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util_factory.py
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util_factory.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Fleet Utils."""
"""distributed operations"""
"""basic collective operations in python"""
"""remote file system"""
from ..utils.fs import FS, LocalFS, HDFSClient
from paddle.fluid.proto import framework_pb2
from paddle.fluid.framework import Program
from paddle.fluid import debugger
from google.protobuf import text_format
import paddle.fluid as fluid
from collections import OrderedDict
from paddle.fluid import core
import subprocess
import os
import numpy as np
__all__ = []
class UtilFactory(object):
def _create_util(self, context=None):
util = UtilBase()
if context is not None and "valid_strategy" in context:
util._set_strategy(context["valid_strategy"])
if context is not None and "role_maker" in context:
util._set_role_maker(context["role_maker"])
return util
class UtilBase(object):
def __init__(self):
self.role_maker = None
self.dist_strategy = None
def _set_strategy(self, dist_strategy):
self.dist_strategy = dist_strategy
def _set_role_maker(self, role_maker):
self.role_maker = role_maker
def _set_file_system(self, fs_client):
assert isinstance(
fs_client, FS
), "fs_client must be the instance of paddle.distributed.fleet.utils.FS"
self.fs_client = fs_client
def all_reduce(self, input, mode="sum", comm_world="worker"):
"""
All reduce `input` between specified collection. This is a distributed API.
Args:
input (list|numpy.array): The input variable to do all_reduce between specified collection.
mode (str): "sum" or "min" or "max".
comm_world (str, optional): Collection used to execute all_reduce operation. Supported collections incude `worker` , `server` and `all` . The default is `worker` .
Returns:
output(Numpy.array|None): A numpy array with the same shape as the `input` .
Examples:
.. code-block:: python
# Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` .
import paddle.distributed.fleet as fleet
from paddle.distributed.fleet import PaddleCloudRoleMaker
import sys
import numpy as np
import os
os.environ["PADDLE_WITH_GLOO"] = "2"
def train():
role = PaddleCloudRoleMaker(
is_collective=False,
init_gloo=True,
path="./tmp_gloo")
fleet.init(role)
if fleet.is_server():
input = [1, 2]
output = fleet.util.all_reduce(input, "sum", "server")
print(output)
# [2, 4]
elif fleet.is_worker():
input = np.array([3, 4])
output = fleet.util.all_reduce(input, "sum", "worker")
print(output)
# [6, 8]
output = fleet.util.all_reduce(input, "sum", "all")
print(output)
# [8, 12]
if __name__ == "__main__":
train()
"""
return self.role_maker._all_reduce(input, mode, comm_world)
def barrier(self, comm_world="worker"):
"""
Barrier between specified collection.
Args:
comm_world (str, optional): Collection used to execute barrier operation. Supported collections incude `worker` , `server` and `all` . The default is `worker` .
Examples:
.. code-block:: python
# Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` .
import paddle.distributed.fleet as fleet
from paddle.distributed.fleet import PaddleCloudRoleMaker
import sys
import os
os.environ["PADDLE_WITH_GLOO"] = "2"
def train():
role = PaddleCloudRoleMaker(
is_collective=False,
init_gloo=True,
path="./tmp_gloo")
fleet.init(role)
if fleet.is_server():
fleet.util.barrier("server")
print("all server arrive here")
elif fleet.is_worker():
fleet.util.barrier("worker")
print("all server arrive here")
fleet.util.barrier("all")
print("all servers and workers arrive here")
if __name__ == "__main__":
train()
"""
self.role_maker._barrier(comm_world)
def all_gather(self, input, comm_world="worker"):
"""
All gather `input` between specified collection.
Args:
input (Int|Float): The input variable to do all_gather between specified collection.
comm_world (str, optional): Collection used to execute all_reduce operation. Supported collections incude `worker` , `server` and `all` . The default is `worker` .
Returns:
output (List): A list of gathered values.
