remove direct dependency on fused dense lib (#2027)

* remove fastchat and sharegpt

* remove imports

* remove more fastchat imports

* chore: remove unused functions

* feat: remove sharegpt and deprecate from docs

* chore: remove unused sharegpt checks

* fix: remove sharegpt type from tests

* feat: add sharegpt deprecation error

* feat: update readme

---------

Co-authored-by: NanoCode012 <nano@axolotl.ai>

.github/workflows/base.yml

@@ -28,7 +28,19 @@ jobs:
             cuda_version: 12.4.1
             cudnn_version: ""
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+          - cuda: "124"
+            cuda_version: 12.4.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.4.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+          - cuda: "124"
+            cuda_version: 12.4.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.5.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout

.github/workflows/main.yml

@@ -27,7 +27,12 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
+            axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -84,7 +89,12 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
+            axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:

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

@@ -21,10 +21,17 @@ jobs:
             pytorch: 2.3.1
             axolotl_extras:
             num_gpus: 2
-          - cuda: 121
-            cuda_version: 12.1.1
+          - cuda: 124
+            cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.3.1
+            pytorch: 2.4.1
+            axolotl_extras:
+            num_gpus: 2
+            nightly_build: "true"
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
             axolotl_extras:
             num_gpus: 2
             nightly_build: "true"

.github/workflows/nightlies.yml

@@ -26,7 +26,12 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
+            axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -83,7 +88,12 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.4.1
+            axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:

.github/workflows/pypi.yml

@@ -4,6 +4,7 @@ on:
   push:
     tags:
       - '*'
+  workflow_dispatch:
 
 jobs:
   pypi-publish:
@@ -27,7 +28,7 @@ jobs:
         run: |
           pip3 install wheel packaging
           pip3 install -e .
-          pip3 install -r requirements-tests.txt
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Extract tag name
         id: tag

.github/workflows/tests-nightly.yml

@@ -25,7 +25,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.0"]
+        pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
     timeout-minutes: 20
 
     steps:
@@ -47,13 +47,14 @@ jobs:
           sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt
           sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt
           sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt
+          sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt
 
       - name: Install dependencies
         run: |
           pip3 install --upgrade pip
           pip3 install --upgrade packaging
           pip3 install -U -e .
-          pip3 install -r requirements-tests.txt
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Run tests
         run: |
@@ -81,17 +82,17 @@ jobs:
             num_gpus: 1
             axolotl_extras: mamba-ssm
             nightly_build: "true"
-          - cuda: 121
-            cuda_version: 12.1.1
+          - cuda: 124
+            cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.3.1
+            pytorch: 2.4.1
             num_gpus: 1
-            axolotl_extras: mamba-ssm
+            axolotl_extras:
             nightly_build: "true"
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.0
+            pytorch: 2.5.1
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"

.github/workflows/tests.yml

@@ -36,7 +36,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.0"]
+        pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
     timeout-minutes: 20
 
     steps:
@@ -49,16 +49,20 @@ jobs:
           python-version: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
 
+      - name: upgrade pip
+        run: |
+          pip3 install --upgrade pip
+          pip3 install --upgrade packaging setuptools wheel
+
       - name: Install PyTorch
         run: |
-          pip3 install torch==${{ matrix.pytorch_version }} --index-url https://download.pytorch.org/whl/cpu
+          pip3 install torch==${{ matrix.pytorch_version }}
 
       - name: Install dependencies
         run: |
-          pip3 install --upgrade pip
-          pip3 install --upgrade packaging
+          pip3 show torch
           pip3 install -U -e .
-          pip3 install -r requirements-tests.txt
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Run tests
         run: |
@@ -68,33 +72,67 @@ jobs:
         run: |
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
-  docker-e2e-tests:
+  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: 60
+    timeout-minutes: 90
     needs: [pre-commit, pytest]
 
     strategy:
       fail-fast: false
       matrix:
         include:
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.10"
-            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: 2.4.0
+            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]
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: 121
+            cuda_version: 12.1.1
+            python_version: "3.10"
+            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
             num_gpus: 1
             axolotl_extras:
     steps:

.isort.cfg

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

README.md

@@ -14,7 +14,7 @@ Features:
 - Integrated with xformer, flash attention, [liger kernel](https://github.com/linkedin/Liger-Kernel), rope scaling, and multipacking
 - Works with single GPU or multiple GPUs via FSDP or Deepspeed
 - Easily run with Docker locally or on the cloud
-- Log results and optionally checkpoints to wandb or mlflow
+- Log results and optionally checkpoints to wandb, mlflow or Comet
 - And more!
 
 <a href="https://www.phorm.ai/query?projectId=e315ba4a-4e14-421f-ab05-38a1f9076f25">
@@ -121,7 +121,7 @@ Features:
 
 Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.
 
-**Requirements**: Python >=3.10 and Pytorch >=2.1.1.
+**Requirements**: Nvidia GPU (Ampere architecture or newer for `bf16` and Flash Attention), Python >=3.10 and PyTorch >=2.3.1.
 
 ```bash
 git clone https://github.com/axolotl-ai-cloud/axolotl
@@ -383,11 +383,10 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
         - typescript
       type: ... # unimplemented custom format
 
-      # fastchat conversation
-      # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+      # chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template
     - path: ...
-      type: sharegpt
-      conversation: chatml # default: vicuna_v1.1
+      type: chat_template
+      chat_template: chatml # defaults to tokenizer's chat_template
 
       # local
     - path: data.jsonl # or json
@@ -515,6 +514,22 @@ wandb_name:
 wandb_log_model:
 ```
 
+##### Comet Logging
+
+Make sure your `COMET_API_KEY` environment variable is set (recommended) or you login to wandb with `comet login`.
+
+- wandb options
+```yaml
+use_comet:
+comet_api_key:
+comet_workspace:
+comet_project_name:
+comet_experiment_key:
+comet_mode:
+comet_online:
+comet_experiment_config:
+```
+
 ##### Special Tokens
 
 It is important to have special tokens like delimiters, end-of-sequence, beginning-of-sequence in your tokenizer's vocabulary.  This will help you avoid tokenization issues and help your model train better.  You can do this in axolotl like this:
@@ -546,7 +561,8 @@ plugins:
   - axolotl.integrations.liger.LigerPlugin
 liger_rope: true
 liger_rms_norm: true
-liger_swiglu: true
+liger_glu_activation: true
+liger_layer_norm: true
 liger_fused_linear_cross_entropy: true
 ```
 

cicd/Dockerfile.jinja

@@ -23,11 +23,11 @@ RUN git fetch origin +$GITHUB_REF && \
     git checkout FETCH_HEAD
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
-RUN pip install causal_conv1d
 RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt; \
         sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
         sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
+        sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt; \
     fi
 
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
@@ -37,7 +37,7 @@ RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
     fi
 
 # So we can test the Docker image
-RUN pip install -r requirements-tests.txt
+RUN pip install -r requirements-dev.txt -r requirements-tests.txt
 
 # fix so that git fetch/pull from remote works
 RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \

cicd/cicd.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 set -e
 
-pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
+pytest -n4 --ignore=tests/e2e/ /workspace/axolotl/tests/
 pytest -n1 --dist loadfile -v /workspace/axolotl/tests/e2e/patched/ /workspace/axolotl/tests/e2e/integrations/
 pytest --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

cicd/multigpu.py

@@ -64,7 +64,7 @@ def run_cmd(cmd: str, run_folder: str):
 @stub.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
-    timeout=45 * 60,
+    timeout=60 * 60,
     cpu=8.0,
     memory=131072 * N_GPUS,
 )

cicd/tests.py

@@ -65,7 +65,7 @@ def run_cmd(cmd: str, run_folder: str):
 @stub.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
-    timeout=45 * 60,
+    timeout=60 * 60,
     cpu=8.0,
     memory=131072,
 )

deepspeed_configs/zero3_bf16.json

@@ -14,15 +14,6 @@
   "bf16": {
     "enabled": true
   },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

deepspeed_configs/zero3_bf16_cpuoffload_all.json

@@ -24,15 +24,6 @@
   "bf16": {
     "enabled": true
   },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

deepspeed_configs/zero3_bf16_cpuoffload_params.json

@@ -20,15 +20,6 @@
   "bf16": {
     "enabled": true
   },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

devtools/dev_chat_template.yml

@@ -1,4 +1,4 @@
-# Example config for debugging the sharegpt prompt format
+# Example config for debugging the chat_template prompt format
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
@@ -7,8 +7,8 @@ load_in_8bit: true
 load_in_4bit: false
 
 datasets:
-  - path: philschmid/guanaco-sharegpt-style
-    type: sharegpt
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
     shards: 10
 val_set_size: 0
 output_dir: temp_debug/axolotl_outputs/model

docker/Dockerfile

@@ -20,7 +20,6 @@ RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
 WORKDIR /workspace/axolotl
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
-RUN pip install causal_conv1d
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \

docker/Dockerfile-base

@@ -35,3 +35,7 @@ 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

@@ -83,22 +83,14 @@ 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, sharegpt, gpteacher, oasst, reflection]
+    # The type of prompt to use for training. [alpaca, 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
     shards: # Optional[int] number of shards to split data into
     name: # Optional[str] name of dataset configuration to load
     train_on_split: train # Optional[str] name of dataset split to load from
-
-    # 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]].
+    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.
 
   # Custom user instruction prompt
   - path: repo
@@ -123,6 +115,48 @@ datasets:
       # For `completion` datsets only, uses the provided field instead of `text` column
       field:
 
+  # Using chat template
+  - path: ...
+    # Set type to `chat_template` to use this strategy
+    type: chat_template
+    # Specify the name of the chat template to use
+    # 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.
+    # - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
+    # - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to if the tokenizer does not have a chat template else default to tokenizer. E.g. tokenizer_default_fallback_chatml.
+    # - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
+    chat_template: tokenizer_default
+    # Custom jinja template for chat template. This will be only used if `chat_template` is set to `jinja` or empty (in which case chat_template is automatically set to `jinja`).
+    chat_template_jinja:
+    # The key in the data example that contains the messages. Default is "messages".
+    field_messages: messages
+    # The key in the message turn that contains the role. Default is "role".
+    message_field_role: role
+    # The key in the message turn that contains the content. Default is "content".
+    message_field_content: content
+    # Optional[Dict[str, List]]. Roles mapping for the messages.
+    roles:
+      user: ["human", "user"]
+      assistant: ["gpt", "assistant", "ai"]
+      system: ["system"]
+
+    ## NOTE: Leaving the below empty will default to using the simple legacy tokenization strategy where only last message is trained on.
+
+    # Optional[List[str]]. Roles to train on. The tokens from these roles will be considered for the loss.
+    roles_to_train: ["gpt", "assistant"]
+    # Optional[str]. Which EOS tokens to train on in the conversation. Possible values are:
+    # - all: train on all EOS tokens
+    # - turn: train on the EOS token at the end of each trainable turn
+    # - last: train on the last EOS token in the conversation
+    train_on_eos: last
+    # The key in the message turn that indicates via boolean whether tokens of a turn should be considered for training. Useful to selectively train on certain turns besides the `roles_to_train`.
+    message_field_training: training
+    # The key in the message turn that contains the training details. Useful to selectively train on certain tokens in a turn.
+    # The value of the key is a List[Dict] containing `begin_offset` (start character index in content), `end_offset` (end character index in content), and `train` (boolean whether to train).
+    # See example at `docs/dataset-formats/conversation.qmd`
+    message_field_training_detail: train_detail
+
+
 # If false, the datasets will not be shuffled and will keep their original order in `datasets`.
 # The same applies to the `test_datasets` option and the `pretraining_dataset` option. Default is true.
 shuffle_merged_datasets: true
@@ -140,10 +174,19 @@ test_datasets:
 
 # use RL training: 'dpo', 'ipo', 'kto'
 rl:
+# whether to perform weighting if doing DPO training. Boolean.
+dpo_use_weighting:
 
-# Saves the desired chat template to the tokenizer_config.json for easier inferencing
-# Currently supports chatml and inst (mistral/mixtral)
-chat_template: chatml
+# 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.
+# - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
+# - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to. E.g. tokenizer_default_fallback_chatml. This is useful when the chat template is not available in the tokenizer.
+# - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
+# The selected chat template will be saved to the tokenizer_config.json for easier inferencing
+# Note: It is recommended to set train_on_inputs to true when using a chat template that is different from the model's default chat template.
+chat_template: tokenizer_default
+# custom jinja template for chat template. This will be only used if chat_template is set to `jinja` or `null` (in which case chat_template is automatically set to `jinja`). Default is null.
+chat_template_jinja: null
 # Changes the default system message
 default_system_message: You are a helpful assistant. Please give a long and detailed answer. # Currently only supports chatml.
 # Axolotl attempts to save the dataset as an arrow after packing the data together so
@@ -265,8 +308,21 @@ wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_step
 # mlflow configuration if you're using it
 mlflow_tracking_uri: # URI to mlflow
 mlflow_experiment_name: # Your experiment name
+mlflow_run_name: # Your run name
 hf_mlflow_log_artifacts:  # set to true to copy each saved checkpoint on each save to mlflow artifact registry
 
+# Comet configuration if you're using it
+# Make sure your `COMET_API_KEY` environment variable is set (recommended) or you login to Comet with `comet login`.
+# Check out our documentation for more details https://www.comet.com/docs/v2/api-and-sdk/python-sdk/reference/Experiment-Creation/#comet_ml.start
+use_comet: # Enable or disable Comet integration.
+comet_api_key: # API key for Comet. Recommended to set via `comet login`.
+comet_workspace: # Workspace name in Comet. Defaults to the user's default workspace.
+comet_project_name: # Project name in Comet. Defaults to Uncategorized.
+comet_experiment_key: # Identifier for the experiment. Used to append data to an existing experiment or control the key of new experiments. Default to a random key.
+comet_mode: # Create a new experiment ("create") or log to an existing one ("get"). Default ("get_or_create") auto-selects based on configuration.
+comet_online: # Set to True to log data to Comet server, or False for offline storage. Default is True.
+comet_experiment_config: # Dictionary for additional configuration settings, see the doc for more details.
+
 # Where to save the full-finetuned model to
 output_dir: ./completed-model
 
@@ -301,7 +357,7 @@ max_steps:
 
 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
-eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", chrf]
+eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", "chrf", "perplexity"]
 
 loss_watchdog_threshold: # High loss value, indicating the learning has broken down (a good estimate is ~2 times the loss at the start of training)
 loss_watchdog_patience: # Number of high-loss steps in a row before the trainer aborts (default: 3)

docs/dataset-formats/conversation.qmd

@@ -6,31 +6,8 @@ order: 3
 
 ## sharegpt
 
-conversations where `from` is `human`/`gpt`. (optional: first row with role `system` to override default system prompt)
+IMPORTANT: ShareGPT is deprecated!. Please see `chat_template` section below.
 
-```{.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
 
@@ -38,34 +15,137 @@ datasets:
 {"conversations": [{"role": "...", "value": "..."}]}
 ```
 
-## sharegpt.load_role
 
-conversations where `role` is used instead of `from`
+## chat_template
+
+Chat Template strategy uses a jinja2 template that converts a list of messages into a prompt. Support using tokenizer's template, a supported template, or custom jinja2.
 
 ```{.json filename="data.jsonl"}
-{"conversations": [{"role": "...", "value": "..."}]}
+{"conversations": [{"role": "...", "content": "..."}]}
 ```
 
-## sharegpt.load_guanaco
+See `config.qmd` for full configs and supported templates.
 
-conversations where `from` is `prompter` `assistant` instead of default sharegpt
+### Migrating from sharegpt
+
+Most configs can be adapted as follows:
+
+```yaml
+# old
+chat_template: chatml
+datasets:
+  - path: ...
+    type: sharegpt
+    conversation: chatml
+
+# new (if using tokenizer's chat_template)
+datasets:
+  - path: ...
+    type: chat_template
+
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
+# new (if setting a new chat_template like chatml, gemma, etc)
+chat_template: chatml
+datasets:
+  - path: ...
+    type: chat_template
+
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+```
+
+We recommend checking the below examples for other usecases.
+
+### Examples
+
+1. Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on only last message.
+
+```yaml
+datasets:
+  - path: ...
+    type: chat_template
+```
+
+2. Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
+
+```yaml
+chat_template: gemma # this overwrites the tokenizer's chat_template
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+3. Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
+
+```yaml
+chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer's chat_template
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+4. Using a custom jinja template on OpenAI messages format, training on all assistant messages.
+
+```yaml
+# chat_template: jinja # `jinja` will be implied if the `chat_template_jinja` is set and this field is empty
+chat_template_jinja: "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|system|>' + '\n' + message['content'] + '<|end|>' + '\n'}}{% elif (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif message['role'] == 'assistant' %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}"
+
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+5. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
+
+For a data sample that looks like:
 
 ```{.json filename="data.jsonl"}
-{"conversations": [{"from": "...", "value": "..."}]}
+{
+  "conversations": [
+    {"from": "system", "value": "You are an AI assistant.", "train": false},
+    {"from": "human", "value": "Hello", "train": false},
+    {"from": "assistant", "value": "Hello", "train": true},
+    {"from": "human", "value": "How are you?", "train": true},
+    {
+      "from": "assistant",
+      "value": "I'm doing very well, thank you!",
+      "train_detail": [
+        {"begin_offset": 0, "end_offset": 8, "train": false},
+        {"begin_offset": 9, "end_offset": 18, "train": true},
+        {"begin_offset": 19, "end_offset": 30, "train": false},
+      ],
+    },
+    {
+        "from": "human",
+        "value": "I'm doing very well, thank you!",
+        "train": true,
+    },
+    {"from": "assistant", "value": "Hi there!", "train": true}
+  ]
+}
 ```
 
-## sharegpt.load_ultrachat
+The configuration would look like:
 
-conversations where the turns field is 'messages', human is 'user' and gpt is 'assistant'.
-
-```{.json filename="data.jsonl"}
-{"messages": [{"user": "...", "assistant": "..."}]}
+```yaml
+datasets:
+  - path: ...
+    type: chat_template
+    chat_template: tokenizer_default
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+    roles_to_train: []
+    train_on_eos: turn
+    message_field_training: train
+    message_field_training_detail: train_detail
 ```
 
-## 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": "..."}]}
-```
+Tip: It is not necessary to use both `message_field_training` and `message_field_training_detail` at a time.

docs/debugging.qmd

@@ -51,12 +51,12 @@ While debugging it's helpful to simplify your test scenario as much as possible.
 
 ### Background
 
-The below example shows how to configure VSCode to debug data preprocessing of the `sharegpt` format.  This is the format used when you have the following in your axolotl config:
+The below example shows how to configure VSCode to debug data preprocessing of the `chat_template` format.  This is the format used when you have the following in your axolotl config:
 
 ```yaml
 datasets:
-  - path: <path to your sharegpt formatted dataset> # example on HF Hub: philschmid/guanaco-sharegpt-style
-    type: sharegpt
+  - path: <path to your chat_template formatted dataset> # example on HF Hub: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
 ```
 
 >[!Important]
@@ -83,7 +83,7 @@ If you developing on a remote host, you can easily use VSCode to debug remotely.
 
 The easiest way to get started is to modify the [.vscode/launch.json](../.vscode/launch.json) file in this project.  This is just an example configuration, so you may need to modify or copy it to suit your needs.
 
-For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_sharegpt.yml`, you would use the below configuration[^1].  Note that we add additional flags that override the axolotl config and incorporate the tips above (see the comments). We also set the working directory to `devtools` and set the `env` variable `HF_HOME` to a temporary folder that is later partially deleted.  This is because we want to delete the HF dataset cache before each run in order to ensure that the data preprocessing code is run from scratch.
+For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_chat_template.yml`, you would use the below configuration[^1].  Note that we add additional flags that override the axolotl config and incorporate the tips above (see the comments). We also set the working directory to `devtools` and set the `env` variable `HF_HOME` to a temporary folder that is later partially deleted.  This is because we want to delete the HF dataset cache before each run in order to ensure that the data preprocessing code is run from scratch.
 
 ```jsonc
 // .vscode/launch.json
@@ -91,12 +91,12 @@ For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 acceler
     "version": "0.2.0",
     "configurations": [
         {
-            "name": "Debug axolotl prompt - sharegpt",
+            "name": "Debug axolotl prompt - chat_template",
             "type": "python",
             "module": "accelerate.commands.launch",
             "request": "launch",
             "args": [
-                "-m", "axolotl.cli.train", "dev_sharegpt.yml",
+                "-m", "axolotl.cli.train", "dev_chat_template.yml",
                 // The flags below simplify debugging by overriding the axolotl config
                 // with the debugging tips above.  Modify as needed.
                 "--dataset_processes=1",      // limits data preprocessing to one process
@@ -240,6 +240,6 @@ style="border-radius: 10px; display: block; margin: auto;" width="560" height="3
 </div>
 <br>
 
-[^1]: The config actually mimics the command `CUDA_VISIBLE_DEVICES=0 python -m accelerate.commands.launch -m axolotl.cli.train devtools/sharegpt.yml`, but this is the same thing.
+[^1]: The config actually mimics the command `CUDA_VISIBLE_DEVICES=0 python -m accelerate.commands.launch -m axolotl.cli.train devtools/chat_template.yml`, but this is the same thing.
 
