pass additional info for fix untrained tokens when using distributed + offloading (#2388)

* pass additional info for fix untrained tokens when using distributed + offloading

* use latest version of vendored lib

* use v0.0.5 of contribs lgpl

* fix for no bad tokens and add tests

* use release

* add multigpu test too

* make sure the multigpu zero3 test actually uses zero3

requirements.txt

@@ -62,5 +62,5 @@ antlr4-python3-runtime==4.13.2
 torchao==0.7.0
 schedulefree==1.3.0
 
-axolotl-contribs-lgpl==0.0.3
+axolotl-contribs-lgpl==0.0.6
 axolotl-contribs-mit==0.0.3

src/axolotl/train.py

@@ -7,7 +7,7 @@ import signal
 import sys
 import weakref
 from pathlib import Path
-from typing import Any
+from typing import Any, Dict
 
 import torch
 import transformers.modelcard
@@ -20,7 +20,7 @@ from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
 from transformers.trainer import Trainer
 
 from axolotl.common.datasets import TrainDatasetMeta
-from axolotl.contribs.lgpl.unsloth import (  # pylint: disable = no-name-in-module
+from axolotl.contribs.lgpl import (  # pylint: disable = no-name-in-module
     fix_untrained_tokens,
 )
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
@@ -382,21 +382,23 @@ def handle_untrained_tokens_fix(
     if not cfg.fix_untrained_tokens:
         return
 
+    is_ds_zero3: bool = False
+    if os.environ.get("ACCELERATE_DEEPSPEED_ZERO_STAGE") == "3":
+        is_ds_zero3 = True
+
     # Check if the `token_ids_to_fix` kwarg exists in the fix_untrained_tokens args
     sig = inspect.signature(fix_untrained_tokens)
 
+    fix_kwargs: Dict[str, Any] = {}
     # If the function has the `token_ids_to_fix` arg, and fix_untrained_tokens is a list
     if "token_ids_to_fix" in sig.parameters and isinstance(
         cfg.fix_untrained_tokens, list
     ):
-        fix_untrained_tokens(
+        fix_kwargs["token_ids_to_fix"] = cfg.fix_untrained_tokens
-            model,
+    if "is_ds_zero3" in sig.parameters:
-            tokenizer,
+        fix_kwargs["is_ds_zero3"] = is_ds_zero3
-            train_dataset,
+
-            token_ids_to_fix=cfg.fix_untrained_tokens,
+    fix_untrained_tokens(model, tokenizer, train_dataset, **fix_kwargs)
-        )
-    else:
-        fix_untrained_tokens(model, tokenizer, train_dataset)
 
     if cfg.local_rank == 0:
         model.save_pretrained(

tests/e2e/multigpu/test_llama.py

@@ -750,3 +750,66 @@ class TestMultiGPULlama:
         check_tensorboard(
             temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
         )
+
+    def test_fix_untrained_tokens(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "fix_untrained_tokens": True,
+                "sequence_len": 512,
+                "val_set_size": 0.0,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                    "bos_token": "<|custom_im_start|>",
+                    "eos_token": "<|custom_im_end|>",
+                },
+                "datasets": [
+                    {
+                        "chat_template": "jinja",
+                        "chat_template_jinja": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|custom_im_start|>' + message['role'] + '\n' + message['content'] + '<|custom_im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|custom_im_start|>assistant\n' }}{% endif %}",
+                        "path": "mlabonne/FineTome-100k",
+                        "type": "chat_template",
+                        "split": "train[:10%]",
+                        "field_messages": "conversations",
+                        "message_field_role": "from",
+                        "message_field_content": "value",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "sample_packing": True,
+                "bf16": True,
+                "save_safetensors": True,
+                "deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero3_bf16.json"),
+                "use_tensorboard": True,
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "axolotl",
+                "train",
+                str(Path(temp_dir) / "config.yaml"),
+                "--num-processes",
+                "2",
+                "--main-process-port",
+                f"{get_torch_dist_unique_port()}",
+            ]
+        )
+
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 4.0, "Train Loss is too high"
+        )

tests/e2e/test_llama.py

@@ -66,6 +66,54 @@ class TestLlama:
         check_model_output_exists(temp_dir, cfg)
 
     def test_fix_untrained_tokens(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "fix_untrained_tokens": True,
+                "sequence_len": 512,
+                "val_set_size": 0.0,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                    "bos_token": "<|custom_im_start|>",
+                    "eos_token": "<|custom_im_end|>",
+                },
+                "datasets": [
+                    {
+                        "chat_template": "jinja",
+                        "chat_template_jinja": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|custom_im_start|>' + message['role'] + '\n' + message['content'] + '<|custom_im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|custom_im_start|>assistant\n' }}{% endif %}",
+                        "path": "mlabonne/FineTome-100k",
+                        "type": "chat_template",
+                        "split": "train[:10%]",
+                        "field_messages": "conversations",
+                        "message_field_role": "from",
+                        "message_field_content": "value",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "sample_packing": True,
+                "bf16": True,
+                "save_safetensors": True,
+            }
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)
+
+    def test_fix_untrained_tokens_already_trained(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {