set download to use revision

* update axolotl ascii art

* spacing for logo

* cleanup dithering

* cleanup ascii logo a bit

.github/workflows/base.yml

@@ -28,7 +28,13 @@ jobs:
             cuda_version: 12.4.1
             cudnn_version: ""
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+          - cuda: "124"
+            cuda_version: 12.4.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.4.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout

.github/workflows/main.yml

@@ -27,7 +27,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -84,7 +84,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:

.github/workflows/nightlies.yml

@@ -26,7 +26,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -83,7 +83,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:

.github/workflows/tests-nightly.yml

@@ -25,7 +25,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.0"]
+        pytorch_version: ["2.3.1", "2.4.1"]
     timeout-minutes: 20
 
     steps:
@@ -91,7 +91,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"

.github/workflows/tests.yml

@@ -36,7 +36,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.0"]
+        pytorch_version: ["2.3.1", "2.4.1"]
     timeout-minutes: 20
 
     steps:
@@ -94,7 +94,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
             num_gpus: 1
             axolotl_extras:
     steps:

.isort.cfg

@@ -1,3 +1,3 @@
 [settings]
 profile=black
-known_third_party=wandb
+known_third_party=wandb,comet_ml

README.md

@@ -14,7 +14,7 @@ Features:
 - Integrated with xformer, flash attention, [liger kernel](https://github.com/linkedin/Liger-Kernel), rope scaling, and multipacking
 - Works with single GPU or multiple GPUs via FSDP or Deepspeed
 - Easily run with Docker locally or on the cloud
-- Log results and optionally checkpoints to wandb or mlflow
+- Log results and optionally checkpoints to wandb, mlflow or Comet
 - And more!
 