Examples:
.. code-block:: python
# Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` .
import paddle.distributed.fleet as fleet
from paddle.distributed.fleet import PaddleCloudRoleMaker
import sys
import os
os.environ["PADDLE_WITH_GLOO"] = "2"
def train():
role = PaddleCloudRoleMaker(
is_collective=False,
init_gloo=True,
path="./tmp_gloo")
fleet.init(role)
if fleet.is_server():
input = fleet.server_index()
output = fleet.util.all_gather(input, "server")
print(output)
# output = [0, 1]
elif fleet.is_worker():
input = fleet.worker_index()
output = fleet.util.all_gather(input, "worker")
# output = [0, 1]
print(output)
output = fleet.util.all_gather(input, "all")
print(output)
# output = [0, 1, 0, 1]
if __name__ == "__main__":
train()
"""
return self.role_maker._all_gather(input, comm_world)
def _broadcast(self):
pass
def _scatter(self):
pass
def get_file_shard(self, files):
"""
Split files before distributed training, and return filelist assigned to the current trainer.
.. code-block:: text
example 1: files is [a, b, c ,d, e] and trainer_num = 2, then trainer
0 gets [a, b, c] and trainer 1 gets [d, e].
example 2: files is [a, b], and trainer_num = 3, then trainer 0 gets
[a], trainer 1 gets [b], trainer 2 gets []
Args:
files(list): File list need to be read.
Returns:
List: Files belong to this worker.
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
from paddle.distributed.fleet import UserDefinedRoleMaker
role = UserDefinedRoleMaker(
is_collective=False,
init_gloo=False,
current_id=0,
role=fleet.Role.WORKER,
worker_endpoints=["127.0.0.1:6003", "127.0.0.1:6004"],
server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"])
fleet.init(role)
files = fleet.util.get_file_shard(["file1", "file2", "file3"])
print(files)
# files = ["file1", "file2"]
"""
if not isinstance(files, list):
raise TypeError("files should be a list of file need to be read.")
trainer_id = self.role_maker._worker_index()
trainers = self.role_maker._worker_num()
remainder = len(files) % trainers
blocksize = int(len(files) / trainers)
blocks = [blocksize] * trainers
for i in range(remainder):
blocks[i] += 1
trainer_files = [[]] * trainers
begin = 0
for i in range(trainers):
trainer_files[i] = files[begin:begin + blocks[i]]
begin += blocks[i]
return trainer_files[trainer_id]
def print_on_rank(self, message, rank_id):
"""
Woker of rank `rank_id` print some message.
Args:
message(str): Log to be printed.
rank_id(int): trainer id.
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
from paddle.distributed.fleet import UserDefinedRoleMaker
role = UserDefinedRoleMaker(
is_collective=False,
init_gloo=False,
current_id=0,
role=fleet.Role.WORKER,
worker_endpoints=["127.0.0.1:6003", "127.0.0.1:6004"],
server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"])
fleet.init(role)
fleet.util.print_on_rank("I'm worker 0", 0)
"""
if self.role_maker._worker_index() != rank_id:
return
print(message)
def _save_program(self, program, model_filename='__model__', is_text=False):
if is_text:
with open(model_filename, "w") as f:
f.write(str(program))
else:
with open(model_filename, "wb") as f:
f.write(program.desc.serialize_to_string())
def _load_program(self, path, is_text):
def load_program_binary(path):
"""load program from binary string file"""
with open(path, "rb") as f:
program_desc_str = f.read()
return Program.parse_from_string(program_desc_str)
def load_program_text(path):
"""load program from human-readable text file"""
with open(path, "r") as f:
program_desc_text = f.read()
prog_desc = framework_pb2.ProgramDesc()
text_format.Merge(program_desc_text, prog_desc)
return Program.parse_from_string(prog_desc.SerializeToString())
if is_text:
return load_program_text(path)
else:
return load_program_binary(path)
def _program_type_trans(self, prog_dir, prog_fn, is_text):
prog = self._load_program(os.path.join(prog_dir, prog_fn), is_text)
prog_out_fn = prog_fn + ".bin" if is_text else prog_fn + ".pbtxt"
self._save_program(prog,
os.path.join(prog_dir, prog_out_fn), 1 - is_text)
return prog_out_fn
def _visualize_graphviz(self, program, output_dir, output_filename):
block = program.global_block()
dot_path = os.path.join(output_dir, output_filename + '.dot')
pdf_path = os.path.join(output_dir, output_filename + '.pdf')
debugger.draw_block_graphviz(block, path=dot_path)
cmd = ["dot", "-Tpdf", dot_path, "-o", pdf_path]
p = subprocess.Popen(
cmd,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
p.wait()
def _proto_check(self, config):
train_prog = self._load_program(config.train_prog_path,
config.is_text_train_program)
pruned_prog = self._load_program(config.pruned_prog_path,
config.is_text_pruned_program)
is_match = True
pruned_vars = [(v.name, v) for v in pruned_prog.list_vars()
if fluid.io.is_persistable(v)]
pruned_vars = OrderedDict(pruned_vars)
pruned_vars_name = [name for name in pruned_vars]
print("persistable vars in pruned program: {}".format(pruned_vars_name))
# feed and fetch op is added in pruned program when pruning, not need to be found in train program
feed_fetch_type_list = [
core.VarDesc.VarType.FEED_MINIBATCH, core.VarDesc.VarType.FETCH_LIST
]
for var_name in pruned_vars:
var = pruned_vars[var_name]
# feed and fetch op is added in pruned program when pruning, not need to be found in train program
if var.type in feed_fetch_type_list:
break
try:
train_prog_var = train_prog.global_block().var(var_name)
except ValueError as e:
print(
"Not find variable '%s' in train program. please check pruning."