 [^2]: Many of the below flags are recommended best practices by Nvidia when using nvidia-container-toolkit.  You can read more about these flags [here](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html).

docs/input_output.qmd

@@ -205,7 +205,7 @@ ds = load_from_disk(f'last_run_prepared/{directory[0]}/')
     hi there!.  goodbye  farewell</s>
 ```
 
-We can check that the right tokens are ingored by comparing the labels
+We can check that the right tokens are ignored by comparing the labels
 to each token:
 
 ```python

docs/multimodal.qmd

@@ -0,0 +1,28 @@
+# MultiModal / Vision Language Models (BETA)
+
+### Supported Models
+
+- Mllama, i.e. llama with vision models
+
+### Usage
+
+Currently multimodal support is limited and doesn't have full feature parity. To finetune a multimodal Llama w/ LoRA,
+you'll need to use the following in YAML in combination with the rest of the required hyperparams.
+
+```yaml
+base_model: alpindale/Llama-3.2-11B-Vision-Instruct
+processor_type: AutoProcessor
+skip_prepare_dataset: true
+
+chat_template: llama3_2_vision
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+remove_unused_columns: false
+sample_packing: false
+
+# only finetune the Language model, leave the vision model and vision tower frozen
+lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+```

docs/optimizers.qmd

@@ -1,17 +0,0 @@
-# Optimizers
-
-## Shampoo
-
-```yaml
-optimizer: shampoo
-optim_shampoo_betas: [0.9, 0.999]
-optim_args:
-    epsilon: 1e-12
-    max_preconditioner_dim: 8192
-    precondition_frequency: 100
-    use_decoupled_weight_decay: true
-optim_shampoo_grafting_config_type: adam
-optim_shampoo_grafting_config_kwargs:
-    beta2: 0.999
-    epsilon: 1e-12
-```

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

@@ -9,14 +9,17 @@ strict: false
 plugins:
   - axolotl.integrations.liger.LigerPlugin
 liger_rms_norm: true
-liger_swiglu: true
+liger_glu_activation: true
 liger_fused_linear_cross_entropy: true
 
 chat_template: deepseek_v2
 datasets:
   - path: mlabonne/FineTome-100k
     type: chat_template
-    split: train
+    split: train[:20%]
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
 
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0

examples/gemma2/qlora.yml

@@ -11,8 +11,11 @@ chat_template: gemma
 datasets:
   - path: cgato/SlimOrcaDedupCleaned
     type: chat_template
-    chat_template: gemma
     drop_system_message: true
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
 val_set_size: 0.0
 output_dir: ./outputs/out
 

examples/gemma2/reward-model.yaml

@@ -0,0 +1,63 @@
+base_model: google/gemma-2-2b
+model_type: AutoModelForSequenceClassification
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+reward_model: true
+chat_template: gemma
+datasets:
+  - path: argilla/distilabel-intel-orca-dpo-pairs
+    type: bradley_terry.chat_template
+val_set_size: 0.0
+output_dir: ./outputs/out
+remove_unused_columns: false
+
+sequence_len: 2048
+sample_packing: false
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch:
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:

examples/jamba/qlora_fsdp_large.yaml

@@ -4,11 +4,15 @@ tokenizer_type: AutoTokenizer
 load_in_4bit: true
 strict: false
 use_tensorboard: true
+chat_template: jamba
 datasets:
   - path: cgato/SlimOrcaDedupCleaned
     type: chat_template
-    chat_template: jamba
     drop_system_message: true
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
 output_dir: jamba-large-fsdp-qlora-ft

examples/llama-3-vision/lora-11b.yaml

@@ -0,0 +1,63 @@
+base_model: alpindale/Llama-3.2-11B-Vision-Instruct
+processor_type: AutoProcessor
+strict: false
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+chat_template: llama3_2_vision
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 8192
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+local_rank:
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:

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_swiglu: true
+liger_glu_activation: true
 liger_fused_linear_cross_entropy: true
 
 strict: false
@@ -14,6 +14,10 @@ datasets:
   - path: mlabonne/FineTome-100k
     type: chat_template
     split: train[:20%]
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.02
 output_dir: ./outputs/out

examples/llama-3/instruct-dpo-lora-8b.yml

@@ -11,7 +11,6 @@ rl: dpo
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_dpo_test
     type: chat_template.default
-    chat_template: llama3
     field_messages: conversation
     field_chosen: chosen
     field_rejected: rejected

examples/llama-3/instruct-lora-8b.yml

@@ -10,7 +10,6 @@ chat_template: llama3
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
-    chat_template: llama3
     field_messages: messages
     message_field_role: role
     message_field_content: content

examples/llama-3/qlora-1b.yml

@@ -0,0 +1,77 @@
+base_model: meta-llama/Llama-3.2-1B
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: teknium/GPT4-LLM-Cleaned
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/qlora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+loss_watchdog_threshold: 5.0
+loss_watchdog_patience: 3
+
+warmup_steps: 10
+evals_per_epoch: 4
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: "<|end_of_text|>"

examples/phi/lora-3.5.yaml

@@ -10,7 +10,6 @@ chat_template: phi_3
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
-    chat_template: phi_3
     field_messages: messages
     message_field_role: role
     message_field_content: content

requirements-dev.txt

@@ -2,3 +2,4 @@ pre-commit
 black
 mypy
 types-requests
+tbparse

requirements.txt

@@ -1,12 +1,12 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
-peft==0.12.0
-transformers @ git+https://github.com/huggingface/transformers.git@0963229e287501bed52ae1dabc17922524de6992
-tokenizers>=0.19.1
-bitsandbytes==0.43.3
-accelerate==0.34.2
-datasets==2.21.0
-deepspeed==0.14.4
+peft==0.13.2
+transformers==4.46.1
+tokenizers>=0.20.1
+bitsandbytes==0.44.1
+accelerate==1.1.0
+datasets==3.0.1
+deepspeed==0.15.3
 pydantic==2.6.3
 addict
 fire
@@ -16,7 +16,7 @@ flash-attn==2.6.3
 sentencepiece
 wandb
 einops
-xformers==0.0.27
+xformers>=0.0.23.post1
 optimum==1.16.2
 hf_transfer
 colorama
@@ -28,14 +28,12 @@ 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==0.2.1
-distributed_shampoo @ git+https://github.com/facebookresearch/optimizers.git@main
+liger-kernel==0.4.0
 
 mamba-ssm==1.2.0.post1
 
@@ -44,6 +42,14 @@ s3fs>=2024.5.0
 gcsfs>=2024.5.0
 # adlfs
 
-trl==0.9.6
+trl==0.12.0
 zstandard==0.22.0
 fastcore
+
+# lm eval harness
+lm_eval==0.4.4
+langdetect==1.0.9
+immutabledict==4.2.0
+antlr4-python3-runtime==4.13.2
+
+torchao==0.5.0

requirements_env.txt

@@ -0,0 +1,315 @@
+accelerate==0.34.1
+addict==2.4.0
+aiofiles==23.2.1
+aiohttp==3.9.0
+aiosignal==1.3.1
+aiostream==0.5.2
+alembic==1.13.1
+annotated-types==0.6.0
+annoy==1.17.3
+ansible==6.7.0
+ansible-core==2.13.13
+ansible-vault==2.1.0
+anyio==3.7.1
+appdirs==1.4.4
+art==6.0
+asgiref==3.7.2
+async-timeout==4.0.2
+attrdict==2.0.1
+attrs==22.2.0
+awscli==1.32.75
+-e git+ssh://git@github.com/OpenAccess-AI-Collective/axolotl.git@6e354682e3c1735d3f7fb9e362280c38e922260f#egg=axolotl
+backoff==2.2.1
+base58==2.1.1
+beartype==0.17.2
+bitnet==0.2.1
+bitsandbytes==0.42.0
+bittensor==6.7.0
+black==23.7.0
+blinker==1.7.0
+boto3==1.34.75
+botocore==1.34.75
+cachetools==5.3.3
+cachy==0.1.1
+certifi==2023.7.22
+cffi==1.16.0
+cfgv==3.3.1
+chai-guanaco==1.2.4
+charset-normalizer==3.2.0
+cleo==0.6.8
+click==8.1.7
+cloudpickle==2.0.0
+cohere==4.11.2
+colorama==0.4.4
+coloredlogs==15.0.1
+CoLT5-attention==0.10.20
+contextlib2==21.6.0
+contourpy==1.2.0
+cryptography==41.0.3
+cycler==0.12.1
+cytoolz==0.12.3
+databricks-cli==0.18.0
+dataclasses-json==0.5.7
+datasets==2.11.0
+ddt==1.6.0
+decorator==5.1.1
+deepspeed==0.15.0
+# Editable Git install with no remote (dialogpt==0.1)
+-e /Users/wing/Projects/ml/dialogpt/src
+dill==0.3.6
+distlib==0.3.6
+docker==7.0.0
+docker-pycreds==0.4.0
+docstring-parser==0.15
+docutils==0.16
+ecdsa==0.18.0
+einops==0.7.0
+einops-exts==0.0.4
+einx==0.1.3
+entrypoints==0.4
+eth-hash==0.6.0
+eth-keys==0.5.0
+eth-typing==4.0.0
+eth-utils==2.3.1
+evaluate==0.4.0
+exceptiongroup==1.1.1
+fastapi==0.109.2
+fastcore==1.5.29
+ffmpy==0.4.0
+filelock==3.12.2
+-e git+https://github.com/NousResearch/finetuning-subnet.git@24e9407d6b4430a7ca39d344692f89ce5a97d27e#egg=finetuning_subnet
+fire==0.5.0
+first==2.0.2
+flake8==7.0.0
+Flask==3.0.1
+fonttools==4.47.2
+frozendict==2.4.1
+frozenlist==1.3.3
+fschat @ git+https://github.com/lm-sys/FastChat.git@27a05b04a35510afb1d767ae7e5990cbd278f8fe
+fsspec==2023.6.0
+fuzzywuzzy==0.18.0
+gitdb==4.0.10
+GitPython==3.1.31
+google-pasta==0.2.0
+gradio==4.42.0
+gradio_client==1.3.0
+greenlet==2.0.2
+grpclib==0.4.7
+gunicorn==21.2.0
+h11==0.14.0
+h2==4.1.0
+hpack==4.0.0
+httpcore==0.17.3
+httpx==0.24.1
+huggingface-hub==0.23.4
+humanfriendly==10.0
+hyperframe==6.0.1
+identify==2.5.24
+idna==3.4
+immutables==0.20
+importlib-metadata==6.7.0
+importlib-resources==6.1.1
+inflection==0.5.1
+iniconfig==2.0.0
+itsdangerous==2.1.2
+Jinja2==3.1.2
+jmespath==1.0.1
+joblib==1.3.2
+jsonlines==3.1.0
+jsonschema==2.6.0
+kiwisolver==1.4.5
+langchain==0.0.144
+Levenshtein==0.24.0
+libcst==1.1.0
+liger-kernel==0.0.0
+lion-pytorch==0.1.2
+llama-cpp-python==0.1.36
+llvmlite==0.40.1
+local-attention==1.9.0
+loguru==0.7.0
+Mako==1.3.2
+Markdown==3.5.2
+markdown-it-py==3.0.0
+markdown2==2.4.10
+MarkupSafe==2.1.2
+marshmallow==3.19.0
+marshmallow-enum==1.5.1
+matplotlib==3.8.2
+mccabe==0.7.0
+mdurl==0.1.2
+MEGABYTE-pytorch==0.0.7
+-e git+https://github.com/cg123/mergekit.git@53c5f414774a0558b8d84858fb6374bc93a8f1c1#egg=mergekit
+mlflow==2.10.0
+modal==0.62.77
+more-itertools==10.2.0
+mpmath==1.2.1
+msgpack==1.0.7
+msgpack-numpy-opentensor==0.5.0
+multidict==6.0.4
+multiprocess==0.70.14
+munch==2.5.0
+mypy==1.3.0
+mypy-extensions==1.0.0
+nest-asyncio==1.6.0
+netaddr==0.10.1
+networkx==3.0rc1
+nh3==0.2.14
+nodeenv==1.8.0
+nomic==2.0.2
+numba==0.57.1
+numexpr==2.8.4
+numpy==1.24.4
+oauthlib==3.2.2
+openai==0.27.4
+openapi==1.1.0
+openapi-schema-pydantic==1.2.4
+optimum==1.8.6
+orjson==3.10.7
+packaging==23.1
+pandas==2.0.0
+parameterized==0.9.0
+password-strength==0.0.3.post2
+pastel==0.1.1
+pathos==0.3.0
+pathspec==0.11.1
+pathtools==0.1.2
+peft==0.11.1
+pendulum==3.0.0
+Pillow==9.5.0
+pip-tools==1.11.0
+platformdirs==3.2.0
+pluggy==1.4.0
+poetry==0.7.1
+pox==0.3.2
+ppft==1.7.6.6
+pre-commit==3.3.2
+prettytable==3.10.0
+prompt-toolkit==3.0.39
+protobuf==3.20.2
+protobuf3-to-dict==0.1.5
+psutil==5.9.5
+psycopg==3.1.18
+PuLP==2.8.0
+py==1.11.0
+py-bip39-bindings==0.1.11
+py-cpuinfo==9.0.0
+py-ed25519-zebra-bindings==1.0.1
+py-sr25519-bindings==0.2.0
+pyarrow==11.0.0
+pyasn1==0.6.0
+pycodestyle==2.11.1
+pycparser==2.21
+pycryptodome==3.20.0
+pydantic==2.5.3
+pydantic_core==2.14.6
+pydub==0.25.1
+pyfiglet==0.8.post1
+pyflakes==3.2.0
+Pygments==2.15.1
+PyJWT==2.8.0
+pylev==1.4.0
+PyNaCl==1.5.0
+pynvml==11.5.0
+pyparsing==2.4.7
+pyrsistent==0.14.11
+pytest==8.0.2
+pytest-asyncio==0.23.4
+python-dateutil==2.8.2
+python-dotenv==1.0.1
+python-Levenshtein==0.24.0
+python-multipart==0.0.9
+pytz==2023.3
+PyYAML==6.0.1
+querystring-parser==1.2.4
+rapidfuzz==3.6.1
+regex==2023.6.3
+requests==2.31.0
+requests-toolbelt==0.8.0
+resolvelib==0.8.1
+responses==0.18.0
+retry==0.9.2
+rich==13.7.0
+rsa==4.7.2
+ruff==0.6.3
+s3transfer==0.10.1
+safetensors==0.4.5
+sagemaker==2.148.0
+scalecodec==1.2.7
+schedulefree==1.2.1
+schema==0.7.5
+scikit-learn==1.4.0
+scipy==1.9.3
+seaborn==0.13.2
+semantic-version==2.10.0
+sentencepiece==0.2.0
+sentry-sdk==1.19.1
+setproctitle==1.3.2
+shellingham==1.5.4
+shortuuid==1.0.11
+shtab==1.6.5
+sigtools==4.0.1
+six==1.16.0
+skypilot==0.4.1
+smdebug-rulesconfig==1.0.1
+smmap==5.0.0
+sniffio==1.3.0
+SQLAlchemy==1.4.47
+sqlparse==0.4.4
+starlette==0.36.3
+substrate-interface==1.5.2
+svgwrite==1.4.3
+sympy==1.11.1
+synchronicity==0.6.7
+tabulate==0.9.0
+tblib==1.7.0
+tenacity==8.2.2
+tensor-parallel==2.0.0
+termcolor==2.2.0
+text2art==0.2.0
+threadpoolctl==3.2.0
+tiktoken==0.6.0
+time-machine==2.14.1
+timm==0.9.16
+tokenizers==0.19.1
+tokenmonster==1.1.12
+toml==0.9.6
+tomli==2.0.1
+tomlkit==0.12.0
+toolz==0.12.1
+torch==2.2.0
+torchdata==0.6.1
+torchdiffeq==0.2.3
+TorchFix==0.4.0
+torchtext==0.15.2
+torchvision==0.17.0
+tqdm==4.66.2
+transformers==4.44.2
+trl==0.9.6
+typer==0.12.5
+types-certifi==2021.10.8.3
+types-requests==2.31.0.20240125
+types-setuptools==69.0.0.20240125
+types-toml==0.10.8.7
+typing==3.7.4.3
+typing-inspect==0.8.0
+typing_extensions==4.9.0
+tyro==0.5.18
+tzdata==2023.3
+unique-names-generator==1.0.2
+urllib3==2.2.2
+uvicorn==0.22.0
+vector_quantize_pytorch==1.14.1
+virtualenv==20.23.0
+voyager==2.0.2
+wandb==0.16.2
+watchfiles==0.21.0
+wavedrom==2.0.3.post3
+wcwidth==0.2.6
+websocket-client==1.7.0
+websockets==12.0
+Werkzeug==3.0.1
+wonderwords==2.2.0
+xxhash==3.2.0
+yarl==1.8.2
+zetascale==2.2.7
+zipp==3.15.0

scripts/chat_datasets.py

@@ -0,0 +1,60 @@
+"""
+helper script to parse chat datasets into a usable yaml
+"""
+import click
+import yaml
+from datasets import load_dataset
+
+
+@click.command()
+@click.argument("dataset", type=str)
+@click.option("--split", type=str, default="train")
+def parse_dataset(dataset=None, split="train"):
+    ds_cfg = {}
+    ds_cfg["path"] = dataset
+    ds_cfg["split"] = split
+    ds_cfg["type"] = "chat_template"
+    ds_cfg["chat_template"] = "<<<Replace based on your model>>>"
+
+    dataset = load_dataset(dataset, split=split)
+    features = dataset.features
+    feature_keys = features.keys()
+    field_messages = None
+    for key in ["conversation", "conversations", "messages"]:
+        if key in feature_keys:
+            field_messages = key
+            break
+    if not field_messages:
+        raise ValueError(
+            f'No conversation field found in dataset: {", ".join(feature_keys)}'
+        )
+    ds_cfg["field_messages"] = field_messages
+
+    message_fields = features["conversations"][0].keys()
+    message_field_role = None
+    for key in ["from", "role"]:
+        if key in message_fields:
+            message_field_role = key
+            break
+    if not message_field_role:
+        raise ValueError(
+            f'No role field found in messages: {", ".join(message_fields)}'
+        )
+    ds_cfg["message_field_role"] = message_field_role
+
+    message_field_content = None
+    for key in ["content", "text", "value"]:
+        if key in message_fields:
+            message_field_content = key
+            break
+    if not message_field_content:
+        raise ValueError(
+            f'No content field found in messages: {", ".join(message_fields)}'
+        )
+    ds_cfg["message_field_content"] = message_field_content
+
+    print(yaml.dump({"datasets": [ds_cfg]}))
+
+
+if __name__ == "__main__":
+    parse_dataset()

setup.py

@@ -30,6 +30,9 @@ def parse_requirements():
 
     try:
         xformers_version = [req for req in _install_requires if "xformers" in req][0]
+        torchao_version = [req for req in _install_requires if "torchao" in req][0]
+        autoawq_version = [req for req in _install_requires if "autoawq" in req][0]
+
         if "Darwin" in platform.system():
             # don't install xformers on MacOS
             _install_requires.pop(_install_requires.index(xformers_version))
@@ -49,20 +52,35 @@ def parse_requirements():
             else:
                 raise ValueError("Invalid version format")
 
-            if (major, minor) >= (2, 3):
+            if (major, minor) >= (2, 5):
+                _install_requires.pop(_install_requires.index(xformers_version))
+                _install_requires.pop(_install_requires.index(autoawq_version))
+            elif (major, minor) >= (2, 4):
+                if patch == 0:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers>=0.0.27")
+                else:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers==0.0.28.post1")
+            elif (major, minor) >= (2, 3):
+                _install_requires.pop(_install_requires.index(torchao_version))
                 if patch == 0:
                     _install_requires.pop(_install_requires.index(xformers_version))
                     _install_requires.append("xformers>=0.0.26.post1")
+                else:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers>=0.0.27")
             elif (major, minor) >= (2, 2):
+                _install_requires.pop(_install_requires.index(torchao_version))
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append("xformers>=0.0.25.post1")
             else:
+                _install_requires.pop(_install_requires.index(torchao_version))
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append("xformers>=0.0.23.post1")
 
     except PackageNotFoundError:
         pass
-
     return _install_requires, _dependency_links
 
