fixed arg passing in masked_lm_dataset

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/715

Differential Revision: D15240723

fbshipit-source-id: 11d7280cb187d68f107902822e878f2a04b840c7

fairseq/data/masked_lm_dataset.py

@@ -54,8 +54,6 @@ class MaskedLMDataset(FairseqDataset):
             replaced with the "MASK" token.
         random_token_prob: specifies the probability of a given token being
             replaced by a random token from the vocabulary.
-        unchanged_prob: specifies the probability of keeping a given
-            token unchanged.
     """
 
     def __init__(
@@ -129,9 +127,6 @@ class MaskedLMDataset(FairseqDataset):
             mask_idx: int,
             pad_idx: int,
             dictionary_token_range: Tuple,
-            masking_ratio: float = 0.15,
-            masking_prob: float = 0.8,
-            random_token_prob: float = 0.1
     ):
         """
         Mask tokens for Masked Language Model training
@@ -149,12 +144,6 @@ class MaskedLMDataset(FairseqDataset):
             dictionary_token_range: range of indices in dictionary which can
                 be used for random word replacement
                 (e.g. without special characters)
-            masking_ratio: specifies what percentage of the blocks should be
-                masked.
-            masking_prob: specifies the probability of a given token being
-                replaced with the "MASK" token.
-            random_token_prob: specifies the probability of a given token being
-                replaced by a random token from the vocabulary
         Return:
             masked_sent: masked sentence
             target: target with words which we are not predicting replaced
@@ -162,7 +151,7 @@ class MaskedLMDataset(FairseqDataset):
         """
         masked_sent = np.copy(sentence)
         sent_length = len(sentence)
-        mask_num = math.ceil(sent_length * masking_ratio)
+        mask_num = math.ceil(sent_length * self.masking_ratio)
         mask = np.random.choice(sent_length, mask_num)
         target = np.copy(sentence)
 
@@ -172,12 +161,12 @@ class MaskedLMDataset(FairseqDataset):
 
                 # replace with mask if probability is less than masking_prob
                 # (Eg: 0.8)
-                if rand < masking_prob:
+                if rand < self.masking_prob:
                     masked_sent[i] = mask_idx
 
                 # replace with random token if probability is less than
                 # masking_prob + random_token_prob (Eg: 0.9)
-                elif rand < (masking_prob + random_token_prob):
+            elif rand < (self.masking_ratio + self.random_token_prob):
                     # sample random token from dictionary
                     masked_sent[i] = (
                         np.random.randint(
@@ -229,8 +218,6 @@ class MaskedLMDataset(FairseqDataset):
                 # mask according to specified probabilities.
                 masked_blk_one, masked_tgt_one = self._mask_block(
                     s["block_one"], self.mask_idx, self.pad_idx, token_range,
-                    masking_ratio=self.masking_ratio, masking_prob=self.masking_prob,
-                    random_token_prob=self.random_token_prob,
                 )
 
                 tokens = np.concatenate([