Fix bugs introduced in #394 #406 and max length bug

Fix automatic batching for greedy_until
Fix stop sequences bug for greedy_until
Fix max length bug when model max length is not specified in tokenizer

lm_eval/models/huggingface.py

@@ -340,6 +340,8 @@ class HuggingFaceAutoLM(BaseLM):
             if hasattr(self._config, attr):
                 return getattr(self._config, attr)
         if hasattr(self.tokenizer, "model_max_length"):
+            if self.tokenizer.model_max_length == 1000000000000000019884624838656:
+                return self._DEFAULT_MAX_LENGTH
             return self.tokenizer.model_max_length
         return self._DEFAULT_MAX_LENGTH
 
@@ -371,13 +373,9 @@ class HuggingFaceAutoLM(BaseLM):
         def _collate(x):
             tokens = self.tok_encode(x[0])
             return len(tokens), x[0]
-
         
-
         results = []
         reorder = utils.Reorderer(requests, _collate)
-        _, context_enc, continuation_enc = reorder.get_reordered()[0]
-        max_context = len((context_enc + continuation_enc)[-(self.max_length + 1) :][:-1])
         
         adaptive_batch_size = None
         if self.batch_size == 'auto': 
@@ -385,7 +383,7 @@ class HuggingFaceAutoLM(BaseLM):
             print('Passed argument batch_size = auto. Detecting largest batch size')
             @find_executable_batch_size(starting_batch_size=512) # if OOM, then halves batch_size and tries again
             def forward_batch(batch_size):
-                test_batch = torch.ones((batch_size, max_context), device=self.device).long()
+                test_batch = torch.ones((batch_size, self.max_length), device=self.device).long()
                 for _ in range(5): 
                     out = F.log_softmax(self._model_call(test_batch), dim = -1).cpu()
                 return batch_size
@@ -400,7 +398,7 @@ class HuggingFaceAutoLM(BaseLM):
             context = [c[0] for c in chunk]
             request_args = chunk[0][1]
             stop = request_args.get('until', None)
-            stop_sequences = [stop] if isinstance(stop, list) else stop
+            stop_sequences = stop if isinstance(stop, list) else [stop]
             max_generation_length = request_args.get("max_length", None)
 
             assert (