use low_cpu_mem_usage with ds zero 1 or 2

* fix(readme): clarify custom user prompt

* chore: update example to show use case of setting field

.github/FUNDING.yml

@@ -3,11 +3,11 @@
 github: OpenAccess-AI-Collective # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
 patreon: # Replace with a single Patreon username
 open_collective: # Replace with a single Open Collective username
-ko_fi: # Replace with a single Ko-fi username
+ko_fi: axolotl_ai # Replace with a single Ko-fi username
 tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
 community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
 liberapay: # Replace with a single Liberapay username
 issuehunt: # Replace with a single IssueHunt username
 otechie: # Replace with a single Otechie username
 lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
-custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
+custom: ['https://quickchart.io/qr?text=bitcoin%3Abc1qxlgwlqwfea5s2cxm42xqsfmwjct0rj8w8ea5np&size=480&centerImageUrl=https%3A%2F%2Fupload.wikimedia.org%2Fwikipedia%2Fcommons%2Fthumb%2F4%2F46%2FBitcoin.svg%2F64px-Bitcoin.svg.png'] # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

.github/PULL_REQUEST_TEMPLATE.md

@@ -20,3 +20,8 @@
 ## Types of changes
 
 <!--- What types of changes does your code introduce? Put an `x` in all the boxes that apply: -->
+
+## Social Handles (Optional)
+
+<!-- Thanks for submitting a bugfix or enhancement. -->
+<!-- We'd love to show our thanks to you on Twitter & Discord if you provide your handle -->

.github/workflows/base.yml

@@ -28,7 +28,12 @@ jobs:
           - cuda: "118"
             cuda_version: 11.8.0
             python_version: "3.10"
-            pytorch: 2.1.0
+            pytorch: 2.1.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
+          - cuda: "121"
+            cuda_version: 12.1.0
+            python_version: "3.10"
+            pytorch: 2.1.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
     steps:
       - name: Checkout

.github/workflows/lint.yml

@@ -0,0 +1,22 @@
+name: lint
+on:
+  # check on PRs, and manual triggers
+  pull_request:
+      paths:
+       - '**.py'
+       - 'requirements.txt'
+       - '.github/workflows/*.yml'
+       - "*.md"
+  workflow_dispatch:
+
+jobs:
+  pre-commit:
+    name: pre-commit
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: "3.9"
+          cache: 'pip' # caching pip dependencies
+      - uses: pre-commit/action@v3.0.0

.github/workflows/main.yml

@@ -27,38 +27,56 @@ jobs:
           - cuda: 118
             cuda_version: 11.8.0
             python_version: "3.10"
-            pytorch: 2.1.0
+            pytorch: 2.1.1
+            axolotl_extras:
+          - cuda: 121
+            cuda_version: 12.1.0
+            python_version: "3.10"
+            pytorch: 2.1.1
             axolotl_extras:
     runs-on: [self-hosted, gpu, docker]
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
       - name: Docker metadata
         id: metadata
-        uses: docker/metadata-action@v3
+        uses: docker/metadata-action@v5
         with:
           images: winglian/axolotl
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
       - name: Login to Docker Hub
-        uses: docker/login-action@v2
+        uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
-      - name: Build
-        uses: docker/build-push-action@v4
+      # guidance for testing before pushing: https://docs.docker.com/build/ci/github-actions/test-before-push/
+      - name: Build and export to Docker
+        uses: docker/build-push-action@v5
         with:
           context: .
+          load: true
           build-args: |
             BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
             CUDA=${{ matrix.cuda }}
             PYTORCH_VERSION=${{ matrix.pytorch }}
           file: ./docker/Dockerfile
-          push: ${{ github.event_name != 'pull_request' }}
           tags: |
             ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
           labels: ${{ steps.metadata.outputs.labels }}
+      - name: Unit Tests
+        run: |
+          docker run --rm ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }} pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
+      - name: Push to Docker Hub
+        if: github.event_name != 'pull_request'
+        run: |
+          docker push ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+          latest_tag=${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
+          if [ -n "$latest_tag" ]; then
+            docker push "$latest_tag"
+          fi
+
   build-axolotl-runpod:
     needs: build-axolotl
     if: github.repository_owner == 'OpenAccess-AI-Collective'
@@ -80,34 +98,41 @@ jobs:
           - cuda: 118
             cuda_version: 11.8.0
             python_version: "3.10"
-            pytorch: 2.1.0
+            pytorch: 2.1.1
+            axolotl_extras:
+          - cuda: 121
+            cuda_version: 12.1.0
+            python_version: "3.10"
+            pytorch: 2.1.1
             axolotl_extras:
     runs-on: [self-hosted, gpu, docker]
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
       - name: Docker metadata
         id: metadata
-        uses: docker/metadata-action@v3
+        uses: docker/metadata-action@v5
         with:
-          images: winglian/axolotl-runpod
+          images: winglian/axolotl-cloud
       - name: Login to Docker Hub
-        uses: docker/login-action@v2
+        uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2
       - name: Build
-        uses: docker/build-push-action@v4
+        uses: docker/build-push-action@v5
         with:
           context: .
           build-args: |
             BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             CUDA=${{ matrix.cuda }}
-          file: ./docker/Dockerfile-runpod
+          file: ./docker/Dockerfile-cloud
           push: ${{ github.event_name != 'pull_request' }}
           tags: |
              ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+             winglian/axolotl-runpod:main-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
              ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
+             ${{ (matrix.is_latest) && format('{0}-latest', 'winglian/axolotl-runpod:main') || '' }}
           labels: ${{ steps.metadata.outputs.labels }}

.github/workflows/pypi.yml

@@ -34,11 +34,11 @@ jobs:
         run: echo ::set-output name=TAG_NAME::$(echo $GITHUB_REF | cut -d / -f 3)
 
       - name: Update version in setup.py
-        run: >-
+        run: |
           sed -i -E 's/version="([0-9.]+)",/version="${{ steps.tag.outputs.TAG_NAME }}",/g' setup.py
 
       - name: Build a binary wheel
-        run: >-
+        run: |
           python setup.py sdist bdist_wheel
 
       - name: Publish package distributions to PyPI

.github/workflows/tests.yml

@@ -7,10 +7,12 @@ on:
     paths:
       - '**.py'
       - 'requirements.txt'
+      - '.github/workflows/*.yml'
   pull_request:
       paths:
        - '**.py'
        - 'requirements.txt'
+       - '.github/workflows/*.yml'
   workflow_dispatch:
 
 jobs:
@@ -31,7 +33,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python_version: ["3.9", "3.10"]
+        python_version: ["3.9", "3.10", "3.11"]
     timeout-minutes: 10
 
     steps:
@@ -53,29 +55,54 @@ jobs:
         run: |
           pytest --ignore=tests/e2e/ tests/
 
-  e2e-test:
-    name: E2E Tests
-    runs-on: [self-hosted, gpu]
-    timeout-minutes: 20
+  docker-e2e-tests:
+    if: github.repository_owner == 'OpenAccess-AI-Collective'
+    # this job needs to be run on self-hosted GPU runners...
+    runs-on: [self-hosted, gpu, docker]
+    timeout-minutes: 30
     needs: [pre-commit, pytest]
 
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: 118
+            cuda_version: 11.8.0
+            python_version: "3.10"
+            pytorch: 2.0.1
+          - cuda: 121
+            cuda_version: 12.1.0
+            python_version: "3.10"
+            pytorch: 2.1.1
     steps:
-      - name: Check out repository code
-        uses: actions/checkout@v3
-
-      - name: Setup Python
-        uses: actions/setup-python@v4
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Docker metadata
+        id: metadata
+        uses: docker/metadata-action@v5
         with:
-          python-version: "3.10"
-#          cache: 'pip' # caching pip dependencies
-
-      - name: Install dependencies
+          images: winglian/axolotl-tests
+      - name: Build Docker image
         run: |
-          pip3 install --extra-index-url https://download.pytorch.org/whl/cu118 -U torch==2.0.1
-          pip3 uninstall -y transformers accelerate
-          pip3 install -U -e .[flash-attn,mamba-ssm]
-          pip3 install -r requirements-tests.txt
-
-      - name: Run e2e tests
+          # Set up build arguments
+          BASE_TAG="main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}"
+          CUDA="${{ matrix.cuda }}"
+          PYTORCH_VERSION="${{ matrix.pytorch }}"
+          # Build the Docker image
+          docker build . \
+            --file ./docker/Dockerfile-tests \
+            --build-arg BASE_TAG=$BASE_TAG \
+            --build-arg CUDA=$CUDA \
+            --build-arg GITHUB_REF=$GITHUB_REF \
+            --build-arg PYTORCH_VERSION=$PYTORCH_VERSION \
+            --tag ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }} \
+            --no-cache
+      - name: Unit Tests w docker image
         run: |
-          pytest tests/e2e/
+          docker run --rm ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }} pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
+      - name: GPU Unit Tests w docker image
+        run: |
+          docker run --privileged --gpus "all" --env WANDB_DISABLED=true --rm ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }} pytest --ignore=tests/e2e/patched/ /workspace/axolotl/tests/e2e/
+      - name: GPU Unit Tests monkeypatched w docker image
+        run: |
+          docker run --privileged --gpus "all" --env WANDB_DISABLED=true --rm ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }} pytest /workspace/axolotl/tests/e2e/patched/

.gitignore

@@ -1,5 +1,7 @@
 **/axolotl.egg-info
 configs
+last_run_prepared/
+.vscode
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

.vscode/README.md

@@ -0,0 +1 @@
+See [docs/debugging.md](../docs/debugging.md) for guidance on how to modify these files to debug axolotl with VSCode.

.vscode/launch.json

@@ -0,0 +1,34 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Debug axolotl prompt - sharegpt",
+            "type": "python",
+            "module": "accelerate.commands.launch",
+            "request": "launch",
+            "args": [
+                "-m", "axolotl.cli.train", "dev_sharegpt.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
+                "--max_steps=1",              // limits training to just one step
+                "--batch_size=1",             // minimizes batch size
+                "--micro_batch_size=1",       // minimizes batch size
+                "--val_set_size=0",           // disables validation
+                "--sample_packing=False",     // disables sample packing which is necessary for small datasets
+                "--eval_sample_packing=False",// disables sample packing on eval set
+                "--dataset_prepared_path=temp_debug/axolotl_outputs/data", // send data outputs to a temp folder
+                "--output_dir=temp_debug/axolotl_outputs/model" // send model outputs to a temp folder
+                ],
+            "console": "integratedTerminal",      // show output in the integrated terminal
+            "cwd": "${workspaceFolder}/devtools", // set working directory to devtools from the root of the project
+            "justMyCode": true,                   // step through only axolotl code
+            "env": {"CUDA_VISIBLE_DEVICES": "0",  // Since we aren't doing distributed training, we need to limit to one GPU
+                    "HF_HOME": "${workspaceFolder}/devtools/temp_debug/.hf-cache"}, // send HF cache to a temp folder
+            "preLaunchTask": "cleanup-for-dataprep", // delete temp folders (see below)
+        }
+    ]
+}

.vscode/tasks.json

@@ -0,0 +1,27 @@
+//this file is used by launch.json
+{
+  "version": "2.0.0",
+  "tasks": [
+    // this task changes into the devtools directory and deletes the temp_debug/axolotl_outputs folder
+    {
+      "label": "delete-outputs",
+      "type": "shell",
+      "command": "rm -rf temp_debug/axolotl_outputs",
+      "options":{ "cwd": "${workspaceFolder}/devtools"},
+      "problemMatcher": []
+    },
+    // this task changes into the devtools directory and deletes the `temp_debug/.hf-cache/datasets` folder
+    {
+      "label": "delete-temp-hf-dataset-cache",
+      "type": "shell",
+      "command": "rm -rf temp_debug/.hf-cache/datasets",
+      "options":{ "cwd": "${workspaceFolder}/devtools"},
+      "problemMatcher": []
+    },
+      // this task combines the two tasks above
+    {
+     "label": "cleanup-for-dataprep",
+     "dependsOn": ["delete-outputs", "delete-temp-hf-dataset-cache"],
+    }
+  ]
+}

README.md

@@ -10,7 +10,7 @@ Features:
 - Integrated with xformer, flash attention, 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
+- Log results and optionally checkpoints to wandb or mlflow
 - And more!
 