 <a href="https://www.phorm.ai/query?projectId=e315ba4a-4e14-421f-ab05-38a1f9076f25">
@@ -515,6 +515,22 @@ wandb_name:
 wandb_log_model:
 ```
 
+##### Comet Logging
+
+Make sure your `COMET_API_KEY` environment variable is set (recommended) or you login to wandb with `comet login`.
+
+- wandb options
+```yaml
+use_comet:
+comet_api_key:
+comet_workspace:
+comet_project_name:
+comet_experiment_key:
+comet_mode:
+comet_online:
+comet_experiment_config:
+```
+
 ##### Special Tokens
 
 It is important to have special tokens like delimiters, end-of-sequence, beginning-of-sequence in your tokenizer's vocabulary.  This will help you avoid tokenization issues and help your model train better.  You can do this in axolotl like this:

docs/config.qmd

@@ -90,6 +90,7 @@ datasets:
     shards: # Optional[int] number of shards to split data into
     name: # Optional[str] name of dataset configuration to load
     train_on_split: train # Optional[str] name of dataset split to load from
+    revision: # Optional[str] The specific revision of the dataset to use when loading from the Hugging Face Hub. This can be a commit hash, tag, or branch name. If not specified, the latest version will be used. This parameter is ignored for local datasets.
 
     # Optional[str] fastchat conversation type, only used with type: sharegpt
     conversation: # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
@@ -267,6 +268,18 @@ mlflow_tracking_uri: # URI to mlflow
 mlflow_experiment_name: # Your experiment name
 hf_mlflow_log_artifacts:  # set to true to copy each saved checkpoint on each save to mlflow artifact registry
 
+# Comet configuration if you're using it
+# Make sure your `COMET_API_KEY` environment variable is set (recommended) or you login to Comet with `comet login`.
+# Check out our documentation for more details https://www.comet.com/docs/v2/api-and-sdk/python-sdk/reference/Experiment-Creation/#comet_ml.start
+use_comet: # Enable or disable Comet integration.
+comet_api_key: # API key for Comet. Recommended to set via `comet login`.
+comet_workspace: # Workspace name in Comet. Defaults to the user's default workspace.
+comet_project_name: # Project name in Comet. Defaults to Uncategorized.
+comet_experiment_key: # Identifier for the experiment. Used to append data to an existing experiment or control the key of new experiments. Default to a random key.
+comet_mode: # Create a new experiment ("create") or log to an existing one ("get"). Default ("get_or_create") auto-selects based on configuration.
+comet_online: # Set to True to log data to Comet server, or False for offline storage. Default is True.
+comet_experiment_config: # Dictionary for additional configuration settings, see the doc for more details.
+
 # Where to save the full-finetuned model to
 output_dir: ./completed-model
 

requirements.txt

@@ -16,7 +16,7 @@ flash-attn==2.6.3
 sentencepiece
 wandb
 einops
-xformers==0.0.27
+xformers==0.0.28.post1
 optimum==1.16.2
 hf_transfer
 colorama
@@ -46,3 +46,9 @@ gcsfs>=2024.5.0
 trl==0.9.6
 zstandard==0.22.0
 fastcore
+
+# lm eval harness
+lm_eval==0.4.4
+langdetect==1.0.9
+immutabledict==4.2.0
+antlr4-python3-runtime==4.13.2

setup.py

@@ -49,10 +49,17 @@ def parse_requirements():
             else:
                 raise ValueError("Invalid version format")
 
+            if (major, minor) >= (2, 4):
+                if patch == 0:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers>=0.0.27")
             if (major, minor) >= (2, 3):
                 if patch == 0:
                     _install_requires.pop(_install_requires.index(xformers_version))
                     _install_requires.append("xformers>=0.0.26.post1")
+                else:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers>=0.0.27")
             elif (major, minor) >= (2, 2):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append("xformers>=0.0.25.post1")

src/axolotl/cli/__init__.py

@@ -31,6 +31,7 @@ from axolotl.integrations.base import PluginManager
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
 from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.comet_ import setup_comet_env_vars
 from axolotl.utils.config import (
     normalize_cfg_datasets,
     normalize_config,
@@ -54,8 +55,22 @@ LOG = logging.getLogger("axolotl.scripts")
 
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
+AXOLOTL_LOGO = """
+     #@@ #@@      @@# @@#
+    @@  @@          @@  @@           =@@#                               @@                 #@    =@@#.
+    @@    #@@@@@@@@@    @@           #@#@=                              @@                 #@     .=@@
+      #@@@@@@@@@@@@@@@@@            =@# @#     ##=     ##    =####=+    @@      =#####+  =#@@###.   @@
