From 87e8727d2c1f5ee7d4d14d7f0b7d2c60af4210b2 Mon Sep 17 00:00:00 2001
From: xuezhong <xuezhongq@gmail.com>
Date: Thu, 1 Nov 2018 10:43:14 +0000
Subject: [PATCH] fix batch offset bug

---
 .../machine_reading_comprehension/rc_model.py |  1 -
 fluid/machine_reading_comprehension/run.py    | 36 +++++++++----------
 2 files changed, 17 insertions(+), 20 deletions(-)

diff --git a/fluid/machine_reading_comprehension/rc_model.py b/fluid/machine_reading_comprehension/rc_model.py
index d95fa9d7..932ccd9c 100644
--- a/fluid/machine_reading_comprehension/rc_model.py
+++ b/fluid/machine_reading_comprehension/rc_model.py
@@ -317,5 +317,4 @@ def rc_model(hidden_size, vocab, args):
     cost.persistable = True
 
     feeding_list = ["q_ids", "start_lables", "end_lables", "p_ids", "q_id0"]
-    layers.Print(ms, message='ms', summarize=3)
     return cost, start_probs, end_probs, ms, feeding_list
diff --git a/fluid/machine_reading_comprehension/run.py b/fluid/machine_reading_comprehension/run.py
index 5959709b..c0fd8d30 100644
--- a/fluid/machine_reading_comprehension/run.py
+++ b/fluid/machine_reading_comprehension/run.py
@@ -236,11 +236,7 @@ def validation(inference_program, avg_cost, s_probs, e_probs, match, feed_order,
         total_loss += np.array(val_fetch_outs[0]).sum()
         start_probs_m = LodTensor_Array(val_fetch_outs[1])
         end_probs_m = LodTensor_Array(val_fetch_outs[2])
-        for data in feed_data:
-            data_len = [[len(y) for y in x[3]] for x in data]
-            logger.info(str(data_len))
         match_lod = val_fetch_outs[3].lod()
-        logger.info(str(match_lod))
         count += len(np.array(val_fetch_outs[0]))
 
         n_batch_cnt += len(np.array(val_fetch_outs[0]))
@@ -252,18 +248,18 @@ def validation(inference_program, avg_cost, s_probs, e_probs, match, feed_order,
                     n_batch_loss / n_batch_cnt)))
             n_batch_loss = 0.0
             n_batch_cnt = 0
-
+        batch_offset = 0
         for idx, batch in enumerate(batch_list):
             #one batch
             batch_size = len(batch['raw_data'])
-            batch_range = match_lod[0][idx * batch_size:(idx + 1) * batch_size +
+            batch_range = match_lod[0][batch_offset:batch_offset + batch_size +
                                        1]
             batch_lod = [[batch_range[x], batch_range[x + 1]]
                          for x in range(len(batch_range[:-1]))]
-            start_prob_batch = start_probs_m[idx * batch_size:(idx + 1) *
-                                             batch_size]
-            end_prob_batch = end_probs_m[idx * batch_size:(idx + 1) *
-                                         batch_size]
+            start_prob_batch = start_probs_m[batch_offset:batch_offset +
+                                             batch_size + 1]
+            end_prob_batch = end_probs_m[batch_offset:batch_offset + batch_size
+                                         + 1]
             for sample, start_prob_inst, end_prob_inst, inst_range in zip(
                     batch['raw_data'], start_prob_batch, end_prob_batch,
                     batch_lod):
@@ -288,6 +284,7 @@ def validation(inference_program, avg_cost, s_probs, e_probs, match, feed_order,
                         'yesno_answers': []
                     }
                     ref_answers.append(ref)
+            batch_offset = batch_offset + batch_size
 
     result_dir = args.result_dir
     result_prefix = args.result_name
@@ -341,8 +338,9 @@ def train(logger, args):
     # build model
     main_program = fluid.Program()
     startup_prog = fluid.Program()
-    main_program.random_seed = args.random_seed
-    startup_prog.random_seed = args.random_seed
+    if args.enable_ce:
+        main_program.random_seed = args.random_seed
+        startup_prog.random_seed = args.random_seed
     with fluid.program_guard(main_program, startup_prog):
         with fluid.unique_name.guard():
             avg_cost, s_probs, e_probs, match, feed_order = rc_model.rc_model(
@@ -402,7 +400,10 @@ def train(logger, args):
             for pass_id in range(1, args.pass_num + 1):
                 pass_start_time = time.time()
                 pad_id = vocab.get_id(vocab.pad_token)
-                train_reader = lambda:brc_data.gen_mini_batches('train', args.batch_size, pad_id, shuffle=False)
+                if args.enable_ce:
+                    train_reader = lambda:brc_data.gen_mini_batches('train', args.batch_size, pad_id, shuffle=False)
+                else:
+                    train_reader = lambda:brc_data.gen_mini_batches('train', args.batch_size, pad_id, shuffle=True)
                 train_reader = read_multiple(train_reader, dev_count)
                 log_every_n_batch, n_batch_loss = args.log_interval, 0
                 total_num, total_loss = 0, 0
@@ -488,8 +489,6 @@ def evaluate(logger, args):
     # build model
     main_program = fluid.Program()
     startup_prog = fluid.Program()
-    main_program.random_seed = args.random_seed
-    startup_prog.random_seed = args.random_seed
     with fluid.program_guard(main_program, startup_prog):
         with fluid.unique_name.guard():
             avg_cost, s_probs, e_probs, match, feed_order = rc_model.rc_model(
@@ -537,8 +536,6 @@ def predict(logger, args):
     # build model
     main_program = fluid.Program()
     startup_prog = fluid.Program()
-    main_program.random_seed = args.random_seed
-    startup_prog.random_seed = args.random_seed
     with fluid.program_guard(main_program, startup_prog):
         with fluid.unique_name.guard():
             avg_cost, s_probs, e_probs, match, feed_order = rc_model.rc_model(
@@ -606,8 +603,9 @@ def prepare(logger, args):
 if __name__ == '__main__':
     args = parse_args()
 
-    random.seed(args.random_seed)
-    np.random.seed(args.random_seed)
+    if args.enable_ce:
+        random.seed(args.random_seed)
+        np.random.seed(args.random_seed)
 
     logger = logging.getLogger("brc")
     logger.setLevel(logging.INFO)
-- 
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