add smoke test training

.github/workflows/base.yml

@@ -40,7 +40,7 @@ jobs:
             cuda_version: 12.4.1
             cudnn_version: ""
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             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

@@ -32,7 +32,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -94,7 +94,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:

.github/workflows/multi-gpu-e2e.yml

@@ -31,7 +31,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             axolotl_extras:
             num_gpus: 2
             nightly_build: "true"

.github/workflows/nightlies.yml

@@ -31,7 +31,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -93,7 +93,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:

.github/workflows/pypi.yml

@@ -4,7 +4,6 @@ on:
   push:
     tags:
       - '*'
-  workflow_dispatch:
 
 jobs:
   pypi-publish:

.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", "2.4.1", "2.5.1"]
+        pytorch_version: ["2.3.1", "2.4.1", "2.5.0"]
     timeout-minutes: 20
 
     steps:
@@ -82,6 +82,13 @@ jobs:
             num_gpus: 1
             axolotl_extras: mamba-ssm
             nightly_build: "true"
+          - cuda: 121
+            cuda_version: 12.1.1
+            python_version: "3.11"
+            pytorch: 2.3.1
+            num_gpus: 1
+            axolotl_extras: mamba-ssm
+            nightly_build: "true"
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -92,7 +99,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.5.0
             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", "2.4.1", "2.5.1"]
+        pytorch_version: ["2.3.1", "2.4.1", "2.5.0"]
     timeout-minutes: 20
 
     steps:
@@ -72,52 +72,12 @@ jobs:
         run: |
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
-  docker-e2e-tests-1st:
-    if: github.repository_owner == 'axolotl-ai-cloud'
-    # this job needs to be run on self-hosted GPU runners...
-    runs-on: [self-hosted, modal]
-    timeout-minutes: 90
-    needs: [pre-commit, pytest]
-
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            num_gpus: 1
-            axolotl_extras:
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-      - name: Install Modal
-        run: |
-          python -m pip install --upgrade pip
-          pip install modal==0.63.64 jinja2
-      - name: Update env vars
-        run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
-          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
-          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
-          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
-          echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
-          echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
-      - name: Run tests job on Modal
-        run: |
-          modal run cicd.tests
-
   docker-e2e-tests:
     if: github.repository_owner == 'axolotl-ai-cloud'
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
     timeout-minutes: 90
-    needs: [pre-commit, pytest, docker-e2e-tests-1st]
+    needs: [pre-commit, pytest]
 
     strategy:
       fail-fast: false
@@ -129,10 +89,22 @@ jobs:
             pytorch: 2.3.1
             num_gpus: 1
             axolotl_extras: mamba-ssm
+          - cuda: 121
+            cuda_version: 12.1.1
+            python_version: "3.11"
+            pytorch: 2.3.1
+            num_gpus: 1
+            axolotl_extras: mamba-ssm
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.4.1
+            num_gpus: 1
+            axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.0
             num_gpus: 1
             axolotl_extras:
     steps:

README.md

@@ -383,10 +383,11 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
         - typescript
       type: ... # unimplemented custom format
 
-      # chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template
+      # fastchat conversation (deprecation soon, use chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template)
+      # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
     - path: ...
-      type: chat_template
-      chat_template: chatml # defaults to tokenizer's chat_template
+      type: sharegpt
+      conversation: chatml # default: vicuna_v1.1
 
       # local
     - path: data.jsonl # or json
@@ -561,8 +562,7 @@ plugins:
   - axolotl.integrations.liger.LigerPlugin
 liger_rope: true
 liger_rms_norm: true
-liger_glu_activation: true
-liger_layer_norm: true
+liger_swiglu: true
 liger_fused_linear_cross_entropy: true
 ```
 

devtools/dev_chat_template.yml

@@ -1,4 +1,4 @@
-# Example config for debugging the chat_template prompt format
+# Example config for debugging the sharegpt prompt format
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer

docker/Dockerfile-base

@@ -35,7 +35,3 @@ RUN git lfs install --skip-repo && \
     pip3 install awscli && \
     # The base image ships with `pydantic==1.8.2` which is not working
     pip3 install -U --no-cache-dir pydantic==1.10.10
-
-RUN if [ "$PYTHON_VERSION" != "2.5.1" ] ; then \
-        pip3 install flash-attn==2.6.3; \
-    fi

docs/config.qmd

@@ -83,7 +83,7 @@ lora_on_cpu: true
 datasets:
   # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
   - path: vicgalle/alpaca-gpt4
-    # The type of prompt to use for training. [alpaca, gpteacher, oasst, reflection]
+    # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
     type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
     ds_type: # Optional[str] (json|arrow|parquet|text|csv) defines the datatype when path is a file
     data_files: # Optional[str] path to source data files
@@ -92,6 +92,15 @@ datasets:
     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
+    field_human: # Optional[str]. Human key to use for conversation.
+    field_model: # Optional[str]. Assistant key to use for conversation.
+    # Add additional keys from your dataset as input or output roles
+    roles:
+      input: # Optional[List[str]]. These will be masked based on train_on_input
+      output: # Optional[List[str]].
+
   # Custom user instruction prompt
   - path: repo
     type:
@@ -174,8 +183,6 @@ test_datasets:
 
 # use RL training: 'dpo', 'ipo', 'kto'
 rl:
-# whether to perform weighting if doing DPO training. Boolean.
-dpo_use_weighting:
 
 # The name of the chat template to use for training, following values are supported:
 # - tokenizer_default: Uses the chat template that is available in the tokenizer_config.json. If the chat template is not available in the tokenizer, it will raise an error. This is the default value.

docs/dataset-formats/conversation.qmd

@@ -6,8 +6,33 @@ order: 3
 
 ## sharegpt
 
-IMPORTANT: ShareGPT is deprecated!. Please see `chat_template` section below.
+UPDATE: ShareGPT is being deprecated in the next release. Please see `chat_template` section below.
 
+conversations where `from` is `human`/`gpt`. (optional: first row with role `system` to override default system prompt)
+
+```{.json filename="data.jsonl"}
+{"conversations": [{"from": "...", "value": "..."}]}
+```
+
+Note: `type: sharegpt` opens special configs:
+- `conversation`: enables conversions to many Conversation types. Refer to the 'name' [here](https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py) for options.
+- `roles`: allows you to specify the roles for input and output. This is useful for datasets with custom roles such as `tool` etc to support masking.
+- `field_human`: specify the key to use instead of `human` in the conversation.
+- `field_model`: specify the key to use instead of `gpt` in the conversation.
+
+```yaml
+datasets:
+    path: ...
+    type: sharegpt
+
+    conversation: # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+    field_human: # Optional[str]. Human key to use for conversation.
+    field_model: # Optional[str]. Assistant key to use for conversation.
+    # Add additional keys from your dataset as input or output roles
+    roles:
+      input: # Optional[List[str]]. These will be masked based on train_on_input
+      output: # Optional[List[str]].
+```
 
 ## pygmalion
 
@@ -15,6 +40,38 @@ IMPORTANT: ShareGPT is deprecated!. Please see `chat_template` section below.
 {"conversations": [{"role": "...", "value": "..."}]}
 ```
 
+## sharegpt.load_role
+
+conversations where `role` is used instead of `from`
+
+```{.json filename="data.jsonl"}
+{"conversations": [{"role": "...", "value": "..."}]}
+```
+
+## sharegpt.load_guanaco
+
+conversations where `from` is `prompter` `assistant` instead of default sharegpt
+
+```{.json filename="data.jsonl"}
+{"conversations": [{"from": "...", "value": "..."}]}
+```
+
+## sharegpt.load_ultrachat
+
+conversations where the turns field is 'messages', human is 'user' and gpt is 'assistant'.
+
+```{.json filename="data.jsonl"}
+{"messages": [{"user": "...", "assistant": "..."}]}
+```
+
+## sharegpt_jokes
+
+creates a chat where bot is asked to tell a joke, then explain why the joke is funny
+
+```{.json filename="data.jsonl"}
+{"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
+```
+
 
 ## chat_template
 

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

@@ -4,7 +4,7 @@ plugins:
   - axolotl.integrations.liger.LigerPlugin
 liger_rope: true
 liger_rms_norm: true
-liger_glu_activation: true
+liger_swiglu: true
 liger_fused_linear_cross_entropy: true
 
 strict: false

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==4.46.1
+transformers==4.46.0
 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
@@ -28,12 +28,13 @@ scipy
 scikit-learn==1.4.2
 pynvml
 art
+fschat @ git+https://github.com/lm-sys/FastChat.git@27a05b04a35510afb1d767ae7e5990cbd278f8fe
 gradio==3.50.2
 tensorboard
 python-dotenv==1.0.1
 autoawq>=0.2.5
 triton>=2.3.0
-liger-kernel==0.4.0
+liger-kernel==0.3.0
 
 mamba-ssm==1.2.0.post1
 
@@ -42,7 +43,7 @@ s3fs>=2024.5.0
 gcsfs>=2024.5.0
 # adlfs
 
-trl==0.12.0
+trl @ git+https://github.com/huggingface/trl.git@31d02cfb795284591a084416b9dcb7bef5d08924
 zstandard==0.22.0
 fastcore
 

setup.py

@@ -89,7 +89,7 @@ install_requires, dependency_links = parse_requirements()
 
 setup(
     name="axolotl",
-    version="0.5.0",
+    version="0.4.1",
     description="LLM Trainer",
     long_description="Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.",
     package_dir={"": "src"},
@@ -100,6 +100,9 @@ setup(
         "flash-attn": [
             "flash-attn==2.6.3",
         ],
+        "fused-dense-lib": [
+            "fused-dense-lib  @ git+https://github.com/Dao-AILab/flash-attention@v2.6.2#subdirectory=csrc/fused_dense_lib",
+        ],
         "deepspeed": [
             "deepspeed==0.14.4",
             "deepspeed-kernels",

src/axolotl/cli/preprocess.py

@@ -23,6 +23,10 @@ from axolotl.cli import (
 )
 from axolotl.common.cli import PreprocessCliArgs
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
+from axolotl.prompt_strategies.sharegpt import (
+    register_chatml_template,
+    register_llama3_template,
+)
 from axolotl.utils.trainer import disable_datasets_caching
 
 LOG = logging.getLogger("axolotl.cli.preprocess")
@@ -40,6 +44,23 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         return_remaining_strings=True
     )
 
+    if parsed_cfg.chat_template == "chatml":
+        if parsed_cfg.default_system_message:
+            LOG.info(
+                f"ChatML set. Adding default system message: {parsed_cfg.default_system_message}"
+            )
+            register_chatml_template(parsed_cfg.default_system_message)
+        else:
+            register_chatml_template()
+    elif parsed_cfg.chat_template == "llama3":
+        if parsed_cfg.default_system_message:
+            LOG.info(
+                f"LLaMA-3 set. Adding default system message: {parsed_cfg.default_system_message}"
+            )
+            register_llama3_template(parsed_cfg.default_system_message)
+        else:
+            register_llama3_template()
+
     if not parsed_cfg.dataset_prepared_path:
         msg = (
             Fore.RED

src/axolotl/cli/train.py

@@ -19,6 +19,10 @@ from axolotl.cli import (
 )
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.integrations.base import PluginManager
+from axolotl.prompt_strategies.sharegpt import (
+    register_chatml_template,
+    register_llama3_template,
+)
 from axolotl.train import train
 
 LOG = logging.getLogger("axolotl.cli.train")
@@ -38,6 +42,21 @@ def do_train(cfg, cli_args) -> None:
     print_axolotl_text_art()
     check_accelerate_default_config()
     check_user_token()
+    if cfg.chat_template == "chatml" and cfg.default_system_message:
+        LOG.info(
+            f"ChatML set. Adding default system message: {cfg.default_system_message}"
+        )
+        register_chatml_template(cfg.default_system_message)
+    else:
+        register_chatml_template()
+
+    if cfg.chat_template == "llama3" and cfg.default_system_message:
+        LOG.info(
+            f"LLaMA-3 set. Adding default system message: {cfg.default_system_message}"
+        )
+        register_llama3_template(cfg.default_system_message)
+    else:
+        register_llama3_template()
 
     if cfg.rl:  # and cfg.rl != "orpo":
         dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)

src/axolotl/core/trainer_builder.py

@@ -48,7 +48,6 @@ from trl import (
 )
 from trl.trainer.utils import RewardDataCollatorWithPadding, pad_to_length
 
-from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils import is_comet_available, is_mlflow_available
@@ -436,7 +435,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         if (
             self.args.loraplus_lr_ratio is None
             and self.args.alternate_optimizer
-            not in ["optimi_adamw", "ao_adamw_8bit", "ao_adamw_4bit", "ao_adamw_fp8"]
+            not in [
+                "optimi_adamw",
+                "ao_adamw_8bit",
+                "ao_adamw_4bit",
+                "ao_adamw_fp8",
+                "soap",
+            ]
         ):
             return super().create_optimizer()
 
@@ -479,6 +484,25 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
                     loraplus_lr_embedding=loraplus_lr_embedding,
                     **optimizer_kwargs,
                 )
+            elif self.args.alternate_optimizer == "soap":
+                from axolotl.utils.optimizers.soap import SOAP
+
+                optim_args = {
+                    "lr": optimizer_kwargs.pop("lr"),
+                    "eps": optimizer_kwargs.pop("eps"),
+                }
+
+                if self.cfg.optim_args:
+                    optim_args.update(self.cfg.optim_args)
+
+                optim_args["betas"] = (
+                    self.args.optim_soap_beta1,
+                    self.args.optim_soap_beta2,
+                )
+                self.optimizer = SOAP(  # pylint: disable=attribute-defined-outside-init
+                    optimizer_grouped_parameters,
+                    **optim_args,
+                )
             elif self.args.alternate_optimizer == "optimi_adamw":
                 from optimi import AdamW
 
@@ -896,13 +920,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         for key, value in metrics.items():
             self._stored_metrics[train_eval][key].append(value)
 
-    def _save_checkpoint(self, model, trial, **kwargs):
+    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, **kwargs)
+        return super()._save_checkpoint(model, trial, metrics=metrics)
 
 
 class AxolotlMambaTrainer(AxolotlTrainer):
@@ -1148,12 +1172,6 @@ class TrainerBuilderBase(abc.ABC):
 
     def get_callbacks(self) -> List[TrainerCallback]:
         callbacks = []
-
-        plugin_manager = PluginManager.get_instance()
-        callbacks.extend(
-            plugin_manager.add_callbacks_pre_trainer(cfg=self.cfg, model=self.model)
-        )
-
         if self.cfg.use_wandb:
             callbacks.append(
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
@@ -1180,17 +1198,11 @@ class TrainerBuilderBase(abc.ABC):
 
         return callbacks
 
+    @abstractmethod
     def get_post_trainer_create_callbacks(self, trainer):
         """
         Callbacks added after the trainer is created, usually b/c these need access to the trainer
         """
-        callbacks = []
-
-        plugin_manager = PluginManager.get_instance()
-        callbacks.extend(
-            plugin_manager.add_callbacks_post_trainer(cfg=self.cfg, trainer=trainer)
-        )
-        return callbacks
 
     def hook_pre_create_training_args(self, training_arguments_kwargs):
         # TODO
@@ -1236,7 +1248,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
+        callbacks = []
         if self.cfg.use_wandb and self.cfg.eval_table_size > 0:
             LogPredictionCallback = log_prediction_callback_factory(
                 trainer, self.tokenizer, "wandb"
@@ -1626,10 +1638,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             trainer_kwargs["max_length"] = self.cfg.sequence_len
 
         if self.cfg.optimizer in [
+            # pylint: disable=duplicate-code
             "optimi_adamw",
             "ao_adamw_4bit",
             "ao_adamw_8bit",
             "ao_adamw_fp8",
+            "soap",
         ]:
             # Set default so transformers doesn't throw
             training_arguments_kwargs["optim"] = "adamw_hf"
@@ -1804,7 +1818,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
+        callbacks = []
         return callbacks
 
     def build_training_arguments(self, total_num_steps):
@@ -1890,18 +1904,17 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             # default to saving each epoch if not defined
             training_args_kwargs["save_strategy"] = "epoch"
 
