Merge pull request #216 from tianxin1860/develop

add utils/init.py

ELMo/train.py

@@ -17,24 +17,27 @@
 from __future__ import print_function
 
 import six
-import numpy as np
+import os
 import random
 import time
-import os
 import math
+import pickle
+import logging
+
+import numpy as np
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import paddle.fluid.framework as framework
 from paddle.fluid.executor import Executor
+
 import data
-from args import *
-from utils.cards import get_cards
 import lm_model
-import logging
+from args import parse_args
+from utils.cards import get_cards
+from utils.init import init_pretraining_params
 
 logging.basicConfig()
-import pickle
 
 
 def prepare_batch_input(batch, args):
@@ -576,11 +579,8 @@ def train_loop(args,
             ce_time = ce_info[-2][1]
         except:
             print("ce info error")
-        print("kpis\ttrain_duration_card%s\t%s" %
-            (card_num, ce_time))
-        print("kpis\ttrain_loss_card%s\t%f" %
-            (card_num, ce_loss))
-
+        print("kpis\ttrain_duration_card%s\t%s" % (card_num, ce_time))
+        print("kpis\ttrain_loss_card%s\t%f" % (card_num, ce_loss))
 
     end_time = time.time()
     total_time += end_time - start_time

ELMo/utils/init.py

@@ -0,0 +1,67 @@
+#   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 print_function
+
+import os
+import six
+import ast
+import copy
+
+import numpy as np
+import paddle.fluid as fluid
+
+
+def init_checkpoint(exe, init_checkpoint_path, main_program, use_fp16=False):
+    assert os.path.exists(
+        init_checkpoint_path), "[%s] cann't be found." % init_checkpoint_path
+
+    def existed_persitables(var):
+        if not fluid.io.is_persistable(var):
+            return False
+        return os.path.exists(os.path.join(init_checkpoint_path, var.name))
+
+    fluid.io.load_vars(
+        exe,
+        init_checkpoint_path,
+        main_program=main_program,
+        predicate=existed_persitables)
+    print("Load model from {}".format(init_checkpoint_path))
+
+    if use_fp16:
+        cast_fp32_to_fp16(exe, main_program)
+
+
+def init_pretraining_params(exe,
+                            pretraining_params_path,
+                            main_program,
+                            use_fp16=False):
+    assert os.path.exists(pretraining_params_path
+                          ), "[%s] cann't be found." % pretraining_params_path
+
+    def existed_params(var):
+        if not isinstance(var, fluid.framework.Parameter):
+            return False
+        return os.path.exists(os.path.join(pretraining_params_path, var.name))
+
+    fluid.io.load_vars(
+        exe,
+        pretraining_params_path,
+        main_program=main_program,
+        predicate=existed_params)
+    print("Load pretraining parameters from {}.".format(
+        pretraining_params_path))
+
+    if use_fp16:
+        cast_fp32_to_fp16(exe, main_program)