alltoall :align: center Args: in_tensor_list (list): A list of input Tensors. Every element in the list must be a Tensor whose data type should be float16, float32, float64, int32 or int64. out_tensor_list (Tensor): A list of output Tensors. The data type of its elements should be the same as the data type of the input Tensors. group (Group, optional): The group instance return by new_group or None for global default group. Default: None. use_calc_stream (bool, optional): Wether to use calculation stream (True) or communication stream. Default: True. Returns: None. Examples: .. code-block:: python # required: distributed import numpy as np import paddle from paddle.distributed import init_parallel_env init_parallel_env() out_tensor_list = [] if paddle.distributed.ParallelEnv().rank == 0: np_data1 = np.array([[1, 2, 3], [4, 5, 6]]) np_data2 = np.array([[7, 8, 9], [10, 11, 12]]) else: np_data1 = np.array([[13, 14, 15], [16, 17, 18]]) np_data2 = np.array([[19, 20, 21], [22, 23, 24]]) data1 = paddle.to_tensor(np_data1) data2 = paddle.to_tensor(np_data2) paddle.distributed.alltoall([data1, data2], out_tensor_list) # out for rank 0: [[[1, 2, 3], [4, 5, 6]], [[13, 14, 15], [16, 17, 18]]] # out for rank 1: [[[7, 8, 9], [10, 11, 12]], [[19, 20, 21], [22, 23, 24]]] """ if group is not None and not group.is_member(): return ring_id = 0 if group is None else group.id temp = paddle.concat(in_tensor_list, axis=0) nranks = len(in_tensor_list) if in_dygraph_mode(): out = _C_ops.alltoall(temp, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id) else: op_type = 'alltoall' helper = LayerHelper(op_type, **locals()) out = helper.create_variable_for_type_inference( dtype=in_tensor_list[0].dtype) if not isinstance(in_tensor_list, list): raise ValueError("The type of 'in_tensor_list' for all_to_all " "should be list.") for elem in in_tensor_list: check_variable_and_dtype( elem, 'in_tensor_list', ['float16', 'float32', 'float64', 'int32', 'int64'], 'all_to_all') if not isinstance(out_tensor_list, list): raise ValueError("The type of 'out_tensor_list' for all_to_all " "should be list.") if len(out_tensor_list) != 0: raise ValueError("The 'out_tensor_list' for all_to_all " "must be an empty list.") helper.append_op( type=op_type, inputs={'X': [temp]}, outputs={'Out': [out]}, attrs={ 'ring_id': ring_id, 'use_calc_stream': use_calc_stream, }) out_tensor_list.extend(paddle.split(out, nranks, 0)) def send(tensor, dst=0, group=None, use_calc_stream=True): """ Send a tensor to the receiver. Args: tensor (Tensor): The Tensor to send. Its data type should be float16, float32, float64, int32 or int64. dst (int): The destination rank id. group (Group, optional): The group instance return by new_group or None for global default group. Default: None. use_calc_stream (bool, optional): Whether to use calculate stream or communication stream. Default: True. Returns: None. Examples: .. code-block:: python # required: distributed import paddle from paddle.distributed import init_parallel_env init_parallel_env() if paddle.distributed.ParallelEnv().rank == 0: data = paddle.to_tensor([7, 8, 9]) paddle.distributed.send(data, dst=1) else: data = paddle.to_tensor([1,2,3]) paddle.distributed.recv(data, src=0) out = data.numpy() """ if group is not None and not group.is_member(): return ring_id = 0 if group is None else group.id if in_dygraph_mode(): return _C_ops.send_v2(tensor, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id, 'peer', dst) op_type = 'send_v2' check_variable_and_dtype( tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'], 'send') helper = LayerHelper(op_type, **locals()) helper.append_op( type=op_type, inputs={'X': [tensor]}, attrs={ 'ring_id': ring_id, 'peer': dst, 'use_calc_stream': use_calc_stream, }) def recv(tensor, src=0, group=None, use_calc_stream=True): """ Receive a tensor to the sender. Args: tensor (Tensor): The Tensor to receive. Its data type should be float16, float32, float64, int32 or int64. src (int): The source rank id. group (Group, optional): The group instance return by new_group or None for global default group. Default: None. use_calc_stream (bool, optional): Whether to use calculate stream or communication stream. Default: True. Returns: None. Examples: .. code-block:: python # required: distributed import paddle from paddle.distributed import init_parallel_env init_parallel_env() if paddle.distributed.ParallelEnv().rank == 0: data = paddle.to_tensor([7, 8, 9]) paddle.distributed.send(data, dst=1) else: data = paddle.to_tensor([1,2,3]) paddle.distributed.recv(data, src=0) out = data.numpy() """ if group is not None and not group.is_member(): return ring_id = 0 if group is None else group.id if in_dygraph_mode(): return _C_ops.recv_v2(tensor, 'use_calc_stream', use_calc_stream, 'ring_id', ring_id, 'peer', src, 'dtype', tensor.dtype, 'out_shape', tensor.shape) op_type = 'recv_v2' check_variable_and_dtype( tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'], 'recv') helper = LayerHelper(op_type, **locals()) helper.append_op( type=op_type, outputs={'Out': [tensor]}, attrs={ 'ring_id': ring_id, 'peer': src, 'out_shape': tensor.shape, 'dtype': tensor.dtype, 'use_calc_stream': use_calc_stream, })