fix train reader usage error

PaddleNLP/dialogue_domain_classification/run_classifier.py

@@ -144,33 +144,29 @@ def train(args, train_exe, compiled_prog, build_res, place):
     time_begin = time.time()
     test_exe = train_exe
     logger.info("Begin training")
-    feed_data = []
     for i in range(args.epoch):
         try:
             for data in train_pyreader(): 
-                feed_data.extend(data)
-                if len(feed_data) == DEV_COUNT:
-                    avg_cost_np, avg_pred_np, pred_label, label = train_exe.run(feed=feed_data, program=compiled_prog, \
-                                                                                fetch_list=fetch_list)
-                    feed_data = []
-                    steps += 1
-                    if steps % int(args.skip_steps) == 0:
-                        time_end = time.time()
-                        used_time = time_end - time_begin
-                        get_score(pred_label, label, eval_phase = "Train")
-                        logger.info('loss is {}'.format(avg_cost_np))
-                        logger.info("epoch: %d, step: %d, speed: %f steps/s" % (i, steps, args.skip_steps / used_time))
-                        time_begin = time.time()
-                    if steps % args.save_steps == 0:
-                        save_path = os.path.join(args.checkpoints,
-                            "step_" + str(steps))
-                        fluid.io.save_persistables(train_exe, save_path, train_prog)
-                        logger.info("[save]step %d : save at %s" % (steps, save_path))
-                    if steps % args.validation_steps == 0:
-                        if args.do_eval:
-                            evaluate(args, test_exe, build_res["eval_prog"], build_res, place, "eval")
-                        if args.do_test:
-                            evaluate(args, test_exe, build_res["test_prog"], build_res, place, "test")
+                avg_cost_np, avg_pred_np, pred_label, label = train_exe.run(feed=data, program=compiled_prog, \
+                                                                            fetch_list=fetch_list)
+                steps += 1
+                if steps % int(args.skip_steps) == 0:
+                    time_end = time.time()
+                    used_time = time_end - time_begin
+                    get_score(pred_label, label, eval_phase = "Train")
+                    logger.info('loss is {}'.format(avg_cost_np))
+                    logger.info("epoch: %d, step: %d, speed: %f steps/s" % (i, steps, args.skip_steps / used_time))
+                    time_begin = time.time()
+                if steps % args.save_steps == 0:
+                    save_path = os.path.join(args.checkpoints,
+                        "step_" + str(steps))
+                    fluid.io.save_persistables(train_exe, save_path, train_prog)
+                    logger.info("[save]step %d : save at %s" % (steps, save_path))
+                if steps % args.validation_steps == 0:
+                    if args.do_eval:
+                        evaluate(args, test_exe, build_res["eval_prog"], build_res, place, "eval")
+                    if args.do_test:
+                        evaluate(args, test_exe, build_res["test_prog"], build_res, place, "test")
         except Exception as e:
             logger.exception(str(e))
             logger.error("Train error : %s" % str(e))
@@ -400,14 +396,14 @@ def main(args):
     random.seed(args.random_seed)
     model_config = ConfigReader.read_conf(args.config_path)
     if args.use_cuda:
-        place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0')))
+        place = fluid.cuda_places()
         DEV_COUNT = fluid.core.get_cuda_device_count()
     else:
-        place = fluid.CPUPlace()
+        place = fluid.cpu_places()
         os.environ['CPU_NUM'] = str(args.cpu_num)
         DEV_COUNT = args.cpu_num
     logger.info("Dev Num is %s" % str(DEV_COUNT))
-    exe = fluid.Executor(place)
+    exe = fluid.Executor(place[0])
     if args.do_train and args.build_dict:
         DataProcesser.build_dict(args.data_dir + "train.txt", args.data_dir)
     # read dict
@@ -436,8 +432,12 @@ def main(args):
     
     if args.do_train:
         build_strategy = fluid.compiler.BuildStrategy()
+        build_strategy.fuse_all_reduce_ops = False
+        exec_strategy = fluid.ExecutionStrategy()
+        exec_strategy.num_threads = 1
         compiled_prog = fluid.compiler.CompiledProgram(build_res["train_prog"]).with_data_parallel( \
-                                        loss_name=build_res["cost"].name, build_strategy=build_strategy)
+                                        loss_name=build_res["cost"].name, build_strategy=build_strategy,
+                                        exec_strategy=exec_strategy)
         build_res["compiled_prog"] = compiled_prog
         train(args, exe, compiled_prog, build_res, place)
     if args.do_eval: