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distribute_transpiler.py
2078 lines (1851 loc) · 86.1 KB
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distribute_transpiler.py
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# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __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
PRINT_LOG = False
def log(*args):
if PRINT_LOG:
print(args)
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).
Args:
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.
Returns:
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(var.name, block_id, curr_block_size)
blocks.append(str(block))
return blocks
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 : https://github.com/PaddlePaddle/Paddle/issues/8638#issuecomment-369912156
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.
"""
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
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
# for pserver mode
pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
current_endpoint = "192.168.0.1:6174"
trainer_id = 0
trainers = 4
role = os.getenv("PADDLE_TRAINING_ROLE")
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
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
t = fluid.DistributeTranspiler(config=config)
t.transpile(trainer_id, workers=workers, current_endpoint=curr_ep)
exe = fluid.ParallelExecutor(
use_cuda,
loss_name=loss_var.name,
num_trainers=len(trainers.split(",)),
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)
startup_program.global_block().append_op(
type="gen_nccl_id",
inputs={},
outputs={"NCCLID": nccl_id_var},
attrs={
"endpoint": current_endpoint,
"endpoint_list": worker_endpoints,
"trainer_id": trainer_id
})
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
def transpile(self,
trainer_id,
program=None,
pservers="127.0.0.1:6174",
trainers=1,
sync_mode=True,
startup_program=None,
current_endpoint="127.0.0.1:6174"):
"""
Run the transpiler.
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.
"""
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._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[param_var.name] = grad_var.name
self.grad_name_to_param_name[grad_var.name] = param_var.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)
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
# 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": splited_vars},
outputs={"Out": dummy_output},
attrs={
"epmap": eplist,
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 = [v.name for v in recv_vars].index(var.name)
eps.append(eplist[index])
table_names.append(var.name)
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:
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,
"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:
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
def get_trainer_program(self, wait_port=True):
"""
Get transpiled trainer side program.
Returns:
Program: trainer side 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 = [v.name for v in recv_vars].index(var.name)
eps.append(eplist[index])
for var in splited_var:
if startup_program.global_block().has_var(var.name):
continue
startup_program.global_block().create_var(
name=var.name,
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,
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,
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
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.
"""
# 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 = v.name.find(".trainer_")
if suff_idx >= 0:
orig_var_name = v.name[:suff_idx]
else:
orig_var_name = v.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" % (p.name, 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 = []
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)
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(origin_block_desc.id)
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,
}
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
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"
"""
pserver_prog = self.get_pserver_program(endpoint)
pserver_startup = self.get_startup_program(
endpoint, pserver_program=pserver_prog)
return pserver_prog, pserver_startup
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.
"""
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 = splited_param.name
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[var.name] = 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"
]:
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[p.name]
startup_tmpvar = s_prog.global_block().vars[p_bak.name]
# copy init random value to param_bak
s_prog.global_block().append_op(