 
@@ -71,7 +89,7 @@ install_requires, dependency_links = parse_requirements()
 
 setup(
     name="axolotl",
-    version="0.4.1",
+    version="0.5.0",
     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"},
@@ -82,15 +100,13 @@ 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",
         ],
         "mamba-ssm": [
             "mamba-ssm==1.2.0.post1",
+            "causal_conv1d",
         ],
         "auto-gptq": [
             "auto-gptq==0.5.1",

src/axolotl/cli/__init__.py

@@ -30,6 +30,8 @@ from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
 from axolotl.integrations.base import PluginManager
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
+from axolotl.utils.chat_templates import get_chat_template
+from axolotl.utils.comet_ import setup_comet_env_vars
 from axolotl.utils.config import (
     normalize_cfg_datasets,
     normalize_config,
@@ -39,7 +41,7 @@ from axolotl.utils.data import load_prepare_dpo_datasets, prepare_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
-from axolotl.utils.models import load_tokenizer
+from axolotl.utils.models import load_processor, load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
 from axolotl.utils.trainer import prepare_opinionated_env, prepare_optim_env
 from axolotl.utils.wandb_ import setup_wandb_env_vars
@@ -53,8 +55,22 @@ LOG = logging.getLogger("axolotl.scripts")
 
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
+AXOLOTL_LOGO = """
+     #@@ #@@      @@# @@#
+    @@  @@          @@  @@           =@@#                               @@                 #@    =@@#.
+    @@    #@@@@@@@@@    @@           #@#@=                              @@                 #@     .=@@
+      #@@@@@@@@@@@@@@@@@            =@# @#     ##=     ##    =####=+    @@      =#####+  =#@@###.   @@
+    @@@@@@@@@@/  +@@/  +@@          #@  =@=     #@=   @@   =@#+  +#@#   @@    =@#+  +#@#   #@.      @@
+    @@@@@@@@@@  ##@@  ##@@         =@#   @#      =@# @#    @@      @@   @@    @@      #@   #@       @@
+     @@@@@@@@@@@@@@@@@@@@          #@=+++#@=      =@@#     @@      @@   @@    @@      #@   #@       @@
+                                  =@#=====@@     =@# @#    @@      @@   @@    @@      #@   #@       @@
+    @@@@@@@@@@@@@@@@  @@@@        #@      #@=   #@=  +@@   #@#    =@#   @@.   =@#    =@#   #@.      @@
+                                 =@#       @#  #@=     #@   =#@@@@#=    +#@@=  +#@@@@#=    .##@@+   @@
+    @@@@  @@@@@@@@@@@@@@@@
+"""
 
-def print_axolotl_text_art(suffix=None):
+
+def print_legacy_axolotl_text_art(suffix=None):
     font = "nancyj"
     ascii_text = "  axolotl"
     if suffix:
@@ -67,6 +83,13 @@ def print_axolotl_text_art(suffix=None):
     print_dep_versions()
 
 
+def print_axolotl_text_art(
+    **kwargs,  # pylint: disable=unused-argument
+):
+    if is_main_process():
+        print(AXOLOTL_LOGO)
+
+
 def print_dep_versions():
     packages = ["accelerate", "peft", "transformers", "trl", "torch", "bitsandbytes"]
     max_len = max(len(pkg) for pkg in packages)
@@ -234,7 +257,8 @@ def do_inference_gradio(
 
     model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
     prompter = cli_args.prompter
-    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+    # default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+    default_tokens: Dict[str, str] = {}
 
     for token, symbol in default_tokens.items():
         # If the token isn't already specified in the config, add it
@@ -242,10 +266,13 @@ def do_inference_gradio(
             tokenizer.add_special_tokens({token: symbol})
 
     prompter_module = None
+    chat_template_str = None
     if prompter:
         prompter_module = getattr(
             importlib.import_module("axolotl.prompters"), prompter
         )
+    elif cfg.chat_template:
+        chat_template_str = get_chat_template(cfg.chat_template, tokenizer=tokenizer)
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -259,7 +286,24 @@ def do_inference_gradio(
             )
         else:
             prompt = instruction.strip()
-        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+
+        if chat_template_str:
+            batch = tokenizer.apply_chat_template(
+                [
+                    {
+                        "role": "user",
+                        "content": prompt,
+                    }
+                ],
+                return_tensors="pt",
+                add_special_tokens=True,
+                add_generation_prompt=True,
+                chat_template=chat_template_str,
+                tokenize=True,
+                return_dict=True,
+            )
+        else:
+            batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
         model.eval()
         with torch.no_grad():
@@ -282,6 +326,7 @@ def do_inference_gradio(
             streamer = TextIteratorStreamer(tokenizer)
             generation_kwargs = {
                 "inputs": batch["input_ids"].to(cfg.device),
+                "attention_mask": batch["attention_mask"].to(cfg.device),
                 "generation_config": generation_config,
                 "streamer": streamer,
             }
@@ -398,6 +443,8 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
 
     setup_mlflow_env_vars(cfg)
 
+    setup_comet_env_vars(cfg)
+
     return cfg
 
 
@@ -407,12 +454,20 @@ def load_datasets(
     cli_args: TrainerCliArgs,
 ) -> TrainDatasetMeta:
     tokenizer = load_tokenizer(cfg)
+    processor = load_processor(cfg, tokenizer=tokenizer) if cfg.processor_type else None
 
     train_dataset, eval_dataset, total_num_steps, prompters = prepare_dataset(
-        cfg, tokenizer
+        cfg,
+        tokenizer,
+        processor=processor,
     )
 
-    if cli_args.debug or cfg.debug:
+    if (
+        cli_args.debug
+        or cfg.debug
+        or cli_args.debug_text_only
+        or int(cli_args.debug_num_examples) > 0
+    ):
         LOG.info("check_dataset_labels...")
         check_dataset_labels(
             train_dataset.select(

src/axolotl/cli/preprocess.py

@@ -23,10 +23,7 @@ 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")
 
@@ -43,23 +40,6 @@ 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
@@ -70,10 +50,11 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         LOG.warning(msg)
         parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
 
-    if parsed_cfg.rl:  # and parsed_cfg.rl != "orpo":
-        load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
-    else:
-        load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    with disable_datasets_caching():
+        if parsed_cfg.rl:  # and parsed_cfg.rl != "orpo":
+            load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+        else:
+            load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
 
     if parsed_cli_args.download:
         model_name = parsed_cfg.base_model

src/axolotl/cli/train.py

@@ -3,13 +3,11 @@ CLI to run training on a model
 """
 import logging
 from pathlib import Path
-from typing import Tuple, Union
+from typing import Union
 
 import fire
 from dotenv import load_dotenv
 from transformers.hf_argparser import HfArgumentParser
-from transformers.modeling_utils import PreTrainedModel
-from transformers.tokenization_utils import PreTrainedTokenizer
 
 from axolotl.cli import (
     check_accelerate_default_config,
@@ -20,10 +18,7 @@ from axolotl.cli import (
     print_axolotl_text_art,
 )
 from axolotl.common.cli import TrainerCliArgs
-from axolotl.prompt_strategies.sharegpt import (
-    register_chatml_template,
-    register_llama3_template,
-)
+from axolotl.integrations.base import PluginManager
 from axolotl.train import train
 
 LOG = logging.getLogger("axolotl.cli.train")
@@ -39,32 +34,23 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
     return do_train(parsed_cfg, parsed_cli_args)
 
 
-def do_train(cfg, cli_args) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
+def do_train(cfg, cli_args) -> None:
     print_axolotl_text_art()
     check_accelerate_default_config()
     check_user_token()
-    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)
     else:
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-    return train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+    model, tokenizer = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+    plugin_manager = PluginManager.get_instance()
+
+    del model
+    del tokenizer
+
+    plugin_manager.post_train_unload(cfg)
 
 
 if __name__ == "__main__":

src/axolotl/common/cli.py

@@ -23,7 +23,7 @@ class TrainerCliArgs:
 
     debug: bool = field(default=False)
     debug_text_only: bool = field(default=False)
-    debug_num_examples: int = field(default=5)
+    debug_num_examples: int = field(default=0)
     inference: bool = field(default=False)
     merge_lora: bool = field(default=False)
     prompter: Optional[str] = field(default=None)

src/axolotl/core/chat/format/chatml.py

@@ -0,0 +1,34 @@
+"""
+ChatML transformation functions for MessageContents
+"""
+from typing import Optional
+
+from ..messages import MessageContents, Messages
+from .shared import wrap_tools
+
+
+def format_message(
+    message: Messages,
+    message_index: Optional[int] = None,  # pylint: disable=unused-argument
+) -> Messages:
+    if message.is_chat_formatted:
+        return message
+
+    # prepend the role prefix within a MessageContents to message.content
+    message.content.insert(
+        0,
+        MessageContents(
+            type="text",
+            value=f"<|im_start|>{message.role}\n",
+            weight=0,
+        ),
+    )
+    message.content.append(
+        MessageContents(type="text", value="<|im_end|>", weight=message.weight)
+    )
+    message.content.append(MessageContents(type="text", value="\n", weight=0))
+
+    message = wrap_tools(message)
+
+    message.is_chat_formatted = True
+    return message

src/axolotl/core/chat/format/llama3x.py

@@ -0,0 +1,45 @@
+"""
+Llama 3.x chat formatting functions for MessageContents
+"""
+from typing import Optional
+
+from ..messages import MessageContents, Messages
+from .shared import wrap_tools
+
+
+def format_message(message: Messages, message_index: Optional[int] = None) -> Messages:
+    if message.is_chat_formatted:
+        return message
+
+    message_role = message.role
+    if message.role == "tool":
+        message_role = "ipython"
+
+    # prepend the role prefix within a MessageContents to message.content
+    message.content.insert(
+        0,
+        MessageContents(
+            type="text",
+            value=f"<|start_header_id|>{message_role}<|end_header_id|>\n\n",
+            weight=0,
+        ),
+    )
+
+    message.content.append(
+        MessageContents(type="text", value="<|eot_id|>", weight=message.weight)
+    )
+
+    message = wrap_tools(message)
+
+    if message_index == 0:
+        message.content.insert(
+            0,
+            MessageContents(
+                type="text",
+                value="<|begin_of_text|>",
+                weight=0,
+            ),
+        )
+
+    message.is_chat_formatted = True
+    return message

src/axolotl/core/chat/format/shared.py

@@ -0,0 +1,47 @@
+"""
+shared functions for format transforms
+"""
+from axolotl.core.chat.messages import MessageContents, Messages
+
+
+def wrap_tools(message: Messages):
+    # loop over message.content by index to find tool calls, we need to wrap each with tags,
+    # so be wary of indexing issues when changing the list while iterating.
+    # iterate over the range in reverse order to avoid index shifting
+    for i in range(len(message.content) - 1, -1, -1):
+        if message.content[i].type == "tool_call":
+            # append a </tool_call> MessageContents text tag after
+            message.content.insert(
+                i + 1,
+                MessageContents(
+                    type="text", value="</tool_call>\n", weight=message.weight
+                ),
+            )
+            # make sure the actual tool call content ends with a newline
+            message.content[i].has_newline = True
+            # prepend a <tool_call> MessageContents text tag before
+            message.content.insert(
+                i,
+                MessageContents(
+                    type="text", value="<tool_call>\n", weight=message.weight
+                ),
+            )
+        elif message.content[i].type == "tool_response":
+            # append a </tool_call> MessageContents text tag after
+            message.content.insert(
+                i + 1,
+                MessageContents(
+                    type="text", value="</tool_response>\n", weight=message.weight
+                ),
+            )
+            # make sure the actual tool response content ends with a newline
+            message.content[i].has_newline = True
+            # prepend a <tool_call> MessageContents text tag before
+            message.content.insert(
+                i,
+                MessageContents(
+                    type="text", value="<tool_response>\n", weight=message.weight
+                ),
+            )
+
+    return message

src/axolotl/core/chat/messages.py

@@ -0,0 +1,230 @@
+"""
+internal message representations of chat messages
+"""
+import json
+from enum import Enum
+from typing import Any, Callable, List, Optional, Union
+
+from pydantic import BaseModel
+from transformers import PreTrainedTokenizer
+
+
+class MessageRoles(str, Enum):
+    """
+    Message roles for the system, user, assistant, and tools
+    """
+
+    system = "system"  # pylint: disable=invalid-name
+    user = "user"  # pylint: disable=invalid-name
+    assistant = "assistant"  # pylint: disable=invalid-name
+    tool = "tool"  # pylint: disable=invalid-name
+    ipython = (  # pylint: disable=invalid-name
+        # for responses from builtin tools
+        "ipython"
+    )
+
+
+class MessageContentTypes(str, Enum):
+    """
+    Message content types for text, image, audio, tool calls, and tool responses
+    """
+
+    special_token = "special_token"  # pylint: disable=invalid-name  # nosec B105
+    text = "text"  # pylint: disable=invalid-name
+    image = "image"  # pylint: disable=invalid-name
+    audio = "audio"  # pylint: disable=invalid-name
+    tool_call = "tool_call"  # pylint: disable=invalid-name  # to differentiate regular responses from tool calls from the assistant
+    tool_response = "tool_response"  # pylint: disable=invalid-name
+
+
+class SpecialToken(str, Enum):
+    """
+    Special tokens for beginning of string and end of string
+    """
+
+    bos_token = "bos_token"  # pylint: disable=invalid-name  # nosec B105
+    eos_token = "eos_token"  # pylint: disable=invalid-name  # nosec B105
+
+
+class ToolCallFunction(BaseModel):
+    """
+    Tool call function with name and arguments
+    """
+
+    name: str
+    arguments: dict[str, str]
+
+
+class Tool(BaseModel):
+    """
+    Tool with description, function, and parameters
+    """
+
+    description: str
+    function: ToolCallFunction
+    parameters: dict[str, str]  # .properties
+
+
+class ToolCallContents(BaseModel):
+    """
+    Tool call contents with name, arguments, and optional id
+    """
+
+    name: str
+    arguments: dict[str, Union[str, int]]
+    id: Optional[str] = None  # pylint: disable=invalid-name
+
+    def __str__(self) -> str:
+        data = {"name": self.name, "arguments": self.arguments}
+        if self.id is not None:
+            data["id"] = self.id
+        return json.dumps(data)
+
+
+class ToolResponseContents(BaseModel):
+    """
+    Tool response contents with name, content, and optional id
+    """
+
+    name: str
+    content: Union[str, dict[str, Union[str, int, float]]]
+    id: Optional[str] = None  # pylint: disable=invalid-name
+
+    def __str__(self) -> str:
+        data = {"name": self.name, "content": self.content}
+        if self.id is not None:
+            data["id"] = self.id
+        return json.dumps(data)
+
+
+class MessageContents(BaseModel):
+    """
+    Message contents with type, value, metadata, weight, newline, and end of contents
+    """
+
+    type: Union[str, MessageContentTypes]
+    value: Union[str, ToolCallContents, ToolResponseContents, SpecialToken]
+    meta: Optional[dict[str, Any]] = None  # support additional arbitrary metadata
+    weight: Optional[Union[int, float]] = None
+    has_newline: bool = False
+    eoc: bool = False  # end of contents
+
+    def __str__(self) -> str:
+        str_val = str(self.value)
+        if self.has_newline and not str_val.endswith("\n"):
+            str_val += "\n"
+        return str_val
+
+
+class Messages(BaseModel):
+    """
+    Messages with role, content, metadata, weight, and chat formatting
+    """
+
+    role: Union[MessageRoles, str]  # allows for arbitrary roles
+    content: List["MessageContents"]
+    meta: Optional[dict[str, Any]] = None  # support additional arbitrary metadata
+    weight: Optional[Union[int, float]] = None
+    is_chat_formatted: bool = False
+
+    def __str__(self) -> str:
+        return "".join(str(c) for c in self.content)
+
+    def tokenized(
+        self, tokenizer: PreTrainedTokenizer, ignore_index=-100
+    ) -> dict[str, List[int]]:
+        # iterate over the contents, tokenizing the concatenated string values up to the current MessageContents
+        # returns a dictionary mapping w input_ids, attention_mask, and labels
+        input_ids: List[int] = []
+        labels: List[int] = []
+        pending_input_ids: List[int] = []
+        pending_weight = self.weight
+        running_content = ""
+        for _, msg_content in enumerate(self.content):
+            # TODO also handle non-text content types
+            if msg_content.type in [
+                MessageContentTypes.text.value,
+                MessageContentTypes.tool_call.value,
+                MessageContentTypes.tool_response.value,
+            ]:
+                running_content += str(msg_content)
+                tok_results = tokenizer(running_content, add_special_tokens=False)
+                tok_input_ids = tok_results["input_ids"]
+                if pending_input_ids:
+                    new_pending_inputs = tok_input_ids[
+                        len(input_ids) : len(input_ids) + len(pending_input_ids)
+                    ]
+                    if new_pending_inputs != pending_input_ids:
+                        # logging.warning("tokenization mismatch from concatenation.")
+                        pending_input_ids = new_pending_inputs
+                    input_ids.extend(pending_input_ids)
+                    if pending_weight:
+                        labels.extend(pending_input_ids)
+                    else:
+                        labels.extend([ignore_index] * len(pending_input_ids))
+                pending_input_ids = tok_results["input_ids"][len(input_ids) :]
+                pending_weight = self.weight and msg_content.weight not in [0, 0.0]
+        input_ids.extend(pending_input_ids)
+        if pending_weight:
+            labels.extend(pending_input_ids)
+        else:
+            labels.extend([ignore_index] * len(pending_input_ids))
+        attention_mask = [1] * len(input_ids)
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "labels": labels,
+        }
+
+
+class Chats(BaseModel):
+    """
+    top level data structure for chat conversations
+    """
+
+    conversation: List[Messages]
+
+    def __str__(self) -> str:
+        return "".join(str(c) for c in self.conversation)
+
+    def tokenized(
+        self, tokenizer: Callable[[str], dict[str, List[int]]], ignore_index=-100
+    ) -> dict[str, List[int]]:
+        input_ids = []
+        attention_mask = []
+        labels = []
+        for msg in self.conversation:
+            msg_results = msg.tokenized(tokenizer, ignore_index)
+            input_ids.extend(msg_results["input_ids"])
+            attention_mask.extend(msg_results["attention_mask"])
+            labels.extend(msg_results["labels"])
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "labels": labels,
+        }
+
+
+class ChatFormattedChats(Chats):
+    """
+    Chat formatted chats with formatter and optional train on inputs
+    """
+
+    formatter: Callable  # [[Union[dict, Chats]], Chats]
+    train_on_inputs: bool = False
+
+    def model_post_init(self, __context):
+        for i, msg in enumerate(self.conversation):
+            self.conversation[i] = self.formatter(msg, message_index=i)
+            if self.train_on_inputs:
+                self.conversation[i].weight = 1
+
+
+class PreferenceChats(BaseModel):
+    """
+    representation for preference data for chat
+    """
+
+    prompt: List[Messages]
+    chosen: Messages
+    rejected: Messages

src/axolotl/core/datasets/chat.py

@@ -0,0 +1,55 @@
+"""
+chat dataset module
+"""
+import os
+from typing import Callable, Optional, Union
+
+from datasets import Dataset
+from transformers import PreTrainedTokenizer
+
+from axolotl.core.chat.messages import ChatFormattedChats
+
+
+class TokenizedChatDataset(Dataset):
+    """
+    Tokenized chat dataset
+    """
+
+    def __init__(
+        self,
+        data: Dataset,
+        model_transform: Union[PreTrainedTokenizer, Callable],
+        *args,
+        message_transform: Optional[Callable] = None,
+        formatter=None,
+        process_count: Optional[int] = None,
+        keep_in_memory: Optional[bool] = False,
+        **kwargs,
+    ):
+        def map_fn(ex):
+            if message_transform is not None:
+                ex = message_transform(ex)
+            if formatter is not None:
+                ex = ChatFormattedChats(
+                    formatter=formatter,
+                    **ex,
+                )
+            else:
+                ex = ChatFormattedChats(
+                    **ex,
+                )
+            return ex.tokenized(model_transform)
+
+        process_or_cpu_count: int = (
+            process_count or os.cpu_count()  # type: ignore[assignment]
+        )
+        num_proc = min(64, process_or_cpu_count)
+        features = data.features.keys()
+        tokenized_data = data.map(
+            map_fn,
+            num_proc=num_proc,
+            keep_in_memory=keep_in_memory,
+            remove_columns=features,
+            desc="Tokenizing Chats",
+        )
+        super().__init__(tokenized_data.data, *args, **kwargs)

