Source code for paddle.fluid.transpiler.distribute_transpiler

#   Copyright (c) 2018 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import print_function
Steps to transpile trainer:
1. split variable to multiple blocks, aligned by product(dim[1:]) (width).
2. rename splited grad variables to add trainer_id suffix ".trainer_%d".
3. modify trainer program add split_op to each grad variable.
4. append send_op to send splited variables to server and
5. add recv_op to fetch params(splited blocks or origin param) from server.
6. append concat_op to merge splited blocks to update local weights.

Steps to transpile pserver:
1. create new program for parameter server.
2. create params and grad variables that assigned to current server instance.
3. create a sub-block in the server side program
4. append ops that should run on current server instance.
5. add listen_and_serv op

import sys
import math
from functools import reduce

import collections
import six
import logging

import numpy as np

from .ps_dispatcher import RoundRobin, PSDispatcher
from .. import core, framework, unique_name
from ..framework import Program, default_main_program, \
    default_startup_program, Block, Parameter, grad_var_name
from .details import wait_server_ready, UnionFind, VarStruct, VarsDistributed
from .details import delete_ops, find_op_by_output_arg
from ..distribute_lookup_table import find_distributed_lookup_table

LOOKUP_TABLE_TYPE = "lookup_table"
LOOKUP_TABLE_GRAD_TYPE = "lookup_table_grad"
OP_ROLE_VAR_ATTR_NAME = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
RPC_OP_ROLE_ATTR_NAME = op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName(
OPT_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.Optimize
RPC_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.RPC
DIST_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.Dist
LR_SCHED_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.LRSched


def log(*args):
    if PRINT_LOG:

class VarBlock:
    def __init__(self, varname, offset, size):
        self.varname = varname
        # NOTE: real offset is offset * size
        self.offset = offset
        self.size = size

    def __str__(self):
        return "%s:%d:%d" % (self.varname, self.offset, self.size)

def same_or_split_var(p_name, var_name):
    return p_name == var_name or p_name.startswith(var_name + ".block")

def slice_variable(var_list, slice_count, min_block_size):
    We may need to split dense tensor to one or more blocks and put
    them equally onto parameter server. One block is a sub-tensor
    aligned by dim[0] of the tensor.

    We need to have a minimal block size so that the calculations in
    the parameter server side can gain better performance. By default
    minimum block size 8K elements (maybe 16bit or 32bit or 64bit).

        var_list (list): List of variables.
        slice_count (int): Numel of count that variables will be sliced, which
            could be the pserver services' count.
        min_block_size (int): Minimum splitted block size.
        blocks (list[(varname, block_id, current_block_size)]): A list
            of VarBlocks. Each VarBlock specifies a shard of the var.
    blocks = []
    for var in var_list:
        split_count = slice_count
        var_numel = reduce(lambda x, y: x * y, var.shape)
        max_pserver_count = int(math.floor(var_numel / float(min_block_size)))
        if max_pserver_count == 0:
            max_pserver_count = 1
        if max_pserver_count < slice_count:
            split_count = max_pserver_count
        block_size = int(math.ceil(var_numel / float(split_count)))

        if len(var.shape) >= 2:
            # align by dim1(width)
            dim1 = reduce(lambda x, y: x * y, var.shape[1:])
            remains = block_size % dim1
            if remains != 0:
                block_size += dim1 - remains
        # update split_count after aligning
        split_count = int(math.ceil(var_numel / float(block_size)))
        for block_id in range(split_count):
            curr_block_size = min(block_size, var_numel - (
                (block_id) * block_size))
            block = VarBlock(, block_id, curr_block_size)
    return blocks

