fix GPTQ quantization?

GPTQ.py

@@ -4,25 +4,20 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 import torch
-import os, sys
-lm_evaluation_harness_path = "/".join(
-    os.getcwd().split("/")[:-1] + ["lm-evaluation-harness"]
-)
-sys.path.insert(0, lm_evaluation_harness_path)
-import main as lm_evaluation_harness_main
 
 import torch.fx as fx
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.utils._pytree import tree_flatten, tree_unflatten
 
 from eval import setup_cache_padded_seq_input_pos_max_seq_length_for_prefill
-from generate import encode_tokens
 
 aten = torch.ops.aten
 
 try:
-    import lm_eval
+    import lm_eval.base
+    import lm_eval.tasks
+    import lm_eval.evaluator
     class InputRecorder(lm_eval.base.BaseLM):
         """
         This is a fake evaluation wrapper that just records the inputs
@@ -88,8 +83,9 @@ def device(self):
             return self._device
 
         def tok_encode(self, string: str):
+            from generate import encode_tokens
             encoded = encode_tokens(
-                self._tokenizer, string, bos=True, eos=False, device=self._device
+                self._tokenizer, string, bos=True, device=self._device
             )
             # encoded is a pytorch tensor, but some internal logic in the
             # eval harness expects it to be a list instead

eval.py

@@ -22,24 +22,10 @@
 wd = Path(__file__).parent.parent.resolve()
 sys.path.append(str(wd))
 
-from model import LLaMA
+from model import Transformer as LLaMA
 from sentencepiece import SentencePieceProcessor
 
-# hacky path setup for lm-evaluation-harness
-import os
-import sys
-lm_evaluation_harness_path = '/'.join(
-    os.getcwd().split('/')[:-1] + ['lm-evaluation-harness'])
-sys.path.insert(0, lm_evaluation_harness_path)
-import main as lm_evaluation_harness_main
-import lm_eval
-
-from generate import (
-    _load_model,
-    encode_tokens,
-    model_forward,
-)
-
+import lm_eval.base
 
 def setup_cache_padded_seq_input_pos_max_seq_length_for_prefill(
     model: LLaMA,
@@ -116,6 +102,7 @@ def device(self):
         return self._device
 
     def tok_encode(self, string: str):
+        from generate import encode_tokens
         encoded = encode_tokens(self._tokenizer,
             string, bos=True, eos=False, device=self._device)
         # encoded is a pytorch tensor, but some internal logic in the
@@ -130,6 +117,8 @@ def tok_decode(self, tokens):
 
     def _model_call(self, inps):
         # TODO: make batches work
+        from generate import model_forward
+
         inps = inps.squeeze(0)
 
         max_new_tokens = 1
@@ -205,6 +194,7 @@ def main(
         max_seq_length (Optional[int]): The maximum sequence length allowed for input text.
 
     """
+    from generate import _load_model, model_forward
 
     assert checkpoint_path.is_file(), checkpoint_path