src/axolotl/core/datasets/transforms/chat_builder.py

@@ -0,0 +1,150 @@
+"""
+This module contains a function that builds a transform that takes a row from the dataset and converts it to a Chat.
+"""
+from typing import Any, Mapping, Union
+
+
+def chat_message_transform_builder(  # pylint: disable=dangerous-default-value
+    train_on_inputs=False,
+    conversations_field: str = "conversations",
+    message_field_role: Union[str, list[str]] = ["role", "from"],  # commonly "role"
+    message_field_content: Union[str, list[str]] = [
+        "value",
+        "text",
+        "content",
+    ],  # commonly "content"
+    message_field_training: Union[str, list[str]] = [
+        "train",
+        "weight",
+    ],  # commonly "weight"
+):
+    """Builds a transform that takes a row from the dataset and converts it to a Chat
+
+    Args:
+        train_on_inputs (bool, optional):
+            If True, the transform will train on the inputs. If False, the transform will train on the targets.
+            Defaults to False.
+        conversations_field (str, optional):
+            The field name of the conversations. Defaults to "conversations".
+        message_field_role (str | list[str], optional):
+            The field name of the role. Defaults to "role".
+        message_field_content (str | list[str], optional):
+            The field name of the message content. Defaults to "content".
+        message_field_training (str | list[str], optional):
+            The field name of the train/weight. Defaults to "weight".
+
+    Returns:
+        Callable:
+            A function that takes a list of conversations and returns a list of messages.
+    """
+
+    message_field_role = (
+        [message_field_role]
+        if isinstance(message_field_role, str)
+        else message_field_role
+    )
+    message_field_content = (
+        [message_field_content]
+        if isinstance(message_field_content, str)
+        else message_field_content
+    )
+    message_weight_fields = (
+        [message_field_training]
+        if isinstance(message_field_training, str)
+        else message_field_training
+    )
+
+    role_value_mappings = {
+        "system": "system",
+        "user": "user",
+        "human": "user",
+        "assistant": "assistant",
+        "gpt": "assistant",
+        "tool": "tool",
+        "ipython": "ipython",
+    }
+    if train_on_inputs:
+        role_default_weights_mappings = {
+            "system": 1,
+            "user": 1,
+            "assistant": 1,
+            "tool": 1,
+            "ipython": 1,
+        }
+    else:
+        role_default_weights_mappings = {
+            "system": 0,
+            "user": 0,
+            "assistant": 1,
+            "tool": 0,
+            "ipython": 0,
+        }
+
+    def transform_builder(sample: Mapping[str, Any]):
+        if conversations_field not in sample:
+            raise ValueError(f"Field '{conversations_field}' not found in sample.")
+        # if none of the role fields are in the message, raise an error
+        if not any(
+            role in sample[conversations_field][0] for role in message_field_role
+        ):
+            raise ValueError("No role field found in message.")
+        role_field = next(
+            role
+            for role in message_field_role
+            if role in sample[conversations_field][0]
+        )
+        if not any(
+            field in sample[conversations_field][0] for field in message_field_content
+        ):
+            raise ValueError("No message_content field found in message.")
+        message_content_field = next(
+            field
+            for field in message_field_content
+            if field in sample[conversations_field][0]
+        )
+        if not any(
+            field in sample[conversations_field][0] for field in message_field_training
+        ):
+            message_weight_field = None
+        else:
+            message_weight_field = next(
+                field
+                for field in message_weight_fields
+                if field in sample[conversations_field][0]
+            )
+
+        messages = []
+        for message in sample[conversations_field]:
+            role = role_value_mappings[message[role_field]]
+            weight = (
+                int(message[message_weight_field])
+                if message_weight_field
+                else role_default_weights_mappings[role]
+            )
+
+            # TODO if "tool_calls" in message[message_content_field]: then convert tool call to ToolCallContents
+            if isinstance(message[message_content_field], str):
+                messages.append(
+                    {
+                        "role": role,
+                        "content": [
+                            {
+                                "type": "text",
+                                "value": message[message_content_field],
+                            }
+                        ],
+                        "weight": weight,
+                    }
+                )
+            else:
+                messages.append(
+                    {
+                        "role": role,
+                        "content": message[message_content_field],
+                        "weight": weight,
+                    }
+                )
+
+        return {"conversation": messages}
+
+    return transform_builder

src/axolotl/core/trainer_builder.py

@@ -7,6 +7,7 @@ import abc
 import gc
 import importlib
 import importlib.util
+import inspect
 import logging
 import math
 import os
@@ -16,17 +17,17 @@ from collections import defaultdict
 from dataclasses import dataclass, field
 from functools import wraps
 from pathlib import Path
-from typing import Any, Dict, List, Literal, Optional, Tuple, Type, Union
+from typing import Any, Dict, List, Literal, Optional, Type, Union
 
 import torch
 import transformers
 from datasets import Dataset
+from peft.optimizers import create_loraplus_optimizer
 from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import (
     EarlyStoppingCallback,
-    PreTrainedModel,
     Trainer,
     TrainerCallback,
     TrainingArguments,
@@ -42,13 +43,15 @@ from trl import (
     KTOTrainer,
     ORPOConfig,
     ORPOTrainer,
+    RewardConfig,
+    RewardTrainer,
 )
-from trl.trainer.utils import pad_to_length
+from trl.trainer.utils import RewardDataCollatorWithPadding, pad_to_length
 
-from axolotl.loraplus import create_loraplus_optimizer
+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_mlflow_available
+from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
     GPUStatsCallback,
@@ -61,12 +64,14 @@ from axolotl.utils.callbacks import (
     log_prediction_callback_factory,
 )
 from axolotl.utils.callbacks.lisa import lisa_callback_factory
+from axolotl.utils.chat_templates import get_chat_template
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
     MambaDataCollator,
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
+from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
 from axolotl.utils.models import ensure_dtype
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 from axolotl.utils.schedulers import (
@@ -250,11 +255,10 @@ class AxolotlTrainingMixins:
             "help": "workaround to pass an alternate lr scheduler to the HF trainer"
         },
     )
-    optim_shampoo_grafting_config_type: Optional[
-        Literal["adam", "sgd", "adagrad"]
-    ] = None
-    optim_shampoo_grafting_config_kwargs: Optional[Dict[str, Any]] = None
-    optim_shampoo_betas: Optional[Tuple[float, float]] = None
+    chat_template: Optional[str] = field(
+        default=None,
+        metadata={"help": "Chat template converting chat messages to text"},
+    )
 
 
 @dataclass
@@ -300,6 +304,13 @@ class AxolotlCPOConfig(AxolotlTrainingMixins, CPOConfig):
     )
 
 
+@dataclass
+class AxolotlRewardConfig(AxolotlTrainingMixins, RewardConfig):
+    """
+    Reward config for Reward training
+    """
+
+
 class SchedulerMixin(Trainer):
     """
     Mixin class for scheduler setup in CausalTrainer.
@@ -397,12 +408,10 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
     def __init__(
         self,
         *_args,
-        num_epochs=1,
         bench_data_collator=None,
         eval_data_collator=None,
         **kwargs,
     ):
-        self.num_epochs = num_epochs
         self.bench_data_collator = bench_data_collator
         self.eval_data_collator = eval_data_collator
         super().__init__(*_args, **kwargs)
@@ -427,13 +436,7 @@ 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",
-                "shampoo",
-            ]
+            not in ["optimi_adamw", "ao_adamw_8bit", "ao_adamw_4bit", "ao_adamw_fp8"]
         ):
             return super().create_optimizer()
 
@@ -467,110 +470,14 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
             if self.args.loraplus_lr_ratio is not None:
                 loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
                 loraplus_lr_embedding = getattr(
-                    self.args, "loraplus_lr_embedding", None
+                    self.args, "loraplus_lr_embedding", 1e-6
                 )
                 self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
                     opt_model,
                     optimizer_cls,
-                    optimizer_kwargs,
-                    loraplus_lr_ratio,
-                    loraplus_lr_embedding,
-                )
-            elif self.args.alternate_optimizer == "shampoo":
-                from distributed_shampoo.distributed_shampoo import DistributedShampoo
-                from distributed_shampoo.shampoo_types import (
-                    AdaGradGraftingConfig,
-                    AdamGraftingConfig,
-                    CommunicationDType,
-                    DDPShampooConfig,
-                    FSDPShampooConfig,
-                    PrecisionConfig,
-                    SGDGraftingConfig,
-                )
-                from distributed_shampoo.utils.shampoo_fsdp_utils import (
-                    compile_fsdp_parameter_metadata,
-                )
-
-                # parse args.optim_args
-                optim_args = {}
-                if self.args.optim_args:
-                    for mapping in self.args.optim_args.replace(" ", "").split(","):
-                        key, value = mapping.split("=")
-                        optim_args[key] = value
-
-                optim_args["betas"] = self.args.optim_shampoo_betas
-                if "max_preconditioner_dim" in optim_args:
-                    optim_args["max_preconditioner_dim"] = int(
-                        optim_args["max_preconditioner_dim"]
-                    )
-                if "precondition_frequency" in optim_args:
-                    optim_args["precondition_frequency"] = int(
-                        optim_args["precondition_frequency"]
-                    )
-                if "use_decoupled_weight_decay" in optim_args:
-                    optim_args["use_decoupled_weight_decay"] = bool(
-                        optim_args["use_decoupled_weight_decay"]
-                    )
-                if isinstance(optim_args["epsilon"], str):
-                    optim_args["epsilon"] = float(optim_args["epsilon"])
-                optim_args["lr"] = self.args.learning_rate
-                optim_args["weight_decay"] = self.args.weight_decay
-
-                if "epsilon" in self.args.optim_shampoo_grafting_config_kwargs:
-                    if isinstance(
-                        self.args.optim_shampoo_grafting_config_kwargs["epsilon"], str
-                    ):
-                        self.args.optim_shampoo_grafting_config_kwargs[
-                            "epsilon"
-                        ] = float(
-                            self.args.optim_shampoo_grafting_config_kwargs["epsilon"]
-                        )
-                if self.args.optim_shampoo_grafting_config_type == "adam":
-                    grafting_config = AdamGraftingConfig(
-                        **self.args.optim_shampoo_grafting_config_kwargs
-                    )
-                elif self.args.optim_shampoo_grafting_config_type == "sgd":
-                    grafting_config = SGDGraftingConfig(
-                        **self.args.optim_shampoo_grafting_config_kwargs
-                    )
-                elif self.args.optim_shampoo_grafting_config_type == "adagrad":
-                    grafting_config = AdaGradGraftingConfig(
-                        **self.args.optim_shampoo_grafting_config_kwargs
-                    )
-
-                distributed_config = None
-                if self.args.world_size > 1:
-                    if self.args.fsdp and self.args.fsdp_config:
-                        distributed_config = FSDPShampooConfig(
-                            param_to_metadata=compile_fsdp_parameter_metadata(
-                                self.model_wrapped
-                            )
-                        )
-                    else:
-                        distributed_config = DDPShampooConfig(
-                            communication_dtype=CommunicationDType.BF16,
-                            num_trainers_per_group=self.args.world_size,
-                            communicate_params=False,
-                        )
-
-                precision_config = None
-                if self.args.bf16:
-                    precision_config = PrecisionConfig(
-                        computation_dtype=torch.bfloat16,
-                        factor_matrix_dtype=torch.bfloat16,
-                        inv_factor_matrix_dtype=torch.bfloat16,
-                        filtered_grad_dtype=torch.bfloat16,
-                        momentum_dtype=torch.bfloat16,
-                        grafting_state_dtype=torch.bfloat16,
-                    )
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    DistributedShampoo(
-                        optimizer_grouped_parameters,
-                        grafting_config=grafting_config,
-                        distributed_config=distributed_config,
-                        precision_config=precision_config,
-                        **optim_args,
-                    )
+                    loraplus_lr_ratio=loraplus_lr_ratio,
+                    loraplus_lr_embedding=loraplus_lr_embedding,
+                    **optimizer_kwargs,
                 )
             elif self.args.alternate_optimizer == "optimi_adamw":
                 from optimi import AdamW
@@ -760,7 +667,9 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         return DataLoader(bench_dataset, **dataloader_params)
         # return self.accelerator.prepare(DataLoader(bench_dataset, **dataloader_params))
 
-    def compute_loss(self, model, inputs, return_outputs=False):
+    def compute_loss(
+        self, model, inputs, return_outputs=False, num_items_in_batch=None
+    ):
         # use one's weighted cross entropy loss calc
         # if self.args.sample_packing:
         #     labels = inputs.pop("labels")
@@ -768,8 +677,18 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         #     loss = trainer_weighted_loss(outputs, labels, shift_labels=True)
         #     return (loss, outputs) if return_outputs else loss
         if self.args.orpo_alpha:
-            return self.orpo_compute_loss(model, inputs, return_outputs=return_outputs)
-        return super().compute_loss(model, inputs, return_outputs=return_outputs)
+            return self.orpo_compute_loss(
+                model,
+                inputs,
+                return_outputs=return_outputs,
+                num_items_in_batch=num_items_in_batch,
+            )
+        return super().compute_loss(
+            model,
+            inputs,
+            return_outputs=return_outputs,
+            num_items_in_batch=num_items_in_batch,
+        )
 
     @staticmethod
     def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
@@ -865,7 +784,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         ).squeeze(2)
         return torch.mul(per_token_logps, mask).sum(dim=1) / mask.sum(dim=1)
 
-    def orpo_compute_loss(self, model, inputs, return_outputs=False):
+    def orpo_compute_loss(
+        self,
+        model,
+        inputs,
+        return_outputs=False,
+        num_items_in_batch=None,  # pylint: disable=unused-argument
+    ):
         concat_inputs = AxolotlTrainer.orpo_concatenate_inputs(
             inputs,
             label_pad_token=-100,
@@ -971,17 +896,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         for key, value in metrics.items():
             self._stored_metrics[train_eval][key].append(value)
 
-    def _save_checkpoint(self, model, trial, metrics=None):
+    def _save_checkpoint(self, model, trial, **kwargs):
         # 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)
-        try:
-            return super()._save_checkpoint(model, trial, metrics=metrics)
-        except NotImplementedError as exc:
-            LOG.warning(f"Failed to save checkpoint: {exc}")
-            return None
+        return super()._save_checkpoint(model, trial, **kwargs)
 
 
 class AxolotlMambaTrainer(AxolotlTrainer):
@@ -996,6 +917,7 @@ class AxolotlMambaTrainer(AxolotlTrainer):
         model,
         inputs,
         return_outputs=False,  # pylint: disable=unused-argument
+        num_items_in_batch=None,  # pylint: disable=unused-argument
     ):
         input_ids = inputs.pop("input_ids")
         lm_logits = model(input_ids).logits
@@ -1080,9 +1002,9 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
             self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
                 opt_model,
                 optimizer_cls,
-                optimizer_kwargs,
-                loraplus_lr_ratio,
-                loraplus_lr_embedding,
+                loraplus_lr_ratio=loraplus_lr_ratio,
+                loraplus_lr_embedding=loraplus_lr_embedding,
+                **optimizer_kwargs,
             )
 
         if is_sagemaker_mp_enabled():
@@ -1103,18 +1025,32 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
         return super().push_to_hub(*args, **kwargs)
 
     def tokenize_row(
-        self, feature, model: Optional[Union[PreTrainedModel, torch.nn.Module]] = None
+        self,
+        features,
+        processing_class,
+        max_prompt_length,
+        max_completion_length,
+        add_special_tokens,
     ) -> Dict:
-        res = super().tokenize_row(feature, model=model)
-        if self.tokenizer.bos_token_id is None and res["prompt_input_ids"][0] is None:
+        res = super().tokenize_row(
+            features,
+            processing_class,
+            max_prompt_length,
+            max_completion_length,
+            add_special_tokens,
+        )
+        if processing_class.bos_token_id is None and res["prompt_input_ids"][0] is None:
             for key in res.keys():
                 res[key] = res[key][1:]
         return res
 
     def training_step(
-        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
+        self,
+        model: nn.Module,
+        inputs: Dict[str, Union[torch.Tensor, Any]],
+        num_items_in_batch=None,
     ) -> torch.Tensor:
-        loss: torch.Tensor = super().training_step(model, inputs)
+        loss: torch.Tensor = super().training_step(model, inputs, num_items_in_batch)
         gc.collect()
         torch.cuda.empty_cache()
         return loss
@@ -1144,6 +1080,14 @@ class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
     tag_names = ["axolotl", "cpo"]
 
 
+class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
+    """
+    Extend the base RewardTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "reward"]
+
+
 class TrainerBuilderBase(abc.ABC):
     """
     Base class for trainer builder
@@ -1154,10 +1098,11 @@ class TrainerBuilderBase(abc.ABC):
     _model_ref = None
     _peft_config = None
 
-    def __init__(self, cfg, model, tokenizer):
+    def __init__(self, cfg, model, tokenizer, processor=None):
         self.cfg = cfg
         self.model = model
         self.tokenizer = tokenizer
+        self.processor = processor
 
         # in case the model supports tagging, add the axolotl tag.
         # This makes sure the tag is correctly pushed even if a user calls
@@ -1203,26 +1148,49 @@ 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)
             )
         if self.cfg.use_mlflow and is_mlflow_available():
+            from transformers.integrations.integration_utils import MLflowCallback
+
             from axolotl.utils.callbacks.mlflow_ import (
                 SaveAxolotlConfigtoMlflowCallback,
             )
 
+            callbacks.extend(
+                [
+                    SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path),
+                    MLflowCallback,
+                ]
+            )
+        if self.cfg.use_comet and is_comet_available():
+            from axolotl.utils.callbacks.comet_ import SaveAxolotlConfigtoCometCallback
+
             callbacks.append(
-                SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
+                SaveAxolotlConfigtoCometCallback(self.cfg.axolotl_config_path)
             )
 
         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
@@ -1268,7 +1236,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         if self.cfg.use_wandb and self.cfg.eval_table_size > 0:
             LogPredictionCallback = log_prediction_callback_factory(
                 trainer, self.tokenizer, "wandb"
@@ -1283,6 +1251,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 trainer, self.tokenizer, "mlflow"
             )
             callbacks.append(LogPredictionCallback(self.cfg))
+        if self.cfg.use_comet and is_comet_available() and self.cfg.eval_table_size > 0:
+            LogPredictionCallback = log_prediction_callback_factory(
+                trainer, self.tokenizer, "comet_ml"
+            )
+            callbacks.append(LogPredictionCallback(self.cfg))
 
         if self.cfg.do_bench_eval:
             callbacks.append(bench_eval_callback_factory(trainer, self.tokenizer))
@@ -1307,6 +1280,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             return ReLoRATrainer
         if self.cfg.model_config_type == "mamba":
             return AxolotlMambaTrainer
+        if self.cfg.reward_model:
+            return AxolotlRewardTrainer
         return AxolotlTrainer
 
     def build(self, total_num_steps):
@@ -1528,15 +1503,22 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         report_to = []
         if self.cfg.use_wandb:
             report_to.append("wandb")
+            if self.cfg.wandb_name:
+                training_arguments_kwargs["run_name"] = self.cfg.wandb_name
         if self.cfg.use_mlflow:
             report_to.append("mlflow")
         if self.cfg.use_tensorboard:
             report_to.append("tensorboard")
+        if self.cfg.use_comet:
+            report_to.append("comet_ml")
 
         training_arguments_kwargs["report_to"] = report_to
-        training_arguments_kwargs["run_name"] = (
-            self.cfg.wandb_name if self.cfg.use_wandb else None
-        )
+        if self.cfg.use_wandb:
+            training_arguments_kwargs["run_name"] = self.cfg.wandb_name
+        elif self.cfg.use_mlflow:
+            training_arguments_kwargs["run_name"] = self.cfg.mlflow_run_name
+        else:
+            training_arguments_kwargs["run_name"] = None
         training_arguments_kwargs["optim"] = (
             self.cfg.optimizer if self.cfg.optimizer else "adamw_hf"
         )
@@ -1552,21 +1534,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs[
                 "optim_target_modules"
             ] = self.cfg.optim_target_modules
-
-        # shampoo optimizer config
-        if self.cfg.optim_shampoo_betas:
-            training_arguments_kwargs[
-                "optim_shampoo_betas"
-            ] = self.cfg.optim_shampoo_betas
-        if self.cfg.optim_shampoo_grafting_config_type:
-            training_arguments_kwargs[
-                "optim_shampoo_grafting_config_type"
-            ] = self.cfg.optim_shampoo_grafting_config_type
-            if self.cfg.optim_shampoo_grafting_config_kwargs:
-                training_arguments_kwargs[
-                    "optim_shampoo_grafting_config_kwargs"
-                ] = self.cfg.optim_shampoo_grafting_config_kwargs
-
         training_arguments_kwargs["loraplus_lr_ratio"] = self.cfg.loraplus_lr_ratio
         training_arguments_kwargs[
             "loraplus_lr_embedding"
@@ -1639,6 +1606,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         )
         training_arguments_kwargs["model_type"] = self.cfg.model_config_type
         training_arguments_kwargs["pretraining"] = bool(self.cfg.pretraining_dataset)
+        if self.cfg.chat_template:
+            training_arguments_kwargs["chat_template"] = get_chat_template(
+                self.cfg.chat_template,
+                tokenizer=self.tokenizer,
+            )
 
         if self.cfg.rl == "orpo":
             training_arguments_kwargs["orpo_alpha"] = self.cfg.orpo_alpha
@@ -1650,13 +1622,14 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         trainer_kwargs = {}
 