-        training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
-
         if self.cfg.rl_beta:
             training_args_kwargs["beta"] = self.cfg.rl_beta
         if self.cfg.orpo_alpha:
             # trl does some odd mapping of alpha to beta to reuse the beta parameter ???
             training_args_kwargs["beta"] = self.cfg.orpo_alpha
 
+        training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
+        training_args_cls = AxolotlDPOConfig
         if self.cfg.rpo_alpha is not None:
             training_args_kwargs["rpo_alpha"] = self.cfg.rpo_alpha
 
-        training_args_cls = None
         if self.cfg.rl == "simpo":
             training_args_cls = AxolotlCPOConfig
             training_args_kwargs["loss_type"] = "simpo"
@@ -1910,13 +1923,13 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
-        elif self.cfg.rl == "orpo":
+        if self.cfg.rl == "orpo":
             training_args_cls = AxolotlORPOConfig
             training_args_kwargs["max_length"] = self.cfg.sequence_len
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
-        elif self.cfg.rl == "kto":
+        if self.cfg.rl == "kto":
             training_args_cls = AxolotlKTOConfig
 
             training_args_kwargs["desirable_weight"] = (
@@ -1931,11 +1944,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
-        else:
-            training_args_cls = AxolotlDPOConfig
-            if self.cfg.dpo_use_weighting is not None:
-                training_args_kwargs["use_weighting"] = self.cfg.dpo_use_weighting
-
         training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
             output_dir=self.cfg.output_dir,
             per_device_train_batch_size=self.cfg.micro_batch_size,
@@ -2019,11 +2027,11 @@ class HFPPOTrainerBuilder(TrainerBuilderBase):
     """
 
     def get_callbacks(self):
-        callbacks = super().get_callbacks()
+        callbacks = []
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
+        callbacks = []
         return callbacks
 
     def build(self, total_num_steps):

src/axolotl/integrations/base.py

@@ -18,10 +18,9 @@ Plugins can be used to integrate third-party models, modify the training process
 
 To create a new plugin, you need to inherit from the BasePlugin class and implement the required methods.
 """
-import collections
 import importlib
 import logging
-from typing import OrderedDict
+from typing import List
 
 
 class BasePlugin:
@@ -48,7 +47,7 @@ class BasePlugin:
         Initializes the BasePlugin.
         """
 
-    def register(self, cfg):  # pylint: disable=unused-argument
+    def register(self, cfg):
         """
         Registers the plugin with the given configuration.
 
@@ -64,7 +63,7 @@ class BasePlugin:
         Returns a pydantic model for the plugin's input arguments.
         """
 
-    def pre_model_load(self, cfg):  # pylint: disable=unused-argument
+    def pre_model_load(self, cfg):
         """
         Performs actions before the model is loaded.
 
@@ -75,7 +74,7 @@ class BasePlugin:
         None
         """
 
-    def post_model_load(self, cfg, model):  # pylint: disable=unused-argument
+    def post_model_load(self, cfg, model):
         """
         Performs actions after the model is loaded.
 
@@ -87,7 +86,7 @@ class BasePlugin:
         None
         """
 
-    def pre_lora_load(self, cfg, model):  # pylint: disable=unused-argument
+    def pre_lora_load(self, cfg, model):
         """
         Performs actions before LoRA weights are loaded.
 
@@ -99,7 +98,7 @@ class BasePlugin:
         None
         """
 
-    def post_lora_load(self, cfg, model):  # pylint: disable=unused-argument
+    def post_lora_load(self, cfg, model):
         """
         Performs actions after LoRA weights are loaded.
 
@@ -111,7 +110,7 @@ class BasePlugin:
         None
         """
 
-    def create_optimizer(self, cfg, trainer):  # pylint: disable=unused-argument
+    def create_optimizer(self, cfg, trainer):
         """
         Creates and returns an optimizer for training.
 
@@ -123,9 +122,7 @@ class BasePlugin:
         object: The created optimizer.
         """
 
-    def create_lr_scheduler(
-        self, cfg, trainer, optimizer
-    ):  # pylint: disable=unused-argument
+    def create_lr_scheduler(self, cfg, trainer, optimizer):
         """
         Creates and returns a learning rate scheduler.
 
@@ -138,7 +135,7 @@ class BasePlugin:
         object: The created learning rate scheduler.
         """
 
-    def add_callbacks_pre_trainer(self, cfg, model):  # pylint: disable=unused-argument
+    def add_callbacks_pre_trainer(self, cfg, model):
         """
         Adds callbacks to the trainer before training.
 
@@ -149,11 +146,8 @@ class BasePlugin:
         Returns:
         List[callable]: A list of callback functions to be added to the TrainingArgs
         """
-        return []
 
-    def add_callbacks_post_trainer(
-        self, cfg, trainer
-    ):  # pylint: disable=unused-argument
+    def add_callbacks_post_trainer(self, cfg, trainer):
         """
         Adds callbacks to the trainer after training.
 
@@ -164,9 +158,8 @@ class BasePlugin:
         Returns:
         List[callable]: A list of callback functions to be added to the TrainingArgs
         """
-        return []
 
-    def post_train(self, cfg, model):  # pylint: disable=unused-argument
+    def post_train(self, cfg, model):
         """
         Performs actions after training is complete.
 
@@ -178,7 +171,7 @@ class BasePlugin:
         None
         """
 
-    def post_train_unload(self, cfg):  # pylint: disable=unused-argument
+    def post_train_unload(self, cfg):
         """
         Performs actions after training is complete and the model is unloaded.
 
@@ -234,7 +227,7 @@ class PluginManager:
     pre_model_load(cfg): Calls the pre_model_load method of all registered plugins.
     """
 
-    plugins: OrderedDict[str, BasePlugin] = collections.OrderedDict()
+    plugins: List[BasePlugin] = []
 
     _instance = None
 
@@ -244,7 +237,7 @@ class PluginManager:
         """
         if cls._instance is None:
             cls._instance = super(PluginManager, cls).__new__(cls)
-            cls._instance.plugins = collections.OrderedDict()
+            cls._instance.plugins: List[BasePlugin] = []
         return cls._instance
 
     @staticmethod
@@ -272,7 +265,7 @@ class PluginManager:
         """
         try:
             plugin = load_plugin(plugin_name)
-            self.plugins[plugin_name] = plugin
+            self.plugins.append(plugin)
         except ImportError:
             logging.error(f"Failed to load plugin: {plugin_name}")
 
@@ -284,7 +277,7 @@ class PluginManager:
         list[str]: A list of Pydantic classes for all registered plugins' input arguments.'
         """
         input_args = []
-        for plugin in self.plugins.values():
+        for plugin in self.plugins:
             input_args_from_plugin = plugin.get_input_args()
             if input_args_from_plugin is not None:
                 input_args.append(input_args_from_plugin)
@@ -300,7 +293,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins.values():
+        for plugin in self.plugins:
             plugin.pre_model_load(cfg)
 
     def post_model_load(self, cfg, model):
@@ -314,7 +307,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins.values():
+        for plugin in self.plugins:
             plugin.post_model_load(cfg, model)
 
     def pre_lora_load(self, cfg, model):
@@ -328,7 +321,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins.values():
+        for plugin in self.plugins:
             plugin.pre_lora_load(cfg, model)
 
     def post_lora_load(self, cfg, model):
@@ -342,7 +335,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins.values():
+        for plugin in self.plugins:
             plugin.post_lora_load(cfg, model)
 
     def create_optimizer(self, cfg, trainer):
@@ -356,7 +349,7 @@ class PluginManager:
         Returns:
         object: The created optimizer, or None if none was found.
         """
-        for plugin in self.plugins.values():
+        for plugin in self.plugins:
             optimizer = plugin.create_optimizer(cfg, trainer)
             if optimizer is not None:
                 return optimizer
@@ -374,7 +367,7 @@ class PluginManager:
         Returns:
         object: The created learning rate scheduler, or None if none was found.
         """
-        for plugin in self.plugins.values():
+        for plugin in self.plugins:
             scheduler = plugin.create_lr_scheduler(cfg, trainer, optimizer)
             if scheduler is not None:
                 return scheduler
@@ -392,7 +385,7 @@ class PluginManager:
         List[callable]: A list of callback functions to be added to the TrainingArgs.
         """
         callbacks = []
-        for plugin in self.plugins.values():
+        for plugin in self.plugins:
             callbacks.extend(plugin.add_callbacks_pre_trainer(cfg, model))
         return callbacks
 
@@ -408,7 +401,7 @@ class PluginManager:
         List[callable]: A list of callback functions to be added to the TrainingArgs.
         """
         callbacks = []
-        for plugin in self.plugins.values():
+        for plugin in self.plugins:
             callbacks.extend(plugin.add_callbacks_post_trainer(cfg, trainer))
         return callbacks
 
@@ -423,5 +416,5 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins.values():
+        for plugin in self.plugins:
             plugin.post_train_unload(cfg)

src/axolotl/integrations/liger/__init__.py

@@ -18,23 +18,20 @@ Module for the Plugin for LIGER integraton with Axolotl.
 Liger Kernel is the collection of Triton-native kernels for LLM Training.
 It is designed to be performant, correct, and light-weight.
 """
-import inspect
 import logging
 import sys
+from functools import partial
 
 from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
-from liger_kernel.transformers.monkey_patch import MODEL_TYPE_TO_APPLY_LIGER_FN
+from liger_kernel.transformers.geglu import LigerGEGLUMLP
 from liger_kernel.transformers.rms_norm import LigerRMSNorm
 from liger_kernel.transformers.rope import liger_rotary_pos_emb
 from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
 
 from axolotl.integrations.base import BasePlugin
 
-from ...utils.distributed import zero_only
 from .args import LigerArgs  # pylint: disable=unused-import. # noqa: F401
 
-LOG = logging.getLogger("axolotl.integrations.liger")
-
 
 class LigerPlugin(BasePlugin):
     """
@@ -45,31 +42,59 @@ class LigerPlugin(BasePlugin):
         return "axolotl.integrations.liger.LigerArgs"
 
     def pre_model_load(self, cfg):
-        if cfg.model_config_type in MODEL_TYPE_TO_APPLY_LIGER_FN:
-            apply_liger_fn = MODEL_TYPE_TO_APPLY_LIGER_FN[cfg.model_config_type]
-            liger_fn_sig = inspect.signature(apply_liger_fn)
-            kwargs = {}
-            if "rope" in liger_fn_sig.parameters:
-                kwargs["rope"] = cfg.liger_rope
-            if "cross_entropy" in liger_fn_sig.parameters:
-                kwargs["cross_entropy"] = cfg.liger_cross_entropy
-            if "fused_linear_cross_entropy" in liger_fn_sig.parameters:
-                kwargs[
-                    "fused_linear_cross_entropy"
-                ] = cfg.liger_fused_linear_cross_entropy
-            if "rms_norm" in liger_fn_sig.parameters:
-                kwargs["rms_norm"] = cfg.liger_rms_norm
-            if "layer_norm" in liger_fn_sig.parameters:
-                kwargs["layer_norm"] = cfg.liger_layer_norm
-            if "geglu" in liger_fn_sig.parameters:
-                kwargs["geglu"] = cfg.liger_glu_activation
-            elif "swiglu" in liger_fn_sig.parameters:
-                kwargs["swiglu"] = cfg.liger_glu_activation
-            with zero_only():
-                LOG.info(
-                    f"Applying LIGER to {cfg.model_config_type} with kwargs: {kwargs}"
+        if cfg.model_config_type == "llama":
+            from liger_kernel.transformers.model.llama import (
+                lce_forward as llama_lce_forward,
+            )
+            from transformers.models.llama import modeling_llama
+
+            if cfg.liger_rope:
+                modeling_llama.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_llama.LlamaRMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_llama.LlamaMLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_llama.CrossEntropyLoss = LigerCrossEntropyLoss
+            elif cfg.liger_fused_linear_cross_entropy:
+                modeling_llama.LlamaForCausalLM.forward = llama_lce_forward
+
+        elif cfg.model_config_type == "mistral":
+            from liger_kernel.transformers.model.mistral import (
+                lce_forward as mistral_lce_forward,
+            )
+            from transformers.models.mistral import modeling_mistral
+
+            if cfg.liger_rope:
+                modeling_mistral.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_mistral.MistralRMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_mistral.MistralMLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_mistral.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_mistral.MistralForCausalLM.forward = mistral_lce_forward
+
+        elif cfg.model_config_type == "gemma":
+            from liger_kernel.transformers.model.gemma import (
+                lce_forward as gemma_lce_forward,
+            )
+            from transformers.models.gemma import modeling_gemma
+
+            if cfg.liger_rope:
+                modeling_gemma.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_gemma.GemmaRMSNorm = partial(
+                    LigerRMSNorm, offset=1.0, init_fn="zeros", casting_mode="gemma"
                 )
-            apply_liger_fn(**kwargs)
+            if cfg.liger_swiglu:
+                modeling_gemma.GemmaMLP = LigerGEGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_gemma.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_gemma.GemmaForCausalLM.forward = gemma_lce_forward
+
         elif cfg.model_config_type == "jamba":
             from transformers.models.jamba import modeling_jamba
 
@@ -79,12 +104,30 @@ class LigerPlugin(BasePlugin):
                 modeling_jamba.apply_rotary_pos_emb = liger_rotary_pos_emb
             if cfg.liger_rms_norm:
                 modeling_jamba.JambaRMSNorm = LigerRMSNorm
-            if cfg.liger_glu_activation:
+            if cfg.liger_swiglu:
                 modeling_jamba.JambaMLP = LigerSwiGLUMLP
             if cfg.liger_cross_entropy:
                 modeling_jamba.CrossEntropyLoss = LigerCrossEntropyLoss
             if cfg.liger_fused_linear_cross_entropy:
                 modeling_jamba.JambaForCausalLM.forward = jamba_lce_forward
+
+        elif cfg.model_config_type == "qwen2":
+            from liger_kernel.transformers.model.qwen2 import (
+                lce_forward as qwen2_lce_forward,
+            )
+            from transformers.models.qwen2 import modeling_qwen2
+
+            if cfg.liger_rope:
+                modeling_qwen2.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_qwen2.Qwen2RMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_qwen2.Qwen2MLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_qwen2.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_qwen2.Qwen2ForCausalLM.forward = qwen2_lce_forward
+
         elif cfg.model_config_type == "deepseek_v2":
             from accelerate import init_empty_weights
             from transformers import AutoModelForCausalLM
@@ -103,9 +146,44 @@ class LigerPlugin(BasePlugin):
                 logging.warning("Fused liger_rope is not supported for DeepseekV2.")
             if cfg.liger_rms_norm:
                 modeling_mod.DeepseekV2RMSNorm = LigerRMSNorm
-            if cfg.liger_glu_activation:
+            if cfg.liger_swiglu:
                 modeling_mod.DeepseekV2MLP.forward = LigerSwiGLUMLP.forward
             if cfg.liger_cross_entropy:
                 modeling_mod.CrossEntropyLoss = LigerCrossEntropyLoss
             if cfg.liger_fused_linear_cross_entropy:
                 modeling_mod.DeepseekV2ForCausalLM.forward = deepseekv2_lce_forward
+
+        elif cfg.model_config_type == "gemma2":
+            from transformers.models.gemma2 import modeling_gemma2
+
+            if cfg.liger_rope:
+                modeling_gemma2.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_gemma2.Gemma2RMSNorm = partial(
+                    LigerRMSNorm, offset=1.0, init_fn="zeros", casting_mode="gemma"
+                )
+            if cfg.liger_swiglu:
+                modeling_gemma2.Gemma2MLP = LigerGEGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_gemma2.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                logging.warning(
+                    "Fused linear cross entropy is not supported for Gemma 2."
+                )
+
+        elif cfg.model_config_type == "phi3":
+            from liger_kernel.transformers.model.phi3 import (
+                lce_forward as phi3_lce_forward,
+            )
+            from transformers.models.phi3 import modeling_phi3
+
+            if cfg.liger_rope:
+                modeling_phi3.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+                modeling_phi3.Phi3RMSNorm = LigerRMSNorm
+            if cfg.liger_swiglu:
+                modeling_phi3.Phi3MLP = LigerSwiGLUMLP
+            if cfg.liger_cross_entropy:
+                modeling_phi3.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                modeling_phi3.Phi3ForCausalLM.forward = phi3_lce_forward

src/axolotl/integrations/liger/args.py

@@ -15,12 +15,9 @@
 """
 Module for handling LIGER input arguments.
 """
-import logging
 from typing import Optional
 
-from pydantic import BaseModel, model_validator
-
-LOG = logging.getLogger("axolotl.integrations.liger.args")
+from pydantic import BaseModel
 
 
 class LigerArgs(BaseModel):
@@ -30,24 +27,6 @@ class LigerArgs(BaseModel):
 
     liger_rope: Optional[bool] = None
     liger_rms_norm: Optional[bool] = None
-    liger_layer_norm: Optional[bool] = None
     liger_swiglu: Optional[bool] = None
-    liger_glu_activation: Optional[bool] = None
     liger_cross_entropy: Optional[bool] = None
     liger_fused_linear_cross_entropy: Optional[bool] = None
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_deprecated_swiglu(cls, data):
-        if data.get("liger_swiglu") is not None:
-            if data.get("liger_glu_activation") is not None:
-                raise ValueError(
-                    "You cannot have both `liger_swiglu` and `liger_glu_activation` set."
-                )
-
-            LOG.warning(
-                "The 'liger_swiglu' argument is deprecated and will be removed in a future release. "
-                "Please use 'liger_glu_activation' instead."
-            )
-            data["liger_glu_activation"] = data.pop("liger_swiglu")
-        return data

src/axolotl/monkeypatch/fastchat_conversation_turns.py

@@ -0,0 +1,231 @@
+"""
+monkeypatch to add a get_turns method
+"""
+
+import logging
+from typing import Generator, Tuple
+
+from fastchat.conversation import SeparatorStyle
+
+LOG = logging.getLogger("axolotl.monkeypatch.fastchat_conversation_turns")
+
+
+def get_prompt(self) -> str:
+    ret = ""
+    for role, msg in self.get_turns():
+        ret += role + msg
+    return ret
+
+
+def get_turns(  # pylint: disable=too-many-return-statements
+    self,
+) -> Generator[Tuple[str, str], None, None]:
+    """Get the prompt for generation."""
+    system_prompt = self.system_template.format(system_message=self.system_message)
+    if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
+        yield "", system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + ": ", message + self.sep
+            else:
+                yield role + ":", ""
+        return
+    if self.sep_style == SeparatorStyle.ADD_COLON_TWO:
+        seps = [self.sep, self.sep2]
+        yield "", system_prompt + seps[0]
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                yield role + ": ", message + seps[i % 2]
+            else:
+                yield role + ":", ""
+        return
+    if self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
+        yield "", system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + ": ", message + self.sep
+            else:
+                yield role + ": ", ""  # must be end with a space
+        return
+    if self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
+        yield "", "" if system_prompt == "" else system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + "\n", message + self.sep
+            else:
+                yield role + "\n", ""
+        return
+    if self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
+        yield "", system_prompt
+        for role, message in self.messages:
+            if message:
+                yield role, message + self.sep
+            else:
+                yield role, ""
+        return
+    if self.sep_style == SeparatorStyle.NO_COLON_TWO:
+        seps = [self.sep, self.sep2]
+        yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                yield role, message + seps[i % 2]
+            else:
+                yield role, ""
+        return
+    if self.sep_style == SeparatorStyle.RWKV:
+        yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                yield role + ": ", message.replace("\r\n", "\n").replace(
+                    "\n\n", "\n"
+                ) + "\n\n"
+            else:
+                yield role + ":", ""
+        return
+    if self.sep_style == SeparatorStyle.LLAMA2 and self.name != "mistral":
+        if self.system_message:
+            if self.messages:
+                # For llama, the system message is incorporated into the first human instruction
+                first_role, first_msg = self.messages[0]
+                if first_role == self.roles[0]:
+                    system_prompt += first_msg
+                    self.messages.pop(0)
+            yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                if (i % 2 == 0 and not self.system_message) or (
+                    i % 2 != 0 and self.system_message
+                ):
+                    role = "<s> " + role
+                yield role + " ", message
+            else:
+                yield role, ""
+        return
+    if self.sep_style == SeparatorStyle.LLAMA2 and self.name == "mistral":
+        contains_sys_msg = False
+        if self.system_message:
+            contains_sys_msg = True
+            if self.messages:
+                # There is no clear guidance on how to handle system messages in Mistral so we just prepend it to the first human instruction separated by a newline
+                first_role, first_msg = self.messages[0]
+                if first_role == self.roles[0]:
+                    system_prompt = self.system_template.format(
+                        system_message=" " + self.system_message
+                    )
+                    system_prompt += first_msg
+                    self.messages.pop(0)
+            yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message and i == 0 and not contains_sys_msg:
+                yield "", system_prompt.strip() + " " + message  # if there is no system message, we need to make sure there is the a `<s> [INST]` at the beginning of the first instruction.
+            elif message:
+                yield role + " ", message
+            else:
+                yield role, ""
+        return
+    if self.sep_style == SeparatorStyle.LLAMA3:
+        if self.system_message:
+            # For llama3, the system message is NOT incorporated into the first human instruction
+            # All messages follow <|start_header_id|>' + role + '<|end_header_id|>\n\n'+ message + '<|eot_id|>
+            yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                yield f"<|start_header_id|>{role}<|end_header_id|>\n\n", f"{message.strip()}<|eot_id|>"
+            else:
+                yield f"<|start_header_id|>{role}<|end_header_id|>\n\n", ""
+        return
+    if self.sep_style == SeparatorStyle.GEMMA:
+        if self.system_message:
+            raise ValueError("Gemma chat template does not support system messages")
+        for i, (role, message) in enumerate(self.messages):
+            prefix = "<bos>" if i == 0 else ""
+            message_str = message if message else ""
+            yield prefix + "<start_of_turn>" + role + "\n", message_str + "<end_of_turn>\n"
+        return
+    if self.sep_style == SeparatorStyle.CHATGLM:
+        # source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
+        # source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
+        round_add_n = 1 if self.name == "chatglm2" else 0
+        if system_prompt:
+            yield "", system_prompt + self.sep
+
+        for i, (role, message) in enumerate(self.messages):
+            if i % 2 == 0:
+                yield "", f"[Round {i//2 + round_add_n}]{self.sep}"
+
+            if message:
+                yield f"{role}：", f"{message}{self.sep}"
+            else:
+                yield f"{role}：", ""
+        return
+    if self.sep_style == SeparatorStyle.CHATML:
+        yield "", "" if system_prompt == "" else system_prompt + self.sep + "\n"
+        for role, message in self.messages:
+            if message:
+                yield role + "\n", message + self.sep + "\n"
+            else:
+                yield role + "\n", ""
+        return
+    if self.sep_style == SeparatorStyle.CHATGLM3:
+        if self.system_message:
+            yield "", system_prompt
+        for role, message in self.messages:
+            if message:
+                yield role + "\n", " " + message
+            else:
+                yield role
+        return
+    if self.sep_style == SeparatorStyle.CHATINTERN:
+        # source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
+        seps = [self.sep, self.sep2]
+        yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            prefix = "<s>" if i % 2 == 0 else ""
+            if message:
+                yield prefix + role + ":", message + seps[i % 2] + "\n"
+            else:
+                yield role + ":", ""
+        return
+    if self.sep_style == SeparatorStyle.DOLLY:
+        seps = [self.sep, self.sep2]
+        yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                suffix = "\n\n" if i % 2 == 1 else ""
+                yield role + ":\n", message + seps[i % 2] + suffix
+            else:
+                yield role + ":\n", ""
+        return
+    if self.sep_style == SeparatorStyle.PHOENIX:
+        yield "", system_prompt
+        for role, message in self.messages:
+            if message:
+                yield role + ": ", "<s>" + message + "</s>"
+            else:
+                yield role + ": " + "<s>", ""
+        return
+    if self.sep_style == SeparatorStyle.ROBIN:
+        yield "", system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + ":\n", message + self.sep
+            else:
+                yield role + ":\n", ""
+        return
+    if self.sep_style == SeparatorStyle.FALCON_CHAT:
+        if self.system_message:
+            yield "", system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + ": ", message + self.sep
+            else:
+                yield role + ":", ""
+    else:
+        raise ValueError(f"Invalid style: {self.sep_style}")
+
+
+def add_get_turns_to_conversation():
+    import fastchat.conversation
+
+    fastchat.conversation.Conversation.get_turns = get_turns
+    fastchat.conversation.Conversation.get_prompt = get_prompt

src/axolotl/prompt_strategies/instruct.py

@@ -0,0 +1,33 @@
+"""Module containing the InstructShareGPTPromptTokenizingStrategy class"""
+from typing import Any, Dict, Optional
+
+from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
+from axolotl.prompters import ShareGPTPrompterV2
+
+
+def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+    conversation = (
+        ds_cfg["conversation"] if ds_cfg and "conversation" in ds_cfg else None
+    )
+    strategy = InstructShareGPTPromptTokenizingStrategy(
+        # pylint: disable=duplicate-code
+        ShareGPTPrompterV2(
+            conversation=conversation,
+        ),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+    return strategy
+
+
+class InstructShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    basic sharegpt strategy to grab conversations from the sample row
+    """
+
+    def get_conversation_thread(self, prompt):
+        return [
+            {"from": "human", "value": prompt["instruction"]},
+            {"from": "gpt", "value": prompt["output"]},
+        ]

src/axolotl/prompt_strategies/llama2_chat.py

@@ -29,7 +29,7 @@ from dataclasses import dataclass, field
 from typing import Generator, List, Sequence
 
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
-from axolotl.prompters import ALTERNATING_ASSERTION_FAILED_ROLE, IGNORE_TOKEN_ID
+from axolotl.prompters import IGNORE_TOKEN_ID, SHAREGPT_ASSERTION_FAILED_ROLE
 
 
 @dataclass
@@ -75,7 +75,7 @@ class Llama2ChatConversation:
 
 class LLama2ChatTokenizingStrategy(PromptTokenizingStrategy):
     """
-    Tokenizing strategy for Llama2 prompts.
+    Tokenizing strategy for ShareGPT prompts.
     adapted from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/train.py
     """
 
@@ -191,7 +191,7 @@ class Llama2ChatPrompter:  # pylint: disable=too-few-public-methods
         conv.messages = []  # pylint: disable=R0801
         for j, sentence in enumerate(source):
             role = roles[sentence["from"]]
-            assert role == conv.roles[j % 2], ALTERNATING_ASSERTION_FAILED_ROLE
+            assert role == conv.roles[j % 2], SHAREGPT_ASSERTION_FAILED_ROLE
             if sentence["value"]:
                 conv.append_message(role, sentence["value"])
         yield conv

