lookup table utils fix (#14730)

* fix utils config, test=develop

* reweite tools in lookup table utils, test=develop

* merge develop, test=develop

* add copy in setup, test=develop

* update api spec, test=develop

* add doc, test=develop

* prettifying doc, test=develop

* Update API.spec

update api.spec, test=develop

* Update lookup_table_utils.py

test=develop

* Update lookup_table_utils.py

test=develop

* merge develop, test=develop

* merge develop, test=develop

* init fix, test=develop

* fix in downloads, test=develop

* fix in setup.in, test=develop

paddle/fluid/API.spec

@@ -350,6 +350,22 @@ paddle.fluid.contrib.QuantizeTranspiler.__init__ ArgSpec(args=['self', 'weight_b
 paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.contrib.QuantizeTranspiler.freeze_program ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None))
 paddle.fluid.contrib.QuantizeTranspiler.training_transpile ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
+paddle.fluid.contrib.load_persistables_for_increment ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.load_persistables_for_inference ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.convert_dist_to_sparse_program ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.HDFSClient.__init__ ArgSpec(args=['self', 'hadoop_home', 'configs'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.HDFSClient.delete ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.HDFSClient.download ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'unzip'], varargs=None, keywords=None, defaults=(False, False))
+paddle.fluid.contrib.HDFSClient.is_dir ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.contrib.HDFSClient.is_exist ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.contrib.HDFSClient.ls ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.HDFSClient.lsr ArgSpec(args=['self', 'hdfs_path', 'only_file', 'sort'], varargs=None, keywords=None, defaults=(True, True))
+paddle.fluid.contrib.HDFSClient.make_local_dirs ArgSpec(args=['local_path'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.HDFSClient.makedirs ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.HDFSClient.rename ArgSpec(args=['self', 'hdfs_src_path', 'hdfs_dst_path', 'overwrite'], varargs=None, keywords=None, defaults=(False,))
+paddle.fluid.contrib.HDFSClient.upload ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'retry_times'], varargs=None, keywords=None, defaults=(False, 5))
+paddle.fluid.contrib.multi_download ArgSpec(args=['client', 'hdfs_path', 'local_path', 'trainer_id', 'trainers', 'multi_processes'], varargs=None, keywords=None, defaults=(5,))
+paddle.fluid.contrib.multi_upload ArgSpec(args=['client', 'hdfs_path', 'local_path', 'multi_processes', 'overwrite', 'sync'], varargs=None, keywords=None, defaults=(5, False, True))
 paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)

python/paddle/fluid/contrib/__init__.py

@@ -22,9 +22,12 @@ from . import op_frequence
 from .op_frequence import *
 from . import quantize
 from .quantize import *
+from . import utils
+from .utils import *
 
 __all__ = []
 __all__ += decoder.__all__
 __all__ += memory_usage_calc.__all__
 __all__ += op_frequence.__all__
 __all__ += quantize.__all__
+__all__ += utils.__all__

python/paddle/fluid/contrib/utils/__init__.py

@@ -13,10 +13,11 @@
 # limitations under the License.
 
 from __future__ import print_function
-#from . import lookup_table_utils
-#from .lookup_table_utils import *
+from . import lookup_table_utils
+from .lookup_table_utils import *
 from . import hdfs_utils
 from .hdfs_utils import *
 
