add gru4rec ce

fluid/PaddleRec/gru4rec/.run_ce.sh

+#!/bin/bash
+
+export MKL_NUM_THREADS=1
+export OMP_NUM_THREADS=1
+
+
+cudaid=${tagspace:=0} # use 0-th card as default
+export CUDA_VISIBLE_DEVICES=$cudaid
+
+FLAGS_benchmark=true  python train.py --train_dir train_big_data --vocab_path vocab_big.txt --use_cuda 1 --batch_size 500 --model_dir model_output --pass_num 2 --enable_ce | python _ce.py
+
+
+cudaid=${tagspace_4:=0,1,2,3} # use 0-th card as default
+export CUDA_VISIBLE_DEVICES=$cudaid
+
+FLAGS_benchmark=true  python train.py --train_dir train_big_data --vocab_path vocab_big.txt --use_cuda 1 --parallel 1 --num_devices 2 --batch_size 500 --model_dir model_output --pass_num 2 --enable_ce | python _ce.py

fluid/PaddleRec/gru4rec/_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_gpu1_kpi = DurationKpi('each_pass_duration_gpu1', 0.08, 0, actived=True)
+train_ppl_gpu1_kpi = CostKpi('train_ppl_gpu1', 0.08, 0)
+each_pass_duration_gpu4_kpi = DurationKpi('each_pass_duration_gpu4', 0.08, 0, actived=True)
+train_ppl_gpu4_kpi = CostKpi('train_ppl_gpu4', 0.08, 0)
+
+tracking_kpis = [
+        each_pass_duration_gpu1_kpi,
+        train_ppl_gpu1_kpi,
+        each_pass_duration_gpu4_kpi,
+        train_ppl_gpu4_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/gru4rec/train.py

@@ -40,6 +40,10 @@ def parse_args():
         '--base_lr', type=float, default=0.01, help='learning rate')
     parser.add_argument(
         '--num_devices', type=int, default=1, help='Number of GPU devices')
+    parser.add_argument(
+        '--enable_ce',
+        action='store_true',
+        help='If set, run the task with continuous evaluation logs.')
     args = parser.parse_args()
     return args
 
@@ -51,6 +55,9 @@ def get_cards(args):
 def train():
     """ do training """
     args = parse_args()
+    if args.enable_ce:
+       fluid.default_startup_program().random_seed = SEED 
+       fluid.default_main_program().random_seed = SEED 
     hid_size = args.hid_size
     train_dir = args.train_dir
     vocab_path = args.vocab_path
@@ -84,6 +91,7 @@ def train():
     model_dir = args.model_dir
     fetch_list = [avg_cost.name]
 
+    ce_info = []
     total_time = 0.0
     for pass_idx in six.moves.xrange(pass_num):
         epoch_idx = pass_idx + 1
@@ -105,8 +113,11 @@ def train():
                                          fetch_list=fetch_list)
             avg_ppl = np.exp(ret_avg_cost[0])
             newest_ppl = np.mean(avg_ppl)
+            ce_info.append(newest_ppl)
             if i % args.print_batch == 0:
                 print("step:%d ppl:%.3f" % (i, newest_ppl))
+            if args.enable_ce and i > 1000:
+                break
 
         t1 = time.time()
         total_time += t1 - t0
@@ -117,8 +128,43 @@ def train():
         fetch_vars = [avg_cost, acc]
         fluid.io.save_inference_model(save_dir, feed_var_names, fetch_vars, exe)
         print("model saved in %s" % save_dir)
+
+    # only for ce
+    if args.enable_ce:
+        ce_ppl = 0
+        try:
+            ce_ppl = ce_info[-2]
+        except:
+            print("ce info error")
+        epoch_idx = args.pass_num
+        device = get_device(args)
+        if args.use_cuda:
+            gpu_num = device[1]
+            print("kpis\teach_pass_duration_gpu%s\t%s" %
+                (gpu_num, total_time / epoch_idx))
+            print("kpis\ttrain_ppl_gpu%s\t%s" %
+                (gpu_num, ce_ppl))
+        else:
+            cpu_num = device[1]
+            threads_num = device[2]
+            print("kpis\teach_pass_duration_cpu%s_thread%s\t%s" %
+                (cpu_num, threads_num, total_time / epoch_idx))
+            print("kpis\ttrain_ppl_cpu%s_thread%s\t%s" %
+                (cpu_num, threads_num, ce_ppl))
+        
     print("finish training")
 
 
+def get_device(args):
+    if args.use_cuda:
+        gpus = os.environ.get("CUDA_VISIBLE_DEVICES", 1)
+        gpu_num = len(gpus.split(','))
+        return "gpu", gpu_num
+    else:
+        threads_num = os.environ.get('NUM_THREADS', 1)
+        cpu_num = os.environ.get('CPU_NUM', 1)
+        return "cpu", int(cpu_num), int(threads_num)
+        
+
 if __name__ == "__main__":
     train()