add ce for ERNIE

ERNIE/.run_ce.sh

+set -eux
+
+export FLAGS_sync_nccl_allreduce=1
+MODEL_PATH=ERNIE_1.0.1
+TASK_DATA_PATH=task_data
+
+train() {
+python -u run_classifier.py \
+                   --use_cuda true \
+                   --do_train true \
+                   --do_val true \
+                   --do_test true \
+                   --verbose true \
+                   --batch_size 8192 \
+                   --in_tokens true \
+                   --init_pretraining_params ${MODEL_PATH}/params \
+                   --train_set ${TASK_DATA_PATH}/xnli/train.tsv \
+                   --dev_set ${TASK_DATA_PATH}/xnli/dev.tsv \
+                   --test_set ${TASK_DATA_PATH}/xnli/test.tsv \
+                   --vocab_path config/vocab.txt \
+                   --label_map ${TASK_DATA_PATH}/xnli/label_map.json \
+                   --ernie_config_path config/ernie_config.json \
+                   --checkpoints ./checkpoints \
+                   --save_steps 2000 \
+                   --weight_decay  0.01 \
+                   --warmup_proportion 0.0 \
+                   --validation_steps 25 \
+                   --epoch 1 \
+                   --max_seq_len 512 \
+                   --learning_rate 1e-4 \
+                   --skip_steps 10 \
+                   --num_iteration_per_drop_scope 1 \
+                   --num_labels 3 \
+                   --random_seed 100 \
+                   --enable_ce \
+                   --shuffle false
+}
+
+export CUDA_VISIBLE_DEVICES=0
+train | python _ce.py
+
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+train | python _ce.py

ERNIE/_ce.py

+####this file is only used for continuous evaluation test!
+
+import os
+import sys
+sys.path.insert(0, os.environ['ceroot'])
+from kpi import CostKpi, DurationKpi, AccKpi
+
+#### NOTE kpi.py should shared in models in some way!!!!
+
+train_loss_card1_kpi = CostKpi('train_loss_card1', 0.03, 0, actived=True)
+train_acc_card1_kpi = AccKpi('train_acc_card1', 0.06, 0, actived=True)
+train_duration_card1_kpi = DurationKpi(
+    'train_duration_card1', 0.01, 0, actived=True)
+train_loss_card4_kpi = CostKpi('train_loss_card4', 0.01, 0, actived=True)
+train_acc_card4_kpi = AccKpi('train_acc_card4', 0.02, 0, actived=True)
+train_duration_card4_kpi = DurationKpi(
+    'train_duration_card4', 0.02, 0, actived=True)
+
+tracking_kpis = [
+        train_loss_card1_kpi,
+        train_acc_card1_kpi,
+        train_duration_card1_kpi,
+        train_loss_card4_kpi,
+        train_acc_card4_kpi,
+        train_duration_card4_kpi,
+]
+
+
+def parse_log(log):
+    '''
+    This method should be implemented by model developers.
+    The suggestion:
+    each line in the log should be key, value, for example:
+    "
+    train_loss\t1.0
+    test_loss\t1.0
+    train_loss\t1.0
+    train_acc\t1.2
+    "
+    '''
+    for line in log.split('\n'):
+        fs = line.strip().split('\t')
+        print(fs)
+        if len(fs) == 3 and fs[0] == 'kpis':
+            print("-----%s" % fs)
+            kpi_name = fs[1]
+            kpi_value = float(fs[2])
+            yield kpi_name, kpi_value
+
+
+def log_to_ce(log):
+    kpi_tracker = {}
+    for kpi in tracking_kpis:
+        kpi_tracker[kpi.name] = kpi
+
+    for (kpi_name, kpi_value) in parse_log(log):
+        print(kpi_name, kpi_value)
+        kpi_tracker[kpi_name].add_record(kpi_value)
+        kpi_tracker[kpi_name].persist()
+
+
+if __name__ == '__main__':
+    log = sys.stdin.read()
+    print("*****")
+    print(log)
+    print("****")
+    log_to_ce(log)

ERNIE/finetune_args.py

@@ -74,4 +74,7 @@ run_type_g.add_arg("do_train",                     bool,   True,  "Whether to pe
 run_type_g.add_arg("do_val",                       bool,   True,  "Whether to perform evaluation on dev data set.")
 run_type_g.add_arg("do_test",                      bool,   True,  "Whether to perform evaluation on test data set.")
 run_type_g.add_arg("metrics",                      bool,   True,  "Whether to perform evaluation on test data set.")
+run_type_g.add_arg("shuffle",                      bool,   True,  "")
+
+parser.add_argument("--enable_ce", action='store_true', help="The flag indicating whether to run the task for continuous evaluation.")
 # yapf: enable

ERNIE/run_classifier.py

@@ -29,6 +29,7 @@ from finetune.classifier import create_model, evaluate
 from optimization import optimization
 from utils.args import print_arguments, check_cuda
 from utils.init import init_pretraining_params, init_checkpoint
+from utils.cards import get_cards
 from finetune_args import parser
 
 args = parser.parse_args()
@@ -67,7 +68,7 @@ def main(args):
             input_file=args.train_set,
             batch_size=args.batch_size,
             epoch=args.epoch,
-            shuffle=True,
+            shuffle=args.shuffle,
             phase="train")
 
         num_train_examples = reader.get_num_examples(args.train_set)
@@ -85,6 +86,8 @@ def main(args):
         print("Num warmup steps: %d" % warmup_steps)
 
         train_program = fluid.Program()
+        if args.random_seed is not None and args.enable_ce:
+            train_program.random_seed = args.random_seed
 
         with fluid.program_guard(train_program, startup_prog):
             with fluid.unique_name.guard():
@@ -187,6 +190,7 @@ def main(args):
         if warmup_steps > 0:
             graph_vars["learning_rate"] = scheduled_lr
 
+        ce_info = []
         time_begin = time.time()
         while True:
             try:
@@ -213,6 +217,7 @@ def main(args):
                           (current_epoch, current_example, num_train_examples,
                            steps, outputs["loss"], outputs["accuracy"],
                            args.skip_steps / used_time))
+                    ce_info.append([outputs["loss"], outputs["accuracy"], used_time])
                     time_begin = time.time()
 
                 if steps % args.save_steps == 0:
@@ -246,6 +251,24 @@ def main(args):
                 fluid.io.save_persistables(exe, save_path, train_program)
                 train_pyreader.reset()
                 break
+        if args.enable_ce:
+            card_num = get_cards()
+            ce_loss = 0
+            ce_acc = 0
+            ce_time = 0
+            try:
+                ce_loss = ce_info[-2][0]
+                ce_acc = ce_info[-2][1]
+                ce_time = ce_info[-2][2]
+            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_acc_card%s\t%f" %
+                (card_num, ce_acc))
+
 
     # final eval on dev set
     if args.do_val:

ERNIE/utils/cards.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.
+
+
+import os
+
+def get_cards():
+    """
+    get gpu cards number
+    """
+    num = 0
+    cards = os.environ.get('CUDA_VISIBLE_DEVICES', '')
+    if cards != '':
+        num = len(cards.split(","))
+    return num
+
+