-#__all__ = lookup_table_utils.__all__
-__all__ = hdfs_utils.__all__
+__all__ = []
+__all__ += lookup_table_utils.__all__
+__all__ += hdfs_utils.__all__

python/paddle/fluid/contrib/utils/hdfs_utils.py

@@ -14,6 +14,7 @@
 """HDFS Utils"""
 
 import os
+import sys
 import subprocess
 import multiprocessing
 from datetime import datetime
@@ -24,7 +25,7 @@ import errno
 
 import logging
 
-__all__ = ["HDFSClient", "multi_download"]
+__all__ = ["HDFSClient", "multi_download", "multi_upload"]
 
 logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
 _logger = logging.getLogger("hdfs_utils")
@@ -93,13 +94,15 @@ class HDFSClient(object):
 
     def upload(self, hdfs_path, local_path, overwrite=False, retry_times=5):
         """
-            upload the local file to hdfs
-            Args:
-                hdfs_path: hdfs path, target path 
-                local_path: local file path, source path
-                overwrite: will overwrite the original file
-                retry_times: max times retry to upload
-            Returns:
+        upload the local file to hdfs
+
+        Args:
+            hdfs_path(str): the hdfs file path
+            local_path(str): the local file path
+            overwrite(bool|None): will overwrite the file on HDFS or not
+            retry_times(int|5): retry times
+
+        Returns:
                 True or False
         """
         assert hdfs_path is not None
@@ -109,7 +112,7 @@ class HDFSClient(object):
             _logger.warn(
                 "The Local path: {} is dir and I will support it later, return".
                 format(local_path))
-            return
+            return False
 
         base = os.path.basename(local_path)
         if not self.is_exist(hdfs_path):
@@ -141,14 +144,16 @@ class HDFSClient(object):
 
     def download(self, hdfs_path, local_path, overwrite=False, unzip=False):
         """
-            download from hdfs
-            Args:
-                hdfs_path: hdfs path, target path 
-                local_path: local file path, source path
-                overwrite: will remove original file and overwrite it.
-                unzip: ignore this param
-            Returns
-                True or False
+        download file from HDFS
+
+        Args:
+            hdfs_path(str): the hdfs file path
+            local_path(str): the local file path
+            overwrite(bool|None): will overwrite the file on HDFS or not
+            unzip(bool|False): if the download file is compressed by zip, unzip it or not.
+
+        Returns:
+            True or False
         """
         _logger.info('Downloading %r to %r.', hdfs_path, local_path)
         _logger.info('Download of %s to %r complete.', hdfs_path, local_path)
@@ -188,13 +193,13 @@ class HDFSClient(object):
 
     def is_exist(self, hdfs_path=None):
         """
-            whether the remote hdfs path exists?
-            Args:
-                hdfs_path: default value(${OUTPUT_PATH}/${SYS_USER_ID}/${SYS_JOB_ID}/tmp)
-                fs_name: The default values are the same as in the job configuration
-                fs_ugi: The default values are the same as in the job configuration
-            Returns:
-                True or False
+        whether the remote HDFS path exists
+
+        Args:
+            hdfs_path(str): the hdfs file path
+
+        Returns:
+            True or False
         """
         exist_cmd = ['-test', '-e', hdfs_path]
         returncode, output, errors = self.__run_hdfs_cmd(
@@ -211,13 +216,13 @@ class HDFSClient(object):
 
     def is_dir(self, hdfs_path=None):
         """
-            whether the remote hdfs path exists?
-            Args:
-                remote_file_path: default value(${OUTPUT_PATH}/${SYS_USER_ID}/${SYS_JOB_ID}/tmp)
-                fs_name: The default values are the same as in the job configuration
-                fs_ugi: The default values are the same as in the job configuration
-            Returns:
-                True or False
+        whether the remote HDFS path is directory
+
+        Args:
+            hdfs_path(str): the hdfs file path
+
+        Returns:
+            True or False
         """
 
         if not self.is_exist(hdfs_path):
@@ -237,17 +242,17 @@ class HDFSClient(object):
 
     def delete(self, hdfs_path):
         """
-            Remove a file or directory from HDFS.
+        Remove a file or directory from HDFS.
+
+        whether the remote HDFS path exists
 
         Args:
-            param hdfs_path: HDFS path.
-            param recursive: Recursively delete files and directories. By default,
-            this method will raise an :class:`HdfsError` if trying to delete a
-            non-empty directory.
+        hdfs_path: HDFS path.
+
         Returns:
+            True or False
             This function returns `True` if the deletion was successful and `False` if
             no file or directory previously existed at `hdfs_path`.
-
         """
         _logger.info('Deleting %r.', hdfs_path)
 
@@ -273,16 +278,14 @@ class HDFSClient(object):
 
     def rename(self, hdfs_src_path, hdfs_dst_path, overwrite=False):
         """
-        Rename a file or folder.
-        Args:    
-            :param hdfs_src_path: Source path.
-            :param hdfs_dst_path: Destination path. If the path already exists and is
-              a directory, the source will be moved into it. If the path exists and is
-              a file, or if a parent destination directory is missing, this method will
-              raise an :class:`HdfsError`.
+        Move a file or folder on HDFS.
+
+        Args:
+        hdfs_path(str): HDFS path.
+        overwrite(bool|False): If the path already exists and overwrite is False, will return False.
+
         Returns:
-             This function returns `True` if the rename was successful and `False` if
-             rename was faild.       
+            True or False
         """
         assert hdfs_src_path is not None
         assert hdfs_dst_path is not None
@@ -320,17 +323,20 @@ class HDFSClient(object):
                 raise
 
     def makedirs(self, hdfs_path):
-        """Create a remote directory, recursively if necessary.
+        """
+        Create a remote directory, recursively if necessary.
+
         Args:
-            :param hdfs_path: Remote path. Intermediate directories will be created
-              appropriately.
+        hdfs_path(str): Remote path. Intermediate directories will be created appropriately.
+
         Returns:
-            True if make a directories was successful, False when make a directiries was failed. 
+            True or False
         """
         _logger.info('Creating directories to %r.', hdfs_path)
         assert hdfs_path is not None
 
         if self.is_exist(hdfs_path):
+            _logger.error("HDFS path is exist: {}".format(hdfs_path))
             return
 
         mkdirs_commands = ['-mkdir', hdfs_path]
@@ -346,11 +352,13 @@ class HDFSClient(object):
 
     def ls(self, hdfs_path):
         """
-        ls a hdfs_path.
-        Args:    
-            :param hdfs_path: hdfs_path will be ls.
+        ls directory contents about HDFS hdfs_path
+
+        Args:
+        hdfs_path(str): Remote HDFS path will be ls.
+
         Returns:
-             This function returns a `list` that contaion all files in the hdfs_path.        
+            List: a contents list about hdfs_path.
         """
         assert hdfs_path is not None
 
@@ -378,11 +386,15 @@ class HDFSClient(object):
 
     def lsr(self, hdfs_path, only_file=True, sort=True):
         """
-        ls a hdfs_path sort by time.
-        Args:    
-            :param hdfs_path: hdfs_path will be ls.
+        list directory contents about HDFS hdfs_path recursively
+
+        Args:
+        hdfs_path(str): Remote HDFS path.
+        only_file(bool|True): will discard folders.
+        sort(bool|True): will be sorted by create time.
+
         Returns:
-             This function returns a `list` that contaion all files sorted by time in the hdfs_path.        
+            List: a contents list about hdfs_path.
         """
 
         def sort_by_time(v1, v2):
@@ -422,21 +434,106 @@ class HDFSClient(object):
             return ret_lines
 
