+ missing script

finetune_launch.py

+#   Copyright (c) 2019 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 absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+from __future__ import absolute_import
+
+import sys
+import subprocess
+import os
+import six
+import copy
+import argparse
+import time
+import logging
+
+from utils.args import ArgumentGroup, print_arguments, prepare_logger
+from finetune_args import parser as worker_parser
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+multip_g = ArgumentGroup(parser, "multiprocessing", 
+        "start paddle training using multi-processing mode.")
+multip_g.add_arg("node_ips", str, None, 
+        "paddle trainer ips")
+multip_g.add_arg("node_id", int, 0, 
+        "the trainer id of the node for multi-node distributed training.")
+multip_g.add_arg("print_config", bool, True, 
+        "print the config of multi-processing mode.")
+multip_g.add_arg("current_node_ip", str, None, 
+        "the ip of current node.")
+multip_g.add_arg("split_log_path", str, "log",
+        "log path for each trainer.")
+multip_g.add_arg("log_prefix", str, "",
+        "the prefix name of job log.")
+multip_g.add_arg("nproc_per_node", int, 8, 
+        "the number of process to use on each node.")
+multip_g.add_arg("selected_gpus", str, "0,1,2,3,4,5,6,7", 
+        "the gpus selected to use.")
+multip_g.add_arg("training_script", str, None, "the program/script to be lauched "
+        "in parallel followed by all the arguments", positional_arg=True)
+multip_g.add_arg("training_script_args", str, None,
+        "training script args", positional_arg=True, nargs=argparse.REMAINDER)
+# yapf: enable
+
+
+log = logging.getLogger()
+
+def start_procs(args):
+    procs = []
+    log_fns = []
+
+    default_env = os.environ.copy()
+
+    node_id = args.node_id
+    node_ips = [x.strip() for x in args.node_ips.split(',')]
+    current_ip = args.current_node_ip
+    if args.current_node_ip is None:
+        assert len(node_ips) == 1
+        current_ip = node_ips[0]
+        log.info(current_ip)
+
+    num_nodes = len(node_ips)
+    selected_gpus = [x.strip() for x in args.selected_gpus.split(',')]
+    selected_gpu_num = len(selected_gpus)
+
+    all_trainer_endpoints = ""
+    for ip in node_ips:
+        for i in range(args.nproc_per_node):
+            if all_trainer_endpoints != "":
+                all_trainer_endpoints += ","
+            all_trainer_endpoints += "%s:617%d" % (ip, i)
+
+    nranks = num_nodes * args.nproc_per_node
+    gpus_per_proc = args.nproc_per_node % selected_gpu_num 
+    if gpus_per_proc == 0:
+        gpus_per_proc =  selected_gpu_num // args.nproc_per_node
+    else:
+        gpus_per_proc =  selected_gpu_num // args.nproc_per_node + 1
+
+    selected_gpus_per_proc = [selected_gpus[i:i + gpus_per_proc] for i in range(0, len(selected_gpus), gpus_per_proc)]
+
+    if args.print_config:
+        log.info("all_trainer_endpoints: %s"
+              ", node_id: %s"
+              ", current_ip: %s"
+              ", num_nodes: %s"
+              ", node_ips: %s"
+              ", gpus_per_proc: %s"
+              ", selected_gpus_per_proc: %s"
+              ", nranks: %s" % (
+                all_trainer_endpoints, 
+                node_id,
+                current_ip,
+                num_nodes,
+                node_ips,
+                gpus_per_proc,
+                selected_gpus_per_proc,
+                nranks))
+
+
+    current_env = copy.copy(default_env)
+    procs = []
+    cmds = []
+    log_fns = []
+    for i in range(0, args.nproc_per_node):
+        trainer_id = node_id * args.nproc_per_node + i
+        assert current_ip is not None