 
@@ -25,7 +25,7 @@ Features:
 - [Installation](#installation)
   - [Docker](#docker)
   - [Conda/Pip venv](#condapip-venv)
-  - [Runpod](#runpod)
+  - [Cloud GPU](#cloud-gpu) - Runpod, Latitude
   - [LambdaLabs](#lambdalabs)
   - [Windows](#windows)
   - [Launching on public clouds via SkyPilot](#launching-on-public-clouds-via-skypilot)
@@ -36,11 +36,15 @@ Features:
   - [Train](#train)
   - [Inference](#inference)
   - [Merge LORA to Base](#merge-lora-to-base)
+  - [Special Tokens](#special-tokens)
 - [Common Errors](#common-errors-)
+  - [Tokenization Mismatch b/w Training & Inference](#tokenization-mismatch-bw-inference--training)
+- [Debugging Axolotl](#debugging-axolotl)
 - [Need Help?](#need-help-)
 - [Badge](#badge-)
 - [Community Showcase](#community-showcase)
 - [Contributing](#contributing-)
+- [Sponsors](#sponsors-)
 
 </td>
 <td>
@@ -128,6 +132,9 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
   docker compose up -d
   ```
 
+>[!Tip]
+> If you want to debug axolotl or prefer to use Docker as your development environment, see the [debugging guide's section on Docker](docs/debugging.md#debugging-with-docker).
+
   <details>
 
   <summary>Docker advanced</summary>
@@ -135,7 +142,7 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
   A more powerful Docker command to run would be this:
 
   ```bash
-  docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=volume,src=axolotl,target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1
   ```
 
   It additionally:
@@ -165,9 +172,11 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
         ```
         Get the token at huggingface.co/settings/tokens
 
-#### Runpod
+#### Cloud GPU
 
-Use `winglian/axolotl-runpod:main-latest` or use this [direct link](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
+For cloud GPU providers that support docker images, use [`winglian/axolotl-cloud:main-latest`](https://hub.docker.com/r/winglian/axolotl-cloud/tags)
+
+- on RunPod use this [direct link](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
 
 #### LambdaLabs
   <details>
@@ -251,6 +260,13 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"conversations": [{"from": "...", "value": "..."}]}
   ```
+- `llama-2`: the json is the same format as `sharegpt` above, with the following config (see the [config section](#config) for more details)
+    ```yml
+    datasets:
+      - path: <your-path>
+        type: sharegpt
+        conversation: llama-2
+    ```
 - `completion`: raw corpus
   ```json
   {"text": "..."}
@@ -360,7 +376,7 @@ Have dataset(s) in one of the following format (JSONL recommended):
 For a dataset that is preprocessed for instruction purposes:
 
 ```json
-{"instruction": "...", "output": "..."}
+{"input": "...", "output": "..."}
 ```
 
 You can use this example in your YAML config:
@@ -371,6 +387,8 @@ datasets:
     type:
       system_prompt: ""
       field_system: system
+      field_instruction: input
+      field_output: output
       format: "[INST] {instruction} [/INST]"
       no_input_format: "[INST] {instruction} [/INST]"
 ```
@@ -511,6 +529,14 @@ model_config:
     type: # linear | dynamic
     factor: # float
 
+# optional overrides to the bnb 4bit quantization configuration
+# https://huggingface.co/docs/transformers/main/main_classes/quantization#transformers.BitsAndBytesConfig
+bnb_config_kwargs:
+  # These are default values
+  llm_int8_has_fp16_weight: false
+  bnb_4bit_quant_type: nf4
+  bnb_4bit_use_double_quant: true
+
 
 # Whether you are training a 4-bit GPTQ quantized model
 gptq: true
@@ -533,6 +559,11 @@ tf32: true # require >=ampere
 bfloat16: true # require >=ampere
 float16: true
 
+# Limit the memory for all available GPUs to this amount (if an integer, expressed in gigabytes); default: unset
+gpu_memory_limit: 20GiB
+# Do the LoRA/PEFT loading on CPU -- this is required if the base model is so large it takes up most or all of the available GPU VRAM, e.g. during a model and LoRA merge
+lora_on_cpu: true
+
 # A list of one or more datasets to finetune the model with
 datasets:
   # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
@@ -550,10 +581,10 @@ datasets:
     field_human: # Optional[str]. Human key to use for conversation.
     field_model: # Optional[str]. Assistant key to use for conversation.
 
-  # Custom user prompt
+  # Custom user instruction prompt
   - path: repo
     type:
-      # The below are defaults. only set what's needed.
+      # The below are defaults. only set what's needed if you use a different column name.
       system_prompt: ""
       system_format: "{system}"
       field_system: system
@@ -562,6 +593,7 @@ datasets:
       field_output: output
 
       # Customizable to be single line or multi-line
+      # Use {instruction}/{input} as key to be replaced
       # 'format' can include {input}
       format: |-
         User: {instruction} {input}
@@ -572,6 +604,12 @@ datasets:
       # For `completion` datsets only, uses the provided field instead of `text` column
       field:
 
+# use RL training: dpo, ipo, kto_pair
+rl:
+
+# Saves the desired chat template to the tokenizer_config.json for easier inferencing
+# Currently supports chatml and inst (mistral/mixtral)
+chat_template: chatml
 # Axolotl attempts to save the dataset as an arrow after packing the data together so
 # subsequent training attempts load faster, relative path
 dataset_prepared_path: data/last_run_prepared
@@ -623,7 +661,8 @@ max_memory:
 # If you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
 adapter: lora
 # If you already have a lora model trained that you want to load, put that here.
-# This means after training, if you want to test the model, you should set this to the value of `lora_out_dir`.
+# This means after training, if you want to test the model, you should set this to the value of `output_dir`.
+# Note that if you merge an adapter to the base model, a new subdirectory `merged` will be created under the `output_dir`.
 lora_model_dir:
 
 # LoRA hyperparameters
@@ -640,7 +679,8 @@ lora_target_modules:
 #  - gate_proj
 #  - down_proj
 #  - up_proj
-lora_target_linear: # If true, will target all linear layers
+lora_target_linear: # If true, will target all linear modules
+peft_layers_to_transform: # The layer indices to transform, otherwise, apply to all layers
 
 # If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.
 # For LLaMA and Mistral, you need to save `embed_tokens` and `lm_head`. It may vary for other models.
@@ -650,10 +690,6 @@ lora_modules_to_save:
 #  - embed_tokens
 #  - lm_head
 
-# Once you complete training, the model will be saved to the following directory.
-# If you merge the adapter to the base model, a subdirectory `merged` will be created under this directory.
-# Make sure `lora_model_dir` points to this directory if you want to use the trained model.
-lora_out_dir:
 lora_fan_in_fan_out: false
 
 # ReLoRA configuration
@@ -663,6 +699,7 @@ relora_warmup_steps: # Number of per-restart warmup steps
 relora_cpu_offload: # True to perform lora weight merges on cpu during restarts, for modest gpu memory savings
 
 # wandb configuration if you're using it
+# Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`.
 wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
 wandb_project: # Your wandb project name
 wandb_entity: # A wandb Team name if using a Team
@@ -671,6 +708,10 @@ wandb_name: # Set the name of your wandb run
 wandb_run_id: # Set the ID of your wandb run
 wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
 
+# mlflow configuration if you're using it
+mlflow_tracking_uri: # URI to mlflow
+mlflow_experiment_name: # Your experiment name
+
 # Where to save the full-finetuned model to
 output_dir: ./completed-model
 
@@ -691,9 +732,11 @@ warmup_ratio: 0.05  # cannot use with warmup_steps
 learning_rate: 0.00003
 lr_quadratic_warmup:
 logging_steps:
+eval_steps: # Leave empty to eval at each epoch, integers for every N steps. decimal for fraction of total steps
+evals_per_epoch: # number of times per epoch to run evals, mutually exclusive with eval_steps
 save_strategy: # Set to `no` to skip checkpoint saves
 save_steps: # Leave empty to save at each epoch
-eval_steps: # Leave empty to eval at each epoch, integers for every N steps. decimal for fraction of total steps
+saves_per_epoch: # number of times per epoch to save a checkpoint, mutually exclusive with save_steps
 save_total_limit: # Checkpoints saved at a time
 # Maximum number of iterations to train for. It precedes num_epochs which means that
 # if both are set, num_epochs will not be guaranteed.
@@ -718,6 +761,9 @@ group_by_length: false
 
 # Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
 gradient_checkpointing: false
+# additional kwargs to pass to the trainer for gradient checkpointing
+# gradient_checkpointing_kwargs:
+#   use_reentrant: false
 
 # Stop training after this many evaluation losses have increased in a row
 # https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
@@ -726,6 +772,7 @@ early_stopping_patience: 3
 # Specify a scheduler and kwargs to use with the optimizer
 lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine
 lr_scheduler_kwargs:
+cosine_min_lr_ratio: # decay lr to some percentage of the peak lr, e.g. cosine_min_lr_ratio=0.1 for 10% of peak lr
 
 # For one_cycle optim
 lr_div_factor: # Learning rate div factor
@@ -772,7 +819,7 @@ max_grad_norm:
 # Augmentation techniques
 # NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to add noise to embeddings
 # currently only supported on Llama and Mistral
-noisy_embedding_alpha:
+neftune_noise_alpha:
 
 # Whether to bettertransformers
 flash_optimum:
@@ -787,11 +834,6 @@ flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
 # Whether to use scaled-dot-product attention
 # https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
 sdp_attention:
-# Landmark attention (only llama)
-landmark_attention:
-# xpos RoPE see https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
-# LLaMA only
-xpos_rope:
 
 # Resume from a specific checkpoint dir
 resume_from_checkpoint:
@@ -914,8 +956,9 @@ accelerate launch -m axolotl.cli.train your_config.yml
 You can optionally pre-tokenize dataset with the following before finetuning.
 This is recommended for large datasets.
 
-- Set `push_dataset_to_hub: hf_user/repo` to push it to Huggingface.
-- Use `--debug` to see preprocessed examples.
+- Set `dataset_prepared_path:` to a local folder for saving and loading pre-tokenized dataset.
+- (Optional): Set `push_dataset_to_hub: hf_user/repo` to push it to Huggingface.
+- (Optional): Use `--debug` to see preprocessed examples.
 
 ```bash
 python -m axolotl.cli.preprocess your_config.yml
@@ -958,6 +1001,8 @@ fsdp_config:
 
 ##### Weights & Biases Logging
 
+Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`.
+
 - wandb options
 ```yaml
 wandb_mode:
@@ -968,9 +1013,28 @@ wandb_name:
 wandb_log_model:
 ```
 
-### Inference
+##### Special Tokens
 
-Pass the appropriate flag to the train command:
+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:
+
+```yml
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"
+tokens: # these are delimiters
+  - "<|im_start|>"
+  - "<|im_end|>"
+```
+
+When you include these tokens in your axolotl config, axolotl adds these tokens to the tokenizer's vocabulary.
+
+### Inference Playground
+
+Axolotl allows you to load your model in an interactive terminal playground for quick experimentation.
+The config file is the same config file used for training.
+
+Pass the appropriate flag to the inference command, depending upon what kind of model was trained:
 
 - Pretrained LORA:
   ```bash
@@ -996,21 +1060,23 @@ Please use `--sample_packing False` if you have it on and receive the error simi
 
 ### Merge LORA to base
 
-Add below flag to train command above
+The following command will merge your LORA adapater with your base model.  You can optionally pass the argument `--lora_model_dir` to specify the directory where your LORA adapter was saved, otherwhise, this will be inferred from `output_dir` in your axolotl config file.  The merged model is saved in the sub-directory `{lora_model_dir}/merged`.
 
 ```bash
-python3 -m axolotl.cli.merge_lora examples/your_config.yml --lora_model_dir="./completed-model" --load_in_8bit=False --load_in_4bit=False
+python3 -m axolotl.cli.merge_lora your_config.yml --lora_model_dir="./completed-model"
 ```
 
-If you run out of CUDA memory, you can try to merge in system RAM with
+You may need to use the `gpu_memory_limit` and/or `lora_on_cpu` config options to avoid running out of memory. If you still run out of CUDA memory, you can try to merge in system RAM with
 
 ```bash
 CUDA_VISIBLE_DEVICES="" python3 -m axolotl.cli.merge_lora ...
 ```
 
+although this will be very slow, and using the config options above are recommended instead.
+
 ## Common Errors 🧰
 
-See also the [FAQ's](./docs/faq.md).
+See also the [FAQ's](./docs/faq.md) and [debugging guide](docs/debugging.md).
 