 
+def multi_download(client,
+                   hdfs_path,
+                   local_path,
+                   trainer_id,
+                   trainers,
+                   multi_processes=5):
+    """
+    Download files from HDFS using multi process.
+
+    Args:
+        client(HDFSClient): instance of HDFSClient
+        hdfs_path(str): path on hdfs
+        local_path(str): path on local
+        trainer_id(int): current trainer id
+        trainers(int): all trainers number
+        multi_processes(int|5): the download data process at the same time, default=5
+
+    Returns:
+        List:
+        Download files in local folder.
+    """
+
+    def __subprocess_download(datas):
+        for data in datas:
+            re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
+            if re_path == os.curdir:
+                sub_local_re_path = local_path
+            else:
+                sub_local_re_path = os.path.join(local_path, re_path)
+            client.download(data, sub_local_re_path)
+
+    assert isinstance(client, HDFSClient)
+
+    client.make_local_dirs(local_path)
+    _logger.info("Make local dir {} successfully".format(local_path))
+
+    all_need_download = client.lsr(hdfs_path, sort=True)
+    need_download = all_need_download[trainer_id::trainers]
+    _logger.info("Get {} files From all {} files need to be download from {}".
+                 format(len(need_download), len(all_need_download), hdfs_path))
+
+    _logger.info("Start {} multi process to download datas".format(
+        multi_processes))
+    procs = []
+    for i in range(multi_processes):
+        process_datas = need_download[i::multi_processes]
+        p = multiprocessing.Process(
+            target=__subprocess_download, args=(process_datas, ))
+        procs.append(p)
+        p.start()
+
+    # complete the processes
+    for proc in procs:
+        proc.join()
+
+    _logger.info("Finish {} multi process to download datas".format(
+        multi_processes))
+
+    local_downloads = []
+    for data in need_download:
+        data_name = os.path.basename(data)
+        re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
+        if re_path == os.curdir:
+            local_re_path = os.path.join(local_path, data_name)
+        else:
+            local_re_path = os.path.join(local_path, re_path, data_name)
+        local_downloads.append(local_re_path)
+
+    return local_downloads
+
+
+def getfilelist(path):
+    rlist = []
+    for dir, folder, file in os.walk(path):
+        for i in file:
+            t = os.path.join(dir, i)
+            rlist.append(t)
+    for r in rlist:
+        print(r)
+
+
 def multi_upload(client,
                  hdfs_path,
                  local_path,
                  multi_processes=5,
-                 overwrite=False):
+                 overwrite=False,
+                 sync=True):
     """
-    Upload file to hdfs.
+    Upload files to HDFS using multi process.
+
     Args:
-        :param overwrite: will overwrite hdfs file or not
-        :param multi_processes: the upload data process at the same time, default=5
-        :param client: instance of HDFSClient
-        :param hdfs_path: path on hdfs
-        :param local_path: path on local
+        client(HDFSClient): instance of HDFSClient
+        hdfs_path(str): path on hdfs
+        local_path(str): path on local
+        multi_processes(int|5): the upload data process at the same time, default=5
+        overwrite(bool|False): will overwrite file on HDFS or not
+        sync(bool|True): upload files sync or not.
+
     Returns:
-        
+        None
     """
 
     def __subprocess_upload(datas):
@@ -446,13 +543,6 @@ def multi_upload(client,
             client.upload(hdfs_re_path, data, overwrite, retry_times=5)
 
     def get_local_files(path):
-        """
-            Get all local files
-        Args:
-            path: local file path
-        Returns:
-            A list that contation all files in the path.
-        """
         rlist = []
 
         if not os.path.isdir(path):
@@ -488,71 +578,6 @@ def multi_upload(client,
         multi_processes))
 