+        if self.cfg.reward_model:
+            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",
-            "shampoo",
         ]:
             # Set default so transformers doesn't throw
             training_arguments_kwargs["optim"] = "adamw_hf"
@@ -1695,13 +1668,22 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 "accelerator_config"
             ] = self.cfg.accelerator_config
 
-        training_args = (
-            AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
-                **training_arguments_kwargs,
-            )
+        training_args_cls = (
+            AxolotlTrainingArguments
+            if not self.cfg.reward_model
+            else AxolotlRewardConfig
+        )
+        training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
+            **training_arguments_kwargs,
         )
         training_args = self.hook_post_create_training_args(training_args)
 
+        # unset run_name so wandb sets up experiment names
+        if self.cfg.use_wandb and training_args.run_name == training_args.output_dir:
+            training_args.run_name = (  # pylint: disable=attribute-defined-outside-init
+                None
+            )
+
         data_collator_kwargs = {
             "padding": True,  # True/"longest" is the default
         }
@@ -1714,27 +1696,37 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
             data_collator_kwargs["pad_to_multiple_of"] = 64
 
+        if self.cfg.reward_model:
+            data_collator_kwargs["max_length"] = self.cfg.sequence_len
+
         trainer_cls = self._get_trainer_cls()
         trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(
             trainer_kwargs, trainer_cls
         )
+        if eval_data_collator := self.build_collator(
+            training_args, is_eval=True, **data_collator_kwargs
+        ):
+            if not self.cfg.reward_model:
+                trainer_kwargs["eval_data_collator"] = eval_data_collator
+        if not self.cfg.reward_model:
+            trainer_kwargs["bench_data_collator"] = transformers.DataCollatorForSeq2Seq(
+                self.tokenizer,
+                return_tensors="pt",
+                **data_collator_kwargs,
+            )
+        sig = inspect.signature(trainer_cls)
+        if "processing_class" in sig.parameters.keys():
+            trainer_kwargs["processing_class"] = self.tokenizer
+        else:
+            trainer_kwargs["tokenizer"] = self.tokenizer
+
         trainer = trainer_cls(
             model=self.model,
             train_dataset=self.train_dataset,
             eval_dataset=self.eval_dataset,
             args=training_args,
-            tokenizer=self.tokenizer,
             data_collator=self.build_collator(training_args, **data_collator_kwargs),
-            eval_data_collator=self.build_collator(
-                training_args, is_eval=True, **data_collator_kwargs
-            ),
-            bench_data_collator=transformers.DataCollatorForSeq2Seq(
-                self.tokenizer,
-                return_tensors="pt",
-                **data_collator_kwargs,
-            ),
             callbacks=self.get_callbacks(),
-            num_epochs=self.cfg.num_epochs,
             **trainer_kwargs,
         )
         trainer = self.hook_post_create_trainer(trainer)
@@ -1768,9 +1760,14 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 V2BatchSamplerDataCollatorForSeq2Seq,
                 BatchSamplerDataCollatorForSeq2Seq,
                 DataCollatorForSeq2Seq,
+                RewardDataCollatorWithPadding,
             ]
         ]
-        if use_batch_sampler_collator:
+        if self.cfg.reward_model:
+            collator = RewardDataCollatorWithPadding
+            if "max_length" in kwargs:
+                kwargs.pop("max_length")
+        elif use_batch_sampler_collator:
             if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES:
                 collator = V2BatchSamplerDataCollatorForSeq2Seq
             elif (
@@ -1781,7 +1778,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             else:
                 collator = BatchSamplerDataCollatorForSeq2Seq
         else:
-            collator = DataCollatorForSeq2Seq
+            if self.cfg.processor_type and self.processor:
+                collator = MultiModalChatDataCollator
+                kwargs["processor"] = self.processor
+                kwargs["chat_template"] = training_args.chat_template
+            else:
+                collator = DataCollatorForSeq2Seq
 
         return collator(
             self.tokenizer,
@@ -1802,7 +1804,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         return callbacks
 
     def build_training_arguments(self, total_num_steps):
@@ -1888,17 +1890,18 @@ 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"
@@ -1907,13 +1910,13 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
-        if self.cfg.rl == "orpo":
+        elif 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
 
-        if self.cfg.rl == "kto":
+        elif self.cfg.rl == "kto":
             training_args_cls = AxolotlKTOConfig
 
             training_args_kwargs["desirable_weight"] = (
@@ -1928,6 +1931,11 @@ 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,
@@ -1967,7 +1975,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             dpo_trainer_kwargs["max_length"] = self.cfg.sequence_len
             dpo_trainer_kwargs["max_target_length"] = None
             dpo_trainer_kwargs["max_prompt_length"] = self.cfg.sequence_len
-            dpo_trainer_kwargs["generate_during_eval"] = True
+            dpo_trainer_kwargs["generate_during_eval"] = self.cfg.use_wandb
         elif self.cfg.rl == "orpo":
             trainer_cls = AxolotlORPOTrainer
             trainer_cls_args = [self.model]
@@ -1979,11 +1987,17 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             trainer_cls_args = [self.model]
         else:
             raise ValueError(f"Unsupported RL: {self.cfg.rl}")
+
+        sig = inspect.signature(trainer_cls)
+        if "processing_class" in sig.parameters.keys():
+            dpo_trainer_kwargs["processing_class"] = self.tokenizer
+        else:
+            dpo_trainer_kwargs["tokenizer"] = self.tokenizer
+
         dpo_trainer = trainer_cls(
             *trainer_cls_args,
             args=training_args,
             train_dataset=self.train_dataset,
-            tokenizer=self.tokenizer,
             callbacks=self.get_callbacks(),
             **dpo_trainer_kwargs,
         )
@@ -2005,11 +2019,11 @@ class HFPPOTrainerBuilder(TrainerBuilderBase):
     """
 
     def get_callbacks(self):
-        callbacks = []
+        callbacks = super().get_callbacks()
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         return callbacks
 
     def build(self, total_num_steps):

src/axolotl/integrations/base.py

@@ -18,9 +18,10 @@ 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 List
+from typing import OrderedDict
 
 
 class BasePlugin:
@@ -47,7 +48,7 @@ class BasePlugin:
         Initializes the BasePlugin.
         """
 
-    def register(self, cfg):
+    def register(self, cfg):  # pylint: disable=unused-argument
         """
         Registers the plugin with the given configuration.
 
@@ -63,7 +64,7 @@ class BasePlugin:
         Returns a pydantic model for the plugin's input arguments.
         """
 
-    def pre_model_load(self, cfg):
+    def pre_model_load(self, cfg):  # pylint: disable=unused-argument
         """
         Performs actions before the model is loaded.
 
@@ -74,7 +75,7 @@ class BasePlugin:
         None
         """
 
-    def post_model_load(self, cfg, model):
+    def post_model_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions after the model is loaded.
 
@@ -86,7 +87,7 @@ class BasePlugin:
         None
         """
 
-    def pre_lora_load(self, cfg, model):
+    def pre_lora_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions before LoRA weights are loaded.
 
@@ -98,7 +99,7 @@ class BasePlugin:
         None
         """
 
-    def post_lora_load(self, cfg, model):
+    def post_lora_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions after LoRA weights are loaded.
 
@@ -110,7 +111,7 @@ class BasePlugin:
         None
         """
 
-    def create_optimizer(self, cfg, trainer):
+    def create_optimizer(self, cfg, trainer):  # pylint: disable=unused-argument
         """
         Creates and returns an optimizer for training.
 
@@ -122,7 +123,9 @@ class BasePlugin:
         object: The created optimizer.
         """
 
-    def create_lr_scheduler(self, cfg, trainer, optimizer):
+    def create_lr_scheduler(
+        self, cfg, trainer, optimizer
+    ):  # pylint: disable=unused-argument
         """
         Creates and returns a learning rate scheduler.
 
@@ -135,7 +138,7 @@ class BasePlugin:
         object: The created learning rate scheduler.
         """
 
-    def add_callbacks_pre_trainer(self, cfg, model):
+    def add_callbacks_pre_trainer(self, cfg, model):  # pylint: disable=unused-argument
         """
         Adds callbacks to the trainer before training.
 
@@ -146,8 +149,11 @@ 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):
+    def add_callbacks_post_trainer(
+        self, cfg, trainer
+    ):  # pylint: disable=unused-argument
         """
         Adds callbacks to the trainer after training.
 
@@ -158,6 +164,30 @@ 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
+        """
+        Performs actions after training is complete.
+
+        Parameters:
+        cfg (dict): The axolotl configuration
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+
+    def post_train_unload(self, cfg):  # pylint: disable=unused-argument
+        """
+        Performs actions after training is complete and the model is unloaded.
+
+        Parameters:
+        cfg (dict): The configuration for the plugin.
+
+        Returns:
+        None
+        """
 
 
 def load_plugin(plugin_name: str) -> BasePlugin:
@@ -204,7 +234,7 @@ class PluginManager:
     pre_model_load(cfg): Calls the pre_model_load method of all registered plugins.
     """
 
-    plugins: List[BasePlugin] = []
+    plugins: OrderedDict[str, BasePlugin] = collections.OrderedDict()
 
     _instance = None
 
@@ -214,7 +244,7 @@ class PluginManager:
         """
         if cls._instance is None:
             cls._instance = super(PluginManager, cls).__new__(cls)
-            cls._instance.plugins: List[BasePlugin] = []
+            cls._instance.plugins = collections.OrderedDict()
         return cls._instance
 
     @staticmethod
@@ -242,7 +272,7 @@ class PluginManager:
         """
         try:
             plugin = load_plugin(plugin_name)
-            self.plugins.append(plugin)
+            self.plugins[plugin_name] = plugin
         except ImportError:
             logging.error(f"Failed to load plugin: {plugin_name}")
 
@@ -254,7 +284,7 @@ class PluginManager:
         list[str]: A list of Pydantic classes for all registered plugins' input arguments.'
         """
         input_args = []
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             input_args_from_plugin = plugin.get_input_args()
             if input_args_from_plugin is not None:
                 input_args.append(input_args_from_plugin)
@@ -270,7 +300,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.pre_model_load(cfg)
 
     def post_model_load(self, cfg, model):
@@ -284,7 +314,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.post_model_load(cfg, model)
 
     def pre_lora_load(self, cfg, model):
@@ -298,7 +328,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.pre_lora_load(cfg, model)
 
     def post_lora_load(self, cfg, model):
@@ -312,7 +342,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.post_lora_load(cfg, model)
 
     def create_optimizer(self, cfg, trainer):
@@ -326,7 +356,7 @@ class PluginManager:
         Returns:
         object: The created optimizer, or None if none was found.
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             optimizer = plugin.create_optimizer(cfg, trainer)
             if optimizer is not None:
                 return optimizer
@@ -344,7 +374,7 @@ class PluginManager:
         Returns:
         object: The created learning rate scheduler, or None if none was found.
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             scheduler = plugin.create_lr_scheduler(cfg, trainer, optimizer)
             if scheduler is not None:
                 return scheduler
@@ -362,7 +392,7 @@ class PluginManager:
         List[callable]: A list of callback functions to be added to the TrainingArgs.
         """
         callbacks = []
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             callbacks.extend(plugin.add_callbacks_pre_trainer(cfg, model))
         return callbacks
 
@@ -378,6 +408,20 @@ class PluginManager:
         List[callable]: A list of callback functions to be added to the TrainingArgs.
         """
         callbacks = []
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             callbacks.extend(plugin.add_callbacks_post_trainer(cfg, trainer))
         return callbacks
+
+    def post_train_unload(self, cfg):
+        """
+        Calls the post_train_unload method of all registered plugins.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        model (object): The loaded model.
+
+        Returns:
+        None
+        """
+        for plugin in self.plugins.values():
+            plugin.post_train_unload(cfg)

src/axolotl/integrations/liger/__init__.py

@@ -18,20 +18,23 @@ 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.geglu import LigerGEGLUMLP
+from liger_kernel.transformers.monkey_patch import MODEL_TYPE_TO_APPLY_LIGER_FN
 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):
     """
@@ -42,59 +45,31 @@ class LigerPlugin(BasePlugin):
         return "axolotl.integrations.liger.LigerArgs"
 
     def pre_model_load(self, cfg):
-        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"
+        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.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
-
+            apply_liger_fn(**kwargs)
         elif cfg.model_config_type == "jamba":
             from transformers.models.jamba import modeling_jamba
 
@@ -104,30 +79,12 @@ 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_swiglu:
+            if cfg.liger_glu_activation:
                 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
@@ -146,44 +103,9 @@ 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_swiglu:
+            if cfg.liger_glu_activation:
                 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

@@ -15,9 +15,12 @@
 """
 Module for handling LIGER input arguments.
 """
+import logging
 from typing import Optional
 
-from pydantic import BaseModel
+from pydantic import BaseModel, model_validator
+
+LOG = logging.getLogger("axolotl.integrations.liger.args")
 
 
 class LigerArgs(BaseModel):
@@ -27,6 +30,24 @@ 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/integrations/lm_eval/README.md

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

src/axolotl/integrations/lm_eval/__init__.py

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

src/axolotl/integrations/lm_eval/args.py

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

src/axolotl/loraplus.py

@@ -1,133 +0,0 @@
-"""Module for LoRA+"""
-
-# MIT License
-#
-# Copyright (c) 2024 nikhil-ghosh-berkeley
-# https://github.com/nikhil-ghosh-berkeley/loraplus
-
-import logging
-from functools import reduce
-
-from peft.tuners import lora
-from torch import nn
-from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
-from transformers.trainer_pt_utils import get_parameter_names
-
-LOG = logging.getLogger("axolotl.loraplus")
-
-
-def get_module(name, opt_model):
-    """
-    Retrieve a module from a model using its parameter name.
-    Args:
-        name (str): Full name of the parameter, typically including module path.
-        opt_model (torch.nn.Module): The model from which to retrieve the module.
-
-    Returns:
-        Module corresponding to the given name.
-    """
-    parent_idx = 2 if "lora" in name else 1
-    module_names = name.split(sep=".")[:-parent_idx]
-    module = reduce(getattr, module_names, opt_model)
-    return module
-
-
-def create_loraplus_optimizer(
-    opt_model,
-    optimizer_cls,
-    optimizer_kwargs,
-    loraplus_lr_ratio,
-    loraplus_lr_embedding=None,
-):
-    """
-    Creates an optimizer for the given model, applying LoRA-specific learning rate adjustments to different parameter groups.
-
-    Args:
-        opt_model (torch.nn.Module): The model for which the optimizer is being created.
-        optimizer_cls (class): The class of the optimizer to be used (e.g., torch.optim.Adam).
-        optimizer_kwargs (dict): A dictionary of keyword arguments for the optimizer's initialization.
-        loraplus_lr_ratio (float): The learning rate ratio to be applied to LoRA parameters.
-        loraplus_lr_embedding (float, optional): A specific learning rate for embedding parameters, with a default value if not provided.
-
-    Returns:
-        An instance of the specified optimizer class configured with the model's parameters organized into groups with custom learning rates.
-    """
-
-    assert loraplus_lr_ratio is not None, "loraplus_lr_ratio must be provided."
-
-    if loraplus_lr_embedding is None:
-        loraplus_lr_embedding = 1e-6
-
-    decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)
-    decay_parameters = [name for name in decay_parameters if "bias" not in name]
-    param_groups = {
-        "groupA": {},
-        "groupB": {},
-        "groupB_no_decay": {},
-        "embedding": {},
-    }
-
-    for name, param in opt_model.named_parameters():
-        if not param.requires_grad:
-            continue
-
-        module = get_module(name, opt_model)
-        if isinstance(module, lora.Embedding):
-            param_groups["embedding"][name] = param
-        elif "lora_B" in name or param.ndim == 1:
-            if name in decay_parameters:
-                param_groups["groupB"][name] = param
-            else:
-                param_groups["groupB_no_decay"][name] = param
-        else:
-            param_groups["groupA"][name] = param
-
-    assigned_param_groups = ""
-    for group, group_params in param_groups.items():
-        assigned_param_groups += f"{group}\n {list(group_params.keys())}\n\n"
-    LOG.info(assigned_param_groups)
-
-    lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
-    weight_decay = optimizer_kwargs.get("weight_decay", 0.0)
-
-    optimizer_grouped_parameters = [
-        {
-            "params": list(param_groups["groupA"].values()),
-            "weight_decay": weight_decay,
-            "lr": lr,
-        },
-        {
-            "params": list(param_groups["embedding"].values()),
-            "weight_decay": weight_decay,
-            "lr": loraplus_lr_embedding,
-        },
-        {
-            "params": list(param_groups["groupB"].values()),
-            "weight_decay": weight_decay,
-            "lr": lr * loraplus_lr_ratio,
-        },
-        {
-            "params": list(param_groups["groupB_no_decay"].values()),
-            "weight_decay": 0.0,
-            "lr": lr * loraplus_lr_ratio,
-        },
-    ]
-
-    optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
-    if optimizer_cls.__name__ == "Adam8bit":
-        import bitsandbytes
-
-        manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
-
-        skipped = 0
-        for module in opt_model.modules():
-            if isinstance(module, nn.Embedding):
-                skipped += sum(
-                    {p.data_ptr(): p.numel() for p in module.parameters()}.values()
-                )
-                LOG.info(f"skipped {module}: {skipped/2**20}M params")
-                manager.register_module_override(module, "weight", {"optim_bits": 32})
-                LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
-        LOG.info(f"skipped: {skipped/2**20}M params")
-
-    return optimizer

src/axolotl/monkeypatch/attention/mllama.py

@@ -0,0 +1,229 @@
+"""
+Monkeypatch for Vision Llama for FA2 support
+"""
+# pylint: disable=duplicate-code
+
+from typing import Optional, Tuple
+
+import torch
+from flash_attn.flash_attn_interface import flash_attn_func
+from transformers.cache_utils import Cache
+from transformers.modeling_flash_attention_utils import _flash_attention_forward
+from transformers.models.mllama.configuration_mllama import MllamaTextConfig
+from transformers.models.mllama.modeling_mllama import (
+    MllamaTextCrossAttention,
+    MllamaTextSelfAttention,
+    apply_rotary_pos_emb,
+    repeat_kv,
+)
+from transformers.utils import is_flash_attn_greater_or_equal_2_10
+
+
+class MllamaTextCrossFlashAttention2(MllamaTextCrossAttention):
+    """
+    Mllama flash cross-attention module. This module inherits from `MllamaTextCrossAttention` and
+    implements the forward pass using Flash Attention for improved performance.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # Check if flash attention version is greater or equal to 2.1
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cross_attention_states: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Cache] = None,
+        attention_mask: Optional[  # pylint: disable=unused-argument
+            torch.Tensor
+        ] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,  # pylint: disable=unused-argument
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        query_states = self.q_norm(query_states)
+
+        if cross_attention_states is not None:
+            key_states = self.k_proj(cross_attention_states)
+            value_states = self.v_proj(cross_attention_states)
+            key_states = key_states.view(
+                bsz, -1, self.num_key_value_heads, self.head_dim
+            ).transpose(1, 2)
+            value_states = value_states.view(
+                bsz, -1, self.num_key_value_heads, self.head_dim
+            ).transpose(1, 2)
+            key_states = repeat_kv(key_states, self.num_key_value_groups)
+            value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+            key_states = self.k_norm(key_states)
+            if past_key_value is not None:
+                key_states, value_states = past_key_value.update(
+                    key_states,
+                    value_states,
+                    self.layer_idx,
+                    {"cache_position": cache_position},
+                )
+        elif cache_position[0] != 0:
+            key_states, value_states = (
+                past_key_value.key_cache[self.layer_idx],
+                past_key_value.value_cache[self.layer_idx],
+            )
+        else:
+            raise ValueError(
+                "Cross attention layer can't find neither `cross_attn_states` nor cached values for key/values!"
+            )
+
+        # Transpose to get the expected layout for flash attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        # Apply Flash Attention
+        dropout_rate = self.dropout if self.training else 0.0
+        output = flash_attn_func(
+            query_states,
+            key_states,
+            value_states,
+            dropout_p=dropout_rate,
+            softmax_scale=None,
+            causal=False,
+            return_attn_probs=output_attentions,
+        )
+
+        attn_output = output.contiguous().view(bsz, q_len, -1)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class MllamaTextSelfFlashAttention2(MllamaTextSelfAttention):
+    """
+    Mllama flash self-attention module. This module inherits from `MllamaTextSelfAttention` and
+    implements the forward pass using Flash Attention for improved performance.
+    """
+
+    def __init__(self, config: MllamaTextConfig, layer_idx: int, *args, **kwargs):
+        super().__init__(config, layer_idx, *args, **kwargs)
+
+        # Check if flash attention version is greater or equal to 2.1
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,  # pylint: disable=unused-argument
+        past_key_value=None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,  # pylint: disable=unused-argument
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x num_heads x head_dim
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin
+        )
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        # Transpose to get the expected layout for flash attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.dropout if self.training else 0.0
+
+        # Handle potential silent casting to float32
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = (
+                    self.config._pre_quantization_dtype  # pylint: disable=protected-access
+                )
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = _flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+            is_causal=True,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+def patch_mllama():
+    from transformers.models.mllama.modeling_mllama import (
+        MLLAMA_TEXT_ATTENTION_CLASSES,
+        MLLAMA_TEXT_CROSS_ATTENTION_CLASSES,
+        MLLAMA_VISION_ATTENTION_CLASSES,
+        MllamaPreTrainedModel,
+    )
+
+    MllamaPreTrainedModel._supports_flash_attn_2 = (  # pylint: disable=protected-access
+        True
+    )
+    MLLAMA_TEXT_ATTENTION_CLASSES["flash_attention_2"] = MllamaTextSelfFlashAttention2
+    MLLAMA_TEXT_CROSS_ATTENTION_CLASSES[
+        "flash_attention_2"
+    ] = MllamaTextCrossFlashAttention2
+    # fallback to SDPA
+    MLLAMA_VISION_ATTENTION_CLASSES[
+        "flash_attention_2"
+    ] = MLLAMA_VISION_ATTENTION_CLASSES["sdpa"]

src/axolotl/monkeypatch/fastchat_conversation_turns.py

@@ -1,231 +0,0 @@
-"""
-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/monkeypatch/llama_attn_hijack_flash.py