+    @@@@@@@@@@/  +@@/  +@@          #@  =@=     #@=   @@   =@#+  +#@#   @@    =@#+  +#@#   #@.      @@
+    @@@@@@@@@@  ##@@  ##@@         =@#   @#      =@# @#    @@      @@   @@    @@      #@   #@       @@
+     @@@@@@@@@@@@@@@@@@@@          #@=+++#@=      =@@#     @@      @@   @@    @@      #@   #@       @@
+                                  =@#=====@@     =@# @#    @@      @@   @@    @@      #@   #@       @@
+    @@@@@@@@@@@@@@@@  @@@@        #@      #@=   #@=  +@@   #@#    =@#   @@.   =@#    =@#   #@.      @@
+                                 =@#       @#  #@=     #@   =#@@@@#=    +#@@=  +#@@@@#=    .##@@+   @@
+    @@@@  @@@@@@@@@@@@@@@@
+"""
 
-def print_axolotl_text_art(suffix=None):
+
+def print_legacy_axolotl_text_art(suffix=None):
     font = "nancyj"
     ascii_text = "  axolotl"
     if suffix:
@@ -68,6 +83,13 @@ def print_axolotl_text_art(suffix=None):
     print_dep_versions()
 
 
+def print_axolotl_text_art(
+    **kwargs,  # pylint: disable=unused-argument
+):
+    if is_main_process():
+        print(AXOLOTL_LOGO)
+
+
 def print_dep_versions():
     packages = ["accelerate", "peft", "transformers", "trl", "torch", "bitsandbytes"]
     max_len = max(len(pkg) for pkg in packages)
@@ -421,6 +443,8 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
 
     setup_mlflow_env_vars(cfg)
 
+    setup_comet_env_vars(cfg)
+
     return cfg
 
 

src/axolotl/cli/train.py

@@ -3,13 +3,11 @@ CLI to run training on a model
 """
 import logging
 from pathlib import Path
-from typing import Tuple, Union
+from typing import Union
 
 import fire
 from dotenv import load_dotenv
 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,
@@ -20,6 +18,7 @@ from axolotl.cli import (
     print_axolotl_text_art,
 )
 from axolotl.common.cli import TrainerCliArgs
+from axolotl.integrations.base import PluginManager
 from axolotl.prompt_strategies.sharegpt import (
     register_chatml_template,
     register_llama3_template,
@@ -39,7 +38,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
     return do_train(parsed_cfg, parsed_cli_args)
 
 
-def do_train(cfg, cli_args) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
+def do_train(cfg, cli_args) -> None:
     print_axolotl_text_art()
     check_accelerate_default_config()
     check_user_token()
@@ -64,7 +63,13 @@ def do_train(cfg, cli_args) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
     else:
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-    return train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+    model, tokenizer = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+    plugin_manager = PluginManager.get_instance()
+
+    del model
+    del tokenizer
+
+    plugin_manager.post_train_unload(cfg)
 
 
 if __name__ == "__main__":

src/axolotl/core/trainer_builder.py

@@ -48,7 +48,7 @@ from trl.trainer.utils import pad_to_length
 
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
-from axolotl.utils import is_mlflow_available
+from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
     GPUStatsCallback,
@@ -1111,6 +1111,12 @@ class TrainerBuilderBase(abc.ABC):
             callbacks.append(
                 SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
             )
+        if self.cfg.use_comet and is_comet_available():
+            from axolotl.utils.callbacks.comet_ import SaveAxolotlConfigtoCometCallback
+
+            callbacks.append(
+                SaveAxolotlConfigtoCometCallback(self.cfg.axolotl_config_path)
+            )
 
         return callbacks
 
@@ -1179,6 +1185,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 trainer, self.tokenizer, "mlflow"
             )
             callbacks.append(LogPredictionCallback(self.cfg))
+        if self.cfg.use_comet and is_comet_available() and self.cfg.eval_table_size > 0:
+            LogPredictionCallback = log_prediction_callback_factory(
+                trainer, self.tokenizer, "comet_ml"
+            )
+            callbacks.append(LogPredictionCallback(self.cfg))
 
         if self.cfg.do_bench_eval:
             callbacks.append(bench_eval_callback_factory(trainer, self.tokenizer))
@@ -1430,6 +1441,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             report_to.append("mlflow")
         if self.cfg.use_tensorboard:
             report_to.append("tensorboard")
+        if self.cfg.use_comet:
+            report_to.append("comet_ml")
 
         training_arguments_kwargs["report_to"] = report_to
         training_arguments_kwargs["run_name"] = (

src/axolotl/integrations/base.py

@@ -159,6 +159,29 @@ class BasePlugin:
         List[callable]: A list of callback functions to be added to the TrainingArgs