% var_name)
is_match = False
continue
if var.shape != train_prog_var.shape or var.dtype != train_prog_var.dtype:
print(
"variable: {} not match. in pruned program shape: {} dtype:{}, in train program shape: {} dtype: {}".
format(var_name, var.shape, var.dtype, train_prog_var.shape,
train_prog_var.dtype))
is_match = False
return is_match
def _params_check(self, config):
def feed_gen(batch_size, feeded_vars_dims, feeded_vars_filelist):
def reader(batch_size, fn, dim):
data = []
if isinstance(dim, list) or isinstance(dim, tuple):
shape = list(dim)
_temp = 1
for x in dim:
_temp = _temp * x
dim = _temp
else:
shape = [dim]
shape = [batch_size] + shape
dim = dim * batch_size
for line in open(fn, 'r'):
fields = line.strip().split(' ')
fields = [float(d) for d in fields]
while len(fields) >= dim:
tmp = fields[:dim]
fields = fields[dim:]
data.append(np.array(tmp).reshape(shape))
return data
batch_feed = []
for i, fn in enumerate(feeded_vars_filelist):
batch_feed.append(reader(batch_size, fn, feeded_vars_dims[i]))
return batch_feed
prog = self._load_program(
os.path.join(config.dump_model_dir, config.dump_program_filename),
config.is_text_dump_program)
if config.is_text_dump_program:
model_filename = self._program_type_trans(
config.dump_model_dir, config.dump_program_filename,
config.is_text_dump_program)
saved_params = [
v for v in prog.list_vars() if fluid.io.is_persistable(v)
]
print("persistable vars in dump program: {}".format(
[v.name for v in saved_params]))
def check_not_expected_ops(prog, not_expected_op_types):
op_types_set = set()
for op in prog.global_block().ops:
if op.type in not_expected_op_types and op.type not in op_types_set:
op_types_set.add(op.type)
return op_types_set
not_expected_op_types = check_not_expected_ops(prog, ["lookup_table"])
if len(not_expected_op_types) > 0:
print(
"find op type '{}' in program, please check if your program is pruned correctly !".
format(list(not_expected_op_types)))
return False
place = fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.core.Scope()
with fluid.scope_guard(scope):
inference_program, feed_target_names, fetch_targets = \
fluid.io.load_inference_model(config.dump_model_dir, exe, model_filename=model_filename,
params_filename=config.save_params_filename)
# check program vars and saved vars shape
orig_para_shape = {
each_var.name: tuple(each_var.desc.shape())
for each_var in saved_params
}
for each_var in saved_params:
var_temp = fluid.global_scope().find_var(each_var.name)
assert var_temp != None, "can't not find var: " + each_var.name
new_shape = (np.array(var_temp.get_tensor())).shape
assert each_var.name in orig_para_shape, each_var.name + "MUST in var list"
orig_shape = orig_para_shape.get(each_var.name)
if new_shape != orig_shape:
raise RuntimeError(
"Shape not matching: the Program requires a parameter with a shape of ({}), "
"while the loaded parameter (namely [ {} ]) has a shape of ({}).".
format(orig_shape, each_var.name, new_shape))
# check feed/fetch vars in program and config
feed_config = config.feed_config
fetch_config = config.fetch_config
fetch_targets_names = [v.name for v in fetch_targets]
if not feed_target_names:
print("warning! no feed targets in program.")
if not fetch_targets_names:
print("warning! no fetch targets in program.")