+        current_env.update({
+            "FLAGS_selected_gpus": "%s" % ",".join([str(s) for s in selected_gpus_per_proc[i]]),
+            "PADDLE_TRAINER_ID" : "%d" % trainer_id,
+            "PADDLE_CURRENT_ENDPOINT": "%s:617%d" % (current_ip, i),
+            "PADDLE_TRAINERS_NUM": "%d" % nranks,
+            "PADDLE_TRAINER_ENDPOINTS": all_trainer_endpoints,
+            "PADDLE_NODES_NUM": "%d" % num_nodes
+        })
+
+        try:
+            idx = args.training_script_args.index('--is_distributed')
+            args.training_script_args[idx + 1] = 'true'
+        except ValueError:
+            args.training_script_args += ['--is_distributed', 'true']
+
+        cmd = [sys.executable, "-u",
+               args.training_script] + args.training_script_args
+        cmds.append(cmd)
+
+        if args.split_log_path:
+            fn = open("%s/%sjob.log.%d" % (args.split_log_path, args.log_prefix, trainer_id), "a")
+            log_fns.append(fn)
+            process = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn)
+        else:
+            process = subprocess.Popen(cmd, env=current_env)
+        log.info('subprocess launched')
+        procs.append(process)
+
+    try:
+        for i in range(len(procs)):
+            proc = procs[i]
+            proc.wait()
+            if len(log_fns) > 0:
+                log_fns[i].close()
+            if proc.returncode != 0:    
+                raise subprocess.CalledProcessError(returncode=procs[i].returncode,
+                                                    cmd=cmds[i])
+            else:
+                log.info("proc %d finsh" % i)
+    except KeyboardInterrupt as e:
+        for p in procs:
+            log.info('killing %s' % p)
+            p.terminate()
+
+
+def main(args):
+    if args.print_config:
+        print_arguments(args)
+    start_procs(args)
+
+
+if __name__ == "__main__":
+    prepare_logger(log)
+    lanch_args = parser.parse_args()
+    finetuning_args = worker_parser.parse_args(
+            lanch_args.training_script_args)
+    init_path = finetuning_args.init_pretraining_params 
+    log.info("init model: %s" % init_path)
+    if not finetuning_args.use_fp16:
+        os.system('rename .master "" ' + init_path + '/*.master') 
+    main(lanch_args)

pretrain_launch.py

+#   Copyright (c) 2019 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 absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+from __future__ import absolute_import
+from __future__ import division
+
+import sys
+import subprocess
+import os
+import six
+import copy
+import argparse
+import time
+import logging
+
+from utils.args import ArgumentGroup, print_arguments, prepare_logger
+from pretrain_args import parser as worker_parser
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+multip_g = ArgumentGroup(parser, "multiprocessing", 
+        "start paddle training using multi-processing mode.")
+multip_g.add_arg("node_ips", str, None, 
+        "paddle trainer ips")
+multip_g.add_arg("node_id", int, 0, 
+        "the trainer id of the node for multi-node distributed training.")
+multip_g.add_arg("print_config", bool, True, 
+        "print the config of multi-processing mode.")
+multip_g.add_arg("current_node_ip", str, None, 
+        "the ip of current node.")
+multip_g.add_arg("split_log_path", str, "./log",
+        "log path for each trainer.")
+multip_g.add_arg("log_prefix", str, "",
+        "the prefix name of job log.")
+multip_g.add_arg("nproc_per_node", int, 8, 
+        "the number of process to use on each node.")
+multip_g.add_arg("selected_gpus", str, "0,1,2,3,4,5,6,7", 
+        "the gpus selected to use.")