         """
 
+    def post_train(self, cfg, model):
+        """
+        Performs actions after training is complete.
+
+        Parameters:
+        cfg (dict): The axolotl configuration
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def post_train_unload(self, cfg):
+        """
+        Performs actions after training is complete and the model is unloaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+
+        Returns:
+        None
+        """
+
 
 def load_plugin(plugin_name: str) -> BasePlugin:
     """
@@ -381,3 +404,17 @@ class PluginManager:
         for plugin in self.plugins:
             callbacks.extend(plugin.add_callbacks_post_trainer(cfg, trainer))
         return callbacks
+
+    def post_train_unload(self, cfg):
+        """
+        Calls the post_train_unload method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins:
+            plugin.post_train_unload(cfg)

src/axolotl/integrations/lm_eval/README.md

@@ -0,0 +1,13 @@
+# LM Eval Harness
+
+### Usage
+
+```yaml
+plugins:
+  - axolotl.integrations.lm_eval.LMEvalPlugin
+
+lm_eval_tasks:
+  - gsm8k
+  - hellaswag
+  - arc_easy
+```

src/axolotl/integrations/lm_eval/__init__.py

@@ -0,0 +1,42 @@
+"""
+Module for the Plugin for LM Eval Harness
+"""
+import subprocess  # nosec
+from datetime import datetime
+
+from axolotl.integrations.base import BasePlugin
+
+from .args import LMEvalArgs  # pylint: disable=unused-import. # noqa: F401
+
+
+class LMEvalPlugin(BasePlugin):
+    """
+    Plugin for LM Evaluation Harness integraton with Axolotl.
+    """
+
+    def get_input_args(self):
+        return "axolotl.integrations.lm_eval.LMEvalArgs"
+
+    def post_train_unload(self, cfg):
+        tasks = ",".join(cfg.lm_eval_tasks)
+        fa2 = ",attn_implementation=flash_attention_2" if cfg.flash_attention else ""
+        dtype = ",dtype=bfloat16" if cfg.bf16 else ",dtype=float16"
+        output_path = cfg.output_dir
+        output_path += "" if cfg.output_dir.endswith("/") else "/"
+        output_path += "lm_eval_results/" + datetime.now().strftime("%Y%m%d_%H%M%S")
+        subprocess.run(  # nosec
+            [
+                "lm_eval",
+                "--model",
+                "hf",
+                "--model_args",
+                f"pretrained={cfg.output_dir}{fa2}{dtype}",
+                "--tasks",
+                tasks,
+                "--batch_size",
+                str(cfg.lm_eval_batch_size),
+                "--output_path",
+                output_path,
+            ],
+            check=True,
+        )

src/axolotl/integrations/lm_eval/args.py

@@ -0,0 +1,15 @@
+"""
+Module for handling lm eval harness input arguments.
+"""
+from typing import List, Optional
+
+from pydantic import BaseModel
+
+
+class LMEvalArgs(BaseModel):
+    """
+    Input args for lm eval harness
+    """
+
+    lm_eval_tasks: List[str] = []
+    lm_eval_batch_size: Optional[int] = 8

src/axolotl/monkeypatch/relora.py

@@ -44,8 +44,8 @@ def magnitude_pruning_(tensor, prune_ratio):
 def reset_optimizer(
     optimizer: torch.optim.Optimizer,
     *,
-    reset_params: list[str],  # where str is the key to a torch.nn.Parameter
+    reset_params: List[str],  # where str is the key to a torch.nn.Parameter
-    optimizer_state_keys: list[str],
+    optimizer_state_keys: List[str],
     prune_ratio: float = 0.9,
 ):
     pruning_fn = partial(magnitude_pruning_, prune_ratio=prune_ratio)

src/axolotl/utils/__init__.py

@@ -1,8 +1,12 @@
 """
 Basic utils for Axolotl
 """
-import importlib
+import importlib.util
 
 
 def is_mlflow_available():
     return importlib.util.find_spec("mlflow") is not None
+
+
+def is_comet_available():
+    return importlib.util.find_spec("comet_ml") is not None

src/axolotl/utils/callbacks/__init__.py

@@ -29,7 +29,7 @@ from transformers import (
 )
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy
 
-from axolotl.utils import is_mlflow_available
+from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.callbacks.perplexity import Perplexity
 from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
@@ -462,7 +462,7 @@ def causal_lm_bench_eval_callback_factory(trainer: Trainer, tokenizer):
                             references=[[r] for r in references],
                             predictions=predictions,
                         )
-                    scores[metric_name] = score
+                    scores["eval_" + metric_name] = score
                 return scores
 
             def predict_with_generate():
@@ -747,6 +747,15 @@ def log_prediction_callback_factory(trainer: Trainer, tokenizer, logger: str):
