test

README.md

@@ -121,7 +121,7 @@ Features:
 
 Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.
 
-**Requirements**: Nvidia GPU (Ampere architecture or newer for `bf16` and Flash Attention), Python >=3.10 and PyTorch >=2.3.1.
+**Requirements**: Python >=3.10 and Pytorch >=2.1.1.
 
 ```bash
 git clone https://github.com/axolotl-ai-cloud/axolotl
@@ -383,7 +383,7 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
         - typescript
       type: ... # unimplemented custom format
 
-      # fastchat conversation (deprecation soon, use chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template)
+      # fastchat conversation (deprecation soon, use chat_template)
       # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
     - path: ...
       type: sharegpt

examples/deepseek-v2/qlora-fsdp-2_5.yaml

@@ -9,7 +9,7 @@ strict: false
 plugins:
   - axolotl.integrations.liger.LigerPlugin
 liger_rms_norm: true
-liger_glu_activation: true
+liger_swiglu: true
 liger_fused_linear_cross_entropy: true
 
 chat_template: deepseek_v2

examples/llama-3/fft-8b-liger-fsdp.yaml

@@ -9,27 +9,21 @@ liger_fused_linear_cross_entropy: true
 
 strict: false
 
-chat_template: llama3
 datasets:
-  - path: mlabonne/FineTome-100k
+    - path: tatsu-lab/alpaca
-    type: chat_template
+      type: alpaca
-    split: train[:20%]
-    field_messages: conversations
-    message_field_role: from
-    message_field_content: value
-
 dataset_prepared_path: last_run_prepared
-val_set_size: 0.02
+val_set_size: 0
 output_dir: ./outputs/out
 
 sequence_len: 4096
 sample_packing: true
 pad_to_sequence_len: true
 
-wandb_project:
+wandb_project: check_liger_hf_GA_llama_fix-3
-wandb_entity:
+wandb_entity: axolotl-ai
 wandb_watch:
-wandb_name:
+wandb_name: pr/fix333-tr4.46.1
 wandb_log_model:
 
 gradient_accumulation_steps: 4

requirements.txt

@@ -1,10 +1,10 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
 peft==0.13.2
-transformers==4.46.2
+transformers==4.46.1
 tokenizers>=0.20.1
 bitsandbytes==0.44.1
-accelerate==1.1.0
+accelerate==1.0.1
 datasets==3.0.1
 deepspeed==0.15.3
 pydantic==2.6.3
@@ -33,8 +33,8 @@ gradio==3.50.2
 tensorboard
 python-dotenv==1.0.1
 autoawq>=0.2.5
-triton>=2.3.0
+triton>=3.1.0
-liger-kernel==0.4.0
+liger-kernel==0.3.1
 
 mamba-ssm==1.2.0.post1
 

src/axolotl/core/trainer_builder.py

@@ -896,13 +896,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         for key, value in metrics.items():
             self._stored_metrics[train_eval][key].append(value)
 
-    def _save_checkpoint(self, model, trial):
+    def _save_checkpoint(self, model, trial, metrics=None):
         # make sure the checkpoint dir exists, since trainer is flakey
         checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
         run_dir = self._get_output_dir(trial=trial)
         output_dir = os.path.join(run_dir, checkpoint_folder)
         os.makedirs(output_dir, exist_ok=True)
-        return super()._save_checkpoint(model, trial)
+        return super()._save_checkpoint(model, trial, metrics=metrics)
 
 
 class AxolotlMambaTrainer(AxolotlTrainer):

tests/e2e/integrations/liger.py

@@ -1,6 +1,7 @@
 """
 Simple end-to-end test for Liger integration
 """
+
 import unittest
 from pathlib import Path
 

tests/integrations/liger.py

@@ -1,80 +0,0 @@
-"""
-config validation tests for swiglu args
-"""
-# pylint: disable=duplicate-code
-import logging
-from typing import Optional
-
-import pytest
-
-from axolotl.utils.config import validate_config
-from axolotl.utils.dict import DictDefault
-
-
-@pytest.fixture(name="minimal_base_cfg")
-def fixture_cfg():
-    return DictDefault(
-        {
-            "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
-            "learning_rate": 0.000001,
-            "datasets": [
-                {
-                    "path": "mhenrichsen/alpaca_2k_test",
-                    "type": "alpaca",
-                }
-            ],
-            "micro_batch_size": 1,
-            "gradient_accumulation_steps": 1,
-        }
-    )
-
-
-class BaseValidation:
-    """
-    Base validation module to setup the log capture
-    """
-
-    _caplog: Optional[pytest.LogCaptureFixture] = None
-
-    @pytest.fixture(autouse=True)
-    def inject_fixtures(self, caplog):
-        self._caplog = caplog
-
-
-# pylint: disable=too-many-public-methods
-class TestValidation(BaseValidation):
-    """
-    Test the validation module for liger
-    """
-
-    def test_deprecated_swiglu(self, minimal_cfg):
-        test_cfg = DictDefault(
-            {
-                "liger_swiglu": False,
-            }
-            | minimal_cfg
-        )
-
-        with self._caplog.at_level(logging.WARNING):
-            updated_cfg = validate_config(test_cfg)
-            assert (
-                "The 'liger_swiglu' argument is deprecated"
-                in self._caplog.records[0].message
-            )
-            assert updated_cfg.liger_swiglu is None
-            assert updated_cfg.liger_glu_activations is False
-
-    def test_conflict_swiglu_ligergluactivation(self, minimal_cfg):
-        test_cfg = DictDefault(
-            {
-                "liger_swiglu": False,
-                "liger_glu_activations": True,
-            }
-            | minimal_cfg
-        )
-
-        with pytest.raises(
-            ValueError,
-            match=r".*You cannot have both `liger_swiglu` and `liger_glu_activation` set.*",
-        ):
-            validate_config(test_cfg)