 > If you encounter a 'Cuda out of memory' error, it means your GPU ran out of memory during the training process. Here's how to resolve it:
 
@@ -1020,6 +1086,10 @@ Please reduce any below
   - `gradient_accumulation_steps`
   - `sequence_len`
 
+If it does not help, try running without deepspeed and without accelerate (replace "accelerate launch" with "python") in the command.
+
+Using adamw_bnb_8bit might also save you some memory.
+
 > `failed (exitcode: -9)`
 
 Usually means your system has run out of system memory.
@@ -1042,6 +1112,24 @@ It's safe to ignore it.
 
 See the [NCCL](docs/nccl.md) guide.
 
+
+### Tokenization Mismatch b/w Inference & Training
+
+For many formats, Axolotl constructs prompts by concatenating token ids _after_ tokenizing strings.  The reason for concatenating token ids rather than operating on strings is to maintain precise accounting for attention masks.
+
+If you decode a prompt constructed by axolotl, you might see spaces between tokens (or lack thereof) that you do not expect, especially around delimiters and special tokens.  When you are starting out with a new format, you should always do the following:
+
+1. Materialize some data using `python -m axolotl.cli.preprocess your_config.yml --debug`, and then decode the first few rows with your model's tokenizer.
+2. During inference, right before you pass a tensor of token ids to your model, decode these tokens back into a string.
+3. Make sure the inference string from #2 looks **exactly** like the data you fine tuned on from #1, including spaces and new lines.  If they aren't the same adjust your inference server accordingly.
+4. As an additional troubleshooting step, you can look look at the token ids between 1 and 2 to make sure they are identical.
+
+Having misalignment between your prompts during training and inference can cause models to perform very poorly, so it is worth checking this.  See [this blog post](https://hamel.dev/notes/llm/05_tokenizer_gotchas.html) for a concrete example.
+
+## Debugging Axolotl
+
+See [this debugging guide](docs/debugging.md) for tips on debugging Axolotl, along with an example configuration for debugging with VSCode.
+
 ## Need help? 🙋♂️
 
 Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we can help you
@@ -1084,3 +1172,33 @@ pre-commit install
 # test
 pytest tests/
 ```
+
+## Sponsors 🤝❤
+
+OpenAccess AI Collective is run by volunteer contributors such as [winglian](https://github.com/winglian),
+[NanoCode012](https://github.com/NanoCode012), [tmm1](https://github.com/tmm1),
+[mhenrichsen](https://github.com/mhenrichsen), [casper-hansen](https://github.com/casper-hansen),
+[hamelsmu](https://github.com/hamelsmu) and many more who help us accelerate forward by fixing bugs, answering
+community questions and implementing new features. Axolotl needs donations from sponsors for the compute needed to
+run our unit & integration tests, troubleshooting community issues, and providing bounties. If you love axolotl,
+consider sponsoring the project via [GitHub Sponsors](https://github.com/sponsors/OpenAccess-AI-Collective),
+[Ko-fi](https://ko-fi.com/axolotl_ai) or reach out directly to
+[wing@openaccessaicollective.org](mailto:wing@openaccessaicollective.org).
+
+---
+
+#### 💎 Diamond Sponsors - [Contact directly](mailto:wing@openaccessaicollective.org)
+
+---
+
+#### 🥇 Gold Sponsors - $5000/mo
+
+---
+
+#### 🥈 Silver Sponsors - $1000/mo
+
+---
+
+#### 🥉 Bronze Sponsors - $500/mo
+
+---

deepspeed/zero3_bf16.json

@@ -0,0 +1,39 @@
+{
+  "zero_optimization": {
+    "stage": 3,
+    "overlap_comm": true,
+    "contiguous_gradients": true,
+    "sub_group_size": 0,
+    "reduce_bucket_size": "auto",
+    "stage3_prefetch_bucket_size": "auto",
+    "stage3_param_persistence_threshold": "auto",
+    "stage3_max_live_parameters": 0,
+    "stage3_max_reuse_distance": 0,
+    "stage3_gather_16bit_weights_on_model_save": true
+  },
+  "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
+  },
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": "auto",
+      "betas": "auto",
+      "eps": "auto",
+      "weight_decay": "auto"
+    }
+  },
+  "gradient_accumulation_steps": "auto",
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "wall_clock_breakdown": false
+}

devtools/README.md

@@ -0,0 +1 @@
+This directory contains example config files that might be useful for debugging. Please see [docs/debugging.md](../docs/debugging.md) for more information.

devtools/dev_sharegpt.yml

@@ -0,0 +1,49 @@
+# Example config for debugging the sharegpt prompt format
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: true
+load_in_4bit: false
+
+datasets:
+  - path: philschmid/guanaco-sharegpt-style
+    type: sharegpt
+    shards: 10
+val_set_size: 0
+output_dir: temp_debug/axolotl_outputs/model
+dataset_prepared_path: temp_debug/axolotl_outputs/data
+dataset_processes: 1
+
+sequence_len: 4096
+sample_packing: false
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+micro_batch_size: 1
+num_epochs: 1
+max_steps: 10
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: false
+fp16: true
+tf32: false
+
+gradient_checkpointing: true
+logging_steps: 1
+flash_attention: true
+
+warmup_steps: 10
+weight_decay: 0.0

docker/Dockerfile

@@ -10,7 +10,7 @@ ARG PYTORCH_VERSION="2.0.1"
 ENV PYTORCH_VERSION=$PYTORCH_VERSION
 
 RUN apt-get update && \
-    apt-get install -y vim curl
+    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev
 
 WORKDIR /workspace
 
@@ -19,13 +19,15 @@ RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/axolotl.git
 WORKDIR /workspace/axolotl
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
-RUN sed -i "s/torch==.*/torch==$PYTORCH_VERSION/" requirements.txt
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[deepspeed,flash-attn,$AXOLOTL_EXTRAS]; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS]; \
     else \
-        pip install -e .[deepspeed,flash-attn]; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm]; \
     fi
 
+# So we can test the Docker image
+RUN pip install pytest
+
 # fix so that git fetch/pull from remote works
 RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
     git config --get remote.origin.fetch

docker/Dockerfile-cloud

@@ -5,15 +5,18 @@ ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
 ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
 ENV TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub"
 ENV HF_HOME="/workspace/data/huggingface-cache/hub"
+ENV HF_HUB_ENABLE_HF_TRANSFER="1"
 
-COPY scripts/runpod-entrypoint.sh /root/runpod-entrypoint.sh
+COPY scripts/cloud-entrypoint.sh /root/cloud-entrypoint.sh
 
+RUN pip install jupyterlab notebook && \
+    jupyter lab clean
 RUN apt install --yes --no-install-recommends openssh-server tmux && \
     mkdir -p ~/.ssh && \
     chmod 700 ~/.ssh && \
     printf "\n[[ -z \"\$TMUX\"  ]] && { tmux attach-session -t ssh_tmux || tmux new-session -s ssh_tmux; exit; }\n" >> ~/.bashrc && \
-    chmod +x /workspace/axolotl/scripts/runpod-entrypoint.sh && \
-    chmod +x /root/runpod-entrypoint.sh
+    chmod +x /workspace/axolotl/scripts/cloud-entrypoint.sh && \
+    chmod +x /root/cloud-entrypoint.sh
 
-ENTRYPOINT ["/root/runpod-entrypoint.sh"]
+ENTRYPOINT ["/root/cloud-entrypoint.sh"]
 CMD ["sleep", "infinity"]

docker/Dockerfile-tests

@@ -0,0 +1,40 @@
+ARG BASE_TAG=main-base
+FROM winglian/axolotl-base:$BASE_TAG
+
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
+ARG AXOLOTL_EXTRAS=""
+ARG CUDA="118"
+ENV BNB_CUDA_VERSION=$CUDA
+ARG PYTORCH_VERSION="2.0.1"
+ARG GITHUB_REF="main"
+
+ENV PYTORCH_VERSION=$PYTORCH_VERSION
+
+RUN apt-get update && \
+    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev
+
+WORKDIR /workspace
+
+RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/axolotl.git
+
+WORKDIR /workspace/axolotl
+
+RUN git fetch origin +$GITHUB_REF && \
+    git checkout FETCH_HEAD
+
+# If AXOLOTL_EXTRAS is set, append it in brackets
+RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS]; \
+    else \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm]; \
+    fi
+
+# So we can test the Docker image
+RUN pip install pytest
+
+# fix so that git fetch/pull from remote works
+RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
+    git config --get remote.origin.fetch
+
+# helper for huggingface-login cli
+RUN git config --global credential.helper store

docs/debugging.md

@@ -0,0 +1,242 @@
+# Debugging Axolotl
+
+This document provides some tips and tricks for debugging Axolotl.  It also provides an example configuration for debugging with VSCode.  A good debugging setup is essential to understanding how Axolotl code works behind the scenes.
+
+## Table of Contents
+
+- [General Tips](#general-tips)
+- [Debugging with VSCode](#debugging-with-vscode)
+    - [Background](#background)
+    - [Configuration](#configuration)
+    - [Customizing your debugger](#customizing-your-debugger)
+    - [Video Tutorial](#video-tutorial)
+- [Debugging With Docker](#debugging-with-docker)
+    - [Setup](#setup)
+    - [Attach To Container](#attach-to-container)
+    - [Video - Attaching To Docker On Remote Host](#video---attaching-to-docker-on-remote-host)
+
+## General Tips
+
+While debugging it's helpful to simplify your test scenario as much as possible.  Here are some tips for doing so:
+
+> [!Important]
+> All of these tips are incorporated into the [example configuration](#configuration) for debugging with VSCode below.
+
+1. **Make sure you are using the latest version of axolotl**:  This project changes often and bugs get fixed fast.  Check your git branch and make sure you have pulled the latest changes from `main`.
+1. **Eliminate concurrency**: Restrict the number of processes to 1 for both training and data preprocessing:
+    - Set `CUDA_VISIBLE_DEVICES` to a single GPU, ex: `export CUDA_VISIBLE_DEVICES=0`.
+    - Set `dataset_processes: 1` in your axolotl config or run the training command with `--dataset_processes=1`.
+2. **Use a small dataset**: Construct or use a small dataset from HF Hub. When using a small dataset, you will often have to make sure `sample_packing: False` and `eval_sample_packing: False` to avoid errors.  If you are in a pinch and don't have time to construct a small dataset but want to use from the HF Hub, you can shard the data (this will still tokenize the entire dataset, but will only use a fraction of the data for training.  For example, to shard the dataset into 20 pieces, add the following to your axolotl config):
+    ```yaml
+    dataset:
+        ...
+        shards: 20
+    ```
+3. **Use a small model**: A good example of a small model is [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0).
+4. **Minimize iteration time**: Make sure the training loop finishes as fast as possible, with these settings.
+    - `micro_batch_size: 1`
+    - `max_steps: 1`
+    - `val_set_size: 0`
+5. **Clear Caches:** Axolotl caches certain steps and so does the underlying HuggingFace trainer.  You may want to clear some of these caches when debugging.
+    - Data preprocessing: When debugging data preprocessing, which includes prompt template formation, you may want to delete the directory set in `dataset_prepared_path:` in your axolotl config.  If you didn't set this value, the default is `last_run_prepared`.
+    - HF Hub: If you are debugging data preprocessing, you should clear the relevant HF cache [HuggingFace cache](https://huggingface.co/docs/datasets/cache), by deleting the appropriate `~/.cache/huggingface/datasets/...` folder(s).
+    - **The recommended approach is to redirect all outputs and caches to a temporary folder and delete selected subfolders before each run.  This is demonstrated in the example configuration below.**
+
+
+## Debugging with VSCode
+
+### 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:
+
+```yaml
+datasets:
+  - path: <path to your sharegpt formatted dataset> # example on HF Hub: philschmid/guanaco-sharegpt-style
+    type: sharegpt
+```
+
+>[!Important]
+> If you are already familiar with advanced VSCode debugging, you can skip the below explanation and look at the files [.vscode/launch.json](../.vscode/launch.json) and [.vscode/tasks.json](../.vscode/tasks.json) for an example configuration.
+
+>[!Tip]
+> If you prefer to watch a video, rather than read, you can skip to the [video tutorial](#video-tutorial) below (but doing both is recommended).
+
+### Setup
+
+Make sure you have an [editable install](https://setuptools.pypa.io/en/latest/userguide/development_mode.html) of Axolotl, which ensures that changes you make to the code are reflected at runtime.  Run the following commands from the root of this project:
+
+```bash
+pip3 install packaging
+pip3 install -e '.[flash-attn,deepspeed]'
+```
+
+#### Remote Hosts
+
+If you developing on a remote host, you can easily use VSCode to debug remotely.  To do so, you will need to follow this [remote - SSH guide](https://code.visualstudio.com/docs/remote/ssh).  You can also see the video below on [Docker and Remote SSH debugging](#video---attaching-to-docker-on-remote-host).
+
+```bash
+
+### Configuration
+
+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.
+
+```jsonc
+// .vscode/launch.json
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Debug axolotl prompt - sharegpt",
+            "type": "python",
+            "module": "accelerate.commands.launch",
+            "request": "launch",
+            "args": [
+                "-m", "axolotl.cli.train", "dev_sharegpt.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
+                "--max_steps=1",              // limits training to just one step
+                "--batch_size=1",             // minimizes batch size
+                "--micro_batch_size=1",       // minimizes batch size
+                "--val_set_size=0",           // disables validation
+                "--sample_packing=False",     // disables sample packing which is necessary for small datasets
+                "--eval_sample_packing=False",// disables sample packing on eval set
+                "--dataset_prepared_path=temp_debug/axolotl_outputs/data", // send data outputs to a temp folder
+                "--output_dir=temp_debug/axolotl_outputs/model" // send model outputs to a temp folder
+                ],
+            "console": "integratedTerminal",      // show output in the integrated terminal
+            "cwd": "${workspaceFolder}/devtools", // set working directory to devtools from the root of the project
+            "justMyCode": true,                   // step through only axolotl code
+            "env": {"CUDA_VISIBLE_DEVICES": "0",  // Since we aren't doing distributed training, we need to limit to one GPU
+                    "HF_HOME": "${workspaceFolder}/devtools/temp_debug/.hf-cache"}, // send HF cache to a temp folder
+            "preLaunchTask": "cleanup-for-dataprep", // delete temp folders (see below)
+        }
+    ]
+}
+```
+
+**Additional notes about this configuration:**
+
+- The argument `justMyCode` is set to `true` such that you step through only the axolotl code.  If you want to step into dependencies, set this to `false`.
+- The `preLaunchTask`: `cleanup-for-dataprep` is defined in [.vscode/tasks.json](../.vscode/tasks.json) and is used to delete the following folders before debugging, which is essential to ensure that the data pre-processing code is run from scratch:
+    -  `./devtools/temp_debug/axolotl_outputs`
+    - `./devtools/temp_debug/.hf-cache/datasets`
+
+>[!Tip]
+> You may not want to delete these folders. For example, if you are debugging model training instead of data pre-processing, you may NOT want to delete the cache or output folders. You may also need to add additional tasks to the `tasks.json` file depending on your use case.
+
+Below is the [./vscode/tasks.json](../.vscode/tasks.json) file that defines the `cleanup-for-dataprep` task.  This task is run before each debugging session when you use the above configuration.  Note how there are two tasks that delete the two folders mentioned above.  The third task `cleanup-for-dataprep` is a composite task that combines the two tasks.  A composite task is necessary because VSCode does not allow you to specify multiple tasks in the `preLaunchTask` argument of the `launch.json` file.
+
+```jsonc
+// .vscode/tasks.json
+// this file is used by launch.json
+{
+    "version": "2.0.0",
+    "tasks": [
+      // this task changes into the devtools directory and deletes the temp_debug/axolotl_outputs folder
+      {
+        "label": "delete-outputs",
+        "type": "shell",
+        "command": "rm -rf temp_debug/axolotl_outputs",
+        "options":{ "cwd": "${workspaceFolder}/devtools"},
+        "problemMatcher": []
+      },
+      // this task changes into the devtools directory and deletes the `temp_debug/.hf-cache/datasets` folder
+      {
+        "label": "delete-temp-hf-dataset-cache",
+        "type": "shell",
+        "command": "rm -rf temp_debug/.hf-cache/datasets",
+        "options":{ "cwd": "${workspaceFolder}/devtools"},
+        "problemMatcher": []
+      },
+        // this task combines the two tasks above
+      {
+       "label": "cleanup-for-dataprep",
+       "dependsOn": ["delete-outputs", "delete-temp-hf-dataset-cache"],
+      }
+    ]
+}
+```
+
+### Customizing your debugger
+
+Your debugging use case may differ from the example above.  The easiest thing to do is to put your own axolotl config in the `devtools` folder and modify the `launch.json` file to use your config.  You may also want to modify the `preLaunchTask` to delete different folders or not delete anything at all.
+
+### Video Tutorial
+
+The following video tutorial walks through the above configuration and demonstrates how to debug with VSCode, (click the image below to watch):
+
+<div style="text-align: center; line-height: 0;">
+
+<a href="https://youtu.be/xUUB11yeMmc" target="_blank"
+title="How to debug Axolotl (for fine tuning LLMs)"><img
+src="https://i.ytimg.com/vi/xUUB11yeMmc/maxresdefault.jpg"
+style="border-radius: 10px; display: block; margin: auto;" width="560" height="315" /></a>
+
+<figcaption style="font-size: smaller;"><a href="https://hamel.dev">Hamel Husain's</a> tutorial: <a href="https://www.youtube.com/watch?v=xUUB11yeMmc">Debugging Axolotl w/VSCode</a></figcaption>
+
+</div>
+<br>
+
+## Debugging With Docker
+
+Using [official Axolotl Docker images](https://hub.docker.com/r/winglian/axolotl/tags) is a great way to debug your code, and is a very popular way to use Axolotl.  Attaching VSCode to Docker takes a few more steps.
+
+### Setup
+
+On the host that is running axolotl (ex: if you are using a remote host), clone the axolotl repo and change your current directory to the root:
+
+```bash
+git clone https://github.com/OpenAccess-AI-Collective/axolotl
+cd axolotl
+```
+
+>[!Tip]
+> If you already have axolotl cloned on your host, make sure you have the latest changes and change into the root of the project.
+
+Next, run the desired docker image and mount the current directory. Below is a docker command you can run to do this:[^2]
+
+```bash
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1
+```
+
+>[!Tip]
+> To understand which containers are available, see the [Docker section of the README](../README.md#docker) and the [DockerHub repo](https://hub.docker.com/r/winglian/axolotl/tags).  For details of how the Docker containers are built, see axolotl's [Docker CI builds](../.github/workflows/main.yml).
+
+You will now be in the container.  Next, perform an editable install of Axolotl:
+
+```bash
+pip3 install packaging