+multip_g.add_arg("training_script", str, None, "the program/script to be lauched "
+        "in parallel followed by all the arguments", positional_arg=True)
+multip_g.add_arg("training_script_args", str, None,
+        "training script args", positional_arg=True, nargs=argparse.REMAINDER)
+# yapf: enable
+
+
+log = logging.getLogger()
+
+def start_procs(args):
+    procs = []
+    log_fns = []
+
+    default_env = os.environ.copy()
+
+    node_id = args.node_id
+    node_ips = [x.strip() for x in args.node_ips.split(',')]
+    current_ip = args.current_node_ip
+    if args.current_node_ip is None:
+        assert len(node_ips) == 1
+        current_ip = node_ips[0]
+        log.info(current_ip)
+
+    num_nodes = len(node_ips)
+    selected_gpus = [x.strip() for x in args.selected_gpus.split(',')]
+    selected_gpu_num = len(selected_gpus)
+
+    all_trainer_endpoints = ""
+    for ip in node_ips:
+        for i in range(args.nproc_per_node):
+            if all_trainer_endpoints != "":
+                all_trainer_endpoints += ","
+            all_trainer_endpoints += "%s:617%d" % (ip, i)
+
+    nranks = num_nodes * args.nproc_per_node
+    gpus_per_proc = args.nproc_per_node % selected_gpu_num 
+    if gpus_per_proc == 0:
+        gpus_per_proc =  selected_gpu_num // args.nproc_per_node
+    else:
+        gpus_per_proc =  selected_gpu_num // args.nproc_per_node + 1
+
+    log.info(gpus_per_proc)
+    selected_gpus_per_proc = [selected_gpus[i:i + gpus_per_proc] for i in range(0, len(selected_gpus), gpus_per_proc)]
+
+    if args.print_config:
+        log.info("all_trainer_endpoints: %s"
+              ", node_id: %s"
+              ", current_ip: %s"
+              ", num_nodes: %s"
+              ", node_ips: %s"
+              ", gpus_per_proc: %s"
+              ", selected_gpus_per_proc: %s"
+              ", nranks: %s" % (
+                all_trainer_endpoints, 
+                node_id,
+                current_ip,
+                num_nodes,
+                node_ips,
+                gpus_per_proc,
+                selected_gpus_per_proc,
+                nranks))
+
+    current_env = copy.copy(default_env)
+    procs = []
+    cmds = []
+    log_fns = []
+    for i in range(0, args.nproc_per_node):
+        trainer_id = node_id * args.nproc_per_node + i
+        current_env.update({
+            "FLAGS_selected_gpus": "%s" % ",".join([str(s) for s in selected_gpus_per_proc[i]]),
+            "PADDLE_TRAINER_ID" : "%d" % trainer_id,
+            "PADDLE_CURRENT_ENDPOINT": "%s:617%d" % (current_ip, i),
+            "PADDLE_TRAINERS_NUM": "%d" % nranks,
+            "PADDLE_TRAINER_ENDPOINTS": all_trainer_endpoints,
+            "PADDLE_NODES_NUM": "%d" % num_nodes
+        })
+
+        try:
+            idx = args.training_script_args.index('--is_distributed')
+            args.training_script_args[idx + 1] = 'true'
+        except ValueError:
+            args.training_script_args += ['--is_distributed', 'true']
+
+        cmd = [sys.executable, "-u",
+               args.training_script] + args.training_script_args
+        cmds.append(cmd)
+
+        if args.split_log_path:
+            fn = open("%s/%sjob.log.%d" % (args.split_log_path, args.log_prefix, trainer_id), "a")
+            log_fns.append(fn)
+            process = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn)
+        else:
+            process = subprocess.Popen(cmd, env=current_env)
+        log.info('subprocess launched')
+        procs.append(process)
+
+    try:
+        for i in range(len(procs)):
+            proc = procs[i]
+            proc.wait()
+            if len(log_fns) > 0:
+                log_fns[i].close()
+            if proc.returncode != 0:    
+                raise subprocess.CalledProcessError(returncode=procs[i].returncode,
+                                                    cmd=cmds[i])
+            else:
+                log.info("proc %d finsh" % i)
+    except KeyboardInterrupt as e:
+        for p in procs:
+            log.info('killing %s' % p)
+            p.terminate()
+
+
+def main(args):
+    if args.print_config:
+        print_arguments(args)
+    start_procs(args)
+
+
+if __name__ == "__main__":
+    prepare_logger(log)
+    lanch_args = parser.parse_args()
+    pretraining_args = worker_parser.parse_args(
+            lanch_args.training_script_args)
+
+    init_path = pretraining_args.init_checkpoint
+    if init_path and not pretraining_args.use_fp16:
+        os.system('rename .master "" ' + init_path + '/*.master')
+    main(lanch_args)