[docs]class DistributeTranspilerConfig(object): """ .. py:attribute:: slice_var_up (bool) Do Tensor slice for pservers, default is True. .. py:attribute:: split_method (PSDispatcher) RoundRobin or HashName can be used. Try to choose the best method to balance loads for pservers. .. py:attribute:: min_block_size (int) Minimum number of splitted elements in block. According to : We can use bandwidth effiently when data size is larger than 2MB.If you want to change it, please be sure you have read the slice_variable function. Examples: .. code-block:: python config = fluid.DistributeTranspilerConfig() config.slice_var_up = True """ slice_var_up = True split_method = None min_block_size = 8192 enable_dc_asgd = False # supported modes: pserver, nccl2 mode = "pserver" print_log = False wait_port = True # split the send recv var in runtime runtime_split_send_recv = False sync_mode = True nccl_comm_num = 1 #The picture here illustrates the principle: # use_hierarchical_allreduce = False #Nccl ranks in a node when use hierarchical allreduce, it's setted to gpu cards' number in most cases. hierarchical_allreduce_inter_nranks = 0 #Nccl ranks bewteen nodes when use hierarchical allreduce, it's setted to nodes number. hierarchical_allreduce_exter_nranks = 0
[docs]class DistributeTranspiler(object): """ **DistributeTranspiler** Convert the fluid program to distributed data-parallelism programs. Supports two modes: pserver mode and nccl2 mode. In pserver mode, the main_program will be transformed to use a remote parameter server to do parameter optimization. And the optimization graph will be put into a parameter server program. In nccl2 mode, the transpiler will append a NCCL_ID broadcasting op in startup_program to share the NCCL_ID across the job nodes. After transpile_nccl2 called, you ***must*** pass trainer_id and num_trainers argument to ParallelExecutor to enable NCCL2 distributed mode. Examples: .. code-block:: python x ='x', shape=[13], dtype='float32') y ='y', shape=[1], dtype='float32') y_predict = fluid.layers.fc(input=x, size=1, act=None) cost = fluid.layers.square_error_cost(input=y_predict, label=y) avg_loss = fluid.layers.mean(cost) sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) sgd_optimizer.minimize(avg_loss) # for pserver mode pserver_endpoints = "," trainer_endpoints = "," current_endpoint = "" trainer_id = 0 trainers = 4 role = "PSERVER" t = fluid.DistributeTranspiler() t.transpile( trainer_id, pservers=pserver_endpoints, trainers=trainers) if role == "PSERVER": pserver_program = t.get_pserver_program(current_endpoint) pserver_startup_program = t.get_startup_program(current_endpoint, pserver_program) elif role == "TRAINER": trainer_program = t.get_trainer_program() # for nccl2 mode trainer_num = 2 trainer_id = 0 config = fluid.DistributeTranspilerConfig() config.mode = "nccl2" trainer_endpoints = "," t = fluid.DistributeTranspiler(config=config) t.transpile(trainer_id=trainer_id, trainers=trainer_endpoints, current_endpoint="") exe = fluid.ParallelExecutor( use_cuda=True,, num_trainers=trainer_num, trainer_id=trainer_id ) """ def __init__(self, config=None): if config is not None: self.config = config else: self.config = DistributeTranspilerConfig() if self.config.split_method is None: self.config.split_method = RoundRobin global PRINT_LOG if self.config.print_log: PRINT_LOG = True assert (self.config.min_block_size >= 8192) assert (self.config.split_method.__bases__[0] == PSDispatcher) def _transpile_nccl2(self, trainer_id, trainers, current_endpoint, startup_program=None, wait_port=True): if not startup_program: startup_program = default_startup_program() if trainer_id >= 0: worker_endpoints = trainers.split(",") # send NCCL_ID to others or recv from trainer 0 worker_endpoints.remove(current_endpoint) if trainer_id == 0 and wait_port: wait_server_ready(worker_endpoints) nccl_id_var = startup_program.global_block().create_var( name="NCCLID", persistable=True, type=core.VarDesc.VarType.RAW) for i in range(1, self.config.nccl_comm_num): startup_program.global_block().create_var( name="NCCLID_{}".format(i), persistable=True, type=core.VarDesc.VarType.RAW) if self.config.use_hierarchical_allreduce: for i in range(0, self.config.nccl_comm_num): startup_program.global_block().create_var( name="Hierarchical_inter_NCCLID_{}".format(i), persistable=True, type=core.VarDesc.VarType.RAW) startup_program.global_block().create_var( name="Hierarchical_exter_NCCLID_{}".format(i), persistable=True, type=core.VarDesc.VarType.RAW) startup_program.global_block().append_op( type="gen_nccl_id", inputs={}, outputs={"NCCLID": nccl_id_var}, attrs={ "trainers": trainers.split(","), "trainer_id": trainer_id, "nccl_comm_num": self.config.nccl_comm_num, "use_hierarchical_allreduce": self.config.use_hierarchical_allreduce, "hierarchical_allreduce_inter_nranks": self.config.hierarchical_allreduce_inter_nranks }) return nccl_id_var else: raise ValueError("must set trainer_id > 0") def _get_all_remote_sparse_update_op(self, main_program): sparse_update_ops = [] sparse_update_op_types = ["lookup_table", "nce", "hierarchical_sigmoid"] for op in main_program.global_block().ops: if op.type in sparse_update_op_types and op.attr( 'remote_prefetch') is True: sparse_update_ops.append(op) return sparse_update_ops def _update_remote_sparse_update_op(self, param_varname, height_sections, endpint_map, table_names): for op in self.sparse_update_ops: if param_varname in op.input_arg_names: op._set_attr('epmap', endpint_map) op._set_attr('table_names', table_names) op._set_attr('height_sections', height_sections) op._set_attr('trainer_id', self.trainer_id) def _is_input_of_remote_sparse_update_op(self, param_name): for op in self.sparse_update_ops: if param_name in op.input_arg_names: return True return False
[docs] def transpile(self, trainer_id, program=None, pservers="", trainers=1, sync_mode=True, startup_program=None, current_endpoint=""): """ Run the transpiler. Transpile the input program. Args: trainer_id (int): id for current trainer worker, if you have n workers, the id may range from 0 ~ n-1 program (Program|None): program to transpile, default is fluid.default_main_program(). startup_program (Program|None): startup_program to transpile, default is fluid.default_startup_program(). pservers (str): comma separated ip:port string for the pserver list. trainers (int|str): in pserver mode this is the number of trainers, in nccl2 mode this is a string of trainer endpoints. sync_mode (bool): Do sync training or not, default is True. startup_program (Program|None): startup_program to transpile, default is fluid.default_main_program(). current_endpoint (str): need pass current endpoint when transpile as nccl2 distributed mode. In pserver mode this argument is not used. Examples: .. code-block:: python transpiler = fluid.DistributeTranspiler() t.transpile( trainer_id=0, pservers=",", trainers=2, sync_mode=False, current_endpoint="") """ if program is None: program = default_main_program() if startup_program is None: startup_program = default_startup_program() self.origin_program = program self.startup_program = startup_program self.origin_startup_program = self.startup_program.clone() if self.config.mode == "nccl2": assert (isinstance(trainers, str)) self.origin_program._trainers_endpoints = trainers.split(",") self.origin_program._nccl_comm_num = self.config.nccl_comm_num self.origin_program._use_hierarchical_allreduce = self.config.use_hierarchical_allreduce self.origin_program._hierarchical_allreduce_inter_nranks = \ int(self.config.hierarchical_allreduce_inter_nranks) self.origin_program._hierarchical_allreduce_exter_nranks = \ int(self.config.hierarchical_allreduce_exter_nranks) self._transpile_nccl2( trainer_id, trainers, current_endpoint, startup_program=startup_program, wait_port=self.config.wait_port) return self.trainer_num = trainers self.sync_mode = sync_mode self.trainer_id = trainer_id pserver_endpoints = pservers.split(",") self.pserver_endpoints = pserver_endpoints self.vars_overview = VarsDistributed() self.optimize_ops, self.params_grads = self._get_optimize_pass() ps_dispatcher = self.config.split_method(self.pserver_endpoints) self.table_name = find_distributed_lookup_table(self.origin_program) self.has_distributed_lookup_table = self.table_name != None self.param_name_to_grad_name = dict() self.grad_name_to_param_name = dict() for param_var, grad_var in self.params_grads: self.param_name_to_grad_name[] = self.grad_name_to_param_name[] = # get all sparse update ops self.sparse_update_ops = self._get_all_remote_sparse_update_op( self.origin_program) # use_sparse_update_param_name -> split_height_section self.sparse_param_to_height_sections = dict() # add distributed attrs to program self.origin_program._is_distributed = True self.origin_program._endpoints = self.pserver_endpoints self.origin_program._ps_endpoint = current_endpoint self.origin_program._is_chief = self.trainer_id == 0 self.origin_program._distributed_lookup_table = self.table_name if