+pip3 install -e '.[flash-attn,deepspeed]'
+```
+
+### Attach To Container
+
+Next, if you are using a remote host, [Remote into this host with VSCode](https://code.visualstudio.com/docs/remote/ssh).  If you are using a local host, you can skip this step.
+
+Next, select `Dev Containers: Attach to Running Container...` using the command palette (`CMD + SHIFT + P`) in VSCode.  You will be prompted to select a container to attach to.  Select the container you just created.  You will now be in the container with a working directory that is at the root of the project.  Any changes you make to the code will be reflected both in the container and on the host.
+
+Now you are ready to debug as described above (see [Debugging with VSCode](#debugging-with-vscode)).
+
+### Video - Attaching To Docker On Remote Host
+
+Here is a short video that demonstrates how to attach to a Docker container on a remote host:
+
+<div style="text-align: center; line-height: 0;">
+
+<a href="https://youtu.be/0AuoR7QnHR0" target="_blank"
+title="Debugging Axolotl Part 2: Attaching to Docker on a Remote Host"><img
+src="https://i.ytimg.com/vi/0AuoR7QnHR0/hqdefault.jpg"
+style="border-radius: 10px; display: block; margin: auto;" width="560" height="315" /></a>
+
+<figcaption style="font-size: smaller;"><a href="https://hamel.dev">Hamel Husain's</a> tutorial: <a href="https://youtu.be/0AuoR7QnHR0">Debugging Axolotl Part 2: Attaching to Docker on a Remote Host
+</a></figcaption>
+
+</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.
+
+[^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/rlhf.md

@@ -0,0 +1,44 @@
+# RLHF (Beta)
+
+### Overview
+
+Reinforcement Learning from Human Feedback is a method whereby a language model is optimized from data using human
+feedback. Various methods include, but not limited to:
+
+- Proximal Policy Optimization (PPO) (not yet supported in axolotl)
+- Direct Preference Optimization (DPO)
+- Identity Preference Optimization (IPO)
+
+
+### RLHF using Axolotl
+
+[!IMPORTANT]
+This is a BETA feature and many features are not fully implemented. You are encouraged to open new PRs to improve the integration and functionality.
+
+The various RL training methods are implemented in trl and wrapped via axolotl. Below are various examples with how you can use various preference datasets to train models that use ChatML
+
+#### DPO
+```yaml
+rl: true
+datasets:
+  - path: Intel/orca_dpo_pairs
+    split: train
+    type: intel_apply_chatml
+  - path: argilla/ultrafeedback-binarized-preferences
+    split: train
+    type: argilla_apply_chatml
+```
+
+#### IPO
+```yaml
+rl: ipo
+```
+
+#### Trl autounwrap for peft
+
+Trl supports autounwrapping peft models, so that a ref model does not need to be additionally loaded, leading to less VRAM needed. This is on by default. To turn it off, pass the following config.
+
+```yaml
+# load ref model when adapter training.
+rl_adapter_ref_model: true
+```

examples/cerebras/btlm-ft.yml

@@ -72,8 +72,8 @@ gptq_groupsize:
 gptq_model_v1:
 
 warmup_steps: 32
-eval_steps:
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 save_total_limit:
 
 debug:

examples/cerebras/qlora.yml

@@ -49,8 +49,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 10
-eval_steps: 0.05
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.1

examples/code-llama/13b/lora.yml

@@ -54,8 +54,8 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 0.05
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/code-llama/13b/qlora.yml

@@ -56,8 +56,8 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 0.05
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/code-llama/34b/lora.yml

@@ -54,8 +54,8 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 0.05
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/code-llama/34b/qlora.yml

@@ -56,8 +56,8 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 0.05
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/code-llama/7b/lora.yml

@@ -54,8 +54,8 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 0.05
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/code-llama/7b/qlora.yml

@@ -56,8 +56,8 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 0.05
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/falcon/config-7b-lora.yml

@@ -51,8 +51,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 40
-eval_steps: 5
-save_steps: 43
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/falcon/config-7b-qlora.yml

@@ -80,8 +80,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 10
-eval_steps: 5
-save_steps: 10
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.000001

examples/falcon/config-7b.yml

@@ -51,8 +51,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 40
-eval_steps: 5
-save_steps: 43
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/gptj/qlora.yml

@@ -46,8 +46,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 10
-eval_steps: 0.05
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.1

examples/jeopardy-bot/config.yml

@@ -42,8 +42,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
-eval_steps: 110
-save_steps: 660
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.1

examples/llama-2/fft_optimized.yml

@@ -58,9 +58,9 @@ flash_attn_fuse_qkv: false
 flash_attn_fuse_mlp: true
 
 warmup_steps: 100
-eval_steps: 0.05
+evals_per_epoch: 4
 eval_table_size:
-save_steps:
+saves_per_epoch: 1
 debug:
 deepspeed: #deepspeed/zero2.json # multi-gpu only
 weight_decay: 0.1

examples/llama-2/gptq-lora.yml

@@ -62,8 +62,8 @@ flash_attention:
 sdp_attention:
 flash_optimum:
 warmup_steps: 100
-eval_steps:
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.1

examples/llama-2/lora.yml

@@ -54,10 +54,10 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 0.05
+evals_per_epoch: 4
 eval_table_size:
 eval_table_max_new_tokens: 128
-save_steps:
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/llama-2/qlora.yml

@@ -56,9 +56,9 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 0.05
+evals_per_epoch: 4
 eval_table_size:
-save_steps:
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/llama-2/relora.yml

@@ -60,8 +60,8 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 0.05
-save_steps: 50
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/mamba/config.yml

@@ -47,10 +47,10 @@ xformers_attention:
 flash_attention:
 
 warmup_steps: 10
-eval_steps:
+evals_per_epoch: 4
 eval_table_size:
 eval_table_max_new_tokens: 128
-save_steps:  0.25
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/mistral/config.yml

@@ -17,6 +17,7 @@ output_dir: ./out
 sequence_len: 8192
 sample_packing: true
 pad_to_sequence_len: true
+eval_sample_packing: false
 
 wandb_project:
 wandb_entity:
@@ -46,10 +47,10 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 0.05
+evals_per_epoch: 4
 eval_table_size:
 eval_table_max_new_tokens: 128
-save_steps:
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/mistral/mixtral.yml

@@ -1,5 +1,5 @@
-base_model: DiscoResearch/mixtral-7b-8expert
-model_type: MixtralForCausalLM
+base_model: mistralai/Mixtral-8x7B-v0.1
+model_type: AutoModelForCausalLM
 tokenizer_type: LlamaTokenizer
 trust_remote_code: true
 
@@ -14,6 +14,18 @@ dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
 output_dir: ./qlora-out
 
+## You can optionally freeze the entire model and unfreeze a subset of parameters
+unfrozen_parameters:
+#  - lm_head.*
+#  - model.embed_tokens.*
+#  - model.layers.2[0-9]+.block_sparse_moe.gate.*
+#  - model.layers.2[0-9]+.block_sparse_moe.experts.*
+#  - model.layers.3[0-9]+.block_sparse_moe.gate.*
+#  - model.layers.3[0-9]+.block_sparse_moe.experts.*
+
+model_config:
+  output_router_logits: true
+
 adapter: qlora
 lora_model_dir:
 
@@ -67,10 +79,10 @@ loss_watchdog_threshold: 5.0
 loss_watchdog_patience: 3
 
 warmup_steps: 10
-eval_steps:
+evals_per_epoch: 4
 eval_table_size:
 eval_table_max_new_tokens: 128
-save_steps:
+saves_per_epoch: 1
 debug:
 deepspeed: deepspeed/zero2.json
 weight_decay: 0.0

examples/mistral/qlora.yml

@@ -11,7 +11,7 @@ datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 dataset_prepared_path: last_run_prepared
-val_set_size: 0.05
+val_set_size: 0.1
 output_dir: ./qlora-out
 
 adapter: qlora
@@ -66,10 +66,10 @@ loss_watchdog_threshold: 5.0
 loss_watchdog_patience: 3
 
 warmup_steps: 10
-eval_steps: 0.05
+evals_per_epoch: 4
 eval_table_size:
 eval_table_max_new_tokens: 128
-save_steps:
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/mpt-7b/config.yml

@@ -44,8 +44,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
-eval_steps: 110
-save_steps: 660
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0001

examples/openllama-3b/config.yml

@@ -49,8 +49,8 @@ flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
-eval_steps: 0.05
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.1

examples/openllama-3b/lora.yml

@@ -54,8 +54,8 @@ flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
-eval_steps: 0.05
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.1

examples/openllama-3b/qlora.yml

@@ -48,8 +48,8 @@ flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
-eval_steps: 0.05
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.1

examples/phi/phi-ft.yml

@@ -59,8 +59,8 @@ xformers_attention:
 flash_attention:
 
 warmup_steps: 100
-eval_steps: 0.05
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.1

examples/phi/phi-qlora.yml

@@ -59,8 +59,8 @@ xformers_attention:
 flash_attention:
 
 warmup_steps: 100
-eval_steps: 0.05
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.1

examples/phi/phi2-ft.yml

@@ -0,0 +1,74 @@
+base_model: microsoft/phi-2
+model_revision:  834565c  # pin model repo to the previous architecture
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+trust_remote_code: true
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: garage-bAInd/Open-Platypus
+    type: alpaca
+
+dataset_prepared_path:
+val_set_size: 0.05
+output_dir: ./phi-sft-out
+
+sequence_len: 2048
+sample_packing: false  # currently unsupported
+pad_to_sequence_len:
+
+adapter:
+lora_model_dir:
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0.1
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_modules_to_save:
+  - embd
+  - lm_head
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 4
+optimizer: paged_adamw_8bit
+adam_beta2: 0.95
+adam_epsilon: 0.00001
+max_grad_norm: 1.0
+lr_scheduler: cosine
+learning_rate: 1e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: true
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 4
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.1
+fsdp:
+fsdp_config:
+resize_token_embeddings_to_32x: true
+special_tokens:
+  pad_token: "<|endoftext|>"

examples/pythia/lora.yml

@@ -33,5 +33,5 @@ early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
 weight_decay: 0.1
-eval_steps: 0.05
+evals_per_epoch: 4
 logging_steps: 1

examples/qwen/lora.yml

@@ -56,10 +56,10 @@ xformers_attention:
 flash_attention:
 
 warmup_steps: 10
-eval_steps: 0.05
+evals_per_epoch: 4
 eval_table_size:
 eval_table_max_new_tokens: 128
-save_steps:
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/qwen/qlora.yml

@@ -56,10 +56,10 @@ xformers_attention:
 flash_attention:
 
 warmup_steps: 10
-eval_steps: 0.05
+evals_per_epoch: 4
 eval_table_size:
 eval_table_max_new_tokens: 128
-save_steps:
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/redpajama/config-3b.yml

@@ -45,8 +45,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
-eval_steps: 110
-save_steps: 660
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0001

examples/replit-3b/config-lora.yml

@@ -45,8 +45,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 20
-eval_steps: 50
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0

examples/tiny-llama/README.md

@@ -0,0 +1,17 @@
+# Overview
+
+This is a simple example of how to finetune TinyLlama1.1B using either lora or qlora:
+
+LoRa:
+
+```
+accelerate launch -m axolotl.cli.train examples/tiny-llama/lora.yml
+```
+
+qLoRa:
+
+```
+accelerate launch -m axolotl.cli.train examples/tiny-llama/qlora.yml
+```
+
+Both take about 10 minutes to complete on a 4090.

examples/tiny-llama/lora.yml

@@ -1,5 +1,4 @@
-base_model: PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T
-
+base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 is_llama_derived_model: true
@@ -17,6 +16,7 @@ output_dir: ./lora-out
 
 sequence_len: 4096
 sample_packing: true
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:
@@ -54,15 +54,11 @@ xformers_attention:
 flash_attention: true
 
 warmup_steps: 10
-eval_steps: 0.05
-eval_table_size:
-save_steps:
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0
 fsdp:
 fsdp_config:
 special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

examples/tiny-llama/pretrain.yml

@@ -0,0 +1,58 @@
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+max_steps: 200
+pretraining_dataset:
+  path: c4
+  name: en
+dataset_prepared_path:
+val_set_size: 0.0
+output_dir: ./model-out
+
+sequence_len: 2048
+sample_packing: 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: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+evals_per_epoch:
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:

examples/tiny-llama/qlora.yml

@@ -0,0 +1,66 @@
+base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path:
+val_set_size: 0.05
+output_dir: ./qlora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 4
+optimizer: paged_adamw_32bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+evals_per_epoch: 4
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:

examples/xgen-7b/xgen-7b-8k-qlora.yml

@@ -78,8 +78,8 @@ flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_steps: 10
-eval_steps: 50
-save_steps: 50
+evals_per_epoch: 4
+saves_per_epoch: 1
 debug:
 deepspeed:
 weight_decay: 0.0

examples/yi-34B-chat/README.md

@@ -0,0 +1,5 @@
+# Overview
+
+This is an example of a Yi-34B-Chat configuration. It demonstrates that it is possible to finetune a 34B model on a GPU with 24GB of VRAM.
+
+Tested on an RTX 4090 with `python -m axolotl.cli.train examples/mistral/qlora.yml`, a single epoch of finetuning on the alpaca dataset using qlora runs in 47 mins, using 97% of available memory.

examples/yi-34B-chat/qlora.yml

@@ -0,0 +1,76 @@
+base_model: 01-ai/Yi-34B-Chat
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+is_mistral_derived_model: false
+is_llama_derived_model: true
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+sequence_len: 1024
+bf16: true
+fp16: false
+tf32: false
+flash_attention: true
+special_tokens:
+  bos_token: "<|startoftext|>"
+  eos_token: "<|endoftext|>"
+  unk_token: "<unk>"
+
+# Data
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+warmup_steps: 10
+
+# Iterations
+num_epochs: 1
+
+# Evaluation
+val_set_size: 0.1
+evals_per_epoch: 5
+eval_table_size:
+eval_table_max_new_tokens: 128
+eval_sample_packing: false
+eval_batch_size: 1
+
+# LoRA
+output_dir: ./qlora-out
+adapter: qlora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_target_modules:
+
+# Sampling
+sample_packing: false
+pad_to_sequence_len: false
+
+# Batching
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+gradient_checkpointing: true
+
+# wandb
+wandb_project:
+
+# Optimizer
+optimizer: paged_adamw_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+# Misc
+train_on_inputs: false
+group_by_length: false
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+debug:
+deepspeed:
+weight_decay: 0
+fsdp:
+fsdp_config:

requirements.txt

@@ -1,11 +1,10 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
-auto-gptq==0.5.1
-packaging
-peft==0.6.0
-transformers @ git+https://github.com/huggingface/transformers.git@df5c5c62ae253055336f5bb0828ca8e3e15ab6bd
+packaging==23.2
+peft==0.7.0
+transformers @ git+https://github.com/huggingface/transformers.git@3cefac1d974db5e2825a0cb2b842883a628be7a0
 tokenizers==0.15.0
 bitsandbytes>=0.41.1
-accelerate==0.24.1
+accelerate @ git+https://github.com/huggingface/accelerate.git@0d2280dadc6a93413a5496613b7fdda3a4d2551b
 deepspeed
 addict
 fire
@@ -21,6 +20,7 @@ hf_transfer
 colorama
 numba
 numpy>=1.24.4
+mlflow
 # qlora things
 bert-score==0.3.13
 evaluate==0.4.0
@@ -29,11 +29,15 @@ scipy
 scikit-learn==1.2.2
 pynvml
 art
-fschat==0.2.29
+fschat==0.2.34
 gradio==3.50.2
 tensorboard
 
+mamba-ssm==1.1.1
+
 # remote filesystems
 s3fs
 gcsfs
 # adlfs
+
+trl>=0.7.9

scripts/cloud-entrypoint.sh

@@ -17,5 +17,16 @@ else
     echo "No PUBLIC_KEY ENV variable provided, not starting openSSH daemon"
 fi
 
+# Check if JUPYTER_PASSWORD is set and not empty
+if [ -n "$JUPYTER_PASSWORD" ]; then
+    # Set JUPYTER_TOKEN to the value of JUPYTER_PASSWORD
+    export JUPYTER_TOKEN="$JUPYTER_PASSWORD"
+fi
+
+if [ "$JUPYTER_DISABLE" != "1" ]; then
+    # Run Jupyter Lab in the background
+    jupyter lab --allow-root --ip 0.0.0.0 &
+fi
+
 # Execute the passed arguments (CMD)
 exec "$@"

setup.py

@@ -1,5 +1,7 @@
 """setup.py for axolotl"""
 
+from importlib.metadata import PackageNotFoundError, version
+
 from setuptools import find_packages, setup
 
 
@@ -9,25 +11,27 @@ def parse_requirements():
     with open("./requirements.txt", encoding="utf-8") as requirements_file:
         lines = [r.strip() for r in requirements_file.readlines()]
         for line in lines:
+            is_extras = (
+                "flash-attn" in line
+                or "flash-attention" in line
+                or "deepspeed" in line
+                or "mamba-ssm" in line
+            )