@@ -22,7 +22,6 @@ from transformers.models.llama.modeling_llama import (
     apply_rotary_pos_emb,
     repeat_kv,
 )
-from xformers.ops import SwiGLU
 
 from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids, set_module_name
 
@@ -44,7 +43,19 @@ except ImportError:
 LOG = logging.getLogger("axolotl")
 
 
+def is_xformers_available() -> bool:
+    try:
+        import xformers  # pylint: disable=unused-import  # noqa: F401
+
+        return True
+    except ImportError:
+        return False
+
+
 def is_xformers_swiglu_available() -> bool:
+    if not is_xformers_available():
+        return False
+
     from xformers.ops.common import get_xformers_operator
 
     try:
@@ -57,6 +68,11 @@ def is_xformers_swiglu_available() -> bool:
 
 
 def replace_llama_mlp_with_swiglu(model):
+    if is_xformers_swiglu_available():
+        from axolotl.monkeypatch.xformers_ import FusedMLP
+    else:
+        raise RuntimeError("xformers SwiGLU not available for this environment")
+
     for name, module in model.named_modules():
         if isinstance(module, LlamaMLP):
             mlp = FusedMLP(
@@ -181,49 +197,6 @@ class FusedAttention(LlamaAttention):
         set_module_name(model, name, new_attn)
 
 
-class FusedMLP(torch.nn.Module):
-    """
-    Fused MLP layer for incrementally improved training efficiency
-    """
-
-    def __init__(
-        self,
-        config,
-        gate_proj: torch.nn.Linear,
-        up_proj: torch.nn.Linear,
-        down_proj: torch.nn.Linear,
-    ):
-        super().__init__()
-        self.config = config
-        self.swiglu = SwiGLU(
-            in_features=config.hidden_size,
-            hidden_features=config.intermediate_size,
-            bias=False,
-            _pack_weights=True,
-        )
-        # overwrite initialized weights with pretrained weights
-        self.swiglu.w12.weight.data = torch.cat(
-            (gate_proj.weight.data, up_proj.weight.data), dim=0
-        )
-        self.swiglu.w3.weight.data = down_proj.weight.data
-
-    def _post_training(self, model, name):
-        w1, w2 = torch.split(  # pylint: disable=invalid-name
-            self.swiglu.w12.weight.data, self.config.intermediate_size, dim=0
-        )
-
-        # Assign the split weights back to the original layers
-        new_mlp = LlamaMLP(self.config)
-        new_mlp.gate_proj.weight.data = w1
-        new_mlp.up_proj.weight.data = w2
-        new_mlp.down_proj.weight.data = self.swiglu.w3.weight.data
-
-        set_module_name(model, name, new_mlp)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:  # pylint: disable=invalid-name
-        return self.swiglu(x)
-
-
 # Disable the transformation of the attention mask in LlamaModel as the flash attention
 # requires the attention mask to be the same as the key_padding_mask
 def _prepare_decoder_attention_mask(

src/axolotl/monkeypatch/multipack.py

@@ -10,6 +10,7 @@ from axolotl.monkeypatch.mixtral import patch_mixtral_moe_forward_zero3
 from axolotl.monkeypatch.utils import get_unpad_data
 
 SUPPORTED_MULTIPACK_MODEL_TYPES = [
+    "mllama_text_model",
     "llama",
     "mistral",
     "mixtral",

src/axolotl/monkeypatch/relora.py

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

src/axolotl/monkeypatch/stablelm_attn_hijack_flash.py

@@ -16,6 +16,7 @@
 # This code is based off the following work:
 # https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
 # https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
+# pylint: disable=duplicate-code
 """ PyTorch StableLM Epoch model. """
 import importlib
 import math

src/axolotl/monkeypatch/unsloth_.py

@@ -16,26 +16,6 @@ from transformers.models.llama.modeling_llama import (
 
 LOG = get_logger("axolotl.monkeypatch.unsloth")
 
-ORIGINAL_CEL_CODE = """# Shift so that tokens < n predict n
-        shift_logits = logits[..., :-1, :].contiguous()
-        shift_labels = labels[..., 1:].contiguous()
-        # Flatten the tokens
-        loss_fct = CrossEntropyLoss()
-        shift_logits = shift_logits.view(-1, self.config.vocab_size)
-        shift_labels = shift_labels.view(-1)
-        # Enable model parallelism
-        shift_labels = shift_labels.to(shift_logits.device)
-        loss = loss_fct(shift_logits, shift_labels)
-"""
-
-PATCHED_CEL_CODE = """shift_logits = logits[..., :-1, :].contiguous()
-        shift_labels = labels[..., 1:].contiguous()
-        loss = fast_cross_entropy_loss(
-            logits = shift_logits,
-            labels = shift_labels,
-        )
-"""
-
 ORIGINAL_QKV_CODE = """
     query_states = self.q_proj(hidden_states)
     key_states = self.k_proj(hidden_states)
@@ -80,12 +60,6 @@ def get_forward_code() -> str:
     return forward
 
 
-def check_cel_is_patchable() -> bool:
-    forward = get_forward_code()
-    forward, _ = detab_code(forward)
-    return ORIGINAL_CEL_CODE in forward
-
-
 def get_self_attn_code() -> str:
     forward = inspect.getsource(LlamaFlashAttention2.forward)
     return forward
@@ -98,48 +72,31 @@ def check_self_attn_is_patchable() -> bool:
 
 
 def integrate_cross_entropy_loss_patch(model_type: str = "llama") -> None:
+    from unsloth.kernels.cross_entropy_loss import fast_cross_entropy_loss
+
+    def UnslothForCausalLMLoss(  # pylint: disable=invalid-name
+        logits,
+        labels,
+        vocab_size: int,  # pylint: disable=unused-argument
+        num_items_in_batch: int = None,
+        ignore_index: int = -100,  # pylint: disable=unused-argument
+        **kwargs,  # pylint: disable=unused-argument
+    ):
+        # Upcast to float if we need to compute the loss to avoid potential precision issues
+        logits = logits.float()
+        # Shift so that tokens < n predict n
+        shift_logits = logits[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+
+        loss = fast_cross_entropy_loss(
+            logits=shift_logits, labels=shift_labels, n_items=num_items_in_batch
+        )
+        return loss
+
     if model_type == "llama":
-        forward = get_forward_code()
-        LlamaForCausalLM._original_forward = forward  # pylint: disable=protected-access
-        forward, _ = detab_code(forward)
-        assert ORIGINAL_CEL_CODE in forward, "Original forward code not found"
+        from transformers.loss import loss_utils
 
-        forward = forward.replace(
-            "@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)", ""
-        )
-        forward = forward.replace(
-            "@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)",
-            "",
-        )
-        forward = forward.replace(ORIGINAL_CEL_CODE, PATCHED_CEL_CODE)
-        forward = forward.replace(
-            "def forward(",
-            "def fast_cross_entropy_loss_forward(",
-            1,
-        )
-
-        # load imports necessary
-        import transformers.models.llama.modeling_llama
-
-        items_to_import = []
-        for item in dir(transformers.models.llama.modeling_llama):
-            if item in forward:
-                items_to_import.append(item)
-
-        exec(  # pylint: disable=exec-used  # nosec B102
-            "from unsloth.kernels.cross_entropy_loss import fast_cross_entropy_loss",
-            globals(),
-        )
-
-        exec(  # pylint: disable=exec-used  # nosec B102
-            "from transformers.models.llama.modeling_llama import ("
-            + ", ".join(x for x in items_to_import)
-            + ")",
-            globals(),
-        )
-        exec(forward, globals())  # pylint: disable=exec-used  # nosec B102
-        LOG.info("patching unsloth fast_cross_entropy_loss", main_process_only=True)
-        LlamaForCausalLM.forward = fast_cross_entropy_loss_forward  # pylint: disable=undefined-variable  # noqa: F821
+        loss_utils.ForCausalLMLoss = UnslothForCausalLMLoss  # type: ignore[assignment]
     else:
         raise ValueError("Unsupported model type")
 

src/axolotl/monkeypatch/xformers_/__init__.py

@@ -0,0 +1,51 @@
+"""
+Fused MLP layer for incrementally improved training efficiency
+"""
+import torch
+from transformers.models.llama.modeling_llama import LlamaMLP
+from xformers.ops import SwiGLU
+
+from axolotl.monkeypatch.utils import set_module_name
+
+
+class FusedMLP(torch.nn.Module):
+    """
+    Fused MLP layer for incrementally improved training efficiency
+    """
+
+    def __init__(
+        self,
+        config,
+        gate_proj: torch.nn.Linear,
+        up_proj: torch.nn.Linear,
+        down_proj: torch.nn.Linear,
+    ):
+        super().__init__()
+        self.config = config
+        self.swiglu = SwiGLU(
+            in_features=config.hidden_size,
+            hidden_features=config.intermediate_size,
+            bias=False,
+            _pack_weights=True,
+        )
+        # overwrite initialized weights with pretrained weights
+        self.swiglu.w12.weight.data = torch.cat(
+            (gate_proj.weight.data, up_proj.weight.data), dim=0
+        )
+        self.swiglu.w3.weight.data = down_proj.weight.data
+
+    def _post_training(self, model, name):
+        w1, w2 = torch.split(  # pylint: disable=invalid-name
+            self.swiglu.w12.weight.data, self.config.intermediate_size, dim=0
+        )
+
+        # Assign the split weights back to the original layers
+        new_mlp = LlamaMLP(self.config)
+        new_mlp.gate_proj.weight.data = w1
+        new_mlp.up_proj.weight.data = w2
+        new_mlp.down_proj.weight.data = self.swiglu.w3.weight.data
+
+        set_module_name(model, name, new_mlp)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:  # pylint: disable=invalid-name
+        return self.swiglu(x)

src/axolotl/prompt_strategies/__init__.py

@@ -9,8 +9,12 @@ from axolotl.prompt_strategies.user_defined import UserDefinedDatasetConfig
 LOG = logging.getLogger("axolotl.prompt_strategies")
 
 
-def load(strategy, tokenizer, cfg, ds_cfg):
+def load(strategy, tokenizer, cfg, ds_cfg, processor=None):
     try:
+        if strategy == "messages":
+            from .messages import load as messages_load
+
+            return messages_load(tokenizer, cfg, ds_cfg, processor=processor)
         load_fn = "load"
         if strategy.split(".")[-1].startswith("load_"):
             load_fn = strategy.split(".")[-1]
@@ -24,9 +28,12 @@ def load(strategy, tokenizer, cfg, ds_cfg):
             sig = inspect.signature(func)
             if "ds_cfg" in sig.parameters:
                 load_kwargs["ds_cfg"] = ds_cfg
+            if "processor" in sig.parameters:
+                load_kwargs["processor"] = processor
         return func(tokenizer, cfg, **load_kwargs)
     except ModuleNotFoundError:
         return None
     except Exception as exc:  # pylint: disable=broad-exception-caught
         LOG.error(f"Failed to load prompt strategy `{strategy}`: {str(exc)}")
-        return None
+        raise exc
+    return None

src/axolotl/prompt_strategies/bradley_terry/README.md

@@ -0,0 +1,10 @@
+### example yaml
+
+```yaml
+chat_template: gemma
+datasets:
+  - path: argilla/distilabel-intel-orca-dpo-pairs
+    type: bradley_terry.chat_template
+val_set_size: 0.0
+output_dir: ./outputs/out
+```

src/axolotl/prompt_strategies/bradley_terry/__init__.py

@@ -0,0 +1,35 @@
+"""Module to load prompt strategies."""
+
+import importlib
+import inspect
+import logging
+
+from axolotl.prompt_strategies.user_defined import UserDefinedDatasetConfig
+
+LOG = logging.getLogger("axolotl.prompt_strategies.bradley_terry")
+
+
+def load(strategy, tokenizer, cfg, ds_cfg):
+    # pylint: disable=duplicate-code
+    try:
+        load_fn = "load"
+        if strategy.split(".")[-1].startswith("load_"):
+            load_fn = strategy.split(".")[-1]
+            strategy = ".".join(strategy.split(".")[:-1])
+        mod = importlib.import_module(
+            f".{strategy}", "axolotl.prompt_strategies.bradley_terry"
+        )
+        func = getattr(mod, load_fn)
+        load_kwargs = {}
+        if strategy == "user_defined":
+            load_kwargs["ds_cfg"] = UserDefinedDatasetConfig(**ds_cfg)
+        else:
+            sig = inspect.signature(func)
+            if "ds_cfg" in sig.parameters:
+                load_kwargs["ds_cfg"] = ds_cfg
+        return func(tokenizer, cfg, **load_kwargs)
+    except ModuleNotFoundError:
+        return None
+    except Exception as exc:  # pylint: disable=broad-exception-caught
+        LOG.error(f"Failed to load prompt strategy `{strategy}`: {str(exc)}")
+        return None

src/axolotl/prompt_strategies/bradley_terry/chat_template.py

@@ -0,0 +1,102 @@
+"""
+Bradley-Terry model with chat template prompt strategy.
+"""
+
+import logging
+from typing import Any, Dict, Optional
+
+from axolotl.prompt_strategies.chat_template import (
+    ChatTemplatePrompter,
+    ChatTemplateStrategy,
+)
+from axolotl.utils.chat_templates import get_chat_template_from_config
+
+# Configure the logger
+LOG = logging.getLogger("axolotl.prompt_strategies.bradley_terry.chat_template")
+LOG.setLevel(logging.INFO)
+
+
+class BTChatTemplateStrategy(ChatTemplateStrategy):
+    """
+    Bradley-Terry reward model pairwise chat template prompt strategy.
+    """
+
+    def tokenize_prompt(self, prompt):
+        """
+
+        :param prompt: the actual row of data from the underlying dataset
+        :return:
+        """
+
+        self.messages = "chosen_messages"
+        # pylint: disable=duplicate-code
+        prompt[self.messages] = []
+        if prompt["system"]:
+            prompt[self.messages].append(
+                {"role": "system", "content": prompt["system"]}
+            )
+        prompt[self.messages].append({"role": "user", "content": prompt["input"]})
+        prompt[self.messages].append({"role": "assistant", "content": prompt["chosen"]})
+        chosen_tokenized = super().tokenize_prompt(prompt)
+
+        self.messages = "rejected_messages"
+        # pylint: disable=duplicate-code
+        prompt[self.messages] = []
+        if prompt["system"]:
+            prompt[self.messages].append(
+                {"role": "system", "content": prompt["system"]}
+            )
+        prompt[self.messages].append({"role": "user", "content": prompt["input"]})
+        prompt[self.messages].append(
+            {"role": "assistant", "content": prompt["rejected"]}
+        )
+        rejected_tokenized = super().tokenize_prompt(prompt)
+
+        return {
+            "input_ids_chosen": chosen_tokenized["input_ids"],
+            "attention_mask_chosen": chosen_tokenized["attention_mask"],
+            "labels_chosen": 1.0,
+            "input_ids_rejected": rejected_tokenized["input_ids"],
+            "attention_mask_rejected": rejected_tokenized["attention_mask"],
+            "labels_rejected": 0.0,
+        }
+
+
+def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+    ds_cfg = ds_cfg or {}
+    chat_template_string = get_chat_template_from_config(
+        cfg=cfg, ds_cfg=ds_cfg, tokenizer=tokenizer
+    )
+
+    prompter_params = {
+        "tokenizer": tokenizer,
+        "chat_template": chat_template_string,
+        "message_field_role": ds_cfg.get("message_field_role", "role"),
+        "message_field_content": ds_cfg.get("message_field_content", "content"),
+        "message_field_training": ds_cfg.get("message_field_training", None),
+        "message_field_training_detail": ds_cfg.get(
+            "message_field_training_detail", None
+        ),
+        "roles": ds_cfg.get("roles"),
+        "drop_system_message": ds_cfg.get("drop_system_message", False),
+        # we need to add one for detecting sequences with exceeding the `sequence_len` limit.
+        "max_length": cfg.sequence_len + 1
+        if not cfg.reward_model
+        else cfg.sequence_len,
+    }
+
+    strategy_params = {
+        "train_on_inputs": cfg.train_on_inputs,
+        "sequence_len": cfg.sequence_len,
+        "roles_to_train": ds_cfg.get("roles_to_train", []),
+        "train_on_eos": ds_cfg.get("train_on_eos", None),
+    }
+
+    strategy = BTChatTemplateStrategy(
+        ChatTemplatePrompter(**prompter_params), tokenizer=tokenizer, **strategy_params
+    )
+
+    if "field_messages" in ds_cfg and hasattr(strategy, "messages"):
+        strategy.messages = ds_cfg["field_messages"]
+
+    return strategy

src/axolotl/prompt_strategies/bradley_terry/llama3.py

@@ -0,0 +1,27 @@
+"""
+chatml transforms for datasets with system, input, chosen, rejected to match llama3 chat template
+"""
+
+
+def icr(
+    cfg,
+    **kwargs,
+):  # pylint: disable=possibly-unused-variable,unused-argument
+    """
+    chatml transforms for datasets with system, input, chosen, rejected
+    ex. https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
+    """
+
+    def transform_fn(sample):
+        if "system" in sample and sample["system"]:
+            prompt = (
+                f"<|start_header_id|>system<|end_header_id|>\n\n{sample['system']}<|eot_id|>"
+                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['input']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
+        else:
+            prompt = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['input']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+        sample["chosen"] = prompt + f"{sample['chosen']}<|eot_id|>"
+        sample["rejected"] = prompt + f"{sample['rejected']}<|eot_id|>"
+        return sample
+
+    return transform_fn

src/axolotl/prompt_strategies/chat_template.py

@@ -5,9 +5,11 @@ HF Chat Templates prompt strategy
 import logging
 from typing import Any, Dict, List, Optional
 
+from transformers import ProcessorMixin
+
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
 from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import get_chat_template_from_config
 
 # Configure the logger
 LOG = logging.getLogger("axolotl")
@@ -20,6 +22,7 @@ class ChatTemplatePrompter(Prompter):
     def __init__(
         self,
         tokenizer,
+        processor=None,
         chat_template=None,
         max_length=2048,
         message_field_role: str = "from",
@@ -44,11 +47,12 @@ class ChatTemplatePrompter(Prompter):
         self.message_field_training = message_field_training
         self.message_field_training_detail = message_field_training_detail
         self.tokenizer = tokenizer
+        self.processor: ProcessorMixin = processor
         self.chat_template = chat_template
         self.max_length = max_length
         self.drop_system_message = drop_system_message
 