self.table_name else None # split and create vars, then put splited vars in dicts for later use. # step 1: split and create vars, then put splited vars in dicts for later use. self._init_splited_vars() # step 2: insert send op to send gradient vars to parameter servers ps_dispatcher.reset() send_vars = [] # in general cases, the number of pservers is times of 2, and this # will lead to uneven distribution among weights and bias: # fc_w@GRAD_trainer_0, fc_w@GRAD_trainer_1 --> pserver1 # fc_b@GRAD_trainer_0, fc_b@GRAD_trainer_1 --> pserver2 # shuffle the map will avoid the uneven distribution above grad_var_mapping_items = list(six.iteritems(self.grad_var_mapping)) if not self.config.slice_var_up: np.random.seed(self.origin_program.random_seed) np.random.shuffle(grad_var_mapping_items) self.grad_name_to_send_dummy_out = dict() for grad_varname, splited_vars in grad_var_mapping_items: eplist = ps_dispatcher.dispatch(splited_vars) if not self.config.slice_var_up: assert (len(splited_vars) == 1) splited_grad_varname = grad_varname if len(splited_vars) == 1: splited_grad_varname = splited_vars[0].name index = find_op_by_output_arg( program.global_block(), splited_grad_varname, reverse=True) if splited_vars[0].type == core.VarDesc.VarType.SELECTED_ROWS: sparse_param_name = self.grad_name_to_param_name[ grad_varname] if self._is_input_of_remote_sparse_update_op( sparse_param_name): self.sparse_param_to_height_sections[ sparse_param_name] = [splited_vars[0].shape[0]] elif len(splited_vars) > 1: orig_var = program.global_block().vars[splited_grad_varname] index = find_op_by_output_arg( program.global_block(), splited_grad_varname, reverse=True) if not self.config.runtime_split_send_recv: self._insert_split_op(program, orig_var, index, splited_vars) index += 1 else: AssertionError("Can not insert the send op by original " "variable name :", splited_grad_varname) dummy_output = program.global_block().create_var( name=framework.generate_control_dev_var_name()) self.grad_name_to_send_dummy_out[grad_varname] = dummy_output if self.config.runtime_split_send_recv: send_input_vars = [ program.global_block().vars[splited_grad_varname] ] sections = self._get_splited_var_sections(splited_vars) send_varnames = [ for var in splited_vars] else: send_input_vars = splited_vars sections = [] send_varnames = [] # get send op_role_var, if not splited, the grad should have .trainer suffix # if splited, grad should be the original grad var name (split_by_ref and send # will be on the same place). ParallelExecutor # will use op_role_var to get expected device place to run this op. program.global_block()._insert_op( index=index + 1, type="send", inputs={"X": send_input_vars}, outputs={"Out": dummy_output}, attrs={ "epmap": eplist, "sections": sections, "send_varnames": send_varnames, RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE, OP_ROLE_VAR_ATTR_NAME: [ self.grad_name_to_param_name[grad_varname], splited_grad_varname ], "sync_mode": not self.sync_mode, }) for _, var in enumerate(splited_vars): send_vars.append(var) if self.sync_mode: send_barrier_out = program.global_block().create_var( name=framework.generate_control_dev_var_name()) if self.has_distributed_lookup_table: self.grad_name_to_send_dummy_out[ self.table_name] = program.global_block().create_var( name=framework.generate_control_dev_var_name()) input_deps = list(self.grad_name_to_send_dummy_out.values()) program.global_block().append_op( type="send_barrier", inputs={"X": list(input_deps)}, outputs={"Out": send_barrier_out}, attrs={ "endpoints": pserver_endpoints, "sync_mode": self.sync_mode, "trainer_id": self.trainer_id, RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE }) # step 3: insert recv op to receive parameters from parameter server recv_vars = [] for _, var in enumerate(send_vars): recv_vars.append(self.grad_param_mapping[var]) ps_dispatcher.reset() eplist = ps_dispatcher.dispatch(recv_vars) for i, ep in enumerate(eplist): self.param_grad_ep_mapping[ep]["params"].append(recv_vars[i]) self.param_grad_ep_mapping[ep]["grads"].append(send_vars[i]) distributed_var = self.vars_overview.get_distributed_var_by_slice( recv_vars[i].name) distributed_var.endpoint = ep # step4: Concat the parameters splits together after recv. all_recv_outputs = [] for param_varname, splited_var in six.iteritems(self.param_var_mapping): eps = [] table_names = [] for var in splited_var: index = [ for v in recv_vars].index( eps.append(eplist[index]) table_names.append( if self.sync_mode: recv_dep_in = send_barrier_out else: # connect deps to send op in async mode recv_dep_in = self.grad_name_to_send_dummy_out[ self.param_name_to_grad_name[param_varname]] # get recv op_role_var, if not splited, the grad should have .trainer suffix # if splited, grad should be the original grad var name. ParallelExecutor # will use op_role_var to get expected device place to run this op. orig_grad_name = self.param_name_to_grad_name[param_varname] recv_op_role_var_name = orig_grad_name splited_trainer_grad = self.grad_var_mapping[orig_grad_name] if len(splited_trainer_grad) == 1: recv_op_role_var_name = splited_trainer_grad[0].name if param_varname in self.sparse_param_to_height_sections: for table_name in table_names: distributed_var = self.vars_overview.get_distributed_var_by_slice( table_name) distributed_var.vtype = "RemotePrefetch" height_sections = self.sparse_param_to_height_sections[ param_varname] self._update_remote_sparse_update_op( param_varname, height_sections, eps, table_names) else: recv_varnames = [] if self.config.runtime_split_send_recv: orig_param = program.global_block().vars[param_varname] recv_varnames = [ for var in splited_var] splited_var = [orig_param] all_recv_outputs.extend(splited_var) program.global_block().append_op( type="recv", inputs={"X": [recv_dep_in]}, outputs={"Out": splited_var}, attrs={ "epmap": eps, "recv_varnames": recv_varnames, "trainer_id": self.trainer_id, RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE, OP_ROLE_VAR_ATTR_NAME: [param_varname, recv_op_role_var_name], "sync_mode": not self.sync_mode }) if self.sync_mode: # form a WAW dependency program.global_block().append_op( type="fetch_barrier", inputs={}, outputs={"Out": all_recv_outputs}, attrs={ "endpoints": pserver_endpoints, "trainer_id": self.trainer_id, RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE }) for param_varname, splited_var in six.iteritems(self.param_var_mapping): if len(splited_var) <= 1: continue orig_param = program.global_block().vars[param_varname] if param_varname not in self.sparse_param_to_height_sections: if not self.config.runtime_split_send_recv: program.global_block().append_op( type="concat", inputs={"X": splited_var}, outputs={"Out": [orig_param]}, attrs={ "axis": 0, RPC_OP_ROLE_ATTR_NAME: DIST_OP_ROLE_ATTR_VALUE }) self._get_trainer_startup_program(recv_vars=recv_vars, eplist=eplist) if self.has_distributed_lookup_table: self._replace_lookup_table_op_with_prefetch(program, pserver_endpoints) self._split_table_grad_and_add_send_vars(program, pserver_endpoints) self._get_distributed_optimizer_vars() self.origin_program._parameters_on_pservers = self.vars_overview
[docs] def get_trainer_program(self, wait_port=True): """ Get transpiled trainer side program. Returns: Program: trainer side program. Examples: .. code-block:: python import paddle.fluid as fluid #this is an example, find available endpoints in your case pserver_endpoints = "," trainer_id = 0 trainers = 4 t = fluid.DistributeTranspiler() t.transpile(trainer_id, trainers=trainers, pservers=pserver_endpoints) trainer_program = t.get_trainer_program() """ # remove optimize ops and add a send op to main_program # FIXME(typhoonzero): Also ops like clip_gradient, lrn_decay? lr_ops = self._get_lr_ops() delete_ops(self.origin_program.global_block(), self.optimize_ops) delete_ops(self.origin_program.global_block(), lr_ops) # delete table init op if self.has_distributed_lookup_table: table_var = self.startup_program.global_block().vars[ self.table_name] table_param_init_op = [] for op in self.startup_program.global_block().ops: if self.table_name in op.output_arg_names: table_param_init_op.append(op) init_op_num = len(table_param_init_op) if init_op_num != 1: raise ValueError("table init op num should be 1, now is " + str( init_op_num)) table_init_op = table_param_init_op[0] self.startup_program.global_block().append_op( type="fake_init", inputs={}, outputs={"Out": table_var}, attrs={"shape": table_init_op.attr('shape')}) delete_ops(self.startup_program.global_block(), table_param_init_op) self.origin_program.__str__() if wait_port: wait_server_ready(self.pserver_endpoints) return self.origin_program