 
-def multi_download(client,
-                   hdfs_path,
-                   local_path,
-                   trainer_id,
-                   trainers,
-                   file_cnt,
-                   multi_processes=5):
-    """
-    multi_download
-    Args:
-        :param client: instance of HDFSClient
-        :param hdfs_path: path on hdfs
-        :param local_path: path on local
-        :param trainer_id: current trainer id
-        :param trainers: all trainers number
-        :param file_cnt: all file number
-        :param multi_processes: the download data process at the same time, default=5
-        :return: None
-    Returns:
-        A list that be downloaded. 
-    """
-
-    def __subprocess_download(datas):
-        for data in datas:
-            re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
-            local_re_path = os.path.join(local_path, re_path)
-            client.download(data, local_re_path)
-
-    assert isinstance(client, HDFSClient)
-
-    client.make_local_dirs(local_path)
-    _logger.info("Make local dir {} successfully".format(local_path))
-
-    all_need_download = client.lsr(hdfs_path, sort=True)[:file_cnt]
-    need_download = all_need_download[trainer_id::trainers]
-    _logger.info("Get {} files From all {} files need to be download from {}".
-                 format(len(need_download), len(all_need_download), hdfs_path))
-
-    _logger.info("Start {} multi process to download datas".format(
-        multi_processes))
-    procs = []
-    for i in range(multi_processes):
-        process_datas = need_download[i::multi_processes]
-        p = multiprocessing.Process(
-            target=__subprocess_download, args=(process_datas, ))
-        procs.append(p)
-        p.start()
-
-    # complete the processes
-    for proc in procs:
-        proc.join()
-
-    _logger.info("Finish {} multi process to download datas".format(
-        multi_processes))
-
-    local_downloads = []
-    for data in need_download:
-        data_name = os.path.basename(data)
-        re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
-        local_re_path = os.path.join(local_path, re_path, data_name)
-        local_downloads.append(local_re_path)
-
-    return local_downloads
-
-
 if __name__ == "__main__":
     hadoop_home = "/home/client/hadoop-client/hadoop/"
 

python/paddle/fluid/contrib/utils/lookup_table_utils.py

@@ -18,14 +18,12 @@ import os
 import time
 import logging
 
-import paddle
-import paddle.fluid as fluid
 from paddle.fluid import core
 from paddle.fluid import io
 from paddle.fluid import Program
 
 __all__ = [
-    "load_inference_model", "load_persistable_vars",
+    "load_persistables_for_increment", "load_persistables_for_inference",
     "convert_dist_to_sparse_program"
 ]
 
@@ -80,19 +78,28 @@ def __get_prefetch_op_tuples(main_program):
     return prefetch_op_tuples
 
 
-def convert_dist_to_sparse_program(main_program):
-    if not main_program._distributed_lookup_table:
+def convert_dist_to_sparse_program(program):
+    """
+    WARNING: this function will only be used for distributed training with distributed lookup table.
+    when we train model with distributed lookup table but want to do the local inference, we can use
+    this function to convert the train program with distributed lookup table to sparse lookup table.
+
+    :param program(Program): the program must be the trainer program, which will be get by the distribute transpiler.
+    :return:
+        program: The `program` is a Program, it's the program replace distributed lookup table to sparse lookup table.
+    """
+    if not program._distributed_lookup_table:
         _logger.warn(
             "There are no distributed lookup tables need to be converted")
         return
 
     # create table param and grad var in pserver program
-    origin_emb_var = "{}.origin".format(main_program._distributed_lookup_table)
-    emb_var = main_program._distributed_lookup_table
-    main_program.global_block()._rename_var(emb_var, origin_emb_var)
-    origin_param_var = main_program.global_block().vars[origin_emb_var]
+    origin_emb_var = "{}.origin".format(program._distributed_lookup_table)
+    emb_var = program._distributed_lookup_table
+    program.global_block()._rename_var(emb_var, origin_emb_var)
+    origin_param_var = program.global_block().vars[origin_emb_var]
 
-    param_var = main_program.global_block().create_var(
+    param_var = program.global_block().create_var(
         name=emb_var,
         shape=origin_param_var.shape,
         dtype=origin_param_var.dtype,
@@ -100,28 +107,28 @@ def convert_dist_to_sparse_program(main_program):
         persistable=True)
     # parameter must be selected rows
     param_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS)
-    main_program._sync_with_cpp()
+    program._sync_with_cpp()
 