src/axolotl/prompt_strategies/sharegpt.py

@@ -0,0 +1,223 @@
+"""Module containing the SimpleShareGPTPromptTokenizingStrategy class"""
+
+import logging
+from typing import Any, Dict, Optional, Type
+
+from fastchat.conversation import Conversation, SeparatorStyle, register_conv_template
+
+from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
+from axolotl.prompters import ShareGPTPrompterV2
+from axolotl.utils.tokenization import (
+    chatml_to_conversation,
+    merge_consecutive_messages,
+)
+
+LOG = logging.getLogger("axolotl")
+
+
+def register_chatml_template(system_message=None):
+    system_message = system_message or "You are a helpful assistant."
+    register_conv_template(
+        Conversation(
+            name="chatml",
+            system_template="<|im_start|>system\n{system_message}",
+            system_message=system_message,
+            roles=("<|im_start|>user", "<|im_start|>assistant"),
+            sep_style=SeparatorStyle.CHATML,
+            sep="<|im_end|>",
+        )
+    )
+    register_conv_template(
+        Conversation(
+            name="chatml_glaive",
+            system_template="<|im_start|>system\n{system_message}",
+            system_message=system_message,
+            roles=("<|im_start|>user", "<|im_start|>assistant", "<|im_start|>tool"),
+            sep_style=SeparatorStyle.CHATML,
+            sep="<|im_end|>",
+        )
+    )
+
+
+def register_llama3_template(system_message=None):
+    system_message = system_message or "You are a helpful assistant."
+    register_conv_template(
+        Conversation(
+            name="llama3",
+            system_template="<|start_header_id|>system<|end_header_id|>\n\n{system_message}<|eot_id|>",
+            system_message=system_message,
+            roles=("user", "assistant"),
+            sep_style=SeparatorStyle.LLAMA3,
+            sep="",
+            stop_str="<|eot_id|>",
+            stop_token_ids=[128001, 128009],
+        )
+    )
+
+
+def build_loader(
+    tokenization_strategy_cls: Type["ShareGPTPromptTokenizingStrategy"],
+    prompter_cls: Type["ShareGPTPrompterV2"],
+    default_conversation: Optional[str] = None,
+):
+    def _load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+        LOG.warning(
+            "sharegpt type support will be deprecated in the next release of Axolotl. Please use chat_template instead. https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template",
+        )
+        conversation = (
+            ds_cfg["conversation"]
+            if ds_cfg and "conversation" in ds_cfg
+            else default_conversation
+        )
+        field_human = (
+            ds_cfg["field_human"] if ds_cfg and "field_human" in ds_cfg else None
+        )
+        field_model = (
+            ds_cfg["field_model"] if ds_cfg and "field_model" in ds_cfg else None
+        )
+        roles = ds_cfg["roles"].to_dict() if ds_cfg and "roles" in ds_cfg else None
+        strategy = tokenization_strategy_cls(
+            prompter_cls(
+                conversation=conversation,
+                role_key_model=field_model,
+                role_key_human=field_human,
+                roles=roles,
+            ),
+            tokenizer,
+            cfg.train_on_inputs,
+            cfg.sequence_len,
+        )
+        if ds_cfg and "strict" in ds_cfg and hasattr(strategy, "strict"):
+            strategy.strict = ds_cfg["strict"]
+        if ds_cfg and "field_messages" in ds_cfg and hasattr(strategy, "messages"):
+            strategy.messages = ds_cfg["field_messages"]
+        return strategy
+
+    return _load
+
+
+class SimpleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    basic sharegpt strategy to grab conversations from the sample row
+    """
+
+    _strict = False
+    _messages = "conversations"
+
+    @property
+    def strict(self):
+        return self._strict
+
+    @strict.setter
+    def strict(self, strict):
+        self._strict = strict
+
+    @property
+    def messages(self):
+        return self._messages
+
+    @messages.setter
+    def messages(self, messages):
+        self._messages = messages
+
+    def get_conversation_thread(self, prompt):
+        conversations = prompt[self.messages]
+        if self.strict:
+            return conversations
+        role_key = "from"
+        if "role" in conversations[0].keys():
+            role_key = "role"
+        value_key = "value"
+        if "text" in conversations[0].keys():
+            value_key = "text"
+        elif "content" in conversations[0].keys():
+            value_key = "content"
+        # remap roles - allow for assistant turn"
+        role_map = {
+            "user": "human",
+            "human": "human",
+            "assistant": "gpt",
+            "gpt": "gpt",
+            "system": "system",
+        }
+        turns = [
+            {
+                "from": (
+                    role_map[t[role_key]] if t[role_key] in role_map else t[role_key]
+                ),
+                "value": t[value_key],
+                "weight": 1
+                if "weight" not in t or t["weight"] is None
+                else t["weight"],
+            }
+            for t in conversations
+        ]
+        return turns
+
+
+class SimpleRoleShareGPTPromptTokenizingStrategy(
+    SimpleShareGPTPromptTokenizingStrategy
+):
+    """
+    basic sharegpt strategy to grab conversations from the sample row, but uses role instead of from
+    """
+
+    def get_conversation_thread(self, prompt):
+        conversations = prompt["conversations"]
+        # remap role: prompter/assistant, text: ... => from: human/gpt, value: ...
+        turns = [{"from": t["role"], "value": t["value"]} for t in conversations]
+        return turns
+
+
+class GuanacoShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    sharegpt strategy that remaps oasst data to sharegpt format
+    """
+
+    def get_conversation_thread(self, prompt):
+        conversations = prompt["conversations"]
+        # remap role: prompter/assistant, text: ... => from: human/gpt, value: ...
+        role_map = {"prompter": "human", "assistant": "gpt"}
+        turns = [
+            {"from": role_map[t["role"]], "value": t["text"]} for t in conversations
+        ]
+        return turns
+
+
+class UltrachatShareGPTPromptTokenizingStrategy(SimpleShareGPTPromptTokenizingStrategy):
+    """
+    sharegpt strategy that remaps ultrachat data to sharegpt format
+    """
+
+    def get_conversation_thread(self, prompt):
+        conversations = prompt["messages"]
+        role_map = {"user": "human", "assistant": "gpt"}
+        turns = [
+            {"from": role_map[t["role"]], "value": t["content"]} for t in conversations
+        ]
+        return turns
+
+
+class GlaiveShareGPTPromptTokenizingStrategy(SimpleShareGPTPromptTokenizingStrategy):
+    """
+    sharegpt strategy that remaps glaive data to sharegpt format
+    """
+
+    def get_conversation_thread(self, prompt):
+        conversation = chatml_to_conversation(prompt)
+        conversation = merge_consecutive_messages(conversation)
+
+        return conversation
+
+
+load = build_loader(SimpleShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2)
+load_role = build_loader(SimpleRoleShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2)
+load_ultrachat = build_loader(
+    UltrachatShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2
+)
+load_guanaco = build_loader(GuanacoShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2)
+load_glaive = build_loader(
+    GlaiveShareGPTPromptTokenizingStrategy,
+    ShareGPTPrompterV2,
+    default_conversation="chatml_glaive",
+)

src/axolotl/prompt_strategies/sharegpt_jokes.py

@@ -0,0 +1,28 @@
+"""Module for Jokes prompts using sharegpt style """
+from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
+from axolotl.prompters import ShareGPTPrompterV2
+
+
+def load(tokenizer, cfg):
+    return SimpleJokesShareGPTPromptTokenizingStrategy(
+        ShareGPTPrompterV2(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+class SimpleJokesShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    Tokenization strategy for asking bot to tell a joke and then explain why its funny
+    """
+
+    # title, text, explanation
+    def get_conversation_thread(self, prompt):
+        title = "" if not prompt["title"] else prompt["title"] + " "
+        return [
+            {"from": "human", "value": "Tell me a joke."},
+            {"from": "gpt", "value": title + prompt["text"]},
+            {"from": "human", "value": "Why is that joke funny?"},
+            {"from": "gpt", "value": prompt["explanation"]},
+        ]

src/axolotl/prompt_tokenizers.py

@@ -1,12 +1,17 @@
 """Module containing PromptTokenizingStrategy and Prompter classes"""
 
 import abc
+import copy
 import logging
 from typing import Dict, List, Tuple, Union
 
+from fastchat.conversation import Conversation
 from transformers import BatchEncoding, PreTrainedTokenizer
 
-from axolotl.prompters import Prompter
+from axolotl.monkeypatch.fastchat_conversation_turns import (
+    add_get_turns_to_conversation,
+)
+from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
 
 LOG = logging.getLogger("axolotl")
 
@@ -16,6 +21,8 @@ LLAMA_DEFAULT_EOS_TOKEN = "</s>"  # nosec
 LLAMA_DEFAULT_BOS_TOKEN = "<s>"  # nosec
 LLAMA_DEFAULT_UNK_TOKEN = "<unk>"  # nosec
 
+add_get_turns_to_conversation()
+
 
 class InvalidDataException(Exception):
     """
@@ -324,6 +331,154 @@ class AlpacaReflectionPTStrategy(ReflectionPromptTokenizingStrategy):
         )
 
 
+class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
+    """
+    Tokenizing strategy for ShareGPT prompts.
+    """
+
+    def get_conversation_thread(self, prompt):
+        return prompt["conversations"]
+
+    def tokenize_prompt(self, prompt):
+        # Initial values. We will append to these as we go through the conversation.
+        result, current_len = tokenize_prompt_default()
+        conversation: Conversation = (
+            self.prompter._conversation.copy()  # pylint: disable=protected-access
+        )
+
+        input_roles = {conversation.roles[0]}
+        output_roles = {conversation.roles[1]}
+
+        if len(conversation.roles) == 3:
+            tool_role_label = conversation.roles[2]
+            input_roles.add(tool_role_label)
+
+        # Add roles from the config
+        if self.prompter.roles:
+            if "input" in self.prompter.roles and self.prompter.roles["input"]:
+                for role in self.prompter.roles["input"]:
+                    input_roles.add(role)
+
+            if "output" in self.prompter.roles and self.prompter.roles["output"]:
+                for role in self.prompter.roles["output"]:
+                    output_roles.add(role)
+
+        # support for custom roles from the dataset, only useful for vicuna style prompts/roles
+        role_remap = []
+        if (
+            conversation.name == "vicuna_v1.1"
+            and "roles" in prompt
+            and len(prompt["roles"]) >= 2
+        ):
+            role_remap = [
+                {"from": conversation.roles[0], "to": prompt["roles"][0]},
+                {"from": conversation.roles[1], "to": prompt["roles"][1]},
+            ]
+
+        try:
+            for _, part in enumerate(
+                self.prompter.build_prompt(self.get_conversation_thread(prompt))
+            ):
+                if not isinstance(part, tuple):
+                    LOG.warning(f"expected tuple, got {part}")
+                    continue
+
+                if len(part) <= 2:
+                    role, content = part
+                    weight = 1
+                else:
+                    role, content, weight = part
+
+                # Uses "in" because role contains extra characters
+                input_turn = any(r.lower() in role.lower() for r in input_roles)
+                output_turn = any(r.lower() in role.lower() for r in output_roles)
+                empty_role = role.strip() == ""
+
+                if not any([input_turn, output_turn, empty_role]):
+                    LOG.warning(f"unhandled role: {role}")
+                    continue
+
+                if input_turn:
+                    role = (
+                        role.replace(role_remap[0]["from"], role_remap[0]["to"])
+                        if role_remap
+                        else role
+                    )
+                    turn = role + content
+                    # this is still the user query, we should
+                    if not content.strip():
+                        LOG.warning(f"user turn has empty text: {prompt}")
+                    res = self._tokenize(
+                        turn,
+                        add_eos_token=False,
+                        strip_bos_token=True,
+                    )
+                    if self.train_on_inputs and weight == 1:
+                        labels = copy.deepcopy(res["input_ids"])
+                    else:
+                        # everything from this is masked out from the labels
+                        labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
+                elif output_turn:
+                    role = (
+                        role.replace(role_remap[1]["from"], role_remap[1]["to"])
+                        if role_remap
+                        else role
+                    )
+                    turn = role + content
+                    # this should be the assistant response, should end with an eos token
+                    if not content.strip():
+                        LOG.warning(f"assistant turn has empty text: {prompt}")
+                    add_eos_token = not (
+                        conversation.name == "chatml"
+                        and conversation.sep == self.tokenizer.eos_token
+                    )
+                    res = self._tokenize(
+                        turn,
+                        add_eos_token=add_eos_token,
+                        strip_bos_token=True,
+                    )
+                    role_res = self._tokenize(
+                        role.rstrip(),
+                        add_eos_token=False,
+                        strip_bos_token=True,
+                    )
+                    labels = copy.deepcopy(res["input_ids"])
+                    if not self.train_on_inputs:
+                        # mask out role tokens from the labels
+                        len_role = len(role_res["input_ids"])
+                        labels[:len_role] = [IGNORE_TOKEN_ID] * min(
+                            len_role, len(labels)
+                        )
+                    if weight == 0:
+                        # everything from this is masked out from the labels
+                        # (role is masked out too because it makes no sense if contents is masked out)
+                        labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
+
+                elif empty_role:
+                    turn = content
+                    # this is only ever the first part, should include the bos token and the user query
+                    res = self._tokenize(
+                        turn, add_eos_token=False, strip_bos_token=False
+                    )
+                    if self.train_on_inputs and weight == 1:
+                        labels = copy.deepcopy(res["input_ids"])
+                    else:
+                        # everything from this is masked out from the labels
+                        labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
+
+                # pylint: disable=duplicate-code
+                result, current_len = parse_tokenized_to_result(
+                    result,
+                    current_len,
+                    res,
+                    labels,
+                    pad_token_id=self.tokenizer.pad_token_id,
+                )
+            return result
+        except (KeyError, AssertionError, IndexError) as err:
+            raise InvalidDataException(str(err)) from err
+
+
 def tokenize_prompt_default() -> Tuple[Dict[str, List[int]], int]:
     """
     Returns the default values for the tokenize prompt function

src/axolotl/prompters.py

@@ -5,6 +5,7 @@ from enum import Enum
 from typing import Generator, Optional, Union
 
 from colorama import Fore
+from fastchat.conversation import Conversation, get_conv_template
 
 LOG = logging.getLogger("axolotl")
 IGNORE_TOKEN_ID = -100
@@ -261,10 +262,166 @@ class ReflectAlpacaPrompter(Prompter):
         )
 
 
-ALTERNATING_ASSERTION_FAILED_ROLE = (
+SHAREGPT_ASSERTION_FAILED_ROLE = (
     "Role did not alternate between turns (gpt and human). Please check your data."
 )
 
+CONVERSATION_ROLE_FORMAT = {
+    "chatml": "<|im_start|>{ROLE}",
+    "zephyr": "<|{ROLE}|>",
+    "vicuna_v1.1": "{ROLE}",
+    "llama3": "<|start_header_id|>{ROLE}<|end_header_id|>",
+}
+
+
+class ShareGPTPrompter(Prompter):  # pylint: disable=too-few-public-methods
+    """
+    A prompter that generates prompts for the ShareGPT
+    """
+
+    role_key_human = "human"
+    role_key_model = "gpt"
+    # Optional, only used for tool usage datasets.
+    role_key_tool: Optional[str] = None
+    # Optional, role input/output mapping
+    roles: Optional[dict] = None
+
+    def __init__(
+        self,
+        prompt_style=None,  # pylint: disable=unused-argument
+        conversation: Optional[Union[str, Conversation]] = None,
+        role_key_human: Optional[str] = None,
+        role_key_model: Optional[str] = None,
+        role_key_tool: Optional[str] = None,
+        roles: Optional[dict] = None,
+    ):
+        if conversation:
+            if isinstance(conversation, Conversation):
+                self._conversation = conversation
+            else:
+                self._conversation = get_conv_template(conversation)
+        else:
+            self._conversation = get_conv_template("vicuna_v1.1")
+        if role_key_human:
+            self.role_key_human = role_key_human
+        if role_key_model:
+            self.role_key_model = role_key_model
+        if role_key_tool:
+            self.role_key_tool = role_key_tool
+        if roles:
+            self.roles = roles
+
+    def _build_result(self, source):
+        if len(source) < 2:
+            # If there isn't a back and forth conversation, ignore it
+            # also happens on the data splitting leaving empty conversations
+            raise IndexError(
+                f"A conversation entry has less than 2 messages :\n{source}"
+            )
+
+        conv = self._conversation.copy()
+
+        original_source = source.copy()
+        # Add the conversation system prompt if provided, otherwise use the default one
+        if source[0]["from"] == "system":
+            conv.set_system_message(source[0]["value"])
+            source.pop(0)
+
+        roles = {self.role_key_human: conv.roles[0], self.role_key_model: conv.roles[1]}
+        if self.role_key_tool:
+            roles[self.role_key_tool] = conv.roles[2]
+
+        try:
+            # Apply prompt templates
+            if source[0]["from"] not in roles:
+                # Skip the first one if it is not from human
+                source = source[1:]
+        except IndexError as err:
+            # sometimes there is a bing or system chat
+            raise err
+
+        conv.messages = []
+        for _, sentence in enumerate(source):
+            from_role = sentence["from"]
+            if from_role in roles:
+                role = roles[from_role]
+            else:
+                if self._conversation.name not in CONVERSATION_ROLE_FORMAT:
+                    raise NotImplementedError(
+                        f"Role ({role}) not in default roles, and {self._conversation.name} does not support role remapping yet."
+                        "Please help us by creating an Issue to add support for this conversation type."
+                    )
+
+                if self._conversation.name in ["llama3"]:
+                    role = from_role
+                else:
+                    role = CONVERSATION_ROLE_FORMAT[self._conversation.name].format(
+                        ROLE=from_role
+                    )
+
+            if len(conv.messages) > 0 and ((role == conv.messages[-1][0])):
+                if (
+                    role != "assistant"
+                ):  # back to back assistant calls may be okay for tool calls
+                    LOG.warning(f"{SHAREGPT_ASSERTION_FAILED_ROLE}: {sentence}")
+
+            conv.append_message(role, sentence["value"])
+        turns = list(conv.get_turns())
+        original_source_length = len(original_source)
+        assert len(turns) in [
+            original_source_length - 1,
+            original_source_length,
+            original_source_length + 1,
+        ]
+        if len(turns) == original_source_length + 1:
+            original_source = [{"weight": None}] + original_source
+        elif len(turns) == original_source_length - 1:
+            original_source = original_source[1:]
+        return [
+            (*turn, weight)
+            for turn, weight in zip(
+                turns,
+                [
+                    1 if "weight" not in e or e["weight"] is None else e["weight"]
+                    for e in original_source
+                ],
+            )
+        ]
+
+    def build_prompt(self, source) -> Generator[str, None, None]:
+        turns = self._build_result(source)
+
+        for part in turns:
+            if part[0] and not part[1]:
+                LOG.warning(f"role with empty message: {part[0]}")
+            yield part
+
+    def __repr__(self) -> str:
+        turns = self._build_result([{"from": "{from}", "value": "{value}"}])
+        return "\n".join([REPR_TEMPLATE.format(full_prompt=part) for part in turns])
+
+
+class ShareGPTPrompterV2(ShareGPTPrompter):
+    """
+    A V2 prompter that generates prompts for the ShareGPT
+    """
+
+    def __init__(
+        self,
+        conversation: Optional[Union[str, Conversation]] = None,
+        role_key_human: Optional[str] = None,
+        role_key_model: Optional[str] = None,
+        role_key_tool: Optional[str] = None,
+        roles: Optional[dict] = None,
+    ):
+        super().__init__(
+            conversation=conversation,
+            role_key_human=role_key_human,
+            role_key_model=role_key_model,
+            role_key_tool=role_key_tool,
+            roles=roles,
+        )
+
 
