hotfix: make greedy_until work for Accelerate HF model

lm_eval/models/__init__.py

@@ -6,7 +6,8 @@ from . import dummy
 
 MODEL_REGISTRY = {
     "hf": gpt2.HFLM,
-    "hf-causal": huggingface.AutoCausalLM,
+    "hf-causal": gpt2.HFLM,
+    "hf-causal-experimental": huggingface.AutoCausalLM,
     "hf-seq2seq": huggingface.AutoSeq2SeqLM,
     "gpt2": gpt2.GPT2LM,
     "gpt3": gpt3.GPT3LM,

lm_eval/models/gpt2.py

@@ -47,28 +47,8 @@ class HFLM(BaseLM):
             revision=revision,
         )
 
-        assert isinstance(
-            self.tokenizer,
-            (
-                transformers.GPT2Tokenizer,
-                transformers.GPT2TokenizerFast,
-                transformers.T5Tokenizer,
-                transformers.T5TokenizerFast,
-            ),
-        ), "this tokenizer has not been checked for compatibility yet!"
-
         self.vocab_size = self.tokenizer.vocab_size
 
-        if isinstance(
-            self.tokenizer, (transformers.GPT2Tokenizer, transformers.GPT2TokenizerFast)
-        ):
-            assert self.tokenizer.encode("hello\n\nhello") == [
-                31373,
-                198,
-                198,
-                31373,
-            ], self.tokenizer.encode("hello\n\nhello")
-
         # multithreading and batching
         self.batch_size_per_gpu = batch_size  # todo: adaptive batch size
 

lm_eval/models/huggingface.py

@@ -343,7 +343,7 @@ class HuggingFaceAutoLM(BaseLM):
     def tok_decode(self, tokens: torch.LongTensor) -> List[str]:
         return self.tokenizer.batch_decode(tokens, skip_special_tokens=True)
 
-    def greedy_until(self, requests: List[Tuple[str, dict]]) -> List[str]:
+    def greedy_until(self, requests: List[Tuple[str, Union[List[str], str]]]) -> List[str]:
         def _collate(x):
             tokens = self.tok_encode(x[0])
             return len(tokens), x[0]
@@ -355,18 +355,16 @@ class HuggingFaceAutoLM(BaseLM):
         ):
             context = [c[0] for c in chunk]
             request_args = chunk[0][1]
-            stop_sequences = request_args["stop_sequences"]
-            max_generation_length = request_args["max_generation_length"]
-            num_fewshot = request_args["num_fewshot"]
+            stop_sequences = request_args if isinstance(request_args, list) else [request_args] # request_args["stop_sequences"]
+            max_generation_length = self._max_gen_toks # request_args["max_generation_length"]
 
             assert (
                 isinstance(max_generation_length, int) or max_generation_length is None
             )
             assert isinstance(stop_sequences, list) or stop_sequences is None
-            assert isinstance(num_fewshot, int) or num_fewshot is None
-
+            
             # TODO: Find a better way to handle stop sequences for 0-shot.
-            if stop_sequences is None or num_fewshot == 0:
+            if stop_sequences is None:
                 until = [self.eot_token]
             else:
                 until = stop_sequences + [self.eot_token]

lm_eval/utils.py

@@ -5,7 +5,9 @@ import collections
 import functools
 import inspect
 import sys
-from typing import List
+from typing import List, Union
+
+import torch
 
 from omegaconf import OmegaConf
 
@@ -116,6 +118,26 @@ def make_disjoint_window(pair):
     return a[: len(a) - (len(b) - 1)], b
 
 
+def select_continuation_from_batch_left_padding(
+    generations: Union[List[List[int]], torch.Tensor], max_context_size: int
+):
+    """Select the continuation from the batch, removing prompts of different lengths.
+    Args:
+        generations (Union[List[List[int]], torch.Tensor]):
+            A tensor or list-of-lists of shape [batch_size, sequence length].
+        max_context_size (int):
+            The size of the biggest context; generations will proceed from that
+            index.
+    Example:
+        PAD     PAD Continue : The dog chased the cat  [every       day of the week]
+        Riddle  me    this   : The  dog chased the  cat [yesterday] PAD PAD PAD PAD
+    Output:
+        [every day of the week]
+        [yesterday]  PAD PAD PAD PAD
+    """
+    return generations[:, max_context_size:]
+
+
 class Reorderer:
     def __init__(self, arr, fn):
         self.size = len(arr)
@@ -201,3 +223,4 @@ def run_task_tests(task_list: List[str]):
         raise ValueError(
             f"Not all tests for the specified tasks ({task_list}) ran successfully! Error code: {pytest_return_val}"
         )
+