                         artifact_file="PredictionsVsGroundTruth.json",
                         tracking_uri=tracking_uri,
                     )
+                elif logger == "comet_ml" and is_comet_available():
+                    import comet_ml
+
+                    experiment = comet_ml.get_running_experiment()
+                    if experiment:
+                        experiment.log_table(
+                            f"{name} - Predictions vs Ground Truth.csv",
+                            pd.DataFrame(table_data),
+                        )
 
             if is_main_process():
                 log_table_from_dataloader("Eval", eval_dataloader)

src/axolotl/utils/callbacks/comet_.py

@@ -0,0 +1,43 @@
+"""Comet module for trainer callbacks"""
+
+import logging
+from typing import TYPE_CHECKING
+
+import comet_ml
+from transformers import TrainerCallback, TrainerControl, TrainerState
+
+from axolotl.utils.distributed import is_main_process
+
+if TYPE_CHECKING:
+    from axolotl.core.trainer_builder import AxolotlTrainingArguments
+
+LOG = logging.getLogger("axolotl.callbacks")
+
+
+class SaveAxolotlConfigtoCometCallback(TrainerCallback):
+    """Callback to save axolotl config to comet"""
+
+    def __init__(self, axolotl_config_path):
+        self.axolotl_config_path = axolotl_config_path
+
+    def on_train_begin(
+        self,
+        args: "AxolotlTrainingArguments",  # pylint: disable=unused-argument
+        state: TrainerState,  # pylint: disable=unused-argument
+        control: TrainerControl,
+        **kwargs,  # pylint: disable=unused-argument
+    ):
+        if is_main_process():
+            try:
+                comet_experiment = comet_ml.start(source="axolotl")
+                comet_experiment.log_other("Created from", "axolotl")
+                comet_experiment.log_asset(
+                    self.axolotl_config_path,
+                    file_name="axolotl-config",
+                )
+                LOG.info(
+                    "The Axolotl config has been saved to the Comet Experiment under assets."
+                )
+            except (FileNotFoundError, ConnectionError) as err:
+                LOG.warning(f"Error while saving Axolotl config to Comet: {err}")
+        return control

src/axolotl/utils/chat_templates.py

@@ -5,7 +5,9 @@ These templates are used for formatting messages in a conversation.
 
 CHAT_TEMPLATES = {
     "alpaca": "{% for message in messages %}{% if message['role'] == 'user' %}{{ '### Instruction: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ '### Response: ' + message['content'] + eos_token}}{% endif %}{% endfor %}",
-    "inst": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",  # I don't know what this one is called. Used by Mistral/Mixtral.
+    "mistral_v1": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ ' [INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",  # Mistral 7B V1, Mistral 7B V2, Mixtral 8x7B V1...
+    "mistral_v2v3": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + '[/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",  # V3: Mistral 7B V3, Small, Large...
+    "mistral_v3_tekken": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST]' + message['content'] + '[/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",  # V3-Tekken: Nemo, Pixtral...
     "chatml": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
     "gemma": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
     "cohere": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'  + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}",

src/axolotl/utils/comet_.py

@@ -0,0 +1,93 @@
+"""Module for wandb utilities"""
+
+import logging
+import os
+
+from axolotl.utils.dict import DictDefault
+
+LOG = logging.getLogger("axolotl.utils.comet_")
+
+COMET_ENV_MAPPING_OVERRIDE = {
+    "comet_mode": "COMET_START_MODE",
+    "comet_online": "COMET_START_ONLINE",
+}
+COMET_EXPERIMENT_CONFIG_ENV_MAPPING_OVERRIDE = {
+    "auto_histogram_activation_logging": "COMET_AUTO_LOG_HISTOGRAM_ACTIVATIONS",
+    "auto_histogram_epoch_rate": "COMET_AUTO_LOG_HISTOGRAM_EPOCH_RATE",
+    "auto_histogram_gradient_logging": "COMET_AUTO_LOG_HISTOGRAM_GRADIENTS",
+    "auto_histogram_tensorboard_logging": "COMET_AUTO_LOG_HISTOGRAM_TENSORBOARD",
+    "auto_histogram_weight_logging": "COMET_AUTO_LOG_HISTOGRAM_WEIGHTS",
+    "auto_log_co2": "COMET_AUTO_LOG_CO2",
+    "auto_metric_logging": "COMET_AUTO_LOG_METRICS",
+    "auto_metric_step_rate": "COMET_AUTO_LOG_METRIC_STEP_RATE",
+    "auto_output_logging": "COMET_AUTO_LOG_OUTPUT_LOGGER",
+    "auto_param_logging": "COMET_AUTO_LOG_PARAMETERS",
+    "comet_disabled": "COMET_AUTO_LOG_DISABLE",