 class UnsupportedPrompter(Prompter):
     """

src/axolotl/utils/config/__init__.py

@@ -215,6 +215,11 @@ def normalize_cfg_datasets(cfg):
     if cfg.chat_template:
         if cfg.datasets:
             for idx, ds_cfg in enumerate(cfg.datasets):
+                if ds_cfg.type == "sharegpt" and not ds_cfg.conversation:
+                    LOG.info(
+                        f"updating dataset {ds_cfg.path} with `conversation: {cfg.chat_template}` to match your chat_template"
+                    )
+                    cfg.datasets[idx].conversation = cfg.chat_template
                 if (
                     ds_cfg.type in ["orpo.chat_template", "chat_template"]
                     and not ds_cfg.chat_template
@@ -456,6 +461,27 @@ def legacy_validate_config(cfg):
                 "`early_stopping_patience` requires that eval_steps should evenly divide save_steps."
             )
 
+    if cfg.datasets:
+        for idx, ds_cfg in enumerate(cfg.datasets):
+            if not ds_cfg.type:
+                continue
+            if ds_cfg.type == "sharegpt:chat":
+                LOG.warning(
+                    PendingDeprecationWarning(
+                        "`type: sharegpt:chat` will soon be deprecated. simply use `type: sharegpt` instead."
+                    )
+                )
+                cfg.datasets[idx].type = "sharegpt"
+            if "sharegpt_simple" in ds_cfg.type:
+                LOG.warning(
+                    PendingDeprecationWarning(
+                        "`type: sharegpt_simple` will soon be deprecated. simply use `type: sharegpt` instead."
+                    )
+                )
+                cfg.datasets[idx].type = cfg.datasets[idx].type.replace(
+                    "sharegpt_simple", "sharegpt"
+                )
+
     if cfg.saves_per_epoch and cfg.save_steps:
         raise ValueError(
             "save_steps and saves_per_epoch are mutually exclusive and cannot be used together."

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

@@ -427,6 +427,7 @@ class HyperparametersConfig(BaseModel):
                 "ao_adamw_4bit",
                 "ao_adamw_8bit",
                 "ao_adamw_fp8",
+                "soap",
             ],
         ]
     ] = OptimizerNames.ADAMW_HF.value
@@ -439,6 +440,10 @@ class HyperparametersConfig(BaseModel):
             "help": "The target modules to optimize, i.e. the module names that you would like to train."
         },
     )
+
+    optim_soap_beta1: Optional[float] = None
+    optim_soap_beta2: Optional[float] = None
+
     torchdistx_path: Optional[str] = None
     lr_scheduler: Optional[Union[SchedulerType, Literal["one_cycle"]]] = "cosine"
     lr_scheduler_kwargs: Optional[Dict[str, Any]] = None
@@ -588,9 +593,6 @@ class AxolotlInputConfig(
 
     rl: Optional[RLType] = None
     reward_model: Optional[bool] = None
-    dpo_use_weighting: Optional[
-        bool
-    ] = None  # whether to use weighting in DPO trainer. If none, default is false in the trainer.
 
     datasets: Optional[conlist(Union[SFTDataset, DPODataset, KTODataset], min_length=1)] = None  # type: ignore
     test_datasets: Optional[conlist(Union[SFTDataset, DPODataset, KTODataset], min_length=1)] = None  # type: ignore
@@ -783,16 +785,26 @@ class AxolotlInputConfig(
 
     @field_validator("datasets", mode="before")
     @classmethod
-    def deprecate_sharegpt_datasets(cls, datasets):
-        for _, ds_cfg in enumerate(datasets):
-            if not ds_cfg.get("type"):
+    def fix_sharegpt_datasets(cls, datasets):
+        for idx, ds_cfg in enumerate(datasets):
+            if not ds_cfg["type"]:
                 continue
-
-            if ds_cfg["type"].startswith("sharegpt"):
-                raise ValueError(
-                    "`type: sharegpt.*` is deprecated. Please use `type: chat_template` instead."
+            if ds_cfg["type"] == "sharegpt:chat":
+                LOG.warning(
+                    PendingDeprecationWarning(
+                        "`type: sharegpt:chat` will soon be deprecated. simply use `type: sharegpt` instead."
+                    )
+                )
+                datasets[idx]["type"] = "sharegpt"
+            if "sharegpt_simple" in ds_cfg["type"]:
+                LOG.warning(
+                    PendingDeprecationWarning(
+                        "`type: sharegpt_simple` will soon be deprecated. simply use `type: sharegpt` instead."
+                    )
+                )
+                datasets[idx]["type"] = datasets[idx]["type"].replace(
+                    "sharegpt_simple", "sharegpt"
                 )
-
         return datasets
 
     @model_validator(mode="before")

src/axolotl/utils/data/sft.py

@@ -2,11 +2,9 @@
 
 import functools
 import logging
-import time
 from pathlib import Path
 from typing import List, Optional, Tuple, Union
 
-import requests
 from datasets import (
     Dataset,
     DatasetDict,
@@ -55,28 +53,6 @@ from axolotl.utils.trainer import (
 LOG = logging.getLogger("axolotl")
 
 
-def retry_on_request_exceptions(max_retries=3, delay=1):
-    def decorator(func):
-        @functools.wraps(func)
-        def wrapper(*args, **kwargs):  # pylint: disable=inconsistent-return-statements
-            for attempt in range(max_retries):
-                try:
-                    return func(*args, **kwargs)
-                except (
-                    requests.exceptions.ReadTimeout,
-                    requests.exceptions.ConnectionError,
-                ) as exc:
-                    if attempt < max_retries - 1:
-                        time.sleep(delay)
-                    else:
-                        raise exc
-
-        return wrapper
-
-    return decorator
-
-
-@retry_on_request_exceptions(max_retries=3, delay=5)
 def prepare_dataset(cfg, tokenizer, processor=None):
     prompters = []
     if not cfg.pretraining_dataset:

src/axolotl/utils/optimizers/soap/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Nikhil Vyas
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