-    prefetch_op_tuples = __get_prefetch_op_tuples(main_program)
+    prefetch_op_tuples = __get_prefetch_op_tuples(program)
 
     split_ids_id = prefetch_op_tuples[0]
 
     for idx in range(split_ids_id + 2, split_ids_id - 1, -1):
-        main_program.global_block()._remove_op(idx)
-    main_program.desc.flush()
+        program.global_block()._remove_op(idx)
+    program.desc.flush()
 
     in_out_pairs = zip(prefetch_op_tuples[1], prefetch_op_tuples[2])
 
     for in_out_pair in in_out_pairs:
         idx = split_ids_id
-        ids = main_program.global_block().vars[in_out_pair[0]]
-        out = main_program.global_block().vars[in_out_pair[1]]
-        __insert_lookup_sparse_table_op(main_program, idx, ids, param_var, out)
-        main_program.desc.flush()
-    return main_program
+        ids = program.global_block().vars[in_out_pair[0]]
+        out = program.global_block().vars[in_out_pair[1]]
+        __insert_lookup_sparse_table_op(program, idx, ids, param_var, out)
+        program.desc.flush()
+    return program
 
 
-def load_persistable_vars(executor, dirname, program, lookup_table_var):
+def _load_persistable_vars(executor, dirname, program, lookup_table_vars):
     def _is_checkpoint_var(exclude_fluid_vars=None):
         """
         the checkpoint will not save or load all the variables.
@@ -159,8 +166,82 @@ def load_persistable_vars(executor, dirname, program, lookup_table_var):
 
         return is_valid
 
-    def _load_lookup_table_vars(executor, dirname, main_program,
-                                lookup_table_vars):
+    io.load_vars(
+        executor,
+        dirname=dirname,
+        main_program=program,
+        predicate=_is_checkpoint_var(lookup_table_vars),
+        filename=None)
+
+
+def load_persistables_for_increment(dirname, executor, program,
+                                    lookup_table_var, lookup_table_var_path):
+    """
+    WARNING: this function will only be used for distributed training with distributed lookup table.
+    for increment trainning, the pserver will not only load dense variables,
+    but also load the suitable lookup table var. Because of slice lookup table
+    var with HASH, we must load the correct slice var.
+
+
+    :param dirname(str): The directory path
+    :param executor(Executor): The executor to run for loading inference model.
+    :param program(Program): The parameter server program, which will run on Pserver.
+    :param lookup_table_var: the distributed lookup tables var name.
+    :param lookup_table_var_path: the the distributed lookup tables var location.
+    :return: None
+    """
+
+    def __load_lookup_table_vars(executor, main_program, lookup_table_var,
+                                 lookup_table_var_path):
+        emb_var = main_program.global_block().var(lookup_table_var)
+
+        load_program = Program()
+        load_block = load_program.global_block()
+        load_block.append_op(
+            type='load',
+            inputs={},
+            outputs={'Out': [emb_var]},
+            attrs={'file_path': lookup_table_var_path})
+        executor.run(load_program)
+
+    if not os.path.isdir(dirname):
+        raise ValueError("There is no directory named '%s'", dirname)
+
+    if not os.path.exists(lookup_table_var_path):
+        raise ValueError("There is no file named '%s'", lookup_table_var_path)
+
+    if not isinstance(program, Program):
+        raise ValueError("program must be an instance of fluid.Program")
+
+    _logger.info("Start Load Sparse Program With "
+                 "Distributed Lookup Table Vars from {}, time = {}".format(
+                     dirname, time.ctime()))
+
+    _load_persistable_vars(executor, dirname, program, [lookup_table_var])
+    __load_lookup_table_vars(executor, program, lookup_table_var,
+                             lookup_table_var_path)
+
+    _logger.info("Finish Load Sparse Program With "
+                 "Distributed Lookup Table Vars from {}, time = {}".format(
+                     dirname, time.ctime()))
+
+
+def load_persistables_for_inference(dirname, executor, program,
+                                    lookup_table_var_name):
+    """
+    WARNING: this function will only be used for inference with distributed lookup table.
+    Inference with distributed lookup table is a little funky, this function will load distributed
+    lookup table vars into sparse var, can be used in local inference mode.
+
+    :param dirname(str): The directory path
+    :param executor(Executor): The executor to run for loading inference model.
+    :param program(Program): The parameter server program, which will run on Pserver.
+    :param lookup_table_var_name: the distributed lookup tables var name.
+    :return: None
+    """
+
+    def __load_lookup_table_vars(executor, dirname, main_program,
+                                 lookup_table_vars):
         if not os.path.isdir(dirname):
             raise ValueError("There is no directory named '%s'", dirname)
 
