fix repetition penalty mask in tf

src/transformers/configuration_utils.py

@@ -70,6 +70,7 @@ def __init__(self, **kwargs):
         # Parameters for sequence generation
         self.max_length = kwargs.pop("max_length", 20)
         self.do_sample = kwargs.pop("do_sample", False)
+        self.early_stopping = kwargs.pop("early_stopping", False)
         self.num_beams = kwargs.pop("num_beams", 1)
         self.temperature = kwargs.pop("temperature", 1.0)
         self.top_k = kwargs.pop("top_k", 50)

src/transformers/modeling_tf_utils.py

@@ -460,6 +460,7 @@ def generate(
         input_ids=None,
         max_length=None,
         do_sample=True,
+        early_stopping=False,
         num_beams=None,
         temperature=None,
         top_k=None,
@@ -559,11 +560,12 @@ def generate(
         if self.get_output_embeddings() is None:
             raise AttributeError(
                 "You tried to generate sequences with a model that does not have a LM Head."
-                "Please use another model class (e.g. `OpenAIGPTLMHeadModel`, `XLNetLMHeadModel`, `GPT2LMHeadModel`, `CTRLLMHeadModel`, `T5WithLMHeadModel`, `TransfoXLLMHeadModel`)"
+                "Please use another model class (e.g. `TFOpenAIGPTLMHeadModel`, `TFXLNetLMHeadModel`, `TFGPT2LMHeadModel`, `TFCTRLLMHeadModel`, `TFT5WithLMHeadModel`, `TFTransfoXLLMHeadModel`)"
             )
 
         max_length = max_length if max_length is not None else self.config.max_length
         do_sample = do_sample if do_sample is not None else self.config.do_sample
+        early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
         num_beams = num_beams if num_beams is not None else self.config.num_beams
         temperature = temperature if temperature is not None else self.config.temperature
         top_k = top_k if top_k is not None else self.config.top_k
@@ -586,6 +588,7 @@ def generate(
 
         assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictely positive integer."
         assert isinstance(do_sample, bool), "`do_sample` should be a boolean."
+        assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean."
         assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictely positive integer."
         assert temperature > 0, "`temperature` should be strictely positive."
         assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer."
@@ -662,6 +665,7 @@ def generate(
                 cur_len,
                 max_length,
                 do_sample,
+                early_stopping,
                 temperature,
                 top_k,
                 top_p,
@@ -803,6 +807,7 @@ def _generate_beam_search(
         cur_len,
         max_length,
         do_sample,
+        early_stopping,
         temperature,
         top_k,
         top_p,
@@ -820,7 +825,8 @@ def _generate_beam_search(
 
         # generated hypotheses
         generated_hyps = [
-            BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=False) for _ in range(batch_size)
+            BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=early_stopping)
+            for _ in range(batch_size)
         ]
 
         # scores for each sentence in the beam
@@ -1058,10 +1064,11 @@ def _create_next_token_logits_penalties(input_ids, logits, repetition_penalty):
     prev_input_ids = [np.unique(input_id) for input_id in input_ids.numpy()]
     for i, prev_input_id in enumerate(prev_input_ids):
         logit_penalized = logits[i].numpy()[prev_input_id]
+        logit_penalties = np.zeros(logit_penalized.shape)
         # if previous logit score is < 0 then multiply repetition penalty else divide
-        logit_penalized[logit_penalized < 0] = repetition_penalty
-        logit_penalized[logit_penalized > 0] = 1 / repetition_penalty
-        np.put(token_penalties[i], prev_input_id, logit_penalized)
+        logit_penalties[logit_penalized < 0] = repetition_penalty
+        logit_penalties[logit_penalized > 0] = 1 / repetition_penalty
+        np.put(token_penalties[i], prev_input_id, logit_penalties)
     return tf.convert_to_tensor(token_penalties, dtype=tf.float32)