src/axolotl/utils/optimizers/soap/__init__.py

@@ -0,0 +1,475 @@
+# pylint: skip-file
+# Copied from https://github.com/nikhilvyas/SOAP
+from itertools import chain
+
+import torch
+import torch.optim as optim
+
+# Parts of the code are modifications of Pytorch's AdamW optimizer
+# Parts of the code are modifications of code from https://github.com/jiaweizzhao/GaLore/blob/master/galore_torch/galore_projector.py
+
+
+class SOAP(optim.Optimizer):
+    """
+    Implements SOAP algorithm (https://arxiv.org/abs/2409.11321).
+
+    Parameters:
+        params (`Iterable[nn.parameter.Parameter]`):
+            Iterable of parameters to optimize or dictionaries defining parameter groups.
+        lr (`float`, *optional*, defaults to 0.003):
+            The learning rate to use.
+        betas (`Tuple[float,float]`, *optional*, defaults to `(0.95, 0.95)`):
+            Adam's betas parameters (b1, b2).
+        shampoo_beta (`float`, *optional*, defaults to -1):
+            If >= 0, use this beta for the preconditioner (L and R in paper, state['GG'] below) moving average instead of betas[1].
+        eps (`float`, *optional*, defaults to 1e-08):
+            Adam's epsilon for numerical stability.
+        weight_decay (`float`, *optional*, defaults to 0.01): weight decay coefficient.
+        precondition_frequency (`int`, *optional*, defaults to 10):
+            How often to update the preconditioner.
+        max_precond_dim (`int`, *optional*, defaults to 10000):
+            Maximum dimension of the preconditioner.
+            Set to 10000, so that we exclude most common vocab sizes while including layers.
+        merge_dims (`bool`, *optional*, defaults to `False`):
+            Whether or not to merge dimensions of the preconditioner.
+        precondition_1d (`bool`, *optional*, defaults to `False`):
+            Whether or not to precondition 1D gradients.
+        normalize_grads (`bool`, *optional*, defaults to `False`):
+            Whether or not to normalize gradients per layer.
+            Helps at large precondition_frequency (~100 in our experiments),
+            but hurts performance at small precondition_frequency (~10 in our experiments).
+        data_format (`str`, *optional*, defaults to `channels_first`):
+            Data format of the input for convolutional layers.
+            Should be "channels_last" for data_format of NHWC and "channels_first" for NCHW.
+        correct_bias (`bool`, *optional*, defaults to `True`):
+            Whether or not to use bias correction in Adam.
+    """
+
+    def __init__(
+        self,
+        params,
+        lr: float = 3e-3,
+        betas=(0.95, 0.95),
+        shampoo_beta: float = -1,
+        eps: float = 1e-8,
+        weight_decay: float = 0.01,
+        precondition_frequency: int = 10,
+        max_precond_dim: int = 10000,  #
+        merge_dims: bool = False,  # Merge dimensions till the product of the dimensions is less than or equal to max_precond_dim.
+        precondition_1d: bool = False,
+        normalize_grads: bool = False,
+        data_format: str = "channels_first",
+        correct_bias: bool = True,
+    ):
+        defaults = {
+            "lr": lr,
+            "betas": betas,
+            "shampoo_beta": shampoo_beta,
+            "eps": eps,
+            "weight_decay": weight_decay,
+            "precondition_frequency": precondition_frequency,
+            "max_precond_dim": max_precond_dim,
+            "merge_dims": merge_dims,
+            "precondition_1d": precondition_1d,
+            "normalize_grads": normalize_grads,
+            "correct_bias": correct_bias,
+        }
+        super().__init__(params, defaults)
+        self._data_format = data_format
+
+    def merge_dims(self, grad, max_precond_dim):
+        """
+        Merges dimensions of the gradient tensor till the product of the dimensions is less than or equal to max_precond_dim.
+        """
+        assert self._data_format in ["channels_first", "channels_last"]
+        if self._data_format == "channels_last" and grad.dim() == 4:
+            grad = grad.permute(0, 3, 1, 2)
+        shape = grad.shape
+        new_shape = []
+
+        curr_shape = 1
+        for sh in shape:
+            temp_shape = curr_shape * sh
+            if temp_shape > max_precond_dim:
+                if curr_shape > 1:
+                    new_shape.append(curr_shape)
+                    curr_shape = sh
+                else:
+                    new_shape.append(sh)
+                    curr_shape = 1
+            else:
+                curr_shape = temp_shape
+
+        if curr_shape > 1 or len(new_shape) == 0:
+            new_shape.append(curr_shape)
+
+        new_grad = grad.reshape(new_shape)
+        return new_grad
+
+    @torch.no_grad()
+    def step(self):
+        """
+        Performs a single optimization step.
+
+        Arguments:
+            closure (`Callable`, *optional*): A closure that reevaluates the model and returns the loss.
+        """
+        loss = None
+
+        for group in self.param_groups:
+            for p in group["params"]:
+                if p.grad is None:
+                    continue
+                grad = p.grad
+
+                state = self.state[p]
+
+                if "step" not in state:
+                    state["step"] = 0
+
+                    # State initialization
+                if "exp_avg" not in state:
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(grad)
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(grad)
+
+                if "Q" not in state:
+                    self.init_preconditioner(
+                        grad,
+                        state,
+                        precondition_frequency=group["precondition_frequency"],
+                        precondition_1d=group["precondition_1d"],
+                        shampoo_beta=(
+                            group["shampoo_beta"]
+                            if group["shampoo_beta"] >= 0
+                            else group["betas"][1]
+                        ),
+                        max_precond_dim=group["max_precond_dim"],
+                        merge_dims=group["merge_dims"],
+                    )
+                    self.update_preconditioner(
+                        grad,
+                        state,
+                        max_precond_dim=group["max_precond_dim"],
+                        merge_dims=group["merge_dims"],
+                        precondition_1d=group["precondition_1d"],
+                    )
+                    continue  # first step is skipped so that we never use the current gradients in the projection.
+
+                # Projecting gradients to the eigenbases of Shampoo's preconditioner
+                # i.e. projecting to the eigenbases of matrices in state['GG']
+                grad_projected = self.project(
+                    grad,
+                    state,
+                    merge_dims=group["merge_dims"],
+                    max_precond_dim=group["max_precond_dim"],
+                )
+
+                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
+                beta1, beta2 = group["betas"]
+
+                state["step"] += 1
+
+                # Decay the first and second moment running average coefficient
+                # In-place operations to update the averages at the same time
+                exp_avg.mul_(beta1).add_(grad, alpha=(1.0 - beta1))
+                exp_avg_sq.mul_(beta2).add_(
+                    grad_projected.square(), alpha=(1.0 - beta2)
+                )
+
+                denom = exp_avg_sq.sqrt().add_(group["eps"])
+
+                # Projecting the exponential moving average of gradients to the eigenbases of Shampoo's preconditioner
+                # i.e. projecting to the eigenbases of matrices in state['GG']
+                exp_avg_projected = self.project(
+                    exp_avg,
+                    state,
+                    merge_dims=group["merge_dims"],
+                    max_precond_dim=group["max_precond_dim"],
+                )
+
+                step_size = group["lr"]
+                if group["correct_bias"]:
+                    bias_correction1 = 1.0 - beta1 ** (state["step"])
+                    bias_correction2 = 1.0 - beta2 ** (state["step"])
+                    step_size = step_size * (bias_correction2**0.5) / bias_correction1
+
+                # Projecting back the preconditioned (by Adam) exponential moving average of gradients
+                # to the original space
+                norm_grad = self.project_back(
+                    exp_avg_projected / denom,
+                    state,
+                    merge_dims=group["merge_dims"],
+                    max_precond_dim=group["max_precond_dim"],
+                )
+
+                if group["normalize_grads"]:
+                    norm_grad = norm_grad / (1e-30 + torch.mean(norm_grad**2) ** 0.5)
+
+                p.add_(norm_grad, alpha=-step_size)
+
+                # From AdamW code: Just adding the square of the weights to the loss function is *not*
+                # the correct way of using L2 regularization/weight decay with Adam,
+                # since that will interact with the m and v parameters in strange ways.
+                #
+                # Instead we want to decay the weights in a manner that doesn't interact
+                # with the m/v parameters. This is equivalent to adding the square
+                # of the weights to the loss with plain (non-momentum) SGD.
+                # Add weight decay at the end (fixed version)
+                if group["weight_decay"] > 0.0:
+                    p.add_(p, alpha=(-group["lr"] * group["weight_decay"]))
+
+                # Update is done after the gradient step to avoid using current gradients in the projection.
+                self.update_preconditioner(
+                    grad,
+                    state,
+                    max_precond_dim=group["max_precond_dim"],
+                    merge_dims=group["merge_dims"],
+                    precondition_1d=group["precondition_1d"],
+                )
+
+        return loss
+
+    def init_preconditioner(
+        self,
+        grad,
+        state,
+        precondition_frequency=10,
+        shampoo_beta=0.95,
+        max_precond_dim=10000,
+        precondition_1d=False,
+        merge_dims=False,
+    ):
+        """
+        Initializes the preconditioner matrices (L and R in the paper).
+        """
+        state[
+            "GG"
+        ] = []  # Will hold all the preconditioner matrices (L and R in the paper).
+        if grad.dim() == 1:
+            if not precondition_1d or grad.shape[0] > max_precond_dim:
+                state["GG"].append([])
+            else:
+                state["GG"].append(
+                    torch.zeros(grad.shape[0], grad.shape[0], device=grad.device)
+                )
+        else:
+            if merge_dims:
+                grad = self.merge_dims(grad, max_precond_dim)
+
+            for sh in grad.shape:
+                if sh > max_precond_dim:
+                    state["GG"].append([])
+                else:
+                    state["GG"].append(torch.zeros(sh, sh, device=grad.device))
+
+        state["Q"] = None  # Will hold all the eigenbases of the preconditioner.
+        state["precondition_frequency"] = precondition_frequency
+        state["shampoo_beta"] = shampoo_beta
+
+    def project(self, grad, state, merge_dims=False, max_precond_dim=10000):
+        """
+        Projects the gradient to the eigenbases of the preconditioner.
+        """
+        original_shape = grad.shape
+        if merge_dims:
+            if grad.dim() == 4 and self._data_format == "channels_last":
+                permuted_shape = grad.permute(0, 3, 1, 2).shape
+            grad = self.merge_dims(grad, max_precond_dim)
+
+        for mat in state["Q"]:
+            if len(mat) > 0:
+                grad = torch.tensordot(
+                    grad,
+                    mat,
+                    dims=[[0], [0]],
+                )
+            else:
+                permute_order = list(range(1, len(grad.shape))) + [0]
+                grad = grad.permute(permute_order)
+
+        if merge_dims:
+            if self._data_format == "channels_last" and len(original_shape) == 4:
+                grad = grad.reshape(permuted_shape).permute(0, 2, 3, 1)
+            else:
+                grad = grad.reshape(original_shape)
+        return grad
+
+    def update_preconditioner(
+        self,
+        grad,
+        state,
+        max_precond_dim=10000,
+        merge_dims=False,
+        precondition_1d=False,
+    ):
+        """
+        Updates the preconditioner matrices and the eigenbases (L, R, Q_L, Q_R in the paper).
+        """
+        if grad.dim() == 1:
+            if precondition_1d and grad.shape[0] <= max_precond_dim:
+                state["GG"][0].lerp_(
+                    grad.unsqueeze(1) @ grad.unsqueeze(0), 1 - state["shampoo_beta"]
+                )
+        else:
+            if merge_dims:
+                new_grad = self.merge_dims(grad, max_precond_dim)
+                for idx, sh in enumerate(new_grad.shape):
+                    if sh <= max_precond_dim:
+                        outer_product = torch.tensordot(
+                            new_grad,
+                            new_grad,
+                            dims=[
+                                [
+                                    *chain(
+                                        range(idx), range(idx + 1, len(new_grad.shape))
+                                    )
+                                ]
+                            ]
+                            * 2,
+                        )
+                        state["GG"][idx].lerp_(outer_product, 1 - state["shampoo_beta"])
+            else:
+                for idx, sh in enumerate(grad.shape):
+                    if sh <= max_precond_dim:
+                        outer_product = torch.tensordot(
+                            grad,
+                            grad,
+                            # Contracts across all dimensions except for k.
+                            dims=[[*chain(range(idx), range(idx + 1, len(grad.shape)))]]
+                            * 2,
+                        )
+                        state["GG"][idx].lerp_(outer_product, 1 - state["shampoo_beta"])
+
+        if state["Q"] is None:
+            state["Q"] = self.get_orthogonal_matrix(state["GG"])
+        if state["step"] > 0 and state["step"] % state["precondition_frequency"] == 0:
+            state["Q"] = self.get_orthogonal_matrix_QR(
+                state, max_precond_dim, merge_dims
+            )
+
+    def project_back(self, grad, state, merge_dims=False, max_precond_dim=10000):
+        """
+        Projects the gradient back to the original space.
+        """
+        original_shape = grad.shape
+        if merge_dims:
+            if self._data_format == "channels_last" and grad.dim() == 4:
+                permuted_shape = grad.permute(0, 3, 1, 2).shape
+            grad = self.merge_dims(grad, max_precond_dim)
+        for mat in state["Q"]:
+            if len(mat) > 0:
+                grad = torch.tensordot(
+                    grad,
+                    mat,
+                    dims=[[0], [1]],
+                )
+            else:
+                permute_order = list(range(1, len(grad.shape))) + [0]
+                grad = grad.permute(permute_order)
+
+        if merge_dims:
+            if self._data_format == "channels_last" and len(original_shape) == 4:
+                grad = grad.reshape(permuted_shape).permute(0, 2, 3, 1)
+            else:
+                grad = grad.reshape(original_shape)
+        return grad
+
+    def get_orthogonal_matrix(self, mat):
+        """
+        Computes the eigenbases of the preconditioner using torch.linalg.eigh decomposition.
+        """
+        matrix = []
+        for m in mat:
+            if len(m) == 0:
+                matrix.append([])
+                continue
+            if m.data.dtype != torch.float:
+                float_data = False
+                original_type = m.data.dtype
+                original_device = m.data.device
+                matrix.append(m.data.float())
+            else:
+                float_data = True
+                matrix.append(m.data)
+
+        final = []
+        for m in matrix:
+            if len(m) == 0:
+                final.append([])
+                continue
+            try:
+                _, Q = torch.linalg.eigh(
+                    m + 1e-30 * torch.eye(m.shape[0], device=m.device)
+                )
+            except:  # pylint: disable=bare-except # noqa: E722
+                _, Q = torch.linalg.eigh(
+                    m.to(torch.float64) + 1e-30 * torch.eye(m.shape[0], device=m.device)
+                )
+                Q = Q.to(m.dtype)
+            Q = torch.flip(Q, [1])
+
+            if not float_data:
+                Q = Q.to(original_device).type(original_type)
+            final.append(Q)
+        return final
+
+    def get_orthogonal_matrix_QR(self, state, max_precond_dim=10000, merge_dims=False):
+        """
+        Computes the eigenbases of the preconditioner using one round of power iteration
+        followed by torch.linalg.qr decomposition.
+        """
+        precond_list = state["GG"]
+        orth_list = state["Q"]
+
+        matrix = []
+        orth_matrix = []
+        for m, o in zip(precond_list, orth_list):
+            if len(m) == 0:
+                matrix.append([])
+                orth_matrix.append([])
+                continue
+            if m.data.dtype != torch.float:
+                float_data = False
+                original_type = m.data.dtype
+                original_device = m.data.device
+                matrix.append(m.data.float())
+                orth_matrix.append(o.data.float())
+            else:
+                float_data = True
+                matrix.append(m.data.float())
+                orth_matrix.append(o.data.float())
+
+        orig_shape = state["exp_avg_sq"].shape
+        if self._data_format == "channels_last" and len(orig_shape) == 4:
+            permuted_shape = state["exp_avg_sq"].permute(0, 3, 1, 2).shape
+        if merge_dims:
+            exp_avg_sq = self.merge_dims(state["exp_avg_sq"], max_precond_dim)
+        else:
+            exp_avg_sq = state["exp_avg_sq"]
+
+        final = []
+        for ind, (m, o) in enumerate(zip(matrix, orth_matrix)):
+            if len(m) == 0:
+                final.append([])
+                continue
+            est_eig = torch.diag(o.T @ m @ o)
+            sort_idx = torch.argsort(est_eig, descending=True)
+            exp_avg_sq = exp_avg_sq.index_select(ind, sort_idx)
+            o = o[:, sort_idx]
+            power_iter = m @ o
+            Q, _ = torch.linalg.qr(power_iter)
+
+            if not float_data:
+                Q = Q.to(original_device).type(original_type)
+            final.append(Q)
+
+        if merge_dims:
+            if self._data_format == "channels_last" and len(orig_shape) == 4:
+                exp_avg_sq = exp_avg_sq.reshape(permuted_shape).permute(0, 2, 3, 1)
+            else:
+                exp_avg_sq = exp_avg_sq.reshape(orig_shape)
+
+        state["exp_avg_sq"] = exp_avg_sq
+        return final

src/axolotl/utils/tokenization.py

@@ -1,6 +1,8 @@
 """Module for tokenization utilities"""
 
 import logging
+import re
+from typing import Dict, List
 
 from termcolor import colored
 
@@ -91,3 +93,65 @@ def check_rl_example_labels(example, tokenizer, text_only=False):
     LOG.info(f"REJECTED RESPONSE: {delimiter.join(colored_rejecteds)}\n\n\n")
 
     return delimiter.join(colored_tokens)
+
+
+GLAIVE_ROLES = ["USER", "ASSISTANT", "FUNCTION RESPONSE"]
+GLAIVE_TO_SHAREGPT_ROLE = {
+    "SYSTEM": "system",
+    "USER": "human",
+    "ASSISTANT": "gpt",
+    "FUNCTION RESPONSE": "tool",
+}
+
+GLAIVE_MSG_REGEX = re.compile(rf"({'|'.join(GLAIVE_ROLES)}): ")
+
+
+def chatml_to_conversation(row: Dict[str, str]) -> List[Dict[str, str]]:
+    """
+    Converts a ChatML formatted row to a list of messages in ShareGPT format.
+    Initially based off https://github.com/lilacai/lilac/blob/main/notebooks/GlaiveToShareGPT.ipynb.
+    """
+
+    system_prompt = row.get("system")
+    if system_prompt:
+        system_prompt = system_prompt.removeprefix("SYSTEM: ")
+
+    chat_str = row["chat"]
+    chat_msgs = [s.strip() for s in GLAIVE_MSG_REGEX.split(chat_str) if s]
+
+    chat_msg_dicts = [
+        {"from": GLAIVE_TO_SHAREGPT_ROLE[role], "value": value}
+        for role, value in zip(chat_msgs[::2], chat_msgs[1::2])
+    ]
+
+    if system_prompt:
+        chat_msg_dicts = [
+            {"from": GLAIVE_TO_SHAREGPT_ROLE["SYSTEM"], "value": system_prompt}
+        ] + chat_msg_dicts
+
+    return chat_msg_dicts
+
+
+def merge_consecutive_messages(messages):
+    """
+    Merge consecutive messages from the same sender into a single message.
+    This can be useful with datasets that contain multiple consecutive tool calls.
+    """
+
+    merged_messages = []
+    current_from = None
+    current_message = ""
+
+    for msg in messages:
+        if current_from == msg["from"]:
+            current_message += msg["value"]
+        else:
+            if current_from is not None:
+                merged_messages.append({"from": current_from, "value": current_message})
+            current_from = msg["from"]
+            current_message = msg["value"]
+
+    if current_from is not None:
+        merged_messages.append({"from": current_from, "value": current_message})
+
+    return merged_messages

tests/e2e/integrations/liger.py

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

tests/e2e/test_dpo.py

@@ -115,51 +115,6 @@ class TestDPOLlamaLora(unittest.TestCase):
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "checkpoint-20/adapter_model.safetensors").exists()
 
-    @with_temp_dir
-    def test_dpo_use_weighting(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
-                "sequence_len": 1024,
-                "load_in_8bit": True,
-                "adapter": "lora",
-                "lora_r": 64,
-                "lora_alpha": 32,
-                "lora_dropout": 0.1,
-                "lora_target_linear": True,
-                "special_tokens": {},
-                "rl": "dpo",
-                "dpo_use_weighting": True,
-                "datasets": [
-                    {
-                        "path": "arcee-ai/distilabel-intel-orca-dpo-pairs-binarized",
-                        "type": "chatml.ultra",
-                        "split": "train",
-                    },
-                ],
-                "num_epochs": 1,
-                "micro_batch_size": 4,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "paged_adamw_8bit",
-                "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "warmup_steps": 5,
-                "gradient_checkpointing": True,
-                "gradient_checkpointing_kwargs": {"use_reentrant": True},
-            }
-        )
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "checkpoint-20/adapter_model.safetensors").exists()
-
     @pytest.mark.skip("kto_pair no longer supported in trl")
     @with_temp_dir
     def test_kto_pair_lora(self, temp_dir):

tests/e2e/test_optimizers.py

@@ -65,3 +65,44 @@ class TestCustomOptimizers(unittest.TestCase):
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "adapter_model.bin").exists()
+
+    @with_temp_dir
+    def test_soap(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM-135M",
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "vicgalle/alpaca-gpt4",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "soap",
+                "optim_soap_beta1": 0.95,
+                "optim_soap_beta2": 0.95,
+                "lr_scheduler": "cosine",
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "adapter_model.bin").exists()