+    "display_summary_level": "COMET_DISPLAY_SUMMARY_LEVEL",
+    "distributed_node_identifier": "COMET_DISTRIBUTED_NODE_IDENTIFIER",
+    "log_code": "COMET_AUTO_LOG_CODE",
+    "log_env_cpu": "COMET_AUTO_LOG_ENV_CPU",
+    "log_env_details": "COMET_AUTO_LOG_ENV_DETAILS",
+    "log_env_disk": "COMET_AUTO_LOG_ENV_DISK",
+    "log_env_gpu": "COMET_AUTO_LOG_ENV_GPU",
+    "log_env_host": "COMET_AUTO_LOG_ENV_HOST",
+    "log_env_network": "COMET_AUTO_LOG_ENV_NETWORK",
+    "log_git_metadata": "COMET_AUTO_LOG_GIT_METADATA",
+    "log_git_patch": "COMET_AUTO_LOG_GIT_PATCH",
+    "log_graph": "COMET_AUTO_LOG_GRAPH",
+    "name": "COMET_START_EXPERIMENT_NAME",
+    "offline_directory": "COMET_OFFLINE_DIRECTORY",
+    "parse_args": "COMET_AUTO_LOG_CLI_ARGUMENTS",
+    "tags": "COMET_START_EXPERIMENT_TAGS",
+}
+
+
+def python_value_to_environ_value(python_value):
+    if isinstance(python_value, bool):
+        if python_value is True:
+            return "true"
+
+        return "false"
+
+    if isinstance(python_value, int):
+        return str(python_value)
+
+    if isinstance(python_value, list):  # Comet only have one list of string parameter
+        return ",".join(map(str, python_value))
+
+    return python_value
+
+
+def setup_comet_env_vars(cfg: DictDefault):
+    # TODO, we need to convert Axolotl configuration to environment variables
+    # as Transformers integration are call first and would create an
+    # Experiment first
+
+    for key in cfg.keys():
+        if key.startswith("comet_") and key != "comet_experiment_config":
+            value = cfg.get(key, "")
+
+            if value is not None and value != "":
+                env_variable_name = COMET_ENV_MAPPING_OVERRIDE.get(key, key.upper())
+                final_value = python_value_to_environ_value(value)
+                os.environ[env_variable_name] = final_value
+
+    if cfg.comet_experiment_config:
+        for key, value in cfg.comet_experiment_config.items():
+            if value is not None and value != "":
+                config_env_variable_name = (
+                    COMET_EXPERIMENT_CONFIG_ENV_MAPPING_OVERRIDE.get(key)
+                )
+
+                if config_env_variable_name is None:
+                    LOG.warning(
+                        f"Unknown Comet Experiment Config name {key}, ignoring it"
+                    )
+                    continue
+
+                final_value = python_value_to_environ_value(value)
+                os.environ[config_env_variable_name] = final_value
+
+    # Enable comet if project name is present
+    if cfg.comet_project_name and len(cfg.comet_project_name) > 0:
+        cfg.use_comet = True

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -489,6 +489,19 @@ class WandbConfig(BaseModel):
         return data
 
 
+class CometConfig(BaseModel):
+    """Comet configuration subset"""
+
+    use_comet: Optional[bool] = None
+    comet_api_key: Optional[str] = None
+    comet_workspace: Optional[str] = None
+    comet_project_name: Optional[str] = None
+    comet_experiment_key: Optional[str] = None
+    comet_mode: Optional[str] = None
+    comet_online: Optional[bool] = None
+    comet_experiment_config: Optional[Dict[str, Any]] = None
+
+
 class GradioConfig(BaseModel):
     """Gradio configuration subset"""
 
@@ -509,6 +522,7 @@ class AxolotlInputConfig(
     HyperparametersConfig,
     WandbConfig,
     MLFlowConfig,
+    CometConfig,
     LISAConfig,
     GradioConfig,
     RemappedParameters,
@@ -966,6 +980,26 @@ class AxolotlInputConfig(
                 "evaluation_strategy must be empty or set to `steps` when used with evals_per_epoch."
             )
 
+        if data.get("do_bench_eval") and not (
+            data.get("evals_per_epoch") or data.get("eval_steps")
+        ):
+            raise ValueError(
+                "do_bench_eval requires evals_per_epoch or eval_steps to be set."
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_test_datasets_bench(cls, data):
+        if (
+            data.get("do_bench_eval")
+            and not data.get("test_datasets")
+            and not data.get("val_set_size")
+        ):
+            LOG.warning(
+                "`do_bench_eval` needs a test dataset to run evals, adding an empty test_dataset."
+            )
+            data["test_datasets"] = [{"path": "axolotl-ai-co/empty-test-ds"}]
         return data
 
     @model_validator(mode="before")

src/axolotl/utils/data/sft.py

@@ -242,6 +242,7 @@ def load_tokenized_prepared_datasets(
                     name=config_dataset.name,
                     streaming=True,
                     token=use_auth_token,
+                    revision=config_dataset.revision,
