add word2vec ce

fluid/PaddleRec/word2vec/.run_ce.sh

+#!/bin/bash
+
+export MKL_NUM_THREADS=1
+export OMP_NUM_THREADS=1
+
+
+export CPU_NUM=1
+
+FLAGS_benchmark=true  python train.py --train_data_path ./data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled --dict_path data/1-billion_dict --with_hs --is_local --num_passes 10 --enable_ce | python _ce.py
+
+export CPU_NUM=8
+
+FLAGS_benchmark=true  python train.py --train_data_path ./data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled --dict_path data/1-billion_dict --with_hs --is_local --num_passes 10 --enable_ce | python _ce.py

fluid/PaddleRec/word2vec/_ce.py

+# this file is only used for continuous evaluation test!
+
+import os
+import sys
+sys.path.append(os.environ['ceroot'])
+from kpi import CostKpi
+from kpi import DurationKpi
+from kpi import AccKpi
+
+
+each_pass_duration_cpu1_thread1_kpi = DurationKpi('each_pass_duration_cpu1_thread1', 0.08, 0, actived=True)
+train_loss_cpu1_thread1_kpi = CostKpi('train_loss_cpu1_thread1', 0.08, 0)
+each_pass_duration_cpu8_thread8_kpi = DurationKpi('each_pass_duration_cpu8_thread8', 0.08, 0, actived=True)
+train_loss_cpu8_thread8_kpi = CostKpi('train_loss_cpu8_thread8', 0.08, 0)
+
+tracking_kpis = [
+        each_pass_duration_cpu1_thread1_kpi,
+        train_loss_cpu1_thread1_kpi,
+        each_pass_duration_cpu8_thread8_kpi,
+        train_loss_cpu8_thread8_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_cost\t1.0
+    test_cost\t1.0
+    train_cost\t1.0
+    train_cost\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':
+            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()
+    log_to_ce(log)

fluid/PaddleRec/word2vec/train.py

@@ -129,6 +129,11 @@ def parse_args():
         default=4,
         help="find rank_num-nearest result for test (default: 4)")
 
+    parser.add_argument(
+        '--enable_ce',
+        action='store_true',
+        help='If set, run the task with continuous evaluation logs.')
+
     return parser.parse_args()
 
 
@@ -198,6 +203,8 @@ def train_loop(args, train_program, reader, py_reader, loss, trainer_id):
     profiler_step_start = 20
     profiler_step_end = 30
 
+    total_time = 0
+    ce_info = []
     for pass_id in range(args.num_passes):
         py_reader.start()
         time.sleep(10)
@@ -206,11 +213,14 @@ def train_loop(args, train_program, reader, py_reader, loss, trainer_id):
         start = time.time()
 
         try:
-            while True:
+                start_time = time.time()
 
                 loss_val = train_exe.run(fetch_list=[loss.name])
                 loss_val = np.mean(loss_val)
 
+                total_time += time.time() - start_time
+                ce_info.append(loss_val.mean())
+
                 if batch_id % 50 == 0:
                     logger.info(
                         "TRAIN --> pass: {} batch: {} loss: {} reader queue:{}".
@@ -250,6 +260,27 @@ def train_loop(args, train_program, reader, py_reader, loss, trainer_id):
                 fluid.io.save_persistables(executor=exe, dirname=model_dir)
                 with open(model_dir + "/_success", 'w+') as f:
                     f.write(str(pass_id))
+    # only for ce
+    if args.enable_ce:
+        threads_num, cpu_num = get_cards(args)
+        epoch_idx = args.num_passes 
+        ce_loss = 0
+        try:
+            ce_loss = ce_info[-1]
+        except:
+            logger.error("ce info error")
+
+        print("kpis\teach_pass_duration_cpu%s_thread%s\t%s" %
+                (cpu_num, threads_num, total_time / epoch_idx))
+        print("kpis\ttrain_loss_cpu%s_thread%s\t%s" %
+                (cpu_num, threads_num, ce_loss))
+
+
+def get_cards(args):
+    threads_num = os.environ.get('CPU_NUM', 1)
+    cpu_num = os.environ.get('CPU_NUM', 1)
+    return int(threads_num), int(cpu_num)
+
 
 
 def GetFileList(data_path):