def _get_trainer_startup_program(self, recv_vars, eplist): """ Get transpiled trainer side startup program. Args: recv_vars (list): Variable list to recv for current trainer_id eplist (list): A list of strings indicating Returns: Program: trainer side startup program. """ startup_program = self.startup_program # FIXME(gongwb): delete not need ops. # note that: some parameter is not trainable and those ops can't be deleted. for varname, splited_var in six.iteritems(self.param_var_mapping): # Get the eplist of recv vars eps = [] for var in splited_var: index = [ for v in recv_vars].index( eps.append(eplist[index]) for var in splited_var: if startup_program.global_block().has_var( continue startup_program.global_block().create_var(, persistable=False, type=var.type, dtype=var.dtype, shape=var.shape, lod_level=var.lod_level) op = startup_program.global_block().append_op( type="recv", inputs={"X": []}, outputs={"Out": splited_var}, attrs={ "epmap": eps, "trainer_id": self.trainer_id, RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE }) fetch_barrier_out = startup_program.global_block().create_var( name=framework.generate_control_dev_var_name()) startup_program.global_block().append_op( type="fetch_barrier", inputs={}, outputs={"Out": fetch_barrier_out}, attrs={ "endpoints": self.pserver_endpoints, "trainer_id": self.trainer_id, RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE }) for varname, splited_var in six.iteritems(self.param_var_mapping): # add concat ops to merge splited parameters received from parameter servers. if len(splited_var) <= 1: continue # NOTE: if enable memory optimization, origin vars maybe removed. if varname in startup_program.global_block().vars: orig_param = startup_program.global_block().vars[varname] else: origin_param_var = self.origin_program.global_block().vars[ varname] orig_param = startup_program.global_block().create_var( name=varname, persistable=origin_param_var.persistable, type=origin_param_var.type, dtype=origin_param_var.dtype, shape=origin_param_var.shape) startup_program.global_block().append_op( type="concat", inputs={"X": splited_var}, outputs={"Out": [orig_param]}, attrs={"axis": 0}) return startup_program
[docs] def get_pserver_program(self, endpoint): """ Get parameter server side program. Args: endpoint (str): current parameter server endpoint. Returns: Program: the program for current parameter server to run. Examples: .. code-block:: python import paddle.fluid as fluid #this is an example, find available endpoints in your case pserver_endpoints = "," current_endpoint = "" trainer_id = 0 trainers = 4 t = fluid.DistributeTranspiler() t.transpile( trainer_id, pservers=pserver_endpoints, trainers=trainers) pserver_program = t.get_pserver_program(current_endpoint) """ # TODO(panyx0718): Revisit this assumption. what if #blocks > #pservers. # NOTE: assume blocks of the same variable is not distributed # on the same pserver, only change param/grad varnames for # trainers to fetch. sys.stderr.write( "get_pserver_program() is deprecated, call get_pserver_programs() to get pserver main and startup in a single call.\n" ) # step1 pserver_program = Program() pserver_program.random_seed = self.origin_program.random_seed pserver_program._copy_dist_param_info_from(self.origin_program) # step2: Create vars to receive vars at parameter servers. recv_inputs = [] for v in self.param_grad_ep_mapping[endpoint]["params"]: self._clone_var(pserver_program.global_block(), v) for v in self.param_grad_ep_mapping[endpoint]["grads"]: # create vars for each trainer in global scope, so # we don't need to create them when grad arrives. # change client side var name to origin name by # removing ".trainer_%d" suffix suff_idx =".trainer_") if suff_idx >= 0: orig_var_name =[:suff_idx] else: orig_var_name = # NOTE: single_trainer_var must be created for multi-trainer # case to merge grads from multiple trainers single_trainer_var = \ pserver_program.global_block().create_var( name=orig_var_name, persistable=True, type=v.type, dtype=v.dtype, shape=v.shape) if self.sync_mode and self.trainer_num > 1: for trainer_id in range(self.trainer_num): var = pserver_program.global_block().create_var( name="%s.trainer_%d" % (orig_var_name, trainer_id), persistable=False, type=v.type, dtype=v.dtype, shape=v.shape) recv_inputs.append(var) else: recv_inputs.append(single_trainer_var) # step 3 # Create a union-find data structure from optimize ops, # If two ops are connected, we could add these two ops # into one set. ufind = self._create_ufind(self.optimize_ops) # step 3.2 # Iterate through the ops and append optimize op which # located on current pserver opt_op_on_pserver = [] for _, op in enumerate(self.optimize_ops): if self._is_optimizer_op(op) and self._is_opt_op_on_pserver( endpoint, op): opt_op_on_pserver.append(op) # step 3.3 # prepare if dc asgd is enabled if self.config.enable_dc_asgd == True: assert (self.sync_mode == False) self.param_bak_list = [] # add param_bak for each trainer for p in self.param_grad_ep_mapping[endpoint]["params"]: # each parameter should have w_bak for each trainer id for i in range(self.trainer_num): param_bak_name = "%s.trainer_%d_bak" % (, i) tmpvar = pserver_program.global_block().create_var( # NOTE: this var name format is used in `request_get_handler` name=param_bak_name, type=p.type, shape=p.shape, dtype=p.dtype) self.param_bak_list.append((p, tmpvar)) # step 3.4 # Iterate through the ops, and if an op and the optimize ops # which located on current pserver are in one set, then # append it into the sub program. global_ops = [] # sparse grad name to param name sparse_grad_to_param = [] def __append_optimize_op__(op, block, grad_to_block_id, merged_var, lr_ops): if self._is_optimizer_op(op): self._append_pserver_ops(block, op, endpoint, grad_to_block_id, self.origin_program, merged_var, sparse_grad_to_param) elif op not in lr_ops: self._append_pserver_non_opt_ops(block, op) def __clone_lr_op_sub_block__(op, program, lr_block): if not op.has_attr('sub_block'): return origin_block_desc = op.attr('sub_block') origin_block = self.origin_program.block( assert isinstance(origin_block, Block) # we put the new sub block to new block to follow the block # hierarchy of the original blocks new_sub_block = program._create_block(lr_block.idx) # clone vars for var in origin_block.vars: new_sub_block._clone_variable(var) # clone ops for origin_op in origin_block.ops: cloned_op = self._clone_lr_op(program, new_sub_block, origin_op) # clone sub_block of op __clone_lr_op_sub_block__(cloned_op, program, new_sub_block) # reset the block of op op._set_attr('sub_block', new_sub_block) # append lr decay ops to the child block if exists lr_ops = self._get_lr_ops() # record optimize blocks and we can run them on pserver parallel optimize_blocks = [] if len(lr_ops) > 0: lr_decay_block = pserver_program._create_block( pserver_program.num_blocks - 1) optimize_blocks.append(lr_decay_block) for _, op in enumerate(lr_ops): cloned_op = self._append_pserver_non_opt_ops(lr_decay_block, op) # append sub blocks to pserver_program in lr_decay_op __clone_lr_op_sub_block__(cloned_op, pserver_program, lr_decay_block) # append op to the current block grad_to_block_id = [] pre_block_idx = pserver_program.num_blocks - 1 for idx, opt_op in enumerate(opt_op_on_pserver): per_opt_block = pserver_program._create_block(pre_block_idx) optimize_blocks.append(per_opt_block) optimize_target_param_name = opt_op.attr(OP_ROLE_VAR_ATTR_NAME)[0] # append grad merging ops before clip and weight decay # e.g. merge grad -> L2Decay op -> clip op -> optimize merged_var = None for _, op in enumerate(self.optimize_ops): # find the origin grad var before clipping/L2Decay, # merged_var should be the input var name of L2Decay grad_varname_for_block = op.attr(OP_ROLE_VAR_ATTR_NAME)[1] if op.attr(OP_ROLE_VAR_ATTR_NAME)[ 0] == optimize_target_param_name: merged_var = self._append_pserver_grad_merge_ops( per_opt_block, grad_varname_for_block, endpoint, grad_to_block_id, self.origin_program) if merged_var: break # append optimize op once then append other ops. if merged_var: for _, op in enumerate(self.optimize_ops): # optimizer is connected to itself if op.attr(OP_ROLE_VAR_ATTR_NAME)[0] == optimize_target_param_name and \ op not in global_ops: log("append opt op: ", op.type, op.input_arg_names, merged_var) __append_optimize_op__(op, per_opt_block, grad_to_block_id, merged_var, lr_ops) # dedup grad to ids list grad_to_block_id = list(set(grad_to_block_id)) # append global ops if global_ops: opt_state_block = pserver_program._create_block( pserver_program.num_blocks - 1) optimize_blocks.append(opt_state_block) for glb_op in global_ops: __append_optimize_op__(glb_op, opt_state_block, grad_to_block_id, None, lr_ops) # process distributed lookup_table prefetch_var_name_to_block_id = [] if self.has_distributed_lookup_table: pserver_index = self.pserver_endpoints.index(endpoint) table_opt_block = self._create_table_optimize_block( pserver_index, pserver_program, pre_block_idx, grad_to_block_id) optimize_blocks.append(table_opt_block) lookup_table_var_name_to_block_id = self._create_prefetch_block( pserver_index, pserver_program, table_opt_block) checkpoint_block_id = self._create_checkpoint_save_block( pserver_program, table_opt_block.idx) pserver_program._distributed_lookup_table = self.table_name prefetch_var_name_to_block_id.extend( lookup_table_var_name_to_block_id) if len(optimize_blocks) == 0: logging.warn("pserver [" + str(endpoint) + "] has no optimize block!!") pre_block_idx = pserver_program.num_blocks - 1 empty_block = pserver_program._create_block(pre_block_idx) optimize_blocks.append(empty_block) # In some case, some parameter server will have no parameter to optimize # So we give an empty optimize block to parameter server. attrs = { "optimize_blocks": optimize_blocks, "endpoint": endpoint, "Fanin": self.trainer_num, "sync_mode": self.sync_mode, "grad_to_block_id": grad_to_block_id, "sparse_grad_to_param": sparse_grad_to_param, } if self.has_distributed_lookup_table: attrs['checkpint_block_id'] = checkpoint_block_id if self.config.enable_dc_asgd: attrs['dc_asgd'] = True if len(prefetch_var_name_to_block_id) > 0: attrs[ 'prefetch_var_name_to_block_id'] = prefetch_var_name_to_block_id # step5 append the listen_and_serv op pserver_program.global_block().append_op( type="listen_and_serv", inputs={'X': recv_inputs}, outputs={}, attrs=attrs) pserver_program._sync_with_cpp() # save pserver program to generate pserver side startup relatively. self.pserver_program = pserver_program return pserver_program