                 )
                 ds_from_hub = True
             except (FileNotFoundError, ConnectionError, HFValidationError, ValueError):
@@ -346,6 +347,7 @@ def load_tokenized_prepared_datasets(
                     streaming=False,
                     data_files=config_dataset.data_files,
                     token=use_auth_token,
+                    revision=config_dataset.revision,
                     **load_ds_kwargs,
                 )
             elif ds_from_cloud and remote_file_system:
@@ -380,6 +382,7 @@ def load_tokenized_prepared_datasets(
                         repo_id=config_dataset.path,
                         repo_type="dataset",
                         filename=config_dataset.data_files,
+                        revision=config_dataset.revision,
                     )
                 elif isinstance(config_dataset.data_files, list):
                     fp = []
@@ -389,6 +392,7 @@ def load_tokenized_prepared_datasets(
                                 repo_id=config_dataset.path,
                                 repo_type="dataset",
                                 filename=file,
+                                revision=config_dataset.revision,
                             )
                         )
                 else:
@@ -433,8 +437,8 @@ def load_tokenized_prepared_datasets(
                 config_dataset=config_dataset,
                 tokenizer=tokenizer,
                 cfg=cfg,
-                dataset=ds,
                 d_base_type=d_base_type,
+                dataset=ds,
                 d_prompt_style=d_prompt_style,
                 processor=processor,
             )

tests/test_datasets.py

@@ -267,6 +267,74 @@ class TestDatasetPreparation(unittest.TestCase):
             assert "attention_mask" in dataset.features
             assert "labels" in dataset.features
 
+    def test_load_hub_with_revision(self):
+        """Verify that processing data from the hub works with a specific revision"""
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            prepared_path = Path(tmp_dir) / "prepared"
+            cfg = DictDefault(
+                {
+                    "tokenizer_config": "huggyllama/llama-7b",
+                    "sequence_len": 1024,
+                    "datasets": [
+                        {
+                            "path": "mhenrichsen/alpaca_2k_test",
+                            "type": "alpaca",
+                            "revision": "d05c1cb",
+                        },
+                    ],
+                }
+            )
+
+            dataset, _ = load_tokenized_prepared_datasets(
+                self.tokenizer, cfg, prepared_path
+            )
+
+            assert len(dataset) == 2000
+            assert "input_ids" in dataset.features
+            assert "attention_mask" in dataset.features
+            assert "labels" in dataset.features
+
+    def test_load_local_hub_with_revision(self):
+        """Verify that a local copy of a hub dataset can be loaded with a specific revision"""
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            tmp_ds_path = Path("mhenrichsen/alpaca_2k_test")
+            tmp_ds_path.mkdir(parents=True, exist_ok=True)
+            snapshot_download(
+                repo_id="mhenrichsen/alpaca_2k_test",
+                repo_type="dataset",
+                local_dir=tmp_ds_path,
+                revision="d05c1cb",
+            )
+
+            prepared_path = Path(tmp_dir) / "prepared"
+            cfg = DictDefault(
+                {
+                    "tokenizer_config": "huggyllama/llama-7b",
+                    "sequence_len": 1024,
+                    "datasets": [
+                        {
+                            "path": "mhenrichsen/alpaca_2k_test",
+                            "ds_type": "parquet",
+                            "type": "alpaca",
+                            "data_files": [
+                                "mhenrichsen/alpaca_2k_test/alpaca_2000.parquet",
+                            ],
+                            "revision": "d05c1cb",
+                        },
+                    ],
+                }
+            )
+
+            dataset, _ = load_tokenized_prepared_datasets(
+                self.tokenizer, cfg, prepared_path
+            )
+
+            assert len(dataset) == 2000
+            assert "input_ids" in dataset.features
+            assert "attention_mask" in dataset.features
+            assert "labels" in dataset.features
+            shutil.rmtree(tmp_ds_path)
+
 
 if __name__ == "__main__":
     unittest.main()

tests/test_validation.py

@@ -9,6 +9,7 @@ from typing import Optional
