Make outputs between fast_infer and the original python infer alignment in Transformer

fluid/neural_machine_translation/transformer/config.py

@@ -42,9 +42,9 @@ class InferTaskConfig(object):
     # the number of decoded sentences to output.
     n_best = 1
     # the flags indicating whether to output the special tokens.
-    output_bos = False
-    output_eos = False
-    output_unk = False
+    output_bos = True  #False
+    output_eos = True  #False
+    output_unk = True  #False
     # the directory for loading the trained model.
     model_path = "trained_models/pass_1.infer.model"
 

fluid/neural_machine_translation/transformer/infer.py

@@ -275,11 +275,11 @@ def translate_batch(exe,
             top_k_indice = np.argpartition(predict, -beam_size)[-beam_size:]
             top_scores_ids = top_k_indice[np.argsort(predict[top_k_indice])[::
                                                                             -1]]
-            top_scores_ids = np.asarray(
-                sorted(
-                    top_scores_ids,
-                    lambda x, y: x / predict_all.shape[-1] - y / predict_all.shape[-1]
-                ))  # sort by pre_branch and score to compare with fast_infer
+            # top_scores_ids = np.asarray(
+            #     sorted(
+            #         top_scores_ids,
+            #         lambda x, y: x / predict_all.shape[-1] - y / predict_all.shape[-1]
+            #     ))  # sort by pre_branch and score to compare with fast_infer
             top_scores = predict[top_scores_ids]
             scores[beam_idx] = top_scores
             prev_branchs[beam_idx].append(top_scores_ids /
@@ -368,6 +368,7 @@ def infer(args):
         start_mark=args.special_token[0],
         end_mark=args.special_token[1],
         unk_mark=args.special_token[2],
+        max_length=ModelHyperParams.max_length,
         clip_last_batch=False)
 
     trg_idx2word = test_data.load_dict(
@@ -394,6 +395,8 @@ def infer(args):
             seq)
 
     for batch_id, data in enumerate(test_data.batch_generator()):
+        if batch_id != 0:
+            continue
         batch_seqs, batch_scores = translate_batch(
             exe,
             [item[0] for item in data],
@@ -422,6 +425,8 @@ def infer(args):
             scores = batch_scores[i]
             for seq in seqs:
                 print(" ".join([trg_idx2word[idx] for idx in seq]))
+            print scores
+        exit(0)
 
 
 def prepare_batch_input(insts, data_input_names, util_input_names, src_pad_idx,
@@ -522,12 +527,15 @@ def fast_infer(args):
         start_mark=args.special_token[0],
         end_mark=args.special_token[1],
         unk_mark=args.special_token[2],
+        max_length=ModelHyperParams.max_length,
         clip_last_batch=False)
 
     trg_idx2word = test_data.load_dict(
         dict_path=args.trg_vocab_fpath, reverse=True)
 
     for batch_id, data in enumerate(test_data.batch_generator()):
+        if batch_id != 0:
+            continue
         data_input = prepare_batch_input(
             data, encoder_data_input_fields + fast_decoder_data_input_fields,
             encoder_util_input_fields + fast_decoder_util_input_fields,
@@ -540,6 +548,7 @@ def fast_infer(args):
         # print np.array(seq_ids)#, np.array(seq_scores)
         # print seq_ids.lod()#, seq_scores.lod()
         hyps = [[] for i in range(len(data))]
+        scores = [[] for i in range(len(data))]
         for i in range(len(seq_ids.lod()[0]) - 1):  # for each source sentence
             start = seq_ids.lod()[0][i]
             end = seq_ids.lod()[0][i + 1]
@@ -550,8 +559,11 @@ def fast_infer(args):
                     trg_idx2word[idx]
                     for idx in np.array(seq_ids)[sub_start:sub_end]
                 ]))
+                scores[i].append(np.array(seq_scores)[sub_end - 1])
             print hyps[i]
+            print scores[i]
             print len(hyps[i]), [len(hyp.split()) for hyp in hyps[i]]
+        exit(0)
 
 
 if __name__ == "__main__":

fluid/neural_machine_translation/transformer/model.py

@@ -123,15 +123,15 @@ def multi_head_attention(queries,
             act="softmax")
         weights = layers.reshape(
             x=weights, shape=product.shape, actual_shape=post_softmax_shape)
-        global FLAG
-        if FLAG:
-            print "hehehehehe"
-            layers.Print(scaled_q)
-            layers.Print(k)
-            layers.Print(v)
-            layers.Print(product)
-            layers.Print(weights)
-            FLAG = False
+        # global FLAG
+        # if FLAG:
+        #     print "hehehehehe"
+        #     layers.Print(scaled_q)
+        #     layers.Print(k)
+        #     layers.Print(v)
+        #     layers.Print(product)
+        #     layers.Print(weights)
+        #     FLAG = False
         if dropout_rate:
             weights = layers.dropout(
                 weights, dropout_prob=dropout_rate, is_test=False)
@@ -694,7 +694,7 @@ def fast_decode(
                     src_attn_pre_softmax_shape, src_attn_post_softmax_shape),
                 enc_output=pre_enc_output,
                 caches=pre_caches)
-            layers.Print(logits)
+            # layers.Print(logits)
             topk_scores, topk_indices = layers.topk(logits, k=beam_size)
             # layers.Print(topk_scores)
             # layers.Print(topk_indices)
@@ -708,6 +708,7 @@ def fast_decode(
             topk_indices = layers.lod_reset(topk_indices, pre_ids)
             selected_ids, selected_scores = layers.beam_search(
                 pre_ids=pre_ids,
+                pre_scores=pre_scores,
                 ids=topk_indices,
                 scores=accu_scores,
                 beam_size=beam_size,
@@ -735,12 +736,16 @@ def fast_decode(
                     y=attn_post_softmax_shape_delta),
                 slf_attn_post_softmax_shape)
 
-            max_len_cond = layers.less_than(x=step_idx, y=max_len)
-            all_finish_cond = layers.less_than(x=step_idx, y=max_len)
-            layers.logical_or(x=max_len_cond, y=all_finish_cond, out=cond)
-
-        finished_ids, finished_scores = layers.beam_search_decode(ids, scores,
-                                                                  eos_idx)
+            length_cond = layers.less_than(x=step_idx, y=max_len)
+            finish_cond = layers.logical_not(layers.is_empty(x=selected_ids))
+            # layers.Print(length_cond)
+            # layers.Print(finish_cond)
+            layers.logical_and(x=length_cond, y=finish_cond, out=cond)
+        layers.Print(step_idx)
+        # finished_ids, finished_scores = layers.beam_search_decode(ids, scores,
+        #                                                           eos_idx)
+        finished_ids, finished_scores = layers.beam_search_decode(
+            ids, scores, beam_size=beam_size, end_id=eos_idx)
         return finished_ids, finished_scores
 
     finished_ids, finished_scores = beam_search()

fluid/neural_machine_translation/transformer/train.py

@@ -288,6 +288,7 @@ def train(args):
         start_mark=args.special_token[0],
         end_mark=args.special_token[1],
         unk_mark=args.special_token[2],
+        max_length=ModelHyperParams.max_length,
         clip_last_batch=False)
 
     train_data = read_multiple(reader=train_data.batch_generator)
@@ -315,6 +316,7 @@ def train(args):
             start_mark=args.special_token[0],
             end_mark=args.special_token[1],
             unk_mark=args.special_token[2],
+            max_length=ModelHyperParams.max_length,
             clip_last_batch=False,
             shuffle=False,
             shuffle_batch=False)