[docs] def get_pserver_programs(self, endpoint): """ Get pserver side main program and startup program for distributed training. Args: endpoint (str): current pserver endpoint. Returns: tuple: (main_program, startup_program), of type "Program" Examples: .. code-block:: python import paddle.fluid as fluid #this is an example, find available endpoints in your case pserver_endpoints = "," current_endpoint = "" trainer_id = 0 trainers = 4 t = fluid.DistributeTranspiler() t.transpile( trainer_id, pservers=pserver_endpoints, trainers=trainers) pserver_program, pserver_startup_program = t.get_pserver_programs(current_endpoint) """ pserver_prog = self.get_pserver_program(endpoint) pserver_startup = self.get_startup_program( endpoint, pserver_program=pserver_prog) return pserver_prog, pserver_startup
[docs] def get_startup_program(self, endpoint, pserver_program=None, startup_program=None): """ **Deprecated** Get startup program for current parameter server. Modify operator input variables if there are variables that were split to several blocks. Args: endpoint (str): current pserver endpoint. pserver_program (Program): deprecated, call get_pserver_program first. startup_program (Program): deprecated, should pass startup_program when initalizing Returns: Program: parameter server side startup program. Examples: .. code-block:: python pserver_endpoints = "," trainer_endpoints = "," current_endpoint = "" trainer_id = 0 trainers = 4 t = fluid.DistributeTranspiler() t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers) pserver_program = t.get_pserver_program(current_endpoint) pserver_startup_program = t.get_startup_program(current_endpoint, pserver_program) """ s_prog = Program() orig_s_prog = self.startup_program s_prog.random_seed = orig_s_prog.random_seed params = self.param_grad_ep_mapping[endpoint]["params"] def _get_splited_name_and_shape(varname): for idx, splited_param in enumerate(params): pname = if same_or_split_var(pname, varname) and varname != pname: return pname, splited_param.shape return "", [] # 1. create vars in pserver program to startup program pserver_vars = pserver_program.global_block().vars created_var_map = collections.OrderedDict() for _, var in six.iteritems(pserver_vars): tmpvar = s_prog.global_block()._clone_variable(var) created_var_map[] = tmpvar # 2. rename op outputs for op in orig_s_prog.global_block().ops: new_outputs = collections.OrderedDict() # do not append startup op if var is not on this pserver op_on_pserver = False # TODO(gongwb): remove this line. if op.type not in ["recv", "fetch_barrier", "concat"]: for key in op.output_names: newname, _ = _get_splited_name_and_shape(op.output(key)[0]) if newname: op_on_pserver = True new_outputs[key] = created_var_map[newname] elif op.output(key)[0] in pserver_vars: op_on_pserver = True new_outputs[key] = pserver_vars[op.output(key)[0]] if op_on_pserver: # most startup program ops have no inputs new_inputs = self._get_input_map_from_op(pserver_vars, op) if op.type in [ "gaussian_random", "fill_constant", "uniform_random", "truncated_gaussian_random" ]: op._set_attr("shape", list(new_outputs["Out"].shape)) s_prog.global_block().append_op( type=op.type, inputs=new_inputs, outputs=new_outputs, attrs=op.all_attrs()) if self.config.enable_dc_asgd: for p, p_bak in self.param_bak_list: startup_param_var = s_prog.global_block().vars[] startup_tmpvar = s_prog.global_block().vars[] # copy init random value to param_bak s_prog.global_block().append_op( type="assign", inputs={"X": startup_param_var}, outputs={"Out": startup_tmpvar}) return s_prog
# ====================== private transpiler functions ===================== def _get_slice_var_info(self, slice_var): block_suffix = "block" block_idx = 0 offset = 0 is_slice = False orig_var_name, block_name, _ = self._get_varname_parts( if not block_name: return is_slice, block_idx, offset block_idx = int(block_name.split(block_suffix)[1]) skip_dim0 = 0 slice_vars = self.param_var_mapping[orig_var_name] orig_dim1_flatten = 1 if len(slice_vars[0].shape) >= 2: orig_dim1_flatten = reduce(lambda x, y: x * y, slice_vars[0].shape[1:]) for slice_var in slice_vars[:block_idx]: skip_dim0 += slice_var.shape[0] offset = skip_dim0 * orig_dim1_flatten is_slice = True return is_slice, block_idx, offset def _get_distributed_optimizer_vars(self): def _get_distributed_optimizer_var(endpoint): opt_op_on_pserver = [] for _, op in enumerate(self.optimize_ops): if self._is_optimizer_op(op) and self._is_opt_op_on_pserver( endpoint, op): opt_op_on_pserver.append(op) for opt_op in opt_op_on_pserver: dist_var = None for key in opt_op.input_names: if key == "Param": param_name = opt_op.input(key)[0] dist_var = self.vars_overview.get_distributed_var_by_origin_and_ep( param_name, endpoint) break for key in opt_op.input_names: if key in ["Param", "Grad", "LearningRate"]: continue origin_var = self.origin_program.global_block().vars[ opt_op.input(key)[0]] # update accumulator variable shape new_shape = self._get_optimizer_input_shape( opt_op.type, key, origin_var.shape, dist_var.slice.shape) if new_shape == dist_var.slice.shape: splited_var = VarStruct(, shape=new_shape, dtype=origin_var.dtype, type=origin_var.type, lod_level=origin_var.lod_level, persistable=origin_var.persistable) self.vars_overview.add_distributed_var( origin_var=origin_var, slice_var=splited_var, is_slice=dist_var.is_slice, block_id=dist_var.block_id, offset=dist_var.offset, vtype="Optimizer", endpoint=endpoint) else: self.vars_overview.add_distributed_var( origin_var=origin_var, slice_var=origin_var, is_slice=False, block_id=0, offset=0, vtype="Optimizer", endpoint=endpoint) for ep in self.pserver_endpoints: _get_distributed_optimizer_var(ep) def _update_dist_lookup_table_vars(self, param_list, grad_list, params_grads): # TODO(wuyi): put find a way to put dist lookup table stuff all together. # update self.table_param_grad and self.trainer_side_table_grad_list program = self.origin_program if self.has_distributed_lookup_table: param_list = [ param for param in param_list if != self.table_name ] grad_list = [ grad for grad in grad_list if != grad_var_name(self.table_name) ] self.table_param_grad = [ param_grad for param_grad in params_grads if param_grad[0].name == self.table_name ][0] table_grad_var = self.table_param_grad[1] if self.sync_mode: self.trainer_side_table_grad_list = [ program.global_block().create_var( name="%s.trainer_%d.pserver_%d" % (, self.trainer_id, index), type=table_grad_var.type, shape=table_grad_var.shape, dtype=table_grad_var.dtype) for index in range(len(self.pserver_endpoints)) ] else: self.trainer_side_table_grad_list = [ program.global_block().create_var( name="%s.pserver_%d" % (, index), type=table_grad_var.type, shape=table_grad_var.shape, dtype=table_grad_var.dtype) for index in range(len(self.pserver_endpoints)) ] return param_list, grad_list def _init_splited_vars(self): # update these mappings for further transpile: # 1. param_var_mapping: param var name -> [splited params vars] # 2. grad_var_mapping: grad var name -> [splited grads vars] # 3. grad_param_mapping: grad.blockx -> param.blockx # 4. param_grad_ep_mapping: ep -> {"params": [], "grads": []} param_list = [] grad_list = [] param_grad_set = set() for p, g in self.params_grads: # skip parameter marked not trainable if type(p) == Parameter and p.trainable == False: continue if not in param_grad_set: param_list.append(p) param_grad_set.add( if not in param_grad_set: grad_list.append(g) param_grad_set.add( param_list, grad_list = self._update_dist_lookup_table_vars( param_list, grad_list, self.params_grads) if self.config.slice_var_up: # when we slice var up into blocks, we will slice the var according to # pserver services' count. A pserver may have two or more listening ports. grad_blocks = slice_variable(grad_list, len(self.pserver_endpoints), self.config.min_block_size) param_blocks = slice_variable(param_list, len(self.pserver_endpoints), self.config.min_block_size) else: # when we do NOT slice var up into blocks, we will always slice params # grads into one block. grad_blocks = slice_variable(grad_list, 1, self.config.min_block_size) param_blocks = slice_variable(param_list, 1, self.config.min_block_size) assert (len(grad_blocks) == len(param_blocks)) # origin_param_name -> [splited_param_vars] self.param_var_mapping = self._create_vars_from_blocklist( self.origin_program, param_blocks) for orig_name, splited_vars in self.param_var_mapping.items(): orig_var = self.origin_program.global_block().var(orig_name) for splited_var in splited_vars: is_slice, block_id, offset = self._get_slice_var_info( splited_var) self.vars_overview.add_distributed_var( origin_var=orig_var, slice_var=splited_var, block_id=block_id, offset=offset, is_slice=is_slice, vtype="Param") # origin_grad_name -> [splited_grad_vars] self.grad_var_mapping = self._create_vars_from_blocklist( self.origin_program, grad_blocks, add_trainer_suffix=self.trainer_num > 1) # dict(grad_splited_var -> param_splited_var) self.grad_param_mapping = collections.OrderedDict() for g, p in zip(grad_blocks, param_blocks): g_name, g_bid, _ = g.split(":") p_name, p_bid, _ = p.split(":") self.grad_param_mapping[self.grad_var_mapping[g_name][int(g_bid)]] = \ self.param_var_mapping[p_name][int(p_bid)] # create mapping of endpoint -> split var to create pserver side program self.param_grad_ep_mapping = collections.OrderedDict() [ self.param_grad_ep_mapping.update({ ep: { "params": [], "grads": [] } }) for ep in self.pserver_endpoints ] # transpiler function for dis lookup_table def _replace_lookup_table_op_with_prefetch(self, program, pserver_endpoints): # 1. replace lookup_table_op with split_ids_op -> prefetch_op -> sum_op self.all_in_ids_vars = [] self.all_prefetch_input_vars = [] self.all_prefetch_output_vars = [] self.all_out_emb_vars = [] lookup_table_op_index = -1 continue_search_lookup_table_op = True while continue_search_lookup_table_op: continue_search_lookup_table_op = False all_ops = program.global_block().ops for op in all_ops: if op.type == LOOKUP_TABLE_TYPE and self.table_name == op.input( "W")[0]: if not op.attr('is_distributed'): raise RuntimeError( "lookup_table_op that lookup an distributed embedding table" "should set is_distributed to true") continue_search_lookup_table_op = True lookup_table_op_index = lookup_table_op_index if lookup_table_op_index != -1 else list( all_ops).index(op) ids_name = op.input("Ids") out_name = op.output("Out") ids_var = program.global_block().vars[ids_name[0]] self.all_in_ids_vars.append(ids_var) out_var = program.global_block().vars[out_name[0]] self.all_out_emb_vars.append(out_var) # delete lookup_table_op delete_ops(program.global_block(), [op]) # break for loop break for index in range(len(self.pserver_endpoints)): in_var = program.global_block().create_var( name=str("prefetch_compress_in_tmp_" + str(index)), type=self.all_in_ids_vars[0].type, shape=self.all_in_ids_vars[0].shape, dtype=self.all_in_ids_vars[0].dtype) self.all_prefetch_input_vars.append(in_var) out_var = program.global_block().create_var( name=str("prefetch_compress_out_tmp_" + str(index)), type=self.all_out_emb_vars[0].type, shape=self.all_out_emb_vars[0].shape, dtype=self.all_out_emb_vars[0].dtype) self.all_prefetch_output_vars.append(out_var) # insert split_ids_op program.global_block()._insert_op( index=lookup_table_op_index, type="split_ids", inputs={'Ids': self.all_in_ids_vars}, outputs={"Out": self.all_prefetch_input_vars}) # insert prefetch_op program.global_block()._insert_op( index=lookup_table_op_index + 1, type="prefetch", inputs={'X': self.all_prefetch_input_vars}, outputs={"Out": self.all_prefetch_output_vars}, attrs={ "epmap": pserver_endpoints, # FIXME(qiao) temporarily disable this config because prefetch # is not act as other rpc op, it's more like a forward op # RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE }) # insert concat_op program.global_block()._insert_op( index=lookup_table_op_index + 2, type="merge_ids", inputs={ 'Ids': self.all_in_ids_vars, 'Rows': self.all_prefetch_input_vars, 'X': self.all_prefetch_output_vars }, outputs={"Out": self.all_out_emb_vars}) def _split_table_grad_and_add_send_vars(self, program, pserver_endpoints): # 2. add split_ids_op and send_op to send gradient to pservers # there should only be one table_name all_ops = program.global_block().ops table_grad_name = grad_var_name(self.table_name) for op in all_ops: if table_grad_name in op.output_arg_names: op_index = list(all_ops).index(op) # insert split_ids_op program.global_block()._insert_op( index=op_index + 1, type="split_ids", inputs={ 'Ids': [program.global_block().vars[table_grad_name]] }, outputs={"Out": self.trainer_side_table_grad_list}, attrs={RPC_OP_ROLE_ATTR_NAME: DIST_OP_ROLE_ATTR_VALUE}) program.global_block()._insert_op( index=op_index + 2, type="send", inputs={'X': self.trainer_side_table_grad_list}, outputs={ 'Out': [self.grad_name_to_send_dummy_out[self.table_name]] if self.sync_mode else [] }, attrs={ "sync_mode": not self.sync_mode, "epmap": pserver_endpoints, "trainer_id": self.trainer_id, RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE, OP_ROLE_VAR_ATTR_NAME: [ self.grad_name_to_param_name[table_grad_name], table_grad_name ] }) break def _create_prefetch_block(self, pserver_index, pserver_program, optimize_block): # STEP: create prefetch block table_var = pserver_program.global_block().vars[self.table_name] prefetch_var_name_to_block_id = [] prefetch_block = pserver_program._create_block(optimize_block.idx) trainer_ids = self.all_prefetch_input_vars[pserver_index] pserver_ids = pserver_program.global_block().create_var(, type=trainer_ids.type, shape=trainer_ids.shape, dtype=trainer_ids.dtype) trainer_out = self.all_prefetch_output_vars[pserver_index] pserver_out = pserver_program.global_block().create_var(, type=trainer_out.type, shape=trainer_out.shape, dtype=trainer_out.dtype) prefetch_block.append_op( type="lookup_sparse_table", inputs={'Ids': pserver_ids, "W": table_var}, outputs={"Out": pserver_out}, attrs={ "is_sparse": True, # has no effect on lookup_table op "is_distributed": True, "padding_idx": -1 }) prefetch_var_name_to_block_id.append( + ":" + str( prefetch_block.idx)) return prefetch_var_name_to_block_id def _create_table_optimize_block(self, pserver_index, pserver_program, pre_block_idx, grad_to_block_id): # STEP: create table optimize block table_opt_block = pserver_program._create_block(pre_block_idx) # create table param and grad var in pserver program # create table optimize block in pserver program table_opt_op = [ op for op in self.optimize_ops if 'Param' in op.input_names and op.input("Param")[0] == self.table_name ][0] origin_param_var = self.origin_program.global_block().vars[ self.table_name] zero_dim = int( math.ceil(origin_param_var.shape[0] / float( len(self.pserver_endpoints)))) table_shape = list(origin_param_var.shape) table_shape[0] = zero_dim param_var = pserver_program.global_block().create_var(, shape=table_shape, dtype=origin_param_var.dtype, type=core.VarDesc.VarType.SELECTED_ROWS, persistable=True) # parameter must be selected rows param_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS) grad_var = pserver_program.global_block()._clone_variable( self.origin_program.global_block().vars[grad_var_name( self.table_name)]) lr_var = pserver_program.global_block()._clone_variable( self.origin_program.global_block().vars[table_opt_op.input( "LearningRate")[0]]) if self.sync_mode: # create grad vars in pserver program table_grad_var = self.table_param_grad[1] pserver_side_table_grad_list = [ pserver_program.global_block().create_var( name="%s.trainer_%d.pserver_%d" % (, index, pserver_index), type=table_grad_var.type, shape=table_grad_var.shape, dtype=table_grad_var.dtype) for index in range(self.trainer_num) ] # append sum op for pserver_side_table_grad_list table_opt_block.append_op( type="sum", inputs={"X": pserver_side_table_grad_list}, outputs={"Out": [grad_var]}, attrs={"use_mkldnn": False}) else: # in async_mode, for table gradient, it also need to be splited to each parameter server origin_grad_name = splited_grad_name = self.trainer_side_table_grad_list[ pserver_index].name if not splited_grad_name.startswith(origin_grad_name): raise ValueError("origin_grad_var: " + splited_grad_name + " grad_var:" + grad_var = pserver_program.global_block()._rename_var( origin_grad_name, splited_grad_name) inputs = { "Param": [param_var], "Grad": [grad_var], "LearningRate": [lr_var] } outputs = {"ParamOut": [param_var]} # only support sgd now logging.warn( "distribute lookup table only support sgd optimizer, change it's optimizer to sgd instead of " + table_opt_op.type) table_opt_block.append_op(type="sgd", inputs=inputs, outputs=outputs) # add table parameter gradient and it's block id to grad_to_block_id