             if line.startswith("--extra-index-url"):
                 # Handle custom index URLs
                 _, url = line.split()
                 _dependency_links.append(url)
-            elif (
-                "flash-attn" not in line
-                and "deepspeed" not in line
-                and line
-                and line[0] != "#"
-            ):
+            elif not is_extras and line and line[0] != "#":
                 # Handle standard packages
                 _install_requires.append(line)
 
-    # TODO(wing) remove once xformers release supports torch 2.1.0
-    if "torch==2.1.0" in _install_requires:
-        _install_requires.pop(_install_requires.index("xformers>=0.0.22"))
-        _install_requires.append(
-            "xformers @ git+https://github.com/facebookresearch/xformers.git@main"
-        )
+    try:
+        torch_version = version("torch")
+        if torch_version.startswith("2.1.1"):
+            _install_requires.pop(_install_requires.index("xformers==0.0.22"))
+            _install_requires.append("xformers==0.0.23")
+    except PackageNotFoundError:
+        pass
 
     return _install_requires, _dependency_links
 
@@ -48,11 +52,17 @@ setup(
         "flash-attn": [
             "flash-attn==2.3.3",
         ],
+        "fused-dense-lib": [
+            "fused-dense-lib  @ git+https://github.com/Dao-AILab/flash-attention@v2.3.3#subdirectory=csrc/fused_dense_lib",
+        ],
         "deepspeed": [
             "deepspeed",
         ],
         "mamba-ssm": [
             "mamba-ssm==1.0.1",
         ],
+        "auto-gptq": [
+            "auto-gptq==0.5.1",
+        ],
     },
 )

src/axolotl/cli/__init__.py

@@ -2,6 +2,7 @@
 
 import importlib
 import logging
+import math
 import os
 import random
 import sys
@@ -16,6 +17,7 @@ import yaml
 # add src to the pythonpath so we don't need to pip install this
 from accelerate.commands.config import config_args
 from art import text2art
+from datasets import concatenate_datasets, load_dataset
 from huggingface_hub import HfApi
 from huggingface_hub.utils import LocalTokenNotFoundError
 from transformers import GenerationConfig, TextIteratorStreamer, TextStreamer
@@ -23,10 +25,15 @@ from transformers import GenerationConfig, TextIteratorStreamer, TextStreamer
 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
-from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.config import (
+    normalize_cfg_datasets,
+    normalize_config,
+    validate_config,
+)
 from axolotl.utils.data import 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.tokenization import check_dataset_labels
 from axolotl.utils.trainer import prepare_optim_env
@@ -71,7 +78,7 @@ def do_merge_lora(
     safe_serialization = cfg.save_safetensors is True
 
     LOG.info("running merge of LoRA with base model")
-    model = model.merge_and_unload()
+    model = model.merge_and_unload(progressbar=True)
     model.to(dtype=cfg.torch_dtype)
 
     if cfg.local_rank == 0:
@@ -79,6 +86,7 @@ def do_merge_lora(
         model.save_pretrained(
             str(Path(cfg.output_dir) / "merged"),
             safe_serialization=safe_serialization,
+            progressbar=True,
         )
         tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
 
@@ -103,15 +111,7 @@ def do_inference(
             importlib.import_module("axolotl.prompters"), prompter
         )
 
-    if cfg.landmark_attention:
-        from axolotl.monkeypatch.llama_landmark_attn import set_model_mem_id
-
-        set_model_mem_id(model, tokenizer)
-        model.set_mem_cache_args(
-            max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
-        )
-
-    model = model.to(cfg.device)
+    model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
     while True:
         print("=" * 80)
@@ -176,15 +176,7 @@ def do_inference_gradio(
             importlib.import_module("axolotl.prompters"), prompter
         )
 
-    if cfg.landmark_attention:
-        from axolotl.monkeypatch.llama_landmark_attn import set_model_mem_id
-
-        set_model_mem_id(model, tokenizer)
-        model.set_mem_cache_args(
-            max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
-        )
-
-    model = model.to(cfg.device)
+    model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
     def generate(instruction):
         if not instruction:
@@ -301,7 +293,12 @@ def load_cfg(config: Path = Path("examples/"), **kwargs):
 
     normalize_config(cfg)
 
+    normalize_cfg_datasets(cfg)
+
     setup_wandb_env_vars(cfg)
+
+    setup_mlflow_env_vars(cfg)
+
     return cfg
 
 
@@ -341,6 +338,94 @@ def load_datasets(
     )
 
 
+def load_rl_datasets(
+    *,
+    cfg: DictDefault,
+    cli_args: TrainerCliArgs,  # pylint: disable=unused-argument
+) -> TrainDatasetMeta:
+    train_datasets: List[Any] = []
+    for i, ds_cfg in enumerate(cfg.datasets):
+        train_datasets.insert(i, load_dataset(ds_cfg["path"], split=ds_cfg["split"]))
+    # eval_dataset = load_dataset(
+    #     cfg.test_datasets[0]["path"], split=cfg.test_datasets[0]["split"]
+    # )
+    eval_dataset = None
+
+    def argilla_apply_chatml(sample):  # pylint: disable=possibly-unused-variable
+        if "system" in sample and sample["system"]:
+            sample["prompt"] = (
+                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
+                f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
+            )
+        else:
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample['chosen_response']}<|im_end|>"
+        sample["rejected"] = f"{sample['rejected_response']}<|im_end|>"
+        return sample
+
+    def intel_apply_chatml(sample):  # pylint: disable=possibly-unused-variable
+        if "system" in sample and sample["system"]:
+            sample["prompt"] = (
+                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
+                f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
+            )
+        else:
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample['chosen']}<|im_end|>"
+        sample["rejected"] = f"{sample['rejected']}<|im_end|>"
+        return sample
+
+    def apply_chatml(sample):  # pylint: disable=possibly-unused-variable
+        if "system" in sample and sample["system"]:
+            sample["prompt"] = (
+                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
+                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+            )
+        else:
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample['chosen']}<|im_end|>"
+        sample["rejected"] = f"{sample['rejected']}<|im_end|>"
+        return sample
+
+    def ultra_apply_chatml(sample):  # pylint: disable=possibly-unused-variable
+        if "system" in sample and sample["system"]:
+            sample["prompt"] = (
+                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
+                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+            )
+        else:
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample['chosen'][1]['content']}<|im_end|>"
+        sample["rejected"] = f"{sample['rejected'][1]['content']}<|im_end|>"
+        return sample
+
+    for i, data_set in enumerate(train_datasets):
+        _type = cfg.datasets[i]["type"]
+        ds_type_fn = locals()[_type]
+        train_datasets[i] = data_set.map(ds_type_fn)
+    train_dataset = concatenate_datasets(train_datasets)
+
+    # eval_dataset = eval_dataset.map(intel_apply_chatml)
+
+    total_num_steps = int(
+        math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
+    )
+
+    return TrainDatasetMeta(
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        total_num_steps=total_num_steps,
+    )
+
+
 def check_accelerate_default_config():
     if Path(config_args.default_yaml_config_file).exists():
         LOG.warning(

src/axolotl/cli/merge_lora.py

@@ -18,7 +18,26 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
         return_remaining_strings=True
     )
     parsed_cli_args.merge_lora = True
-    parsed_cfg = load_cfg(config, merge_lora=True, **kwargs)
+
+    parsed_cfg = load_cfg(
+        config,
+        merge_lora=True,
+        load_in_8bit=False,
+        load_in_4bit=False,
+        flash_attention=False,
+        **kwargs,
+    )
+
+    if not parsed_cfg.lora_model_dir and parsed_cfg.output_dir:
+        parsed_cfg.lora_model_dir = parsed_cfg.output_dir
+    if not Path(parsed_cfg.lora_model_dir).exists():
+        raise ValueError(
+            f"Target directory for merge: `{parsed_cfg.lora_model_dir}` does not exist."
+        )
+
+    parsed_cfg.load_in_4bit = False
+    parsed_cfg.load_in_8bit = False
+    parsed_cfg.flash_attention = False
 
     do_merge_lora(cfg=parsed_cfg, cli_args=parsed_cli_args)
 

src/axolotl/cli/preprocess.py

@@ -31,6 +31,7 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(
         return_remaining_strings=True
     )
+
     if not parsed_cfg.dataset_prepared_path:
         msg = (
             Fore.RED

src/axolotl/cli/train.py

@@ -12,6 +12,7 @@ from axolotl.cli import (
     check_user_token,
     load_cfg,
     load_datasets,
+    load_rl_datasets,
     print_axolotl_text_art,
 )
 from axolotl.common.cli import TrainerCliArgs
@@ -22,15 +23,19 @@ LOG = logging.getLogger("axolotl.cli.train")
 
 def do_cli(config: Path = Path("examples/"), **kwargs):
     # pylint: disable=duplicate-code
-    print_axolotl_text_art()
     parsed_cfg = load_cfg(config, **kwargs)
+    print_axolotl_text_art()
     check_accelerate_default_config()
     check_user_token()
     parser = transformers.HfArgumentParser((TrainerCliArgs))
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(
         return_remaining_strings=True
     )
-    dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+
+    if parsed_cfg.rl:
+        dataset_meta = load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    else:
+        dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
     train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)
 
 

src/axolotl/core/trainer_builder.py

@@ -1,3 +1,4 @@
+# pylint: disable=too-many-lines
 """
 Builder for the training args and trainer
 """
@@ -9,7 +10,7 @@ import math
 import sys
 from abc import abstractmethod
 from dataclasses import dataclass, field
-from functools import partial
+from functools import wraps
 from pathlib import Path
 from typing import Optional
 
@@ -20,6 +21,7 @@ from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
 from transformers.trainer_utils import seed_worker
+from trl import DPOTrainer
 
 from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils.callbacks import (
@@ -33,10 +35,14 @@ from axolotl.utils.callbacks import (
 )
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
+    DataCollatorForSeq2Seq,
     MambaDataCollator,
 )
-from axolotl.utils.samplers import MultipackBatchSampler
-from axolotl.utils.schedulers import get_cosine_schedule_with_quadratic_warmup
+from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
+from axolotl.utils.schedulers import (
+    get_cosine_schedule_with_min_lr,
+    get_cosine_schedule_with_quadratic_warmup,
+)
 
 try:
     import torch._dynamo  # pylint: disable=ungrouped-imports
@@ -59,6 +65,12 @@ class AxolotlTrainingArguments(TrainingArguments):
         default=False,
         metadata={"help": "Use quadratic warmup for cosine scheduling."},
     )
+    pretraining: bool = field(
+        default=False,
+        metadata={
+            "help": "Indicates to trainer whether we are doing continued pretraining."
+        },
+    )
     sample_packing: bool = field(
         default=False,
         metadata={"help": "Use sample packing for efficient training."},
@@ -112,6 +124,10 @@ class AxolotlTrainingArguments(TrainingArguments):
         default=None,
         metadata={"help": "prefetch_factor argument to the dataloader"},
     )
+    cosine_min_lr_ratio: Optional[float] = field(
+        default=None,
+        metadata={"help": "Minimum learning rate is min_lr_ratio * learning_rate"},
+    )
 
 
 class AxolotlTrainer(Trainer):
@@ -120,11 +136,21 @@ class AxolotlTrainer(Trainer):
     """
 
     args = None  # type: AxolotlTrainingArguments
+    tag_names = ["axolotl"]
 
-    def __init__(self, *args, num_epochs=1, bench_data_collator=None, **kwargs):
+    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
-        super().__init__(*args, **kwargs)
+        self.eval_data_collator = eval_data_collator
+        super().__init__(*_args, **kwargs)
+        self.train_data_collator = self.data_collator
 
     def create_scheduler(
         self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
@@ -150,23 +176,29 @@ class AxolotlTrainer(Trainer):
                     num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
                     num_training_steps=num_training_steps,
                 )
+            elif self.args.lr_scheduler_type == "cosine" and self.args.cosine_min_lr_ratio is not None:
+                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+                if self.args.deepspeed:
+                    LOG.warning("Using cosine scheduler with deepspeed. This may be ignored if a scheduler is set \
+                                in the deepspeed JSON")
+                self.lr_scheduler = get_cosine_schedule_with_min_lr(  # pylint: disable=attribute-defined-outside-init
+                    optimizer,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                    num_training_steps=num_training_steps,
+                    min_lr_ratio=self.args.cosine_min_lr_ratio,
+                )
             else:
                 return super().create_scheduler(num_training_steps, optimizer)
         return self.lr_scheduler
 
     def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
-        if self.args.sample_packing:
+        if self.args.sample_packing and not self.args.pretraining:
             return MultipackBatchSampler(
                 RandomSampler(self.train_dataset),
                 self.args.train_batch_size,
                 drop_last=True,
                 batch_max_len=self._train_batch_size * self.args.max_seq_length,
-                lengths=(
-                    self.train_dataset.data.column("position_ids")
-                    .to_pandas()
-                    .apply(lambda x: x[-1] + 1)
-                    .values
-                ),
+                lengths=get_dataset_lengths(self.train_dataset),
                 packing_efficiency_estimate=self.args.sample_packing_efficiency,
             )
         return super()._get_train_sampler()
@@ -180,18 +212,13 @@ class AxolotlTrainer(Trainer):
                 self.args.per_device_eval_batch_size,
                 drop_last=True,
                 batch_max_len=self.args.eval_batch_size * self.args.max_seq_length,
-                lengths=(
-                    eval_dataset.data.column("position_ids")
-                    .to_pandas()
-                    .apply(lambda x: x[-1] + 1)
-                    .values
-                ),
+                lengths=get_dataset_lengths(eval_dataset),
                 packing_efficiency_estimate=self.args.sample_packing_efficiency,
             )
         return super()._get_eval_sampler(eval_dataset)
 
     def get_train_dataloader(self) -> DataLoader:
-        if self.args.sample_packing:
+        if self.args.sample_packing and not self.args.pretraining:
             train_dataset = self.train_dataset
             train_dataset = train_dataset.remove_columns(["length"])
             data_collator = self.data_collator
@@ -222,6 +249,16 @@ class AxolotlTrainer(Trainer):
         return super().get_train_dataloader()
 
     def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
+        if self.args.sample_packing and self.args.eval_sample_packing is False:
+            self.data_collator = (  # pylint: disable=attribute-defined-outside-init
+                self.eval_data_collator
+            )
+            dataloader = super().get_eval_dataloader(eval_dataset)
+            self.data_collator = (  # pylint: disable=attribute-defined-outside-init
+                self.train_data_collator
+            )
+            return dataloader
+
         if self.args.sample_packing and self.args.eval_sample_packing is not False:
             eval_dataset = (
                 eval_dataset if eval_dataset is not None else self.eval_dataset
@@ -252,6 +289,7 @@ class AxolotlTrainer(Trainer):
             return self.accelerator.prepare_data_loader(
                 DataLoader(eval_dataset, **dataloader_params)
             )
+
         return super().get_eval_dataloader(eval_dataset)
 
     def _get_bench_sampler(
@@ -290,12 +328,41 @@ class AxolotlTrainer(Trainer):
         #     return (loss, outputs) if return_outputs else loss
         return super().compute_loss(model, inputs, return_outputs=return_outputs)
 
+    def _sanitize_kwargs_for_tagging(self, tag_names, kwargs=None):
+        if isinstance(tag_names, str):
+            tag_names = [tag_names]
+
+        if kwargs is not None:
+            if "tags" not in kwargs:
+                kwargs["tags"] = tag_names
+            elif "tags" in kwargs and isinstance(kwargs["tags"], list):
+                kwargs["tags"].extend(tag_names)
+            elif "tags" in kwargs and isinstance(kwargs["tags"], str):
+                tag_names.append(kwargs["tags"])
