fix split files at PY3 (#103)

* fix split files at PY3

* fix linux at PY3

* fix desc error

* fix collective cards and worknum
Co-authored-by: Ntangwei <tangwei12@baidu.com>

core/trainers/framework/dataset.py

@@ -15,13 +15,13 @@
 from __future__ import print_function
 
 import os
-import warnings
 
 import paddle.fluid as fluid
 from paddlerec.core.utils import envs
 from paddlerec.core.utils import dataloader_instance
 from paddlerec.core.reader import SlotReader
 from paddlerec.core.trainer import EngineMode
+from paddlerec.core.utils.util import split_files
 
 __all__ = ["DatasetBase", "DataLoader", "QueueDataset"]
 
@@ -123,7 +123,8 @@ class QueueDataset(DatasetBase):
             for x in os.listdir(train_data_path)
         ]
         if context["engine"] == EngineMode.LOCAL_CLUSTER:
-            file_list = context["fleet"].split_files(file_list)
+            file_list = split_files(file_list, context["fleet"].worker_index(),
+                                    context["fleet"].worker_num())
 
         dataset.set_filelist(file_list)
         for model_dict in context["phases"]:

core/utils/dataloader_instance.py

@@ -19,6 +19,7 @@ from paddlerec.core.utils.envs import get_global_env
 from paddlerec.core.utils.envs import get_runtime_environ
 from paddlerec.core.reader import SlotReader
 from paddlerec.core.trainer import EngineMode
+from paddlerec.core.utils.util import split_files
 
 
 def dataloader_by_name(readerclass,
@@ -39,7 +40,8 @@ def dataloader_by_name(readerclass,
 
     files = [str(data_path) + "/%s" % x for x in os.listdir(data_path)]
     if context["engine"] == EngineMode.LOCAL_CLUSTER:
-        files = context["fleet"].split_files(files)
+        files = split_files(files, context["fleet"].worker_index(),
+                            context["fleet"].worker_num())
         print("file_list : {}".format(files))
 
     reader = reader_class(yaml_file)
@@ -80,7 +82,8 @@ def slotdataloader_by_name(readerclass, dataset_name, yaml_file, context):
 
     files = [str(data_path) + "/%s" % x for x in os.listdir(data_path)]
     if context["engine"] == EngineMode.LOCAL_CLUSTER:
-        files = context["fleet"].split_files(files)
+        files = split_files(files, context["fleet"].worker_index(),
+                            context["fleet"].worker_num())
         print("file_list: {}".format(files))
 
     sparse = get_global_env(name + "sparse_slots", "#")
@@ -133,7 +136,8 @@ def slotdataloader(readerclass, train, yaml_file, context):
 
     files = [str(data_path) + "/%s" % x for x in os.listdir(data_path)]
     if context["engine"] == EngineMode.LOCAL_CLUSTER:
-        files = context["fleet"].split_files(files)
+        files = split_files(files, context["fleet"].worker_index(),
+                            context["fleet"].worker_num())
         print("file_list: {}".format(files))
 
     sparse = get_global_env("sparse_slots", "#", namespace)

core/utils/envs.py

@@ -18,6 +18,7 @@ import copy
 import os
 import socket
 import sys
+import six
 import traceback
 import six
 
@@ -102,6 +103,12 @@ def set_global_envs(envs):
             name = ".".join(["dataset", dataset["name"], "type"])
             global_envs[name] = "DataLoader"
 
+    if get_platform() == "LINUX" and six.PY3:
+        print("QueueDataset can not support PY3, change to DataLoader")
+        for dataset in envs["dataset"]:
+            name = ".".join(["dataset", dataset["name"], "type"])
+            global_envs[name] = "DataLoader"
+
 
 def get_global_env(env_name, default_value=None, namespace=None):
     """

core/utils/util.py

@@ -19,11 +19,8 @@ import time
 import numpy as np
 from paddle import fluid
 
-from paddlerec.core.utils import fs as fs
-
 
 def save_program_proto(path, program=None):
-
     if program is None:
         _program = fluid.default_main_program()
     else:
@@ -171,6 +168,39 @@ def print_cost(cost, params):
     return log_str
 
 
+def split_files(files, trainer_id, trainers):
+    """
+    split files before distributed training,
+    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 belongs to this worker.
+    """
+    if not isinstance(files, list):
+        raise TypeError("files should be a list of file need to be read.")
+
+    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]
+
+
 class CostPrinter(object):
     """
     For count cost time && print cost log

run.py

@@ -139,7 +139,7 @@ def get_engine(args, running_config, mode):
             engine = "LOCAL_CLUSTER_TRAIN"
 
     if engine not in engine_choices:
-        raise ValueError("{} can not be chosen in {}".format(engine_class,
+        raise ValueError("{} can only be chosen in {}".format(engine_class,
                                                               engine_choices))
 
     run_engine = engines[transpiler].get(engine, None)
@@ -439,8 +439,8 @@ def local_cluster_engine(args):
     if fleet_mode == "COLLECTIVE":
         cluster_envs["selected_gpus"] = selected_gpus
         gpus = selected_gpus.split(",")
-        gpu_num = get_worker_num(run_extras, len(gpus))
-        cluster_envs["selected_gpus"] = ','.join(gpus[:gpu_num])
+        worker_num = get_worker_num(run_extras, len(gpus))
+        cluster_envs["selected_gpus"] = ','.join(gpus[:worker_num])
 
     cluster_envs["server_num"] = server_num
     cluster_envs["worker_num"] = worker_num