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)

tests/prompt_strategies/test_sharegpt.py

@@ -0,0 +1,500 @@
+"""
+Test module for sharegpt integration w chatml
+"""
+
+import pytest
+from datasets import Dataset
+from tokenizers import AddedToken
+from transformers import AutoTokenizer
+
+from axolotl.datasets import TokenizedPromptDataset
+from axolotl.prompt_strategies.sharegpt import (
+    GlaiveShareGPTPromptTokenizingStrategy,
+    SimpleShareGPTPromptTokenizingStrategy,
+    register_chatml_template,
+    register_llama3_template,
+)
+from axolotl.prompters import ShareGPTPrompterV2
+
+register_chatml_template()
+register_llama3_template()
+
+
+@pytest.fixture(name="sharegpt_dataset")
+def fixture_sharegpt_dataset():
+    return Dataset.from_list(
+        [
+            {
+                "conversations": [
+                    {
+                        "from": "system",
+                        "value": "repeat",
+                    },
+                    {
+                        "from": "human",
+                        "value": "hello",
+                    },
+                    {
+                        "from": "gpt",
+                        "value": "hello",
+                    },
+                    {
+                        "from": "human",
+                        "value": "goodbye",
+                    },
+                    {
+                        "from": "gpt",
+                        "value": "goodbye",
+                    },
+                ]
+            }
+        ]
+    )
+
+
+@pytest.fixture(name="sharegpt_dataset_with_weights")
+def fixture_sharegpt_dataset_with_weights():
+    return Dataset.from_list(
+        [
+            {
+                "conversations": [
+                    {
+                        "from": "system",
+                        "value": "repeat",
+                    },
+                    {
+                        "from": "human",
+                        "value": "hello",
+                        "weight": 1,
+                    },
+                    {
+                        "from": "gpt",
+                        "value": "hello",
+                        "weight": 0,
+                    },
+                    {
+                        "from": "human",
+                        "value": "rehello",
+                        "weight": 0,
+                    },
+                    {
+                        "from": "gpt",
+                        "value": "rehello",
+                        "weight": 1,
+                    },
+                    {
+                        "from": "human",
+                        "value": "goodbye",
+                    },
+                    {
+                        "from": "gpt",
+                        "value": "goodbye",
+                        "weight": 0,
+                    },
+                ]
+            }
+        ]
+    )
+
+
+@pytest.fixture(name="glaive_dataset")
+def fixture_sharegpt_glaive_dataset():
+    return Dataset.from_list(
+        [
+            {
+                "system": "SYSTEM: This is a system prompt",
+                "chat": "USER: Can you book a flight for me from New York to London? ASSISTANT: I'm sorry, but I don't have the capability to book flights.  <|endoftext|>",
+            }
+        ]
+    )
+
+
+@pytest.fixture(name="multi_role_dataset")
+def fixture_multi_role_dataset():
+    return Dataset.from_list(
+        [
+            {
+                "conversations": [
+                    {
+                        "from": "system",
+                        "value": "use get_weather(city) to get the weather for a city",
+                    },
+                    {
+                        "from": "human",
+                        "value": "hello, what's the weather in New York?",
+                    },
+                    {
+                        "from": "gpt",
+                        "value": "let me get that for you",
+                    },
+                    {
+                        "from": "tool",
+                        "value": "get_weather(New York)",
+                    },
+                    {
+                        "from": "gpt",
+                        "value": "the weather in New York is 70 degrees and sunny",
+                    },
+                ]
+            }
+        ]
+    )
+
+
+@pytest.fixture(name="tokenizer")
+def fixture_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained(
+        "casperhansen/mistral-7b-instruct-v0.1-awq"
+    )
+    tokenizer.add_special_tokens(
+        {
+            "eos_token": AddedToken(
+                "<|im_end|>", rstrip=False, lstrip=False, normalized=False
+            )
+        }
+    )
+    tokenizer.add_tokens(
+        [
+            AddedToken("<|im_start|>", rstrip=False, lstrip=False, normalized=False),
+        ]
+    )
+
+    return tokenizer
+
+
+@pytest.fixture(name="llama3_tokenizer")
+def fixture_llama3_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B")
+    tokenizer.eos_token = "<|eot_id|>"
+
+    return tokenizer
+
+
+class TestSharegptLlama3:
+    """Test class for ShareGPT style datasets with llama-3 prompts"""
+
+    def test_tokenization(self, sharegpt_dataset, llama3_tokenizer):
+        strategy = SimpleShareGPTPromptTokenizingStrategy(
+            ShareGPTPrompterV2(
+                conversation="llama3",
+                role_key_model=None,
+                role_key_human=None,
+            ),
+            llama3_tokenizer,
+            False,  # train_on_inputs
+            2048,  # sequence_len
+        )
+
+        dataset_wrapper = TokenizedPromptDataset(
+            strategy, sharegpt_dataset, process_count=1
+        )
+
+        input_ids = dataset_wrapper[0]["input_ids"]
+
+        # fmt: off
+        # pylint: disable=duplicate-code
+        assert input_ids == [
+            128000,  # bos
+            128006, 9125, 128007,  # system header
+            271, 31724, 128009,  # sys prompt, eot
+            128006, 882, 128007,  # user header
+            271, 15339, 128009,  # user prompt eot
+            128006, 78191, 128007,  # assistant header
+            271, 15339, 128009,   # assistant response eot
+            128006, 882, 128007,
+            271, 19045, 29474, 128009,
+            128006, 78191, 128007,
+            271, 19045, 29474, 128009,
+        ]
+        # fmt: on
+
+    def test_tokenization_with_weights(
+        self, sharegpt_dataset_with_weights, llama3_tokenizer
+    ):
+        strategy = SimpleShareGPTPromptTokenizingStrategy(
+            ShareGPTPrompterV2(
+                conversation="llama3",
+                role_key_model=None,
+                role_key_human=None,
+            ),
+            llama3_tokenizer,
+            False,  # train_on_inputs
+            2048,  # sequence_len
+        )
+
+        dataset_wrapper = TokenizedPromptDataset(
+            strategy, sharegpt_dataset_with_weights, process_count=1
+        )
+
+        input_ids = dataset_wrapper[0]["input_ids"]
+
+        # fmt: off
+        # pylint: disable=duplicate-code
+        assert input_ids == [
+            128000,  # bos
+            128006, 9125, 128007,  # system header
+            271, 31724, 128009,  # sys prompt, eot
+            128006, 882, 128007,  # user header
+            271, 15339, 128009,  # user prompt eot
+            128006, 78191, 128007,  # assistant header
+            271, 15339, 128009,   # assistant response eot
+            128006, 882, 128007,
+            271, 11310, 4896, 128009,
+            128006, 78191, 128007,
+            271, 11310, 4896, 128009,
+            128006, 882, 128007,
+            271, 19045, 29474, 128009,
+            128006, 78191, 128007,
+            271, 19045, 29474, 128009,
+        ]
+        # fmt: on
+
+
+class TestSharegptChatML:
+    """
+    Test class for sharegpt prompter
+    """
+
+    def test_no_double_im_end(self, sharegpt_dataset, tokenizer):
+        strategy = SimpleShareGPTPromptTokenizingStrategy(
+            ShareGPTPrompterV2(
+                conversation="chatml",
+                role_key_model=None,
+                role_key_human=None,
+            ),
+            tokenizer,
+            False,  # train_on_inputs
+            2048,  # sequence_len
+        )
+
+        dataset_wrapper = TokenizedPromptDataset(
+            strategy, sharegpt_dataset, process_count=1
+        )
+
+        input_ids = dataset_wrapper[0]["input_ids"]
+        # fmt: off
+        assert input_ids == [
+            #  28705, 13, is " \n"
+            1,   # bos
+            32001, 1587, 13, 25997, 32000, 28705, 13,  # system
+            32001, 2188, 13, 21558, 32000, 28705, 13,  # human
+            32001, 13892, 13, 21558, 32000, 28705, 13,  # gpt
+            32001, 2188, 13, 12684, 17664, 32000, 28705, 13,   # human
+            32001, 13892, 13, 12684, 17664, 32000, 28705, 13,  # gpt
+        ]
+        # fmt: on
+
+    def test_no_double_im_end_with_weights(
+        self, sharegpt_dataset_with_weights, tokenizer
+    ):
+        strategy = SimpleShareGPTPromptTokenizingStrategy(
+            ShareGPTPrompterV2(
+                conversation="chatml",
+                role_key_model=None,
+                role_key_human=None,
+            ),
+            tokenizer,
+            False,  # train_on_inputs
+            2048,  # sequence_len
+        )
+
+        dataset_wrapper = TokenizedPromptDataset(
+            strategy, sharegpt_dataset_with_weights, process_count=1
+        )
+
+        input_ids = dataset_wrapper[0]["input_ids"]
+        # fmt: off
+        assert input_ids == [
+            #  28705, 13, is " \n"
+            1,   # bos
+            32001, 1587, 13, 25997, 32000, 28705, 13,  # system
+            32001, 2188, 13, 21558, 32000, 28705, 13,  # human
+            32001, 13892, 13, 21558, 32000, 28705, 13,  # gpt
+            32001, 2188, 13, 267, 21558, 32000, 28705, 13,  # human
+            32001, 13892, 13, 267, 21558, 32000, 28705, 13,  # gpt
+            32001, 2188, 13, 12684, 17664, 32000, 28705, 13,   # human
+            32001, 13892, 13, 12684, 17664, 32000, 28705, 13,  # gpt
+        ]
+        # fmt: on
+
+    def test_no_train_on_input(self, sharegpt_dataset, tokenizer):
+        strategy = SimpleShareGPTPromptTokenizingStrategy(
+            ShareGPTPrompterV2(
+                conversation="chatml",
+                role_key_model=None,
+                role_key_human=None,
+            ),
+            tokenizer,
+            False,  # train_on_inputs
+            2048,  # sequence_len
+        )
+
+        dataset_wrapper = TokenizedPromptDataset(
+            strategy, sharegpt_dataset, process_count=1
+        )
+
+        labels = dataset_wrapper[0]["labels"]
+        # fmt: off
+        assert labels == [
+            -100,   # bos
+            -100, -100, -100, -100, -100, -100, -100,  # system
+            -100, -100, -100, -100, -100, -100, -100,  # human
+            -100, -100, 13, 21558, 32000, 28705, 13,  # gpt
+            -100, -100, -100, -100, -100, -100, -100, -100,   # human
+            -100, -100, 13, 12684, 17664, 32000, 28705, 13,  # gpt
+        ]
+        # fmt: on
+
+    def test_no_train_on_input_with_weights(
+        self, sharegpt_dataset_with_weights, tokenizer
+    ):
+        strategy = SimpleShareGPTPromptTokenizingStrategy(
+            ShareGPTPrompterV2(
+                conversation="chatml",
+                role_key_model=None,
+                role_key_human=None,
+            ),
+            tokenizer,
+            False,  # train_on_inputs
+            2048,  # sequence_len
+        )
+
+        dataset_wrapper = TokenizedPromptDataset(
+            strategy, sharegpt_dataset_with_weights, process_count=1
+        )
+
+        labels = dataset_wrapper[0]["labels"]
+        # fmt: off
+        assert labels == [
+            -100,   # bos
+            -100, -100, -100, -100, -100, -100, -100,  # system
+            -100, -100, -100, -100, -100, -100, -100,  # human
+            -100, -100, -100, -100, -100, -100, -100,  # gpt with weight zero
+            -100, -100, -100, -100, -100, -100, -100, -100,  # human
+            -100, -100, 13, 267, 21558, 32000, 28705, 13,  # gpt
+            -100, -100, -100, -100, -100, -100, -100, -100,   # human
+            -100, -100, -100, -100, -100, -100, -100, -100  # gpt with weight zero
+        ]
+        # fmt: on
+
+    def test_w_train_on_input(self, sharegpt_dataset, tokenizer):
+        strategy = SimpleShareGPTPromptTokenizingStrategy(
+            ShareGPTPrompterV2(
+                conversation="chatml",
+                role_key_model=None,
+                role_key_human=None,
+            ),
+            tokenizer,
+            True,  # train_on_inputs
+            2048,  # sequence_len
+        )
+
+        dataset_wrapper = TokenizedPromptDataset(
+            strategy, sharegpt_dataset, process_count=1
+        )
+
+        labels = dataset_wrapper[0]["labels"]
+        # fmt: off
+        assert labels == [
+            1,   # bos
+            32001, 1587, 13, 25997, 32000, 28705, 13,  # system
+            32001, 2188, 13, 21558, 32000, 28705, 13,  # human
+            32001, 13892, 13, 21558, 32000, 28705, 13,  # gpt
+            32001, 2188, 13, 12684, 17664, 32000, 28705, 13,   # human
+            32001, 13892, 13, 12684, 17664, 32000, 28705, 13,  # gpt
+        ]
+        # fmt: on
+
+    def test_w_train_on_input_with_weights(
+        self, sharegpt_dataset_with_weights, tokenizer
+    ):
+        strategy = SimpleShareGPTPromptTokenizingStrategy(
+            ShareGPTPrompterV2(
+                conversation="chatml",
+                role_key_model=None,
+                role_key_human=None,
+            ),
+            tokenizer,
+            True,  # train_on_inputs
+            2048,  # sequence_len
+        )
+
+        dataset_wrapper = TokenizedPromptDataset(
+            strategy, sharegpt_dataset_with_weights, process_count=1
+        )
+
+        labels = dataset_wrapper[0]["labels"]
+        # fmt: off
+        assert labels == [
+            1,   # bos
+            32001, 1587, 13, 25997, 32000, 28705, 13,  # system
+            32001, 2188, 13, 21558, 32000, 28705, 13,  # human
+            -100, -100, -100, -100, -100, -100, -100,  # gpt with weight 0
+            -100, -100, -100, -100, -100, -100, -100, -100,  # human with weight 0
+            32001, 13892, 13, 267, 21558, 32000, 28705, 13,  # gpt
+            32001, 2188, 13, 12684, 17664, 32000, 28705, 13,  # human
+            -100, -100, -100, -100, -100, -100, -100, -100  # gpt with weight 0
+        ]
+        # fmt: on
+
+    def test_chatml_glaive(self, glaive_dataset, tokenizer):
+        strategy = GlaiveShareGPTPromptTokenizingStrategy(
+            ShareGPTPrompterV2(
+                conversation="chatml",
+                role_key_model=None,
+                role_key_human=None,
+            ),
+            tokenizer,
+            True,  # train_on_inputs
+            2048,  # sequence_len
+        )
+
+        dataset_wrapper = TokenizedPromptDataset(
+            strategy, glaive_dataset, process_count=1
+        )
+
+        labels = dataset_wrapper[0]["labels"]
+        # fmt: off
+        assert labels == [
+            1,  # bos
+            32001, 1587, 13, 3260, 349, 264, 1587, 11510, 32000, 28705, 13,  # system
+            32001, 2188, 13, 6325, 368, 1820, 264, 9314, 354, 528, 477, 1450, 2726, 298, 4222, 28804, 32000, 28705, 13,  # human
+            32001, 13892, 13, 28737, 28742, 28719, 7371, 28725, 562, 315, 949, 28742, 28707, 506, 272, 21368, 298, 1820, 22447, 28723, 28705, 523, 28766, 416, 1009, 772, 28766, 28767, 32000, 28705, 13  # gpt
+        ]
+        # fmt: on
+
+    def test_multi_role_dataset(self, multi_role_dataset, tokenizer):
+        strategy = SimpleShareGPTPromptTokenizingStrategy(
+            ShareGPTPrompterV2(conversation="chatml", roles={"input": ["tool"]}),
+            tokenizer,
+            False,  # train_on_inputs
+            2048,  # sequence_len
+        )
+
+        dataset_wrapper = TokenizedPromptDataset(
+            strategy, multi_role_dataset, process_count=1
+        )
+
+        input_ids = dataset_wrapper[0]["input_ids"]
+        # fmt: off
+        assert input_ids == [
+            1,   # bos
+            32001, 1587, 13, 1730, 625, 28730, 769, 1223, 28732, 18373, 28731, 298, 625, 272, 8086, 354, 264, 2990, 32000, 28705, 13,  # system
+            32001, 2188, 13, 21558, 28725, 767, 28742, 28713, 272, 8086, 297, 1450, 2726, 28804, 32000, 28705, 13,  # human
+            32001, 13892, 13, 895, 528, 625, 369, 354, 368, 32000, 28705, 13,  # gpt
+            32001, 3921, 13, 527, 28730, 769, 1223, 28732, 2972, 2726, 28731, 32000, 28705, 13,  # tool
+            32001, 13892, 13, 1237, 8086, 297, 1450, 2726, 349, 28705, 28787, 28734, 11182, 304, 4376, 1780, 32000, 28705, 13  # gpt
+        ]
+        # fmt: on
+
+        labels = dataset_wrapper[0]["labels"]
+        # fmt: off
+        assert labels == [
+            -100,  # bos
+            -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,  # system
+            -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,  # human
+            -100, -100, 13, 895, 528, 625, 369, 354, 368, 32000, 28705, 13,  # gpt
+            -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,  # tool
+            -100, -100, 13, 1237, 8086, 297, 1450, 2726, 349, 28705, 28787, 28734, 11182, 304, 4376, 1780, 32000, 28705, 13  # gpt
+        ]
+        # fmt: on

tests/test_datasets.py

@@ -306,10 +306,6 @@ class TestDatasetPreparation(unittest.TestCase):
         """Verify that processing data from the hub works with a specific revision"""
         with tempfile.TemporaryDirectory() as tmp_dir:
             prepared_path = Path(tmp_dir) / "prepared"
-
-            # make sure prepared_path is empty
-            shutil.rmtree(prepared_path, ignore_errors=True)
-
             cfg = DictDefault(
                 {
                     "tokenizer_config": "huggyllama/llama-7b",

tests/test_normalize_config.py

@@ -39,12 +39,12 @@ class NormalizeConfigTestCase(unittest.TestCase):
                 "datasets": [
                     {
                         "path": "lorem/ipsum",
-                        "type": "chat_template",
-                        "chat_template": "gemma",
+                        "type": "sharegpt",
+                        "conversation": "vicuna_v1.1",
                     },
                     {
                         "path": "sit/amet",
-                        "type": "chat_template",
+                        "type": "sharegpt",
                     },
                 ],
             }
@@ -52,8 +52,8 @@ class NormalizeConfigTestCase(unittest.TestCase):
 
         normalize_cfg_datasets(cfg)
 
