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
6 years ago · 45dd3491db
parent e7675dd542
commit 45dd3491db
6 changed files with 313 additions and 201 deletions
--- a/paddle/fluid/API.spec
+++ b/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)
--- a/python/paddle/fluid/contrib/init.py
+++ b/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__
--- a/python/paddle/fluid/contrib/utils/init.py
+++ b/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__
--- a/python/paddle/fluid/contrib/utils/hdfs_utils.py
+++ b/python/paddle/fluid/contrib/utils/hdfs_utils.py
--- a/python/paddle/fluid/contrib/utils/lookup_table_utils.py
+++ b/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.
@ -159,7 +166,81 @@ def load_persistable_vars(executor, dirname, program, lookup_table_var):

        return is_valid

-    def _load_lookup_table_vars(executor, dirname, main_program,
+    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)
@ -209,30 +290,13 @@ 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)

+    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:
@ -244,13 +308,16 @@ def load_inference_model(dirname, executor, lookup_table_var_name):
            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()))

-    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
-    ]
+    _load_persistable_vars(executor, dirname, program, [lookup_table_var_name])
+    __load_lookup_table_vars(executor, dirname, program,
+                             [lookup_table_var_name])
+
+    _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
--- a/python/setup.py.in
+++ b/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']