🚨🚨 Generate: change order of ops in beam sample to avoid nans (#26843)

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

src/transformers/generation/tf_utils.py

@@ -1430,14 +1430,22 @@ class TFGenerationMixin:
         # instantiate warpers list
         warpers = TFLogitsProcessorList()
 
-        # the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
-        # all samplers can be found in `generation_utils_samplers.py`
+        # In beam methods, we need to keep at least one non-eos token to explore continuations that might have a
+        # better score (i.e. keep len(generation_config.eos_token_id) + 1)
+        if generation_config.num_beams > 1:
+            if isinstance(generation_config.eos_token_id, list):
+                min_tokens_to_keep = len(generation_config.eos_token_id) + 1
+            else:
+                min_tokens_to_keep = 2
+        else:
+            min_tokens_to_keep = 1
+
         if generation_config.temperature is not None and generation_config.temperature != 1.0:
             warpers.append(TFTemperatureLogitsWarper(generation_config.temperature))
         if generation_config.top_k is not None and generation_config.top_k != 0:
-            warpers.append(TFTopKLogitsWarper(top_k=generation_config.top_k, min_tokens_to_keep=1))
+            warpers.append(TFTopKLogitsWarper(top_k=generation_config.top_k, min_tokens_to_keep=min_tokens_to_keep))
         if generation_config.top_p is not None and generation_config.top_p < 1.0:
-            warpers.append(TFTopPLogitsWarper(top_p=generation_config.top_p, min_tokens_to_keep=1))
+            warpers.append(TFTopPLogitsWarper(top_p=generation_config.top_p, min_tokens_to_keep=min_tokens_to_keep))
         return warpers
 
     def _get_logits_processor(
@@ -2366,14 +2374,11 @@ class TFGenerationMixin:
             log_probs = tf.nn.log_softmax(logits)
             log_probs = logits_processor(flatten_beam_dim(running_sequences), flatten_beam_dim(log_probs), cur_len)
             log_probs = unflatten_beam_dim(log_probs, num_beams)
-            log_probs_processed = log_probs
-            log_probs = log_probs + tf.expand_dims(running_scores, axis=2)
             if do_sample:
-                # Note: logits warpers are intentionally applied after adding running beam scores. On some logits
-                # warpers (like top_p) this is indiferent, but on others (like temperature) it is not. For reference,
-                # see https://github.com/huggingface/transformers/pull/5420#discussion_r449779867
                 log_probs = logits_warper(flatten_beam_dim(running_sequences), flatten_beam_dim(log_probs), cur_len)
                 log_probs = unflatten_beam_dim(log_probs, num_beams)
+            log_probs_processed = log_probs
+            log_probs = log_probs + tf.expand_dims(running_scores, axis=2)
             vocab_size = log_probs.shape[2]
             log_probs = tf.reshape(log_probs, (batch_size, num_beams * vocab_size))
 

src/transformers/generation/utils.py

@@ -820,11 +820,20 @@ class GenerationMixin:
         # instantiate warpers list
         warpers = LogitsProcessorList()
 
+        # In beam methods, we need to keep at least one non-eos token to explore continuations that might have a
+        # better score (i.e. keep len(list(generation_config.eos_token_id)) + 1)
+        if generation_config.num_beams > 1:
+            if isinstance(generation_config.eos_token_id, list):
+                min_tokens_to_keep = len(generation_config.eos_token_id) + 1
+            else:
+                min_tokens_to_keep = 2
+        else:
+            min_tokens_to_keep = 1
+
         # the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
         # all samplers can be found in `generation_utils_samplers.py`
         if generation_config.temperature is not None and generation_config.temperature != 1.0:
             warpers.append(TemperatureLogitsWarper(generation_config.temperature))
-        min_tokens_to_keep = 2 if generation_config.num_beams > 1 else 1
         if generation_config.top_k is not None and generation_config.top_k != 0:
             warpers.append(TopKLogitsWarper(top_k=generation_config.top_k, min_tokens_to_keep=min_tokens_to_keep))
         if generation_config.top_p is not None and generation_config.top_p < 1.0:
@@ -3406,18 +3415,15 @@ class GenerationMixin:
             )  # (batch_size * num_beams, vocab_size)
 
             next_token_scores_processed = logits_processor(input_ids, next_token_scores)
+            next_token_scores_processed = logits_warper(input_ids, next_token_scores_processed)
             next_token_scores = next_token_scores_processed + beam_scores[:, None].expand_as(
                 next_token_scores_processed
             )
-            # Note: logits warpers are intentionally applied after adding running beam scores. On some logits warpers
-            # (like top_p) this is indiferent, but on others (like temperature) it is not. For reference, see
-            # https://github.com/huggingface/transformers/pull/5420#discussion_r449779867
-            next_token_scores = logits_warper(input_ids, next_token_scores)
 
             # Store scores, attentions and hidden_states when required
             if return_dict_in_generate:
                 if output_scores:
-                    scores += (logits_warper(input_ids, next_token_scores_processed),)
+                    scores += (next_token_scores_processed,)
                 if output_attentions:
                     decoder_attentions += (
                         (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)