-    def build_prompt(self, conversation, add_generation_prompt=False):
+    def build_prompt(self, conversation, add_generation_prompt=False, images=None):
         turns = [
             {
                 "role": self.roles[t[self.message_field_role]],
@@ -61,6 +65,28 @@ class ChatTemplatePrompter(Prompter):
         if self.drop_system_message and turns[0]["role"] == "system":
             turns = turns[1:]
 
+        if self.processor:
+            text = self.processor.apply_chat_template(
+                turns,
+                chat_template=self.chat_template,
+                tokenize=False,
+                add_generation_prompt=add_generation_prompt,
+            )
+            batch = self.processor(
+                text=text,
+                images=images,
+                return_tensors="pt",
+                truncation=True,
+                max_length=self.max_length,
+            )
+            # workaround since processor works in batches instead of single examples
+            for k, val in batch.items():
+                if k in ["pixel_values"]:
+                    batch[k] = val.tolist()
+                else:
+                    batch[k] = val.squeeze().tolist()
+            return batch
+
         return self.tokenizer.apply_chat_template(
             turns,
             truncation=True,
@@ -191,6 +217,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         super().__init__(prompter, tokenizer, train_on_inputs, sequence_len)
         self.roles_to_train = roles_to_train if roles_to_train is not None else []
         self.train_on_eos = train_on_eos
+        self.images = "images"
 
     @property
     def messages(self):
@@ -209,10 +236,21 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
             and not self.prompter.message_field_training_detail
         ):
             turns = self.get_conversation_thread(prompt)
+            images = self.get_images(prompt)
             prompt_ids = self.prompter.build_prompt(
-                turns[:-1], add_generation_prompt=True
+                turns[:-1],
+                add_generation_prompt=True,
+                images=images,
             )
-            input_ids = self.prompter.build_prompt(turns)
+            tokenized_res = self.prompter.build_prompt(turns, images=images)
+            tokenized_prompt = {}
+            if isinstance(tokenized_res, list):
+                input_ids = prompt_ids + tokenized_res[len(prompt_ids) :]
+                tokenized_prompt["input_ids"] = input_ids
+                tokenized_prompt["attention_mask"] = [1] * len(input_ids)
+            else:
+                input_ids = tokenized_res["input_ids"]
+                tokenized_prompt = tokenized_res
 
             if not self.train_on_inputs:
                 user_prompt_len = len(prompt_ids)
@@ -220,17 +258,9 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
             else:
                 labels = input_ids
 
-            tokenized_prompt = {
-                "input_ids": input_ids,
-                "labels": labels,
-                "attention_mask": [1] * len(input_ids),
-            }
+            tokenized_prompt["labels"] = labels
 
             return tokenized_prompt
-        LOG.info(self.roles_to_train)
-        LOG.info(self.train_on_eos)
-        LOG.info(self.prompter.message_field_training)
-        LOG.info(self.prompter.message_field_training_detail)
 
         turns = prompt[self.messages]
         input_ids = self.prompter.build_prompt(turns)
@@ -368,15 +398,23 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
     def get_conversation_thread(self, prompt):
         return prompt[self.messages]
 
+    def get_images(self, prompt):
+        return prompt.get(self.images, None)
 
-def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+
+def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None, processor=None):
+    # pylint: disable=duplicate-code
     ds_cfg = ds_cfg or {}
+    chat_template_string = get_chat_template_from_config(
+        cfg=cfg, ds_cfg=ds_cfg, tokenizer=tokenizer
+    )
+    LOG.info(f"Using chat template:\n---\n{chat_template_string!s}\n---")
 
     prompter_params = {
         "tokenizer": tokenizer,
-        "chat_template": chat_templates(ds_cfg.get("chat_template", "chatml")),
-        "message_field_role": ds_cfg.get("message_field_role", "from"),
-        "message_field_content": ds_cfg.get("message_field_content", "value"),
+        "chat_template": chat_template_string,
+        "message_field_role": ds_cfg.get("message_field_role", "role"),
+        "message_field_content": ds_cfg.get("message_field_content", "content"),
         "message_field_training": ds_cfg.get("message_field_training", None),
         "message_field_training_detail": ds_cfg.get(
             "message_field_training_detail",
@@ -386,6 +424,7 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
         "drop_system_message": ds_cfg.get("drop_system_message", False),
         # we need to add one for detecting sequences with exceeding the `sequence_len` limit.
         "max_length": cfg.sequence_len + 1,
+        "processor": processor,
     }
 
     strategy_params = {

src/axolotl/prompt_strategies/dpo/chat_template.py

@@ -2,15 +2,16 @@
 DPO prompt strategies for using tokenizer chat templates.
 """
 
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import extract_chat_template_args, get_chat_template
 
 
 def default(
     cfg, dataset_idx=0, **kwargs
 ):  # pylint: disable=possibly-unused-variable,unused-argument
     ds_cfg = cfg["datasets"][dataset_idx]
-    chat_template_str = chat_templates(cfg.chat_template)
-
+    chat_template_choice, chat_template_jinja = extract_chat_template_args(
+        cfg=cfg, ds_cfg=ds_cfg
+    )
     field_messages = ds_cfg.get("field_messages", "messages")
     field_chosen = ds_cfg.get("field_chosen", "chosen")
     field_rejected = ds_cfg.get("field_rejected", "rejected")
@@ -30,6 +31,12 @@ def default(
             role_map[source] = target
 
     def transform_fn(sample, tokenizer=None):
+        chat_template_string = get_chat_template(
+            user_choice=chat_template_choice,
+            jinja_template=chat_template_jinja,
+            tokenizer=tokenizer,
+        )
+
         messages = sample[field_messages]
         messages = [
             {
@@ -46,28 +53,29 @@ def default(
             "role": role_map[sample[field_rejected][field_message_role]],
             "content": sample[field_rejected][field_message_content],
         }
+        dummy_user_message = {"role": "user", "content": "[[dummy_message]]"}
 
         result = {}
         result["prompt"] = tokenizer.apply_chat_template(
             messages,
             add_generation_prompt=True,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )
 
         result["chosen"] = tokenizer.apply_chat_template(
-            [chosen],
+            [dummy_user_message, chosen],
             add_generation_prompt=False,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )
         chosen_strip_index = result["chosen"].find(chosen["content"])
         result["chosen"] = result["chosen"][chosen_strip_index:].rstrip()
 
         result["rejected"] = tokenizer.apply_chat_template(
-            [rejected],
+            [dummy_user_message, rejected],
             add_generation_prompt=False,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )
         rejected_strip_index = result["rejected"].find(rejected["content"])

src/axolotl/prompt_strategies/instruct.py

@@ -1,33 +0,0 @@
-"""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 IGNORE_TOKEN_ID, SHAREGPT_ASSERTION_FAILED_ROLE
+from axolotl.prompters import ALTERNATING_ASSERTION_FAILED_ROLE, IGNORE_TOKEN_ID
 
 
 @dataclass
@@ -75,7 +75,7 @@ class Llama2ChatConversation:
 
 class LLama2ChatTokenizingStrategy(PromptTokenizingStrategy):
     """
-    Tokenizing strategy for ShareGPT prompts.
+    Tokenizing strategy for Llama2 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], SHAREGPT_ASSERTION_FAILED_ROLE
+            assert role == conv.roles[j % 2], ALTERNATING_ASSERTION_FAILED_ROLE
             if sentence["value"]:
                 conv.append_message(role, sentence["value"])
         yield conv

src/axolotl/prompt_strategies/messages/__init__.py

@@ -0,0 +1,34 @@
+"""Module to load message prompt strategies."""
+
+import importlib
+import inspect
+import logging
+
+LOG = logging.getLogger("axolotl.prompt_strategies.messages")
+
+
+def load(tokenizer, cfg, ds_cfg, processor=None):
+    try:
+        strategy = ds_cfg.get("input_transform", "chat")
+        # pylint: disable=duplicate-code
+        load_fn = "load"
+        if strategy.split(".")[-1].startswith("load_"):
+            load_fn = strategy.split(".")[-1]
+            strategy = ".".join(strategy.split(".")[:-1])
+        mod = importlib.import_module(
+            f".{strategy}", "axolotl.prompt_strategies.messages"
+        )
+        func = getattr(mod, load_fn)
+        load_kwargs = {}
+        sig = inspect.signature(func)
+        if "ds_cfg" in sig.parameters:
+            load_kwargs["ds_cfg"] = ds_cfg
+        if "processor" in sig.parameters:
+            load_kwargs["processor"] = processor
+        return func(tokenizer, cfg, **load_kwargs)
+    except ModuleNotFoundError:
+        return None
+    except Exception as exc:  # pylint: disable=broad-exception-caught
+        LOG.error(f"Failed to load prompt strategy `{strategy}`: {str(exc)}")
+        raise exc
+    return None

src/axolotl/prompt_strategies/messages/chat.py

@@ -0,0 +1,84 @@
+"""
+Chat dataset wrapping strategy for new internal messages representations
+"""
+from typing import Any, Callable, Dict, Optional
+
+from axolotl.core.datasets.chat import TokenizedChatDataset
+from axolotl.core.datasets.transforms.chat_builder import chat_message_transform_builder
+from axolotl.prompt_tokenizers import DatasetWrappingStrategy
+
+
+class ChatMessageDatasetWrappingStrategy(DatasetWrappingStrategy):
+    """
+    Chat dataset wrapping strategy for new internal messages representations
+    """
+
+    def __init__(
+        self,
+        processor,
+        message_transform=None,
+        formatter=None,
+        **kwargs,  # pylint: disable=unused-argument
+    ):
+        """
+        :param processor: tokenizer or image processor
+        :param kwargs:
+        """
+        self.processor = processor
+        self.dataset = None
+        self.message_transform = message_transform
+        self.formatter = formatter
+
+    def wrap_dataset(
+        self,
+        dataset,
+        process_count: Optional[int] = None,
+        keep_in_memory: Optional[bool] = False,
+        **kwargs,  # pylint: disable=unused-argument
+    ):
+        self.dataset = TokenizedChatDataset(
+            dataset,
+            message_transform=self.message_transform,
+            model_transform=self.processor,
+            formatter=self.formatter,
+            process_count=process_count,
+            keep_in_memory=keep_in_memory,
+        )
+        return self.dataset
+
+
+def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+    ds_cfg = ds_cfg or {}
+
+    field_messages = ds_cfg.get("field_messages")
+    message_field_role = ds_cfg.get("message_field_role")
+    message_field_content = ds_cfg.get("message_field_content")
+    message_field_training = ds_cfg.get("message_field_training")
+
+    builder_kwargs = {}
+    if field_messages:
+        builder_kwargs["conversations_field"] = field_messages
+    if message_field_role:
+        builder_kwargs["message_field_role"] = message_field_role
+    if message_field_content:
+        builder_kwargs["message_field_content"] = message_field_content
+    if message_field_training:
+        builder_kwargs["message_field_training"] = message_field_training
+
+    chat_template = ds_cfg.get("chat_template", cfg.get("chat_template", "chatml"))
+    format_message = (
+        lambda x: x  # noqa E731  # pylint: disable=unnecessary-lambda-assignment
+    )
+    if chat_template == "chatml":
+        from axolotl.core.chat.format.chatml import format_message  # noqa F811
+    if chat_template.startswith("llama3"):
+        from axolotl.core.chat.format.llama3x import format_message  # noqa F811
+    message_transform: Callable = chat_message_transform_builder(
+        train_on_inputs=ds_cfg.get("train_on_inputs", False),
+        **builder_kwargs,
+    )
+    strategy = ChatMessageDatasetWrappingStrategy(
+        tokenizer, message_transform=message_transform, formatter=format_message
+    )
+
+    return strategy

src/axolotl/prompt_strategies/orpo/chat_template.py

@@ -5,7 +5,7 @@ from pydantic import BaseModel
 
 from axolotl.prompt_tokenizers import IGNORE_INDEX, PromptTokenizingStrategy
 from axolotl.prompters import Prompter
-from axolotl.utils.chat_templates import chat_templates
+from axolotl.utils.chat_templates import get_chat_template_from_config
 
 
 class Message(BaseModel):
@@ -28,18 +28,13 @@ def load(
     """
     chatml transforms for datasets with system, input, chosen, rejected
     """
-
-    chat_template = chat_templates("chatml")
-    if ds_cfg and "chat_template" in ds_cfg:
-        chat_template = ds_cfg["chat_template"]
-        try:
-            chat_template = chat_templates(chat_template)
-        except ValueError:
-            pass
-    tokenizer.chat_template = chat_template
+    chat_template_string = get_chat_template_from_config(
+        cfg=cfg, ds_cfg=ds_cfg, tokenizer=tokenizer
+    )
+    tokenizer.chat_template = chat_template_string
 
     return ORPOTokenizingStrategy(
-        ORPOPrompter(chat_template, tokenizer),
+        ORPOPrompter(chat_template_string, tokenizer),
         tokenizer,
         cfg.train_on_inputs,
         cfg.sequence_len,
@@ -248,28 +243,30 @@ class ORPOPrompter(Prompter):
 def argilla(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-argument
     dataset_parser = ORPODatasetParsingStrategy()
 
-    chat_template_str = chat_templates(cfg.chat_template)
-
     def transform_fn(sample, tokenizer=None):
         res = {}
 
+        chat_template_string = get_chat_template_from_config(
+            cfg=cfg, tokenizer=tokenizer
+        )
+
         res["prompt"] = tokenizer.apply_chat_template(
             [msg.model_dump() for msg in dataset_parser.get_prompt(sample).messages],
             add_generation_prompt=True,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )
         prompt_str_len = len(res["prompt"])
         res["chosen"] = tokenizer.apply_chat_template(
             [msg.model_dump() for msg in dataset_parser.get_chosen(sample).messages],
             add_generation_prompt=False,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )[prompt_str_len:]
         res["rejected"] = tokenizer.apply_chat_template(
             [msg.model_dump() for msg in dataset_parser.get_rejected(sample).messages],
             add_generation_prompt=False,
-            chat_template=chat_template_str,
+            chat_template=chat_template_string,
             tokenize=False,
         )[prompt_str_len:]
 

src/axolotl/prompt_strategies/sharegpt.py

@@ -1,220 +0,0 @@
-"""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):
-        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

@@ -1,28 +0,0 @@
-"""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

@@ -1,17 +1,12 @@
 """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.monkeypatch.fastchat_conversation_turns import (
-    add_get_turns_to_conversation,
-)
-from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
+from axolotl.prompters import Prompter
 
 LOG = logging.getLogger("axolotl")
 
@@ -21,8 +16,6 @@ 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):
     """
@@ -30,6 +23,12 @@ class InvalidDataException(Exception):
     """
 
 
+class DatasetWrappingStrategy(abc.ABC):
+    """
+    Abstract class for wrapping datasets for Chat Messages
+    """
+
+
 class PromptTokenizingStrategy(abc.ABC):
     """
     Abstract class for tokenizing strategies
@@ -325,154 +324,6 @@ 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

@@ -5,7 +5,6 @@ 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
@@ -262,166 +261,10 @@ class ReflectAlpacaPrompter(Prompter):
         )
 
 
-SHAREGPT_ASSERTION_FAILED_ROLE = (
+ALTERNATING_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/train.py

@@ -10,7 +10,6 @@ from typing import Optional, Tuple, Union
 
 import torch
 import transformers.modelcard
-from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import save_fsdp_model
 from datasets import Dataset
@@ -24,7 +23,7 @@ from axolotl.core.tokenizer_utils import fix_untrained_tokens
 from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.freeze import freeze_layers_except
-from axolotl.utils.models import load_model, load_tokenizer
+from axolotl.utils.models import load_model, load_processor, load_tokenizer
 from axolotl.utils.trainer import setup_trainer
 
 try:
@@ -69,6 +68,9 @@ def train(
         main_process_only=True,
     )
     tokenizer = load_tokenizer(cfg)
+    processor = None
+    if cfg.is_multimodal:
+        processor = load_processor(cfg, tokenizer)
 
     train_dataset = dataset_meta.train_dataset
     eval_dataset = dataset_meta.eval_dataset
@@ -94,10 +96,11 @@ def train(
     if cfg.adapter:
         msg += " and peft_config..."
     LOG.debug(msg)
-    # we wait unitl the last possible moment to setup Accelerator
-    Accelerator()
-    model, peft_config = load_model(cfg, tokenizer, inference=cli_args.inference)
-    model.generation_config.do_sample = True
+    model, peft_config = load_model(
+        cfg, tokenizer, processor=processor, inference=cli_args.inference
+    )
+    if model.generation_config is not None:
+        model.generation_config.do_sample = True
 
     model_ref = None
     if cfg.rl and cfg.rl != "orpo":
@@ -122,6 +125,7 @@ def train(
         eval_dataset,
         (model, model_ref, peft_config),
         tokenizer,
+        processor,
         total_num_steps,
     )
 
@@ -256,8 +260,10 @@ def train(
 
     if not cfg.hub_model_id:
         try:
-            trainer.create_model_card(model_name=cfg.output_dir.lstrip("./"))
-        except AttributeError:
+            trainer.create_model_card(
+                model_name=cfg.output_dir.lstrip("./").encode("utf-8").decode("utf-8")
+            )
+        except (AttributeError, UnicodeDecodeError):
             pass
     elif cfg.hub_model_id:
         # defensively push to the hub to ensure the model card is updated

src/axolotl/utils/__init__.py

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

src/axolotl/utils/callbacks/__init__.py

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

src/axolotl/utils/callbacks/comet_.py

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

src/axolotl/utils/collators/__init__.py

@@ -0,0 +1,10 @@
+"""
+shared axolotl collators for multipack, mamba, multimodal
+"""
+from .batching import (  # noqa: F401
+    BatchSamplerDataCollatorForSeq2Seq,
+    DataCollatorForSeq2Seq,
+    PretrainingBatchSamplerDataCollatorForSeq2Seq,
+    V2BatchSamplerDataCollatorForSeq2Seq,
+)
+from .mamba import MambaDataCollator  # noqa: F401

src/axolotl/utils/collators/batching.py

@@ -1,17 +1,14 @@
 """
 DataCollator for axolotl to pad labels and position_ids for packed sequences
 """
+
 from dataclasses import dataclass
-from typing import Any, Dict, Optional, Sequence, Union
+from typing import Any, Optional, Union
 
 import numpy as np
-import torch
-import transformers
 from transformers import PreTrainedTokenizerBase
 from transformers.utils import PaddingStrategy
 
-IGNORE_INDEX = -100
-
 
 @dataclass
 class DataCollatorForSeq2Seq:
@@ -183,34 +180,6 @@ class V2BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
         return super().__call__(out_features, return_tensors=return_tensors)
 