grad_to_block_id.append( + ":" + str(table_opt_block.idx)) return table_opt_block def _create_checkpoint_save_block(self, pserver_program, pre_block_idx): """ create a new block to handle save checkpoint. """ pserver_program.global_block().create_var( name="kLookupTablePath", persistable=True, type=core.VarDesc.VarType.RAW) checkpoint_save_block = pserver_program._create_block(pre_block_idx) # this 'file_path' do not be used in save lookup table variable checkpoint_save_block.append_op( type='save', inputs={'X': [self.table_name]}, outputs={}, attrs={'file_path': "none"}) return checkpoint_save_block.idx def _create_vars_from_blocklist(self, program, block_list, add_trainer_suffix=False): """ Create vars for each split. NOTE: only grads need to be named for different trainers, use add_trainer_suffix to rename the grad vars. Args: program (ProgramDesc): ProgramDesc which gradients blong. block_list (list[(varname, block_id, block_size)]): List of gradient blocks. add_trainer_suffix (Bool): Add trainer suffix to new variable's name if set True. Returns: var_mapping (collections.OrderedDict(varname->[new_varname_variable])):A dict mapping from original var name to each var split. """ # varname->[(block_id, current_block_size)] block_map = collections.OrderedDict() var_mapping = collections.OrderedDict() for block_str in block_list: varname, offset, size = block_str.split(":") if varname not in block_map: block_map[varname] = [] block_map[varname].append((int(offset), int(size))) for varname, splited in six.iteritems(block_map): orig_var = program.global_block().var(varname) if len(splited) == 1: if self.sync_mode and add_trainer_suffix: new_var_name = "%s.trainer_%d" % \ (, self.trainer_id) program.global_block()._rename_var(varname, new_var_name) var_mapping[varname] = \ [program.global_block().var(new_var_name)] else: var_mapping[varname] = \ [program.global_block().var(] continue var_mapping[varname] = [] orig_shape = orig_var.shape orig_dim1_flatten = 1 if len(orig_shape) >= 2: orig_dim1_flatten = reduce(lambda x, y: x * y, orig_shape[1:]) for i, block in enumerate(splited): size = block[1] rows = size // orig_dim1_flatten splited_shape = [rows] if len(orig_shape) >= 2: splited_shape.extend(orig_shape[1:]) new_var_name = "" if self.sync_mode and add_trainer_suffix: new_var_name = "%s.block%d.trainer_%d" % \ (varname, i, self.trainer_id) else: new_var_name = "%s.block%d" % \ (varname, i) var = program.global_block().create_var( name=new_var_name, persistable=False, dtype=orig_var.dtype, type=orig_var.type, shape=splited_shape) # flattend splited var var_mapping[varname].append(var) program.global_block()._sync_with_cpp() return var_mapping def _clone_var(self, block, var, persistable=True): return block.create_var(, shape=var.shape, dtype=var.dtype, type=var.type, lod_level=var.lod_level, persistable=persistable) @staticmethod def _get_splited_var_sections(splited_vars): height_sections = [] for v in splited_vars: height_sections.append(v.shape[0]) return height_sections def _insert_split_op(self, program, orig_var, index, splited_vars): height_sections = self._get_splited_var_sections(splited_vars) if orig_var.type == core.VarDesc.VarType.SELECTED_ROWS: sparse_param_name = self.grad_name_to_param_name[] if self._is_input_of_remote_sparse_update_op(sparse_param_name): self.sparse_param_to_height_sections[ sparse_param_name] = height_sections program.global_block()._insert_op( index=index + 1, type="split_selected_rows", inputs={"X": orig_var}, outputs={"Out": splited_vars}, attrs={ "height_sections": height_sections, RPC_OP_ROLE_ATTR_NAME: DIST_OP_ROLE_ATTR_VALUE }) elif orig_var.type == core.VarDesc.VarType.LOD_TENSOR: program.global_block()._insert_op( index=index + 1, type="split_byref", inputs={"X": orig_var}, outputs={"Out": splited_vars}, attrs={ "sections": height_sections, RPC_OP_ROLE_ATTR_NAME: DIST_OP_ROLE_ATTR_VALUE }) else: AssertionError("Variable type should be in set " "[LOD_TENSOR, SELECTED_ROWS]") def _get_optimizer_input_shape(self, op_type, varkey, orig_shape, param_shape): """ Returns the shape for optimizer inputs that need to be reshaped when Param and Grad is split to multiple servers. """ # HACK(typhoonzero): Should use functions of corresponding optimizer in # to get the shape, do not bind this in the transpiler. if op_type == "adam": if varkey in ["Moment1", "Moment2"]: return param_shape elif op_type == "adagrad": if varkey == "Moment": return param_shape elif op_type == "adamax": if varkey in ["Moment", "InfNorm"]: return param_shape elif op_type in ["momentum", "lars_momentum"]: if varkey == "Velocity": return param_shape elif op_type == "rmsprop": if varkey in ["Moment", "MeanSquare"]: return param_shape elif op_type == "decayed_adagrad": if varkey == "Moment": return param_shape elif op_type == "ftrl": if varkey in ["SquaredAccumulator", "LinearAccumulator"]: return param_shape elif op_type == "sgd": pass else: raise ValueError( "Not supported optimizer for distributed training: %s" % op_type) return orig_shape def _get_varname_parts(self, varname): # returns origin, blockid, trainerid orig_var_name = "" trainer_part = "" block_part = "" trainer_idx = varname.find(".trainer_") if trainer_idx >= 0: trainer_part = varname[trainer_idx + 1:] else: trainer_idx = len(varname) block_index = varname.find(".block") if block_index >= 0: block_part = varname[block_index + 1:trainer_idx] else: block_index = len(varname) orig_var_name = varname[0:min(block_index, trainer_idx)] return orig_var_name, block_part, trainer_part def _orig_varname(self, varname): orig, _, _ = self._get_varname_parts(varname) return orig def _append_pserver_grad_merge_ops(self, optimize_block, grad_varname_for_block, endpoint, grad_to_block_id, origin_program): program = optimize_block.program pserver_block = program.global_block() grad_block = None for g in self.param_grad_ep_mapping[endpoint]["grads"]: if self._orig_varname( == \ self._orig_varname(grad_varname_for_block): grad_block = g break if not grad_block: # do not append this op if current endpoint # is not dealing with this grad block return None orig_varname, block_name, trainer_name = self._get_varname_parts( if block_name: merged_var_name = '.'.join([orig_varname, block_name]) else: merged_var_name = orig_varname merged_var = pserver_block.vars[merged_var_name] grad_to_block_id.append( + ":" + str(optimize_block.idx)) if self.sync_mode and self.trainer_num > 1: vars2merge = [] for i in range(self.trainer_num): per_trainer_name = "%s.trainer_%d" % \ (merged_var_name, i) vars2merge.append(pserver_block.vars[per_trainer_name]) optimize_block.append_op( type="sum", inputs={"X": vars2merge}, outputs={"Out": merged_var}, attrs={"use_mkldnn": False}) optimize_block.append_op( type="scale", inputs={"X": merged_var}, outputs={"Out": merged_var}, attrs={"scale": 1.0 / float(self.trainer_num)}) return merged_var def _append_dc_asgd_ops(self, block, param_var, grad_var): # NOTE: can not use grammar candy here, should put ops in specific block local_param_bak = block.create_var( name="%s.local_bak" %, shape=param_var.shape, type=param_var.type, dtype=param_var.dtype, persistable=False) # trainer_id_var is block local trainer_id_var = block.create_var( name="@TRAINER_ID@", type=core.VarDesc.VarType.LOD_TENSOR, dtype=core.VarDesc.VarType.INT64, shape=[1], persistable=False) # ref_inputs = [x[1] for x in self.param_bak_list] ref_inputs = [] for p, p_bak in self.param_bak_list: if == ref_inputs.append(p_bak) block.append_op( type="ref_by_trainer_id", inputs={"X": ref_inputs, "TrainerId": trainer_id_var}, outputs={"Out": local_param_bak}) def __create_temp_var__(): return block.create_var( name=unique_name.generate("tmp_dc_output"), shape=param_var.shape, type=param_var.type, dtype=param_var.dtype, persistable=False) o1 = __create_temp_var__() block.append_op( type="elementwise_sub", inputs={"X": param_var, "Y": local_param_bak}, outputs={"Out": o1}) o2 = __create_temp_var__() block.append_op( type="elementwise_mul", inputs={"X": o1, "Y": grad_var}, outputs={"Out": o2}) o3 = __create_temp_var__() block.append_op( type="elementwise_mul", inputs={"X": o2, "Y": grad_var}, outputs={"Out": o3}) # TODO(typhoonzero): append scale o4 = __create_temp_var__() block.append_op( type="elementwise_add", inputs={"X": grad_var, "Y": o3}, outputs={"Out": o4}) return o4 def _append_pserver_ops(self, optimize_block, opt_op, endpoint, grad_to_block_id, origin_program, merged_var, sparse_grad_to_param): program = optimize_block.program pserver_block = program.global_block() new_inputs = collections.OrderedDict() def _get_param_block(opt_op): # param is already created on global program param_block = None for p in self.param_grad_ep_mapping[endpoint]["params"]: if same_or_split_var(, opt_op.input("Param")[0]): param_block = p break return param_block if self.config.enable_dc_asgd: param_var = _get_param_block(opt_op) dc = self._append_dc_asgd_ops(optimize_block, param_var, merged_var) for key in opt_op.input_names: if key == "Grad": if self.config.enable_dc_asgd: new_inputs[key] = dc else: # Note!! This is for l2decay on sparse gradient, because it will create a new tensor for # decayed gradient but not inplace modify the origin one origin_grad_name = opt_op.input(key)[0] if core.kNewGradSuffix( ) in origin_grad_name and pserver_block.has_var( origin_grad_name): new_grad = pserver_block.var(origin_grad_name) new_inputs[key] = new_grad else: new_inputs[key] = merged_var elif key == "Param": param_block = _get_param_block(opt_op) if not param_block: return tmpvar = pserver_block.create_var(, persistable=True, dtype=param_block.dtype, shape=param_block.shape) new_inputs[key] = tmpvar elif key == "LearningRate": # learning rate variable has already be created by non-optimize op, # don't create it once again. lr_varname = opt_op.input(key)[0] if lr_varname in pserver_block.vars: new_inputs[key] = pserver_block.vars[opt_op.input(key)[0]] else: origin_var = origin_program.global_block().vars[lr_varname] tmpvar = pserver_block.create_var(, persistable=origin_var.persistable, dtype=origin_var.dtype, shape=origin_var.shape) new_inputs[key] = tmpvar for key in opt_op.input_names: new_shape = None if key in ["Param", "Grad", "LearningRate"]: continue var = self.origin_program.global_block().vars[opt_op.input(key)[0]] param_var = new_inputs["Param"] # update accumulator variable shape new_shape = self._get_optimizer_input_shape( opt_op.type, key, var.shape, param_var.shape) tmpvar = pserver_block.create_var(, persistable=var.persistable, dtype=var.dtype, shape=new_shape) new_inputs[key] = tmpvar # change output's ParamOut variable outputs = self._get_output_map_from_op( self.origin_program.global_block().vars, opt_op) outputs["ParamOut"] = new_inputs["Param"] optimize_block.append_op( type=opt_op.type, inputs=new_inputs, outputs=outputs, attrs=opt_op.all_attrs()) # record sparse grad to param name if new_inputs["Grad"].type == core.VarDesc.VarType.SELECTED_ROWS: sparse_grad_to_param.append( str(new_inputs["Grad"].name) + ":" + str(new_inputs["Param"] .name)) def _get_pserver_grad_param_var(self, var, var_dict): """ Return pserver side grad/param variable, return None if the variable is not grad/param, e.g. a@GRAD -> a@GRAD.block0 a@GRAD -> a@GRAD (a is not splited) fc_0.w_0 -> fc_0.w_0.block_0 fc_0.w_0 -> fc_0.w_0 (weight is not splited) _generated_var_123 -> None """ grad_block = None for _, g in six.iteritems(var_dict): if self._orig_varname( == self._orig_varname( # skip per trainer vars if".trainer_") == -1: # only param or grads have splited blocks if self._orig_varname( in self.grad_name_to_param_name or \ self._orig_varname( in self.param_name_to_grad_name: grad_block = g break return grad_block def _clone_lr_op(self, program, block, op): inputs = self._get_input_map_from_op( self.origin_program.global_block().vars, op) for key, varlist in six.iteritems(inputs): if not isinstance(varlist, list): varlist = [varlist] for var in varlist: if var not in program.global_block().vars: block._clone_variable(var) outputs = self._get_output_map_from_op( self.origin_program.global_block().vars, op) for key, varlist in six.iteritems(outputs): if not isinstance(varlist, list): varlist = [varlist] for var in varlist: if var not in program.global_block().vars: block._clone_variable(var) return block.append_op( type=op.type, inputs=inputs, outputs=outputs, attrs=op.all_attrs()) def _append_pserver_non_opt_ops(self, optimize_block, opt_op): program = optimize_block.program # Append the ops for parameters that do not need to be optimized/updated inputs = self._get_input_map_from_op( self.origin_program.global_block().vars, opt_op) for key, varlist in six.iteritems(inputs): if not isinstance(varlist, list): varlist = [varlist] for i in range(len(varlist)): var = varlist[i] # for ops like clipping and weight decay, get the splited var (xxx.block0) # for inputs/outputs grad_block = self._get_pserver_grad_param_var( var, program.global_block().vars) if grad_block: varlist[i] = grad_block elif not in program.global_block().vars: tmpvar = program.global_block()._clone_variable(var) varlist[i] = tmpvar else: varlist[i] = program.global_block().vars[] inputs[key] = varlist outputs = self._get_output_map_from_op( self.origin_program.global_block().vars, opt_op) for key, varlist in six.iteritems(outputs): if not isinstance(varlist, list): varlist = [varlist] for i in range(len(varlist)): var = varlist[i] grad_block = self._get_pserver_grad_param_var( var, program.global_block().vars) if grad_block: varlist[i] = grad_block elif not in program.global_block().vars: tmpvar = program.global_block()._clone_variable(var) varlist[i] = tmpvar else: varlist[i] = program.global_block().vars[] outputs[key] = varlist return optimize_block.append_op( type=opt_op.type, inputs=inputs, outputs=outputs, attrs=opt_op.all_attrs()) def _is_op_connected(self, op1, op2): # If one op's input is another op's output or # one op's output is another op's input, we say # the two operator is connected. if set(op1.desc.output_arg_names()) & set(op2.desc.input_arg_names()) or \ set(op1.desc.input_arg_names()) & set(op2.desc.output_arg_names()): return True return False def _create_ufind(self, optimize_ops): # Create a unit find data struct by optimize ops ufind = UnionFind(optimize_ops) for i in range(len(optimize_ops)): for j in range(i, len(optimize_ops)): op1 = optimize_ops[i] op2 = optimize_ops[j] if self._is_op_connected(op1, op2): ufind.union(op1, op2) return ufind def _is_optimizer_op(self, op): if "Param" in op.input_names and \ "LearningRate" in op.input_names: return True return False def _is_opt_op_on_pserver(self, endpoint, op): param_names = [ for p in self.param_grad_ep_mapping[endpoint]["params"] ] if op.input("Param")[0] in param_names: return True else: for n in param_names: param = op.input("Param")[0] if same_or_split_var(n, param) and n != param: return True return False def _get_input_map_from_op(self, varmap, op): """Returns a dict from op input name to the vars in varmap.""" iomap = collections.OrderedDict() for key in op.input_names: vars = [] for varname in op.input(key): vars.append(varmap[varname]) if len(vars) == 1: iomap[key] = vars[0] else: iomap[key] = vars return iomap def _get_output_map_from_op(self, varmap, op): """Returns a dict from op output name to the vars in varmap.""" iomap = collections.OrderedDict() for key in op.output_names: vars = [] for varname in op.output(key): vars.append(varmap[varname]) if len(vars) == 1: iomap[key] = vars[0] else: iomap[key] = vars return iomap def _get_lr_ops(self): lr_ops = [] block = self.origin_program.global_block() for op in block.ops: role_id = int(op.attr(RPC_OP_ROLE_ATTR_NAME)) if role_id == int(LR_SCHED_OP_ROLE_ATTR_VALUE) or \ role_id == int(LR_SCHED_OP_ROLE_ATTR_VALUE) | \ int(OPT_OP_ROLE_ATTR_VALUE): lr_ops.append(op) log("append lr op: ", op.type) return lr_ops def _get_lr_ops_deprecated(self): lr_ops = [] # find learning rate variables by optimize op lr_vars = set() for op in self.optimize_ops: if self._is_optimizer_op(op): lr_vars.add(op.input("LearningRate")[0]) find_ops = [] # find ops which output is lr var block = self.origin_program.global_block() for op in block.ops: if set(op.output_arg_names) & lr_vars: find_ops.append(op) # make a union find struct by the ops in default_main_program ufind = UnionFind(block.ops) for op1 in block.ops: for op2 in block.ops: # NOTE: we need to skip all optimize ops, since it is connected # with forward/backward ops and lr ops, we only need the lr ops. if op1 != op2 and self._is_op_connected(op1, op2) and \ not self._is_optimizer_op(op1) and not self._is_optimizer_op(op2): ufind.union(op1, op2) # find all ops which is related with lr var for op1 in block.ops: for op2 in find_ops: if ufind.is_connected(op1, op2): lr_ops.append(op1) # we only need to append op for once break return lr_ops def _is_opt_role_op(self, op): # NOTE: depend on oprole to find out whether this op is for # optimize op_maker = core.op_proto_and_checker_maker optimize_role = core.op_proto_and_checker_maker.OpRole.Optimize if op_maker.kOpRoleAttrName() in op.attr_names and \ int(op.all_attrs()[op_maker.kOpRoleAttrName()]) == int(optimize_role): return True return False def _get_optimize_pass(self): """ Get optimizer operators, parameters and gradients from origin_program Returns: opt_ops (list): optimize operators. params_grads (dict): parameter->gradient. """ block = self.origin_program.global_block() opt_ops = [] params_grads = [] # tmp set to dedup optimize_params = set() origin_var_dict = self.origin_program.global_block().vars for op in block.ops: if self._is_opt_role_op(op): opt_ops.append(op) if op.attr(OP_ROLE_VAR_ATTR_NAME): param_name = op.attr(OP_ROLE_VAR_ATTR_NAME)[0] grad_name = op.attr(OP_ROLE_VAR_ATTR_NAME)[1] if not param_name in optimize_params: optimize_params.add(param_name) log("adding param_grad pair: ", param_name, grad_name) params_grads.append([ origin_var_dict[param_name], origin_var_dict[grad_name] ]) else: pass return opt_ops, params_grads