Peft deepspeed resume (#1227)

* import deepspeed integration

* monkeypatch peft adapater with deepspeed for resume from checkpoint

* fix patch

* fix patches attempt 2

* make sure to set lora_model_dir

* skip pylint for deepspeed.utils

* pick up upstream fix in transformers

* remove monkeypatch for deepspeed/peft fix

* no need to set the lora_model_dir on resume

* unset load_in_*bit when using quant config

* guard before del

* better handling of load_in* kwargs

requirements.txt

@@ -1,7 +1,7 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
 peft @ git+https://github.com/huggingface/peft.git
-transformers==4.37.0
+transformers @ git+https://github.com/huggingface/transformers.git@bebeeee01275c32fccec3fa36d8b148d3813a7dc
 tokenizers==0.15.0
 bitsandbytes>=0.41.1
 accelerate==0.26.1

src/axolotl/cli/train.py

@@ -6,8 +6,9 @@
 from typing import Tuple
 
 import fire
-import transformers
-from transformers import PreTrainedModel, PreTrainedTokenizer
+from transformers.hf_argparser import HfArgumentParser
+from transformers.modeling_utils import PreTrainedModel
+from transformers.tokenization_utils import PreTrainedTokenizer
 
 from axolotl.cli import (
     check_accelerate_default_config,
@@ -27,7 +28,7 @@
 def do_cli(config: Path = Path("examples/"), **kwargs):
     # pylint: disable=duplicate-code
     parsed_cfg = load_cfg(config, **kwargs)
-    parser = transformers.HfArgumentParser((TrainerCliArgs))
+    parser = HfArgumentParser((TrainerCliArgs))
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(
         return_remaining_strings=True
     )

src/axolotl/train.py

@@ -57,6 +57,21 @@ def train(
     eval_dataset = dataset_meta.eval_dataset
     total_num_steps = dataset_meta.total_num_steps
 
+    if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints:
+        possible_checkpoints = [
+            str(cp) for cp in Path(cfg.output_dir).glob("checkpoint-*")
+        ]
+        if len(possible_checkpoints) > 0:
+            sorted_paths = sorted(
+                possible_checkpoints,
+                key=lambda path: int(path.split("-")[-1]),
+            )
+            cfg.resume_from_checkpoint = sorted_paths[-1]
+            LOG.info(
+                f"Using Auto-resume functionality to start with checkpoint at {cfg.resume_from_checkpoint}"
+            )
+    resume_from_checkpoint = cfg.resume_from_checkpoint
+
     # Load the model and tokenizer
     msg = "loading model"
     if cfg.adapter:
@@ -79,21 +94,6 @@ def train(
 
     safe_serialization = cfg.save_safetensors is True
 
-    if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints:
-        possible_checkpoints = [
-            str(cp) for cp in Path(cfg.output_dir).glob("checkpoint-*")
-        ]
-        if len(possible_checkpoints) > 0:
-            sorted_paths = sorted(
-                possible_checkpoints,
-                key=lambda path: int(path.split("-")[-1]),
-            )
-            cfg.resume_from_checkpoint = sorted_paths[-1]
-            LOG.info(
-                f"Using Auto-resume functionality to start with checkpoint at {cfg.resume_from_checkpoint}"
-            )
-    resume_from_checkpoint = cfg.resume_from_checkpoint
-
     if cfg.unfrozen_parameters:
         freeze_parameters_except(model, cfg.unfrozen_parameters)
 

src/axolotl/utils/models.py

@@ -473,6 +473,18 @@ def load_model(
             **bnb_config,
         )
 
+    if cfg.load_in_8bit and cfg.adapter is not None:
+        model_kwargs["load_in_8bit"] = True
+    if cfg.load_in_4bit and cfg.adapter is not None:
+        model_kwargs["load_in_4bit"] = True
+
+    # no longer needed per https://github.com/huggingface/transformers/pull/26610
+    if "quantization_config" in model_kwargs or cfg.gptq:
+        if "load_in_8bit" in model_kwargs:
+            del model_kwargs["load_in_8bit"]
+        if "load_in_4bit" in model_kwargs:
+            del model_kwargs["load_in_4bit"]
+
     # sample packing uses custom FA2 patch
     if cfg.flash_attention:
         if not cfg.sample_packing:
@@ -506,8 +518,6 @@ def load_model(
             model = LlamaForCausalLM.from_pretrained(
                 base_model,
                 config=model_config,
-                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,
                 **model_kwargs,
             )
 
@@ -575,8 +585,6 @@ def load_model(
                 model = getattr(transformers, model_type).from_pretrained(
                     base_model,
                     config=model_config,
-                    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,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
@@ -608,8 +616,6 @@ def load_model(
                 model = AutoModelForCausalLM.from_pretrained(
                     base_model,
                     config=model_config,
-                    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,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
@@ -678,7 +684,9 @@ def load_model(
     skip_prepare_model_for_kbit_training = False
 
     if cfg.model_config_type == "mixtral" and is_deepspeed_zero3_enabled():
-        from deepspeed.utils import set_z3_leaf_modules
+        from deepspeed.utils import (  # pylint: disable=no-name-in-module
+            set_z3_leaf_modules,
+        )
         from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
 
         set_z3_leaf_modules(model, [MixtralSparseMoeBlock])