-        assert cfg.datasets[0].chat_template == "gemma"
-        assert cfg.datasets[1].chat_template == "chatml"
+        assert cfg.datasets[0].conversation == "vicuna_v1.1"
+        assert cfg.datasets[1].conversation == "chatml"
 
     @patch("axolotl.utils.config.is_torch_bf16_gpu_available")
     def test_bf16_auto_setter_available(self, mock_bf16_avail):

tests/test_prompt_tokenizers.py

@@ -3,6 +3,7 @@
 import json
 import logging
 import unittest
+from copy import deepcopy
 from pathlib import Path
 from typing import Optional
 
@@ -20,8 +21,12 @@ from axolotl.prompt_strategies.llama2_chat import (
     LLama2ChatTokenizingStrategy,
 )
 from axolotl.prompt_strategies.orpo.chat_template import load
-from axolotl.prompt_tokenizers import AlpacaPromptTokenizingStrategy
-from axolotl.prompters import AlpacaPrompter, PromptStyle
+from axolotl.prompt_strategies.sharegpt import GlaiveShareGPTPromptTokenizingStrategy
+from axolotl.prompt_tokenizers import (
+    AlpacaPromptTokenizingStrategy,
+    ShareGPTPromptTokenizingStrategy,
+)
+from axolotl.prompters import AlpacaPrompter, PromptStyle, ShareGPTPrompterV2
 from axolotl.utils.dict import DictDefault
 
 LOG = logging.getLogger("axolotl")
@@ -60,6 +65,17 @@ test_data = {
 }
 
 
+def prompt_strat(conversation, tokenizer):
+    "Helper function to create a prompt strategy for testing."
+    prompter = ShareGPTPrompterV2(conversation=conversation)
+    return ShareGPTPromptTokenizingStrategy(
+        prompter,
+        tokenizer,
+        False,
+        2048,
+    )
+
+
 class TestPromptTokenizationStrategies(unittest.TestCase):
     """
     Test class for prompt tokenization strategies.
@@ -82,6 +98,196 @@ class TestPromptTokenizationStrategies(unittest.TestCase):
             }
         )
 
+    def test_sharegpt_integration(self):
+        with open(
+            Path(__file__).parent / "fixtures/conversation.json", encoding="utf-8"
+        ) as fin:
+            data = fin.read()
+            conversation = json.loads(data)
+        with open(
+            Path(__file__).parent / "fixtures/conversation.tokenized.json",
+            encoding="utf-8",
+        ) as fin:
+            data = fin.read()
+            tokenized_conversation = json.loads(data)
+        prompter = ShareGPTPrompterV2()
+        strat = ShareGPTPromptTokenizingStrategy(
+            prompter,
+            self.tokenizer,
+            False,
+            2048,
+        )
+        example = strat.tokenize_prompt(conversation)
+        for fields in ["input_ids", "attention_mask", "labels"]:
+            self.assertEqual(len(example[fields]), len(tokenized_conversation[fields]))
+            self.assertEqual(example[fields], tokenized_conversation[fields])
+
+    def test_sharegpt_warnings_integration(self):
+        with open(
+            Path(__file__).parent / "fixtures/conversation.missingturns.json",
+            encoding="utf-8",
+        ) as fin:
+            data = fin.read()
+            conversation = json.loads(data)
+        prompter = ShareGPTPrompterV2()
+        strat = ShareGPTPromptTokenizingStrategy(
+            prompter,
+            self.tokenizer,
+            False,
+            2048,
+        )
+        with self._caplog.at_level(logging.WARNING):
+            strat.tokenize_prompt(conversation)
+            assert "assistant turn has empty text" in self._caplog.records[1].message
+
+    def test_sharegpt_warnings_turns(self):
+        conversation = {
+            "conversations": [
+                {"from": "system", "value": "lorem"},
+                {"from": "gpt", "value": "ipsum"},
+                {"from": "human", "value": "dolor"},
+                {"from": "human", "value": "dolor"},
+                {"from": "gpt", "value": "sit"},
+            ]
+        }
+        prompter = ShareGPTPrompterV2()
+        strat = ShareGPTPromptTokenizingStrategy(
+            prompter,
+            self.tokenizer,
+            False,
+            2048,
+        )
+        with self._caplog.at_level(logging.WARNING):
+            strat.tokenize_prompt(conversation)
+            assert (
+                "Role did not alternate between turns (gpt and human)"
+                in self._caplog.records[0].message
+            )
+
+    def test_sharegpt_llama(self):
+        "Make sure the sharegpt/llama is tokenized and formatted correctly."
+        strat = prompt_strat("llama-2", self.tokenizer)
+
+        def tokenize(conv):
+            return strat.tokenize_prompt(deepcopy(conv))["input_ids"]
+
+        def decode(ids):
+            return strat.tokenizer.decode(ids)
+
+        # fmt: off
+        # System message, multi-turn conversations
+        mt_ids = tokenize(test_data['multi_turn_sys'])
+        assert decode(mt_ids) == '<s> [INST] <<SYS>>\nlorem\n<</SYS>>\n\nabc [/INST] ipsum</s><s> [INST] 123 [/INST] sit</s>'
+        assert mt_ids == [1, 518, 25580, 29962, 3532, 14816, 29903, 6778, 13, 29880, 3668, 13, 29966, 829, 14816, 29903, 6778, 13, 13, 10736, 518, 29914, 25580, 29962, 23421, 2, 1, 518, 25580, 29962, 29871, 29896, 29906, 29941, 518, 29914, 25580, 29962, 7845, 2]
+
+        # System message, single-turn conversations
+        st_ids = tokenize(test_data['single_turn_sys'])
+        assert decode(st_ids) == '<s> [INST] <<SYS>>\nlorem\n<</SYS>>\n\nabc [/INST] ipsum</s>'
+        assert st_ids == [1, 518, 25580, 29962, 3532, 14816, 29903, 6778, 13, 29880, 3668, 13, 29966, 829, 14816, 29903, 6778, 13, 13, 10736, 518, 29914, 25580, 29962, 23421, 2]
+
+        # No system message, single-turn
+        ns_ids = tokenize(test_data['single_turn_no_sys'])
+        assert decode(ns_ids) == '<s> [INST] abc [/INST] ipsum</s>'
+        assert ns_ids == [1, 518, 25580, 29962, 25638, 518, 29914, 25580, 29962, 23421, 2]
+
+        # No system message, multi-turn
+        ns_mt_ids = tokenize(test_data['multi_turn_no_sys'])
+        assert decode(ns_mt_ids) == '<s> [INST] abc [/INST] ipsum</s><s> [INST] 123 [/INST] sit</s>'
+        assert ns_mt_ids == [1, 518, 25580, 29962, 25638, 518, 29914, 25580, 29962, 23421, 2, 1, 518, 25580, 29962, 29871, 29896, 29906, 29941, 518, 29914, 25580, 29962, 7845, 2]
+        # fmt: on
+
+    def test_sharegpt_mistral(self):
+        "Make sure the sharegpt/mistral is tokenized and formatted correctly."
+        strat = prompt_strat("mistral", self.tokenizer)
+
+        def tokenize(conv):
+            return strat.tokenize_prompt(deepcopy(conv))["input_ids"]
+
+        def decode(ids):
+            return strat.tokenizer.decode(ids)
+
+        # fmt: off
+        # System message, multi-turn conversations
+        mt_ids = tokenize(test_data['multi_turn_sys'])
+        assert decode(mt_ids) == '<s> [INST]  lorem\nabc [/INST] ipsum</s> [INST] 123 [/INST] sit</s>'
+        assert mt_ids == [1, 518, 25580, 29962, 29871, 301, 3668, 13, 10736, 518, 29914, 25580, 29962, 23421, 2, 518, 25580, 29962, 29871, 29896, 29906, 29941, 518, 29914, 25580, 29962, 7845, 2]
+
+        # System message, single-turn conversations
+        st_ids = tokenize(test_data['single_turn_sys'])
+        assert decode(st_ids) == '<s> [INST]  lorem\nabc [/INST] ipsum</s>'
+        assert st_ids == [1, 518, 25580, 29962, 29871, 301, 3668, 13, 10736, 518, 29914, 25580, 29962, 23421, 2]
+
+        # No system message, single-turn
+        ns_ids = tokenize(test_data['single_turn_no_sys'])
+        assert decode(ns_ids) == '<s> [INST] abc [/INST] ipsum</s>'
+        assert ns_ids == [1, 518, 25580, 29962, 25638, 518, 29914, 25580, 29962, 23421, 2]
+
+        # No system message, multi-turn
+        ns_mt_ids = tokenize(test_data['multi_turn_no_sys'])
+        assert decode(ns_mt_ids) == '<s> [INST] abc [/INST] ipsum</s> [INST] 123 [/INST] sit</s>'
+        assert ns_mt_ids == [1, 518, 25580, 29962, 25638, 518, 29914, 25580, 29962, 23421, 2, 518, 25580, 29962, 29871, 29896, 29906, 29941, 518, 29914, 25580, 29962, 7845, 2]
+        # fmt: on
+
+    def test_sharegpt_changes_roles(self):
+        conversation = {
+            "roles": ["USER", "CHARACTER"],
+            "conversations": [
+                {"from": "system", "value": "lorem"},
+                {"from": "gpt", "value": "ipsum"},
+                {"from": "human", "value": "dolor"},
+                {"from": "gpt", "value": "sit"},
+            ],
+        }
+        prompter = ShareGPTPrompterV2()
+        strat = ShareGPTPromptTokenizingStrategy(
+            prompter,
+            self.tokenizer,
+            False,
+            2048,
+        )
+        with self._caplog.at_level(logging.WARNING):
+            res = strat.tokenize_prompt(conversation)
+            assert "CHARACTER" in self.tokenizer.decode(res["input_ids"])
+
+    def test_sharegpt_assistant_label_ignore(self):
+        conversation = {
+            "roles": ["user", "assistant"],
+            "conversations": [
+                {"from": "system", "value": "lorem"},
+                {"from": "gpt", "value": "ipsum"},
+                {"from": "human", "value": "dolor"},
+                {"from": "gpt", "value": "sit"},
+            ],
+        }
+        prompter = ShareGPTPrompterV2()
+        strat = ShareGPTPromptTokenizingStrategy(
+            prompter,
+            self.tokenizer,
+            False,
+            2048,
+        )
+        with self._caplog.at_level(logging.WARNING):
+            res = strat.tokenize_prompt(conversation)
+            idx = res["input_ids"].index(20255)  # assistant token
+            assert res["labels"][idx] == -100
+
+    def test_glaive_tool_label_ignore(self):
+        conversation = {
+            "system": "SYSTEM: This is a system prompt",
+            "chat": "USER: Can you book a flight for me from New York to London? ASSISTANT: I'm sorry, but I don't have the capability to book flights.  <|endoftext|>",
+        }
+        prompter = ShareGPTPrompterV2()
+        strat = GlaiveShareGPTPromptTokenizingStrategy(
+            prompter,
+            self.tokenizer,
+            False,
+            2048,
+        )
+        with self._caplog.at_level(logging.WARNING):
+            res = strat.tokenize_prompt(conversation)
+            idx = res["input_ids"].index(13566)  # assistant token
+            assert res["labels"][idx] == -100
+
     def test_no_sys_prompt(self):
         """
         tests the interface between the user and assistant parts

tests/test_validation.py

@@ -646,6 +646,39 @@ class TestValidation(BaseValidation):
 
         validate_config(cfg)
 
+    def test_sharegpt_deprecation(self, minimal_cfg):
+        cfg = (
+            DictDefault(
+                {"datasets": [{"path": "lorem/ipsum", "type": "sharegpt:chat"}]}
+            )
+            | minimal_cfg
+        )
+        with self._caplog.at_level(logging.WARNING):
+            new_cfg = validate_config(cfg)
+            assert any(
+                "`type: sharegpt:chat` will soon be deprecated." in record.message
+                for record in self._caplog.records
+            )
+        assert new_cfg.datasets[0].type == "sharegpt"
+
+        cfg = (
+            DictDefault(
+                {
+                    "datasets": [
+                        {"path": "lorem/ipsum", "type": "sharegpt_simple:load_role"}
+                    ]
+                }
+            )
+            | minimal_cfg
+        )
+        with self._caplog.at_level(logging.WARNING):
+            new_cfg = validate_config(cfg)
+            assert any(
+                "`type: sharegpt_simple` will soon be deprecated." in record.message
+                for record in self._caplog.records
+            )
+        assert new_cfg.datasets[0].type == "sharegpt:load_role"
+
     def test_no_conflict_save_strategy(self, minimal_cfg):
         cfg = (
             DictDefault(

tests/test_validation_dataset.py

@@ -48,8 +48,9 @@ class TestValidationCheckDatasetConfig(BaseValidation):
             | {
                 "datasets": [
                     {
-                        "path": "mhenrichsen/alpaca_2k_test",
-                        "type": "alpaca",
+                        "path": "LDJnr/Puffin",
+                        "type": "sharegpt",
+                        "conversation": "chatml",
                         "shards": 10,
                     }
                 ]
@@ -61,6 +62,7 @@ class TestValidationCheckDatasetConfig(BaseValidation):
         def _check_config():
             assert checked_cfg.datasets[0].path == cfg.datasets[0].path
             assert checked_cfg.datasets[0].type == cfg.datasets[0].type
+            assert checked_cfg.datasets[0].conversation == cfg.datasets[0].conversation
             assert checked_cfg.datasets[0].shards == cfg.datasets[0].shards
 
         _check_config()