misc fixes to add gptq tests (#621)

* misc fixes to add gptq tests

* set bf16 needed for fa2

src/axolotl/utils/bench.py

@@ -19,7 +19,11 @@ def check_cuda_device(default_value):
         def wrapper(*args, **kwargs):
             device = kwargs.get("device", args[0] if args else None)
 
-            if not torch.cuda.is_available() or device == "auto" or device == "cpu":
+            if (
+                not torch.cuda.is_available()
+                or device == "auto"
+                or torch.device(device).type == "cpu"
+            ):
                 return default_value
 
             return func(*args, **kwargs)

src/axolotl/utils/models.py

@@ -10,6 +10,7 @@ import torch
 import transformers
 from optimum.bettertransformer import BetterTransformer
 from peft import PeftConfig, prepare_model_for_kbit_training
+from peft.tuners.lora import QuantLinear
 from transformers import (  # noqa: F401
     AutoConfig,
     AutoModelForCausalLM,
@@ -309,16 +310,26 @@ def load_model(
             ):
                 config.max_sequence_length = cfg.sequence_len
                 LOG.warning(f"increasing context length to {cfg.sequence_len}")
-            model = AutoModelForCausalLM.from_pretrained(
-                base_model,
-                config=config,
-                device_map=cfg.device_map,
-                load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
-                load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
-                torch_dtype=cfg.torch_dtype,
-                trust_remote_code=cfg.trust_remote_code or False,
-                **model_kwargs,
-            )
+            if cfg.gptq:
+                model = AutoModelForCausalLM.from_pretrained(
+                    base_model,
+                    config=config,
+                    device_map=cfg.device_map,
+                    torch_dtype=cfg.torch_dtype,
+                    trust_remote_code=cfg.trust_remote_code or False,
+                    **model_kwargs,
+                )
+            else:
+                model = AutoModelForCausalLM.from_pretrained(
+                    base_model,
+                    config=config,
+                    device_map=cfg.device_map,
+                    load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
+                    load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
+                    torch_dtype=cfg.torch_dtype,
+                    trust_remote_code=cfg.trust_remote_code or False,
+                    **model_kwargs,
+                )
     except Exception as err:  # pylint: disable=broad-exception-caught
         LOG.error(
             "Exception raised attempting to load model, retrying with AutoModelForCausalLM"
@@ -466,10 +477,10 @@ def load_llama_adapter(model, cfg):
 
 
 def find_all_linear_names(model):
-    cls = (bnb.nn.Linear4bit, bnb.nn.Linear8bitLt, torch.nn.Linear)
+    cls = (bnb.nn.Linear4bit, bnb.nn.Linear8bitLt, torch.nn.Linear, QuantLinear)
     lora_module_names = set()
     for name, module in model.named_modules():
-        if isinstance(module, cls):
+        if isinstance(module, cls) or "Linear" in module.__class__.__name__:
             names = name.split(".")
             lora_module_names.add(names[0] if len(names) == 1 else names[-1])
 

src/axolotl/utils/trainer.py

@@ -676,6 +676,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
             (cfg.load_best_model_at_end is not False or cfg.early_stopping_patience)
             and cfg.val_set_size > 0
             and cfg.save_steps
+            and cfg.eval_steps
             and cfg.save_steps % cfg.eval_steps == 0
         )
         or False,