@@ -209,48 +290,34 @@ def load_persistable_vars(executor, dirname, program, lookup_table_var):
         global_block.append_op(type='delete_var', inputs={'X': sums})
         executor.run(convert_program)
 
-    _logger.info("Start Load Sparse Program With "
-                 "Distributed Lookup Table Vars from {}, time = {}".format(
-                     dirname, time.ctime()))
-
-    lookup_table_vars = [lookup_table_var]
-
-    io.load_vars(
-        executor,
-        dirname=dirname,
-        main_program=program,
-        predicate=_is_checkpoint_var(lookup_table_vars),
-        filename=None)
-
-    _load_lookup_table_vars(executor, dirname, program, lookup_table_vars)
-
-    _logger.info("Finish Load Sparse Program With "
-                 "Distributed Lookup Table Vars from {}, time = {}".format(
-                     dirname, time.ctime()))
-
-
-def load_inference_model(dirname, executor, lookup_table_var_name):
     if not os.path.isdir(dirname):
         raise ValueError("There is no directory named '%s'", dirname)
 
-    local_model = os.path.join(dirname, model_filename)
+    if program:
+        if not isinstance(program, Program):
+            raise ValueError("program must be an instance of fluid.Program")
+    else:
+        local_model = os.path.join(dirname, model_filename)
 
-    with open(local_model, "rb") as f:
-        program_desc_str = f.read()
+        with open(local_model, "rb") as f:
+            program_desc_str = f.read()
 
-    program = Program.parse_from_string(program_desc_str)
+        program = Program.parse_from_string(program_desc_str)
 
-    if not core._is_program_version_supported(program._version()):
-        raise ValueError("Unsupported program version: %d\n" %
-                         program._version())
+        if not core._is_program_version_supported(program._version()):
+            raise ValueError("Unsupported program version: %d\n" %
+                             program._version())
 
-    # Binary data also need version.
-    load_persistable_vars(executor, dirname, program, lookup_table_var_name)
+    _logger.info("Start Load Sparse Program With "
+                 "Distributed Lookup Table Vars from {}, time = {}".format(
+                     dirname, time.ctime()))
+
+    _load_persistable_vars(executor, dirname, program, [lookup_table_var_name])
+    __load_lookup_table_vars(executor, dirname, program,
+                             [lookup_table_var_name])
 
-    feed_target_names = program.desc.get_feed_target_names()
-    fetch_target_names = program.desc.get_fetch_target_names()
-    fetch_targets = [
-        program.global_block().var(name) for name in fetch_target_names
-    ]
+    _logger.info("Finish Load Sparse Program With "
+                 "Distributed Lookup Table Vars from {}, time = {}".format(
+                     dirname, time.ctime()))
 
-    return [program, feed_target_names, fetch_targets]
+    return program

python/setup.py.in

@@ -107,9 +107,9 @@ packages=['paddle',
           'paddle.fluid.distributed',
           'paddle.fluid.layers',
           'paddle.fluid.contrib',
-          'paddle.fluid.contrib.utils',
           'paddle.fluid.contrib.decoder',
           'paddle.fluid.contrib.quantize',
+          'paddle.fluid.contrib.utils',
           'paddle.fluid.transpiler',
           'paddle.fluid.transpiler.details']