fetch_list = fetch_targets
feed_name_list = feed_target_names
if feed_config.feeded_vars_names is not None and feed_target_names != feed_config.feeded_vars_names:
print(
"warning! feed vars in program and config are diff: feed in program: {}. feed in config {}.".
format(feed_target_names, feed_config.feeded_vars_names))
feed_name_list = feed_config.feeded_vars_names
# remove feed op in inference_program. new feed op will be added in exe.run
global_block = inference_program.global_block()
need_to_remove_op_index = []
for i, op in enumerate(global_block.ops):
op.desc.set_is_target(False)
if op.type == "feed": # only remove feed op here
need_to_remove_op_index.append(i)
for index in need_to_remove_op_index[::-1]:
global_block._remove_op(index)
if fetch_config.fetch_vars_names is not None and fetch_targets_names != fetch_config.fetch_vars_names:
print(
"warning! fetch vars in program and config are diff: fetch in program: {}. fetch in config {}.".
format(fetch_targets_names, fetch_config.fetch_vars_names))
fetch_list = [
inference_program.global_block().var(i)
for i in fetch_config.fetch_vars_names
]
# remove fetch op in inference_program. new fetch op will be added in exe.run
global_block = inference_program.global_block()
need_to_remove_op_index = []
for i, op in enumerate(global_block.ops):
op.desc.set_is_target(False)
if op.type == "fetch": # only remove fetch op here
need_to_remove_op_index.append(i)
for index in need_to_remove_op_index[::-1]:
global_block._remove_op(index)
# if fetch_list have lod tensor
return_numpy = all([v.lod_level == 0 for v in fetch_list])
# try dump fetch_targets
feed_tensors = []
assert len(feed_config.feeded_vars_names) == len(
feed_config.feeded_vars_dims) == len(
feed_config.feeded_vars_types)
# check program vars and feed tensor shape in config
for i in range(len(feed_config.feeded_vars_names)):
var = inference_program.global_block().var(
feed_config.feeded_vars_names[i])
if not isinstance(feed_config.feeded_vars_dims[i],
(list, tuple)):
tensor_shape = (feed_config.feeded_vars_dims[i], )
else:
tensor_shape = tuple(feed_config.feeded_vars_dims[i])
feed_config.feeded_vars_dims[i] = tensor_shape
var_shape = var.shape[1:]
if tensor_shape != var_shape:
raise RuntimeError(
"feed variable '{}' shape not match. infer program shape: {}. feed tensor shape: {}".
format(feed_config.feeded_vars_names[i], var_shape,
tensor_shape))
if not feed_config.feeded_vars_filelist:
print("generate random feed vars.")
for i in range(len(feed_config.feeded_vars_names)):
var = inference_program.global_block().var(
feed_config.feeded_vars_names[i])
# create fake feed tensor. if lod_level > 1, should create_lod_tensor()
if var.lod_level == 0:
feed_tensors.append(
np.array(
np.random.random(
tuple([config.batch_size] + list(
feed_config.feeded_vars_dims[i]))),
dtype=feed_config.feeded_vars_types[i]))
elif var.lod_level == 1:
t = np.array(
np.random.random(
tuple([config.batch_size] + list(
feed_config.feeded_vars_dims[i]))),
dtype=feed_config.feeded_vars_types[i])
feed_tensors.append(
fluid.create_lod_tensor(t, [[1] * config.batch_size
], place))
else:
raise RuntimeError(
"vars with lod_level >= 2 is not supported now in this infer program check tool."
)
results = exe.run(inference_program,
feed={
name: feed_tensors[i]
for i, name in enumerate(feed_name_list)
},
fetch_list=fetch_list,
return_numpy=return_numpy)
else:
print("load feed vars from files: {}.".format(
feed_config.feeded_vars_filelist))
feed_vars = [
inference_program.global_block().var(
feed_config.feeded_vars_names[i])
for i in range(len(feed_config.feeded_vars_names))
]
feeder = fluid.DataFeeder(feed_list=feed_vars, place=place)
batch_feed = feed_gen(config.batch_size,
feed_config.feeded_vars_dims,
feed_config.feeded_vars_filelist)
slots = [batch_feed]
results = exe.run(inference_program,
feed=feeder.feed(slots),
fetch_list=fetch_list,
return_numpy=return_numpy)
for i, v in enumerate(fetch_list):
print("fetch_targets name: %s" % v.name)
print("fetch_targets: {}".format(results[i]))
return results