 
-@dataclass
-class MambaDataCollator:
-    """
-    Collator for State Space Models (Mamba)
-    """
-
-    tokenizer: transformers.PreTrainedTokenizer
-
-    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
-        input_ids, labels = tuple(
-            [torch.LongTensor(instance[key]) for instance in instances]
-            for key in ("input_ids", "labels")
-        )
-        input_ids = torch.nn.utils.rnn.pad_sequence(
-            input_ids,
-            batch_first=True,
-            padding_value=self.tokenizer.pad_token_id,
-        )
-        labels = torch.nn.utils.rnn.pad_sequence(
-            labels, batch_first=True, padding_value=IGNORE_INDEX
-        )
-
-        return {
-            "input_ids": input_ids,
-            "labels": labels,
-        }
-
-
 @dataclass
 class PretrainingBatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
     """

src/axolotl/utils/collators/core.py

@@ -0,0 +1,4 @@
+"""
+basic shared collator constants
+"""
+IGNORE_INDEX = -100

src/axolotl/utils/collators/mamba.py

@@ -0,0 +1,38 @@
+"""
+collators for Mamba
+"""
+from dataclasses import dataclass
+from typing import Dict, Sequence
+
+import torch
+import transformers
+
+from axolotl.utils.collators.core import IGNORE_INDEX
+
+
+@dataclass
+class MambaDataCollator:
+    """
+    Collator for State Space Models (Mamba)
+    """
+
+    tokenizer: transformers.PreTrainedTokenizer
+
+    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
+        input_ids, labels = tuple(
+            [torch.LongTensor(instance[key]) for instance in instances]
+            for key in ("input_ids", "labels")
+        )
+        input_ids = torch.nn.utils.rnn.pad_sequence(
+            input_ids,
+            batch_first=True,
+            padding_value=self.tokenizer.pad_token_id,
+        )
+        labels = torch.nn.utils.rnn.pad_sequence(
+            labels, batch_first=True, padding_value=IGNORE_INDEX
+        )
+
+        return {
+            "input_ids": input_ids,
+            "labels": labels,
+        }

src/axolotl/utils/collators/mm_chat.py

@@ -0,0 +1,83 @@
+"""
+Collators for multi-modal chat messages and packing
+"""
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Union
+
+from PIL import Image
+from transformers import PreTrainedTokenizerBase, ProcessorMixin
+from transformers.data.data_collator import DataCollatorMixin
+from transformers.utils import PaddingStrategy
+
+
+@dataclass
+class MultiModalChatDataCollator(DataCollatorMixin):
+    """
+    Collator for multi-modal chat messages
+    """
+
+    tokenizer: PreTrainedTokenizerBase
+    processor: ProcessorMixin
+    return_tensors: str = "pt"
+    chat_template: Optional[str] = None
+    packing: bool = False
+    max_images: int = -1
+    padding: Union[bool, str, PaddingStrategy] = True
+    pad_to_multiple_of: Optional[int] = None
+
+    def __post_init__(self):
+        if self.packing:
+            raise ValueError("Packing is currently not supported.")
+
+    def torch_call(
+        self, examples: List[Union[List[int], Any, Dict[str, Any]]]
+    ) -> Dict[str, Any]:
+        # Handle dict or lists with proper padding and conversion to tensor.
+
+        return self.__class__.process_rows(
+            examples, self.processor, self.chat_template, self.max_images
+        )
+
+    @staticmethod
+    def process_rows(examples, processor, chat_template, max_images, length_only=False):
+        # HINT: use `_torch_collate_batch` to stack and pad tensors
+        # see also DataCollatorWithFlattening and DefaultDataCollator
+
+        # *** This is COPIED from the trl example sft_vlm.py code ***
+        # use this as a starting point
+
+        # Get the texts and images, and apply the chat template
+        texts = [
+            processor.apply_chat_template(
+                example["messages"], chat_template=chat_template, tokenize=False
+            )
+            for example in examples
+        ]
+        images = [
+            Image.open(example["images"])
+            if isinstance(example["images"], str)
+            else example["images"]
+            for example in examples
+        ]
+
+        if max_images > 0:
+            images = [img_batch[:max_images] for img_batch in images]
+
+        # Tokenize the texts and process the images
+        batch = processor(text=texts, images=images, return_tensors="pt", padding=True)
+
+        # The labels are the input_ids, and we mask the padding tokens in the loss computation
+        labels = batch["input_ids"].clone()
+        labels[labels == processor.tokenizer.pad_token_id] = -100  #
+        # Ignore the image token index in the loss computation (model specific)
+        image_token_id = processor.tokenizer.convert_tokens_to_ids(
+            processor.image_token
+        )
+        labels[labels == image_token_id] = -100
+        batch["labels"] = labels
+
+        if length_only:
+            return {
+                "length": [len(sample["input_ids"]) for sample in batch["input_ids"]]
+            }
+        return batch

src/axolotl/utils/comet_.py

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

src/axolotl/utils/config/__init__.py

@@ -121,15 +121,36 @@ def normalize_config(cfg):
         cfg.base_model_config = cfg.base_model
 
     model_config = load_model_config(cfg)
-    cfg.model_config_type = model_config.model_type
 
     cfg.tokenizer_config = (
         cfg.tokenizer_config or cfg.base_model_config or cfg.base_model
     )
 
+    cfg.is_multimodal = (
+        hasattr(model_config, "model_type")
+        and model_config.model_type in ["llava", "mllama"]
+        or any(
+            multimodal_name in cfg.base_model.lower()
+            for multimodal_name in [
+                "pixtral",
+            ]
+        )
+        or cfg.is_multimodal
+    )
+    if cfg.is_multimodal:
+        cfg.processor_config = (
+            cfg.processor_config or cfg.base_model_config or cfg.base_model
+        )
+        model_config = model_config.text_config
+
+    cfg.model_config_type = model_config.model_type
+
     # figure out if the model is llama
     cfg.is_llama_derived_model = (
-        (hasattr(model_config, "model_type") and model_config.model_type == "llama")
+        (
+            hasattr(model_config, "model_type")
+            and model_config.model_type == ["llama", "mllama_text_model"]
+        )
         or cfg.is_llama_derived_model
         or "llama" in cfg.base_model.lower()
         or (cfg.type_of_model and "llama" in cfg.type_of_model.lower())
@@ -194,11 +215,6 @@ 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
@@ -207,6 +223,7 @@ def normalize_cfg_datasets(cfg):
                         f"updating dataset {ds_cfg.path} with `chat_template: {cfg.chat_template}` to match your chat_template"
                     )
                     cfg.datasets[idx].chat_template = cfg.chat_template
+                    cfg.datasets[idx].chat_template_jinja = cfg.chat_template_jinja
 
 
 def validate_config(cfg: DictDefault, capabilities: Optional[dict] = None):
@@ -439,27 +456,6 @@ 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

@@ -8,9 +8,16 @@ import logging
 import os
 from enum import Enum
 from importlib.metadata import version
-from typing import Any, Dict, List, Literal, Optional, Tuple, Union
+from typing import Annotated, Any, Dict, List, Literal, Optional, Tuple, Union
 
-from pydantic import BaseModel, Field, conlist, field_validator, model_validator
+from pydantic import (
+    BaseModel,
+    Field,
+    StringConstraints,
+    conlist,
+    field_validator,
+    model_validator,
+)
 from transformers import SchedulerType
 from transformers.training_args import OptimizerNames
 
@@ -21,6 +28,38 @@ LOG = logging.getLogger("axolotl.utils.config.models.input")
 SUPPORTED_METRICS = {"sacrebleu", "comet", "ter", "chrf", "perplexity"}
 
 
+class RLType(str, Enum):
+    """RL trainer type configuration subset"""
+
+    dpo = "dpo"  # pylint: disable=invalid-name
+    ipo = "ipo"  # pylint: disable=invalid-name
+    orpo = "orpo"  # pylint: disable=invalid-name
+    kto = "kto"  # pylint: disable=invalid-name
+    simpo = "simpo"  # pylint: disable=invalid-name
+
+
+class ChatTemplate(str, Enum):
+    """Chat templates configuration subset"""
+
+    alpaca = "alpaca"  # pylint: disable=invalid-name
+    chatml = "chatml"  # pylint: disable=invalid-name
+    mistral_v1 = "mistral_v1"  # pylint: disable=invalid-name
+    mistral_v2v3 = "mistral_v2v3"  # pylint: disable=invalid-name
+    mistral_v3_tekken = "mistral_v3_tekken"  # pylint: disable=invalid-name
+    gemma = "gemma"  # pylint: disable=invalid-name
+    cohere = "cohere"  # pylint: disable=invalid-name
+    llama3 = "llama3"  # pylint: disable=invalid-name
+    llama3_2_vision = "llama3_2_vision"  # pylint: disable=invalid-name
+    phi_3 = "phi_3"  # pylint: disable=invalid-name
+    phi_35 = "phi_35"  # pylint: disable=invalid-name
+    deepseek_v2 = "deepseek_v2"  # pylint: disable=invalid-name
+    jamba = "jamba"  # pylint: disable=invalid-name
+    jinja = "jinja"  # pylint: disable=invalid-name
+    qwen_25 = "qwen_25"  # pylint: disable=invalid-name
+    tokenizer_default = "tokenizer_default"  # pylint: disable=invalid-name
+    exaone = "exaone"  # pylint: disable=invalid-name
+
+
 class DeprecatedParameters(BaseModel):
     """configurations that are deprecated"""
 
@@ -102,14 +141,22 @@ class SFTDataset(BaseModel):
     path: Optional[str] = None
     split: Optional[str] = None
     type: Optional[Union[str, UserDefinedPrompterType]] = None
+    input_transform: Optional[str] = None
     shards: Optional[int] = None
     conversation: Optional[str] = None
-    chat_template: Optional[str] = None
+    # Do not make this too strict or it will break the validator to choose different dataset class
+    chat_template: Optional[
+        Union[
+            ChatTemplate,
+            str,
+        ]
+    ] = None
+    chat_template_jinja: Optional[str] = None
     data_files: Optional[Union[str, List[str]]] = None
+    input_format: Optional[str] = None
     name: Optional[str] = None
     ds_type: Optional[str] = None
     train_on_split: Optional[str] = None
-
     field: Optional[str] = None
     field_human: Optional[str] = None
     field_model: Optional[str] = None
@@ -120,11 +167,31 @@ class SFTDataset(BaseModel):
     message_field_training_detail: Optional[str] = None
     roles_to_train: Optional[List[str]] = None
     train_on_eos: Optional[str] = None
-
     roles: Optional[Dict[str, List[str]]] = None
     drop_system_message: Optional[bool] = None
-
     trust_remote_code: Optional[bool] = False
+    revision: Optional[str] = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_chat_template_config(cls, data):
+        # Set chat_template to tokenizer_default if not set
+        if data.get("type") == "chat_template" and not data.get("chat_template"):
+            data["chat_template"] = ChatTemplate.tokenizer_default
+
+        # if chat_template is set to jinja, chat_template_jinja is required
+        if data.get("chat_template") == ChatTemplate.jinja and not data.get(
+            "chat_template_jinja"
+        ):
+            raise ValueError(
+                "chat_template_jinja is required when chat_template is set to jinja"
+            )
+
+        # If chat_template_jinja is set, set chat_template to jinja
+        if data.get("chat_template_jinja") and not data.get("chat_template"):
+            data["chat_template"] = ChatTemplate.jinja
+
+        return data
 
 
 class UserDefinedDPOType(BaseModel):
@@ -146,6 +213,7 @@ class DPODataset(BaseModel):
     split: Optional[str] = None
     type: Optional[Union[UserDefinedDPOType, str]] = None
     data_files: Optional[List[str]] = None
+    revision: Optional[str] = None
 
 
 class UserDefinedKTOType(BaseModel):
@@ -167,31 +235,7 @@ class KTODataset(BaseModel):
     type: Optional[Union[UserDefinedKTOType, str]] = None
     data_files: Optional[List[str]] = None
     trust_remote_code: Optional[bool] = False
-
-
-class RLType(str, Enum):
-    """RL trainer type configuration subset"""
-
-    dpo = "dpo"  # pylint: disable=invalid-name
-    ipo = "ipo"  # pylint: disable=invalid-name
-    orpo = "orpo"  # pylint: disable=invalid-name
-    kto = "kto"  # pylint: disable=invalid-name
-    simpo = "simpo"  # pylint: disable=invalid-name
-
-
-class ChatTemplate(str, Enum):
-    """Chat templates configuration subset"""
-
-    alpaca = "alpaca"  # pylint: disable=invalid-name
-    chatml = "chatml"  # pylint: disable=invalid-name
-    inst = "inst"  # pylint: disable=invalid-name
-    gemma = "gemma"  # pylint: disable=invalid-name
-    cohere = "cohere"  # pylint: disable=invalid-name
-    llama3 = "llama3"  # pylint: disable=invalid-name
-    phi_3 = "phi_3"  # pylint: disable=invalid-name
-    phi_35 = "phi_35"  # pylint: disable=invalid-name
-    deepseek_v2 = "deepseek_v2"  # pylint: disable=invalid-name
-    jamba = "jamba"  # pylint: disable=invalid-name
+    revision: Optional[str] = None
 
 
 class LoftQConfig(BaseModel):
@@ -228,11 +272,12 @@ class LoraConfig(BaseModel):
     lora_r: Optional[int] = None
     lora_alpha: Optional[int] = None
     lora_fan_in_fan_out: Optional[bool] = None
-    lora_target_modules: Optional[List[str]] = None
+    lora_target_modules: Optional[Union[str, List[str]]] = None
     lora_target_linear: Optional[bool] = None
     lora_modules_to_save: Optional[List[str]] = None
     lora_dropout: Optional[float] = 0.0
     peft_layers_to_transform: Optional[List[int]] = None
+    peft_layers_pattern: Optional[List[str]] = None
     peft: Optional[PeftConfig] = None
     peft_use_dora: Optional[bool] = None
     peft_use_rslora: Optional[bool] = None
@@ -298,6 +343,13 @@ class LoraConfig(BaseModel):
                     raise ValueError("Require cfg.load_in_4bit to be True for qlora")
         return self
 
+    @field_validator("loraplus_lr_embedding")
+    @classmethod
+    def convert_loraplus_lr_embedding(cls, loraplus_lr_embedding):
+        if loraplus_lr_embedding and isinstance(loraplus_lr_embedding, str):
+            loraplus_lr_embedding = float(loraplus_lr_embedding)
+        return loraplus_lr_embedding
+
 
 class ReLoRAConfig(BaseModel):
     """ReLoRA configuration subset"""
@@ -321,6 +373,9 @@ class ModelInputConfig(BaseModel):
     tokenizer_type: Optional[str] = Field(
         default=None, metadata={"help": "transformers tokenizer class"}
     )
+    processor_type: Optional[str] = Field(
+        default=None, metadata={"help": "transformers processor class"}
+    )
     trust_remote_code: Optional[bool] = None
 
     model_kwargs: Optional[Dict[str, Any]] = None
@@ -372,7 +427,6 @@ class HyperparametersConfig(BaseModel):
                 "ao_adamw_4bit",
                 "ao_adamw_8bit",
                 "ao_adamw_fp8",
-                "shampoo",
             ],
         ]
     ] = OptimizerNames.ADAMW_HF.value
@@ -385,12 +439,6 @@ class HyperparametersConfig(BaseModel):
             "help": "The target modules to optimize, i.e. the module names that you would like to train."
         },
     )
-    optim_shampoo_grafting_config_type: Optional[
-        Literal["adam", "sgd", "adagrad"]
-    ] = None
-    optim_shampoo_grafting_config_kwargs: Optional[Dict[str, Any]] = None
-    optim_shampoo_betas: Optional[Tuple[float, float]] = None
-
     torchdistx_path: Optional[str] = None
     lr_scheduler: Optional[Union[SchedulerType, Literal["one_cycle"]]] = "cosine"
     lr_scheduler_kwargs: Optional[Dict[str, Any]] = None
@@ -439,6 +487,7 @@ class MLFlowConfig(BaseModel):
     use_mlflow: Optional[bool] = None
     mlflow_tracking_uri: Optional[str] = None
     mlflow_experiment_name: Optional[str] = None
+    mlflow_run_name: Optional[str] = None
     hf_mlflow_log_artifacts: Optional[bool] = None
 
 
@@ -484,6 +533,19 @@ class WandbConfig(BaseModel):
         return data
 
 
+class CometConfig(BaseModel):
+    """Comet configuration subset"""
+
+    use_comet: Optional[bool] = None
+    comet_api_key: Optional[str] = None
+    comet_workspace: Optional[str] = None
+    comet_project_name: Optional[str] = None
+    comet_experiment_key: Optional[str] = None
+    comet_mode: Optional[str] = None
+    comet_online: Optional[bool] = None
+    comet_experiment_config: Optional[Dict[str, Any]] = None
+
+
 class GradioConfig(BaseModel):
     """Gradio configuration subset"""
 
@@ -504,6 +566,7 @@ class AxolotlInputConfig(
     HyperparametersConfig,
     WandbConfig,
     MLFlowConfig,
+    CometConfig,
     LISAConfig,
     GradioConfig,
     RemappedParameters,
@@ -521,8 +584,13 @@ class AxolotlInputConfig(
     resume_from_checkpoint: Optional[str] = None
     auto_resume_from_checkpoints: Optional[bool] = None
     resize_token_embeddings_to_32x: Optional[bool] = None
+    mean_resizing_embeddings: Optional[bool] = False
 
     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
@@ -530,6 +598,7 @@ class AxolotlInputConfig(
     dataset_prepared_path: Optional[str] = None
     dataset_shard_num: Optional[int] = None
     dataset_shard_idx: Optional[int] = None
+    skip_prepare_dataset: Optional[bool] = False
 
     pretraining_dataset: Optional[  # type: ignore
         conlist(Union[PretrainingDataset, SFTDataset], min_length=1)
@@ -688,7 +757,13 @@ class AxolotlInputConfig(
     gpu_memory_limit: Optional[Union[int, str]] = None
     low_cpu_mem_usage: Optional[bool] = None
 
-    chat_template: Optional[ChatTemplate] = None
+    chat_template: Optional[
+        Union[
+            ChatTemplate,
+            Annotated[str, StringConstraints(pattern="^tokenizer_default_fallback_")],
+        ]
+    ] = None
+    chat_template_jinja: Optional[str] = None
     default_system_message: Optional[str] = None
 
     fix_untrained_tokens: Optional[bool] = None
@@ -708,26 +783,16 @@ class AxolotlInputConfig(
 
     @field_validator("datasets", mode="before")
     @classmethod
-    def fix_sharegpt_datasets(cls, datasets):
-        for idx, ds_cfg in enumerate(datasets):
-            if not ds_cfg["type"]:
+    def deprecate_sharegpt_datasets(cls, datasets):
+        for _, ds_cfg in enumerate(datasets):
+            if not ds_cfg.get("type"):
                 continue
-            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"
+
+            if ds_cfg["type"].startswith("sharegpt"):
+                raise ValueError(
+                    "`type: sharegpt.*` is deprecated. Please use `type: chat_template` instead."
                 )
+
         return datasets
 
     @model_validator(mode="before")
@@ -797,6 +862,23 @@ class AxolotlInputConfig(
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_chat_template_config(cls, data):
+        # if chat_template is set to jinja, chat_template_jinja is required
+        if data.get("chat_template") == ChatTemplate.jinja and not data.get(
+            "chat_template_jinja"
+        ):
+            raise ValueError(
+                "chat_template_jinja is required when chat_template is set to jinja"
+            )
+
+        # If chat_template_jinja is set, set chat_template to jinja
+        if data.get("chat_template_jinja") and not data.get("chat_template"):
+            data["chat_template"] = ChatTemplate.jinja
+
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_sample_packing_wo_flash(cls, data):
@@ -827,6 +909,17 @@ class AxolotlInputConfig(
             )
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def hint_reward_model_pad(cls, data):
+        if data.get("reward_model") and not data.get("pad_to_sequence_len"):
+            LOG.warning(
+                "`pad_to_sequence_len: true` is recommended when using reward_model"
+            )
+            if data.get("pad_to_sequence_len") is None:
+                data["pad_to_sequence_len"] = True
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_gas_bsz(cls, data):
@@ -960,6 +1053,26 @@ class AxolotlInputConfig(
                 "evaluation_strategy must be empty or set to `steps` when used with evals_per_epoch."
             )
 
+        if data.get("do_bench_eval") and not (
+            data.get("evals_per_epoch") or data.get("eval_steps")
+        ):
+            raise ValueError(
+                "do_bench_eval requires evals_per_epoch or eval_steps to be set."
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_test_datasets_bench(cls, data):
+        if (
+            data.get("do_bench_eval")
+            and not data.get("test_datasets")
+            and not data.get("val_set_size")
+        ):
+            LOG.warning(
+                "`do_bench_eval` needs a test dataset to run evals, adding an empty test_dataset."
+            )
+            data["test_datasets"] = [{"path": "axolotl-ai-co/empty-test-ds"}]
         return data
 
     @model_validator(mode="before")
@@ -997,6 +1110,18 @@ class AxolotlInputConfig(
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_mm_prepare(cls, data):
+        if data.get("skip_prepare_dataset"):
+            if data.get("remove_unused_columns") is None:
+                LOG.info(
+                    "setting `remove_unused_columns: false` for skip_prepare_dataset"
+                )
+                data["remove_unused_columns"] = False
+
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_warmup(cls, data):
@@ -1024,12 +1149,20 @@ class AxolotlInputConfig(
         return neftune_noise_alpha
 
     @model_validator(mode="after")
-    def check(self):
+    def check_rl_beta(self):
         if self.dpo_beta and not self.rl_beta:
             self.rl_beta = self.dpo_beta
             del self.dpo_beta
         return self
 
+    @model_validator(mode="after")
+    def check_simpo_warmup(self):
+        if self.rl == "simpo" and self.warmup_ratio:
+            raise ValueError(
+                "warmup_ratio is not supported with the simpo trainer. Please use `warmup_steps` instead"
+            )
+        return self
+
     @model_validator(mode="before")
     @classmethod
     def check_frozen(cls, data):
@@ -1044,6 +1177,15 @@ class AxolotlInputConfig(
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_peft_layers_pattern(cls, data):
+        if data.get("peft_layers_pattern") and not data.get("peft_layers_to_transform"):
+            raise ValueError(
+                "peft_layers_pattern requires peft_layers_to_transform to be set"
+            )
+        return data
+
     @model_validator(mode="after")
     def check_fft_possible_bad_config(self):
         if (

src/axolotl/utils/data/rl.py

@@ -90,6 +90,7 @@ def load_prepare_dpo_datasets(cfg):
                 ds = load_dataset(  # pylint: disable=invalid-name
                     ds_cfg["path"],
                     split=ds_cfg["split"],
+                    revision=ds_cfg.get("revision", None),
                 )
                 split_datasets.insert(i, ds)