merged conflict resolved

lm_eval/utils.py

@@ -19,11 +19,6 @@ import transformers
 from omegaconf import OmegaConf
 from jinja2 import BaseLoader, Environment, StrictUndefined
 from itertools import islice
-<<<<<<< HEAD
-=======
-
-import transformers
->>>>>>> more pre-commit
 
 from lm_eval.logger import eval_logger
 
@@ -422,34 +417,11 @@ def create_iterator(raw_iterator, rank, world_size, limit=None):
     return islice(raw_iterator, rank, limit, world_size)
 
 
-<<<<<<< HEAD
-def clear_torch_cache():
-    gc.collect()
-    torch.cuda.empty_cache()
-
-
-def get_dtype(dtype: Union[str, torch.dtype]) -> torch.dtype:
-    """Converts `dtype` from `str` to torch.dtype when possible. Does not use an instantiated HF AutoConfig"""
-    if isinstance(dtype, str) and dtype != "auto":
-        # Convert `str` args torch dtype: `float16` -> `torch.float16`
-        _torch_dtype = getattr(torch, dtype)
-    else:
-        _torch_dtype = dtype
-    return _torch_dtype
-
-
-def pad_and_concat(max_length:int, tensors: List[torch.Tensor], padding_side="right"):
-    """ 
-    Method for padding a list of tensors given the maximum tensor 
-    length in the batch. Used for batching inputs and continuations in 
-    seq2seq models. 
-=======
 def pad_and_concat(max_length: int, tensors: List[torch.Tensor], padding_side="right"):
     """
     Method for padding a list of tensors given the maximum tensor
     length in the batch. Used for batching inputs and continuations in
     seq2seq models.
->>>>>>> more pre-commit
     """
     assert (
         padding_side == "left" or padding_side == "right"
@@ -490,16 +462,22 @@ def pad_and_concat(max_length: int, tensors: List[torch.Tensor], padding_side="r
     return torch.cat(tensors, dim=0)
 
 
-<<<<<<< HEAD
-# Multi-token stopping criteria 
-=======
 def clear_torch_cache():
     gc.collect()
     torch.cuda.empty_cache()
 
 
+def get_dtype(dtype: Union[str, torch.dtype]) -> torch.dtype:
+    """Converts `dtype` from `str` to torch.dtype when possible. Does not use an instantiated HF AutoConfig"""
+    if isinstance(dtype, str) and dtype != "auto":
+        # Convert `str` args torch dtype: `float16` -> `torch.float16`
+        _torch_dtype = getattr(torch, dtype)
+    else:
+        _torch_dtype = dtype
+    return _torch_dtype
+
+
 # Multi-token stopping criteria
->>>>>>> more pre-commit
 class MultiTokenEOSCriteria(transformers.StoppingCriteria):
     """Criteria to stop on the specified multi-token sequence."""