various bugfixes

configs/stability_3b.yml

@@ -0,0 +1,33 @@
+base_model: stabilityai/stablelm-base-alpha-3b
+load_in_8bit: true
+datasets:
+  - path: vicgalle/alpaca-gpt4
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.04
+adapter:
+lora_model_dir:
+sequence_len: 4096
+lora_r: 8
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+  - q_proj
+  - v_proj
+lora_fan_in_fan_out: false
+wandb_project: stable-llama-3b
+wandb_watch:
+wandb_run_id:
+wandb_log_model: checkpoint
+output_dir: ./stable-llama-3b
+batch_size: 128
+micro_batch_size: 16
+num_epochs: 1
+learning_rate: 0.00003
+train_on_inputs: false
+group_by_length: false
+bf16: true
+tf32: true
+early_stopping_patience: 3
+resume_from_checkpoint:
+local_rank:

scripts/finetune.py

@@ -159,7 +159,7 @@ def train(
     cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
     cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
     choose_device(cfg)
-    cfg.ddp = cfg.world_size != 1
+    cfg.ddp = cfg.ddp if cfg.ddp is not None else cfg.world_size != 1
     if cfg.ddp:
         cfg.device_map = {"": int(os.environ.get("LOCAL_RANK", 0))}
         cfg.gradient_accumulation_steps = (

src/axolotl/datasets.py

@@ -1,3 +1,4 @@
+import logging
 from typing import List
 
 import torch
@@ -92,11 +93,14 @@ class ConstantLengthDataset(IterableDataset):
                             : self.seq_length
                         ]
                         labels = torch.cat(buffer["labels"], dim=-1)[: self.seq_length]
-                        yield {
+                        if labels.size() == input_ids.size() and attention_mask.size() == input_ids.size():
-                            "input_ids": input_ids,
+                            yield {
-                            "labels": labels,
+                                "input_ids": input_ids,
-                            "attention_mask": attention_mask,
+                                "labels": labels,
-                        }
+                                "attention_mask": attention_mask,
+                            }
+                        else:
+                            logging.warning("dropping batch due to tensor size mismatch")
                     buffer = {"input_ids": [], "attention_mask": [], "labels": []}
                     buffer_len = 0
 

src/axolotl/utils/data.py

@@ -65,7 +65,7 @@ def load_prepare_datasets(tokenizer, cfg, default_dataset_prepared_path):
             elif ds_from_hub:
                 ds = load_dataset(d.path, streaming=True)
             else:
-                raise Exception("unhandled dataset load")
+                raise Exception(f"unhandled dataset load for {d.path}")
 
             if d.type == "alpaca":
                 ds_strategy = AlpacaPromptTokenizingStrategy(

src/axolotl/utils/models.py

@@ -102,13 +102,20 @@ def load_model(
                 torch_dtype=torch_dtype,
                 device_map=cfg.device_map,
             )
-        else:
+        elif model_type:
             model = getattr(transformers, model_type).from_pretrained(
                 base_model,
                 load_in_8bit=cfg.load_in_8bit,
                 torch_dtype=torch_dtype,
                 device_map=cfg.device_map,
             )
+        else:
+            model = AutoModelForCausalLM.from_pretrained(
+                base_model,
+                load_in_8bit=cfg.load_in_8bit,
+                torch_dtype=torch_dtype,
+                device_map=cfg.device_map,
+            )
     except Exception as e:
         logging.error(
             "Exception raised attempting to load model, retrying with AutoModelForCausalLM"
@@ -148,7 +155,7 @@ def load_model(
 
     model, lora_config = load_adapter(model, cfg, adapter)
 
-    if cfg.ddp:
+    if cfg.ddp and not load_in_8bit:
         model.to(f"cuda:{cfg.local_rank}")
 
     if cfg.load_4bit:

src/axolotl/utils/trainer.py

@@ -94,13 +94,22 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
         )
         trainer_kwargs["callbacks"] = [early_stop_cb]
 
+    data_collator_kwargs = {
+        "padding": True,
+    }
+    if cfg.collator_pad_to_longest:
+        data_collator_kwargs["padding"] = "longest"
+    else:
+        data_collator_kwargs["pad_to_multiple_of"] = 8
     trainer = transformers.Trainer(
         model=model,
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,
         args=training_args,
         data_collator=transformers.DataCollatorForSeq2Seq(
-            tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
+            tokenizer,
+            return_tensors="pt",
+            **data_collator_kwargs,
         ),
         **trainer_kwargs,
     )