 import pytest
 from pydantic import ValidationError
 
+from axolotl.utils import is_comet_available
 from axolotl.utils.config import validate_config
 from axolotl.utils.config.models.input.v0_4_1 import AxolotlConfigWCapabilities
 from axolotl.utils.dict import DictDefault
@@ -1329,3 +1330,105 @@ class TestValidationWandb(BaseValidation):
 
         os.environ.pop("WANDB_PROJECT", None)
         os.environ.pop("WANDB_DISABLED", None)
+
+
+@pytest.mark.skipif(is_comet_available() is False, reason="comet_ml is not installed")
+class TestValidationComet(BaseValidation):
+    """
+    Validation test for comet
+    """
+
+    def test_comet_sets_env(self, minimal_cfg):
+        from axolotl.utils.comet_ import setup_comet_env_vars
+
+        comet_config = {
+            "comet_api_key": "foo",
+            "comet_workspace": "some_workspace",
+            "comet_project_name": "some_project",
+            "comet_experiment_key": "some_experiment_key",
+            "comet_mode": "get_or_create",
+            "comet_online": False,
+            "comet_experiment_config": {
+                "auto_histogram_activation_logging": False,
+                "auto_histogram_epoch_rate": 2,
+                "auto_histogram_gradient_logging": True,
+                "auto_histogram_tensorboard_logging": False,
+                "auto_histogram_weight_logging": True,
+                "auto_log_co2": False,
+                "auto_metric_logging": True,
+                "auto_metric_step_rate": 15,
+                "auto_output_logging": False,
+                "auto_param_logging": True,
+                "comet_disabled": False,
+                "display_summary_level": 2,
+                "distributed_node_identifier": "some_distributed_node_identifier",
+                "log_code": True,
+                "log_env_cpu": False,
+                "log_env_details": True,
+                "log_env_disk": False,
+                "log_env_gpu": True,
+                "log_env_host": False,
+                "log_env_network": True,
+                "log_git_metadata": False,
+                "log_git_patch": True,
+                "log_graph": False,
+                "name": "some_name",
+                "offline_directory": "some_offline_directory",
+                "parse_args": True,
+                "tags": ["tag1", "tag2"],
+            },
+        }
+
+        cfg = DictDefault(comet_config) | minimal_cfg
+
+        new_cfg = validate_config(cfg)
+
+        setup_comet_env_vars(new_cfg)
+
+        comet_env = {
+            key: value for key, value in os.environ.items() if key.startswith("COMET_")
+        }
+
+        assert (
+            len(comet_env)
+            == len(comet_config) + len(comet_config["comet_experiment_config"]) - 1
+        )
+
+        assert comet_env == {
+            "COMET_API_KEY": "foo",
+            "COMET_AUTO_LOG_CLI_ARGUMENTS": "true",
+            "COMET_AUTO_LOG_CO2": "false",
+            "COMET_AUTO_LOG_CODE": "true",
+            "COMET_AUTO_LOG_DISABLE": "false",
+            "COMET_AUTO_LOG_ENV_CPU": "false",
+            "COMET_AUTO_LOG_ENV_DETAILS": "true",
+            "COMET_AUTO_LOG_ENV_DISK": "false",
+            "COMET_AUTO_LOG_ENV_GPU": "true",
+            "COMET_AUTO_LOG_ENV_HOST": "false",
+            "COMET_AUTO_LOG_ENV_NETWORK": "true",
+            "COMET_AUTO_LOG_GIT_METADATA": "false",
+            "COMET_AUTO_LOG_GIT_PATCH": "true",
+            "COMET_AUTO_LOG_GRAPH": "false",
+            "COMET_AUTO_LOG_HISTOGRAM_ACTIVATIONS": "false",
+            "COMET_AUTO_LOG_HISTOGRAM_EPOCH_RATE": "2",
+            "COMET_AUTO_LOG_HISTOGRAM_GRADIENTS": "true",
+            "COMET_AUTO_LOG_HISTOGRAM_TENSORBOARD": "false",
+            "COMET_AUTO_LOG_HISTOGRAM_WEIGHTS": "true",
+            "COMET_AUTO_LOG_METRIC_STEP_RATE": "15",
+            "COMET_AUTO_LOG_METRICS": "true",
+            "COMET_AUTO_LOG_OUTPUT_LOGGER": "false",
+            "COMET_AUTO_LOG_PARAMETERS": "true",
+            "COMET_DISPLAY_SUMMARY_LEVEL": "2",
+            "COMET_DISTRIBUTED_NODE_IDENTIFIER": "some_distributed_node_identifier",
+            "COMET_EXPERIMENT_KEY": "some_experiment_key",
+            "COMET_OFFLINE_DIRECTORY": "some_offline_directory",
+            "COMET_PROJECT_NAME": "some_project",
+            "COMET_START_EXPERIMENT_NAME": "some_name",
+            "COMET_START_EXPERIMENT_TAGS": "tag1,tag2",
+            "COMET_START_MODE": "get_or_create",
+            "COMET_START_ONLINE": "false",
+            "COMET_WORKSPACE": "some_workspace",
+        }
+
+        for key in comet_env.keys():
+            os.environ.pop(key, None)