+                kwargs["tags"] = tag_names
+
+        return kwargs
+
+    @wraps(Trainer.push_to_hub)
+    def push_to_hub(self, *args, **kwargs) -> str:
+        """
+        Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
+        model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
+        """
+        kwargs = self._sanitize_kwargs_for_tagging(
+            tag_names=self.tag_names, kwargs=kwargs
+        )
+
+        return super().push_to_hub(*args, **kwargs)
+
 
 class AxolotlMambaTrainer(AxolotlTrainer):
     """
     Mamba specific trainer to handle loss calculation
     """
 
+    tag_names = ["axolotl", "mamba"]
+
     def compute_loss(
         self,
         model,
@@ -322,6 +389,8 @@ class OneCycleLRSchedulerTrainer(AxolotlTrainer):
     Trainer subclass that uses the OneCycleLR scheduler
     """
 
+    tag_names = ["axolotl", "onecycle"]
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.lr_scheduler = None
@@ -351,6 +420,8 @@ class ReLoRATrainer(AxolotlTrainer):
     Trainer subclass that uses the OneCycleLR scheduler
     """
 
+    tag_names = ["axolotl", "relora"]
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.lr_scheduler = None
@@ -386,12 +457,21 @@ class TrainerBuilderBase(abc.ABC):
 
     _train_dataset = None
     _eval_dataset = None
+    _model_ref = None
 
     def __init__(self, cfg, model, tokenizer):
         self.cfg = cfg
         self.model = model
         self.tokenizer = tokenizer
 
+    @property
+    def model_ref(self):
+        return self._model_ref
+
+    @model_ref.setter
+    def model_ref(self, model):
+        self._model_ref = model
+
     @property
     def train_dataset(self):
         return self._train_dataset
@@ -532,6 +612,14 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs[
                 "gradient_checkpointing"
             ] = self.cfg.gradient_checkpointing
+            if self.cfg.gradient_checkpointing_kwargs:
+                training_arguments_kwargs[
+                    "gradient_checkpointing_kwargs"
+                ] = self.cfg.gradient_checkpointing_kwargs
+            else:
+                training_arguments_kwargs["gradient_checkpointing_kwargs"] = {
+                    "use_reentrant": False
+                }
         if self.cfg.fsdp:
             training_arguments_kwargs["fsdp"] = self.cfg.fsdp
             if self.cfg.fsdp_config:
@@ -559,6 +647,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["hub_model_id"] = self.cfg.hub_model_id
             training_arguments_kwargs["push_to_hub"] = True
             training_arguments_kwargs["hub_private_repo"] = True
+            training_arguments_kwargs["hub_always_push"] = True
 
             if self.cfg.hub_strategy:
                 training_arguments_kwargs["hub_strategy"] = self.cfg.hub_strategy
@@ -583,6 +672,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs[
                 "dataloader_prefetch_factor"
             ] = self.cfg.dataloader_prefetch_factor
+        if self.cfg.dataloader_drop_last is not None:
+            training_arguments_kwargs[
+                "dataloader_drop_last"
+            ] = self.cfg.dataloader_drop_last
+        elif self.cfg.sample_packing and self.cfg.eval_sample_packing is False:
+            training_arguments_kwargs["dataloader_drop_last"] = True
 
         if self.cfg.val_set_size == 0:
             # no eval set, so don't eval
@@ -679,7 +774,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             False if self.cfg.ddp else None
         )
         training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
-        training_arguments_kwargs["report_to"] = "wandb" if self.cfg.use_wandb else None
+        report_to = None
+        if self.cfg.use_wandb:
+            report_to = "wandb"
+        if self.cfg.use_mlflow:
+            report_to = "mlflow"
+        training_arguments_kwargs["report_to"] = report_to
         training_arguments_kwargs["run_name"] = (
             self.cfg.wandb_name if self.cfg.use_wandb else None
         )
@@ -692,6 +792,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             and self.cfg.lr_scheduler not in ("one_cycle", "log_sweep")
             else "cosine"
         )
+        training_arguments_kwargs["lr_scheduler_kwargs"] = (
+            self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
+        )
+        training_arguments_kwargs["cosine_min_lr_ratio"] = self.cfg.cosine_min_lr_ratio
         training_arguments_kwargs["weight_decay"] = (
             self.cfg.weight_decay if self.cfg.weight_decay is not None else 0.0
         )
@@ -712,6 +816,13 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs
         )
         training_arguments_kwargs["model_type"] = self.cfg.model_config_type
+        training_arguments_kwargs["pretraining"] = bool(self.cfg.pretraining_dataset)
+
+        if self.cfg.neftune_noise_alpha is not None:
+            training_arguments_kwargs[
+                "neftune_noise_alpha"
+            ] = self.cfg.neftune_noise_alpha
+
         training_args = (
             AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
                 **training_arguments_kwargs,
@@ -737,26 +848,6 @@ 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.is_llama_derived_model and self.cfg.landmark_attention:
-            from axolotl.monkeypatch.llama_landmark_attn import (
-                add_mem_tokens,
-                get_mem_id,
-                set_model_mem_id,
-            )
-
-            set_model_mem_id(self.model, self.tokenizer)
-
-            LOG.info("Adding landmark attention tokens to dataset")
-
-            for dataset in [self.train_dataset, self.eval_dataset]:
-                dataset = dataset.map(
-                    partial(
-                        add_mem_tokens, mem_freq=50, mem_id=get_mem_id(self.tokenizer)
-                    ),
-                    batched=False,
-                    num_proc=32,
-                )
-
         trainer_cls = self._get_trainer_cls()
         trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(
             trainer_kwargs, trainer_cls
@@ -766,7 +857,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             train_dataset=self.train_dataset,
             eval_dataset=self.eval_dataset,
             args=training_args,
-            data_collator=self.build_collator(**data_collator_kwargs),
+            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",
@@ -787,12 +881,125 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         return trainer
 
-    def build_collator(self, **kwargs):
+    def build_collator(
+        self, training_args: AxolotlTrainingArguments, is_eval=False, **kwargs
+    ):
+        if training_args.pretraining:
+            return None
+
         if self.cfg.model_config_type == "mamba":
             return MambaDataCollator(tokenizer=self.tokenizer)
 
-        return BatchSamplerDataCollatorForSeq2Seq(
+        use_batch_sampler_collator = False
+        if is_eval is False and training_args.sample_packing:
+            use_batch_sampler_collator = True
+        if is_eval and training_args.eval_sample_packing:
+            use_batch_sampler_collator = True
+
+        if use_batch_sampler_collator:
+            return BatchSamplerDataCollatorForSeq2Seq(
+                self.tokenizer,
+                return_tensors="pt",
+                **kwargs,
+            )
+
+        return DataCollatorForSeq2Seq(
             self.tokenizer,
             return_tensors="pt",
             **kwargs,
         )
+
+
+class HFDPOTrainerBuilder(TrainerBuilderBase):
+    """
+    Trainer factory class for DPO Trainer
+    """
+
+    def get_callbacks(self):
+        callbacks = []
+        return callbacks
+
+    def get_post_trainer_create_callbacks(self, trainer):
+        callbacks = []
+        return callbacks
+
+    def build_training_arguments(self, total_num_steps):
+        training_args_kwargs = {}
+        for arg in [
+            "adam_beta1",
+            "adam_beta2",
+            "adam_epsilon",
+            "dataloader_num_workers",
+            "dataloader_pin_memory",
+        ]:
+            if hasattr(self.cfg, arg) and getattr(self.cfg, arg) is not None:
+                training_args_kwargs[arg] = getattr(self.cfg, arg)
+        training_args = TrainingArguments(
+            per_device_train_batch_size=self.cfg.micro_batch_size,
+            max_steps=total_num_steps,
+            remove_unused_columns=False,
+            gradient_accumulation_steps=self.cfg.gradient_accumulation_steps,
+            learning_rate=self.cfg.learning_rate,
+            evaluation_strategy="no",
+            # eval_steps=self.cfg.eval_steps,
+            save_strategy="steps",
+            save_steps=self.cfg.save_steps,
+            output_dir=self.cfg.output_dir,
+            warmup_steps=self.cfg.warmup_steps,
+            bf16=True,
+            gradient_checkpointing=self.cfg.gradient_checkpointing,
+            gradient_checkpointing_kwargs={"use_reentrant": False},
+            logging_first_step=True,
+            logging_steps=1,
+            optim=self.cfg.optimizer,
+            save_total_limit=self.cfg.save_total_limit or 5,
+            **training_args_kwargs,
+        )
+
+        return training_args
+
+    def build(self, total_num_steps):
+        training_args = self.build_training_arguments(total_num_steps)
+        dpo_trainer_kwargs = {}
+        if self.cfg.rl == "ipo":
+            dpo_trainer_kwargs["loss_type"] = "ipo"
+            if self.cfg.dpo_label_smoothing:
+                dpo_trainer_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
+        elif self.cfg.rl == "kto_pair":
+            dpo_trainer_kwargs["loss_type"] = "kto_pair"
+
+        dpo_trainer = DPOTrainer(
+            self.model,
+            self.model_ref,
+            args=training_args,
+            beta=self.cfg.dpo_beta or 0.1,
+            train_dataset=self.train_dataset,
+            # eval_dataset=self.eval_dataset,
+            eval_dataset=None,
+            tokenizer=self.tokenizer,
+            max_length=self.cfg.sequence_len,
+            max_target_length=None,
+            max_prompt_length=self.cfg.sequence_len,
+            generate_during_eval=True,
+            **dpo_trainer_kwargs,
+        )
+
+        return dpo_trainer
+
+
+class HFPPOTrainerBuilder(TrainerBuilderBase):
+    """
+    HF Factory class for PPO Trainer
+    """
+
+    def get_callbacks(self):
+        callbacks = []
+        return callbacks
+
+    def get_post_trainer_create_callbacks(self, trainer):
+        callbacks = []
+        return callbacks
+
+    def build(self, total_num_steps):
+        # build PPOConfig
+        pass

src/axolotl/core/trainers/trl.py

@@ -0,0 +1,66 @@
+"""
+module for TRL PPO training
+"""
+import torch
+from tqdm import tqdm
+from trl import PPOTrainer
+
+
+class TRLPPOTrainer(PPOTrainer):
+    """
+    wrapper for ppo trainer to handle customizations
+    """
+
+    def train(
+        self,
+        reward_pipe,
+        resume_from_checkpoint=None,  # pylint: disable=unused-argument
+    ):
+        generation_kwargs = {
+            "min_length": -1,
+            "top_k": 0.0,
+            "top_p": 1.0,
+            "do_sample": True,
+            "pad_token_id": self.tokenizer.eos_token_id,
+            "max_new_tokens": 32,
+        }
+        sent_kwargs = {
+            "return_all_scores": True,
+            "function_to_apply": "none",
+            "batch_size": 16,
+        }
+
+        for epoch, batch in tqdm(  # pylint: disable=unused-variable
+            enumerate(self.dataloader)
+        ):
+            query_tensors = batch["input_ids"]
+
+            # generate model response
+            response_tensors, ref_response_tensors = self.generate(
+                query_tensors,
+                return_prompt=False,
+                generate_ref_response=True,
+                **generation_kwargs
+            )
+            batch["response"] = self.tokenizer.batch_decode(response_tensors)
+            batch["ref_response"] = self.tokenizer.batch_decode(ref_response_tensors)
+
+            # Compute sentiment score
+            texts = [q + r for q, r in zip(batch["query"], batch["response"])]
+            pipe_outputs = reward_pipe(texts, **sent_kwargs)
+            rewards = [torch.tensor(output[1]["score"]) for output in pipe_outputs]
+            ref_texts = [q + r for q, r in zip(batch["query"], batch["ref_response"])]
+            ref_pipe_outputs = reward_pipe(ref_texts, **sent_kwargs)
+            ref_rewards = [
+                torch.tensor(output[1]["score"]) for output in ref_pipe_outputs
+            ]
+            batch["ref_rewards"] = ref_rewards
+
+            # Run PPO step
+            stats = self.step(query_tensors, response_tensors, rewards)
+            self.log_stats(
+                stats,
+                batch,
+                rewards,
+                columns_to_log=["query", "response", "ref_response", "ref_rewards"],
+            )

src/axolotl/models/mamba/__init__.py

@@ -2,8 +2,20 @@
 Modeling module for Mamba models
 """
 
+import importlib
+
+
+def check_mamba_ssm_installed():
+    mamba_ssm_spec = importlib.util.find_spec("mamba_ssm")
+    if mamba_ssm_spec is None:
+        raise ImportError(
+            "MambaLMHeadModel requires mamba_ssm. Please install it with `pip install -e .[mamba-ssm]`"
+        )
+
 
 def fix_mamba_attn_for_loss():
+    check_mamba_ssm_installed()
+
     from mamba_ssm.models import mixer_seq_simple
 
     from .modeling_mamba import MambaLMHeadModel as MambaLMHeadModelFixed

src/axolotl/models/mixtral/__init__.py

@@ -1,6 +0,0 @@
-"""
-Custom modeling code for mixtral
-"""
-
-from .configuration_moe_mistral import MixtralConfig  # noqa
-from .modeling_moe_mistral import MixtralForCausalLM  # noqa

src/axolotl/models/mixtral/configuration_moe_mistral.py

@@ -1,154 +0,0 @@
-# coding=utf-8
-# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Mistral model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "mistralai/Mistral-7B-v0.1": "https://huggingface.co/mistralai/Mistral-7B-v0.1/resolve/main/config.json",
-    "mistralai/Mistral-7B-Instruct-v0.1": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/resolve/main/config.json",
-}
-
-
-class MixtralConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
-    Mistral model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.
-
-    [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
-    [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 32000):
-            Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`MistralModel`]
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 14336):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        num_key_value_heads (`int`, *optional*, defaults to 8):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details checkout [this
-            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
-        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
-            The non-linear activation function (function or string) in the decoder.
-        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
-            The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
-            allows sequence of up to 4096*32 tokens.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
-            The epsilon used by the rms normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        pad_token_id (`int`, *optional*):
-            The id of the padding token.
-        bos_token_id (`int`, *optional*, defaults to 1):
-            The id of the "beginning-of-sequence" token.
-        eos_token_id (`int`, *optional*, defaults to 2):
-            The id of the "end-of-sequence" token.
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether the model's input and output word embeddings should be tied.
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        sliding_window (`int`, *optional*, defaults to 4096):
-            Sliding window attention window size. If not specified, will default to `4096`.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-
-    ```python
-    >>> from transformers import MistralModel, MistralConfig
-
-    >>> # Initializing a Mistral 7B style configuration
-    >>> configuration = MixtralConfig()
-
-    >>> # Initializing a model from the Mistral 7B style configuration
-    >>> model = MixtralModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "mistral"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=32000,
-        hidden_size=4096,
-        intermediate_size=14336,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=8,
-        hidden_act="silu",
-        max_position_embeddings=4096 * 32,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        pad_token_id=None,
-        bos_token_id=1,
-        eos_token_id=2,
-        tie_word_embeddings=False,
-        rope_theta=10000.0,
-        attention_dropout=0.0,
-        num_experts_per_token=2,
-        num_experts=8,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-
-        # for backward compatibility
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.attention_dropout = attention_dropout
-        self.num_experts = num_experts
-        self.num_experts_per_token = num_experts_per_token
-
-        # pylint: disable=duplicate-code
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )

src/axolotl/models/phi/modeling_phi.py

@@ -9,27 +9,32 @@ from __future__ import annotations
 
 import math
 from dataclasses import dataclass, field
-from typing import Any, Dict, Optional, Tuple, Union
+from typing import Any, Callable, Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
 from einops import rearrange, repeat
+from torch.utils.checkpoint import checkpoint
 from transformers import PretrainedConfig, PreTrainedModel
 from transformers.activations import ACT2FN
 from transformers.modeling_outputs import CausalLMOutputWithPast
 
-from ...monkeypatch.utils import get_cu_seqlens_from_pos_ids
 from .configuration_phi import PhiConfig
 
 try:
     from flash_attn.bert_padding import pad_input, unpad_input
     from flash_attn.layers.rotary import RotaryEmbedding as FlashRotaryEmbedding
     from flash_attn.modules.mha import FlashCrossAttention, FlashSelfAttention
-    from flash_attn.ops.fused_dense import FusedDense
-except:  # noqa: E722
+except ImportError:
     pad_input, unpad_input = None, None
     FlashRotaryEmbedding = None
     FlashSelfAttention, FlashCrossAttention = None, None
+
+# this is in a seperate try/except block since sometimes fused_dense isn't available
+# and it shouldn't completely disable flash attn when it isn't
+try:
+    from flash_attn.ops.fused_dense import FusedDense
+except ImportError:
     FusedDense = None
 
 
@@ -224,7 +229,9 @@ class RotaryEmbedding(nn.Module):
 
         # Initialize cached attributes since ONNX can't rely on dynamic initialization
         self._update_cos_sin_cache(
-            max_position_embeddings, device=device, dtype=torch.float32
+            max_position_embeddings,
+            device=device,
+            dtype=torch.float32,
         )
 
     def _compute_inv_freq(self, device: Optional[str] = None) -> torch.FloatTensor:
@@ -281,34 +288,32 @@ class RotaryEmbedding(nn.Module):
         seqlen_offset: int = 0,
         **kwargs,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
-        seq_start = seqlen_offset
-        seq_end = seq_start + qkv.shape[1]
-
         if (
-            self._cos_cached.device != qkv.device
+            self._seq_len_cached < qkv.shape[1] + seqlen_offset
+            or self._cos_cached.device != qkv.device
             or self._cos_cached.dtype != qkv.dtype
             or (self.training and self._cos_cached.is_inference())
         ):
             self._update_cos_sin_cache(
-                self.max_position_embeddings, device=qkv.device, dtype=qkv.dtype
+                qkv.shape[1] + seqlen_offset, device=qkv.device, dtype=qkv.dtype
             )
 
         if kv is None:
             return _apply_rotary_emb_qkv(
                 qkv,
-                self._cos_cached[seq_start:seq_end],
-                self._sin_cached[seq_start:seq_end],
+                self._cos_cached[seqlen_offset:],
+                self._sin_cached[seqlen_offset:],
             )
         else:
             q = _apply_rotary_emb(
                 qkv,
-                self._cos_cached[seq_start:seq_end],
-                self._sin_cached[seq_start:seq_end],
+                self._cos_cached[seqlen_offset:],
+                self._sin_cached[seqlen_offset:],
             )
             kv = _apply_rotary_emb_kv(
                 kv,
-                self._cos_cached[seq_start:seq_end],
-                self._sin_cached[seq_start:seq_end],
+                self._cos_cached[seqlen_offset:],
+                self._sin_cached[seqlen_offset:],
             )
 
             return q, kv
@@ -511,7 +516,7 @@ def _update_kv_cache(
     num_heads, head_dim = kv.shape[-2:]
 
     if layer_idx not in inference_params.key_value_memory_dict:
-        kv_cache = torch.empty(
+        inference_params.key_value_memory_dict[layer_idx] = torch.empty(
             inference_params.max_batch_size,
             inference_params.max_seqlen,
             2,
@@ -520,9 +525,6 @@ def _update_kv_cache(
             dtype=kv.dtype,
             device=kv.device,
         )
-        inference_params.key_value_memory_dict[layer_idx] = kv_cache
-    else:
-        kv_cache = inference_params.key_value_memory_dict[layer_idx]
 
     batch_start = inference_params.batch_size_offset
     batch_end = batch_start + kv.shape[0]
@@ -530,8 +532,19 @@ def _update_kv_cache(
     sequence_start = inference_params.seqlen_offset
     sequence_end = sequence_start + kv.shape[1]
 
-    kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
-    kv = kv_cache[batch_start:batch_end, :sequence_end, ...]
+    # When the current sequence length is equal to or larger than the maximum sequence length,
+    # we need to concatenate the current `kv` with the cached `kv` to expand its length
+    if sequence_end >= inference_params.max_seqlen:
+        inference_params.key_value_memory_dict[layer_idx] = torch.concatenate(
+            (inference_params.key_value_memory_dict[layer_idx], kv), dim=1
+        )
+
+    inference_params.key_value_memory_dict[layer_idx][
+        batch_start:batch_end, sequence_start:sequence_end, ...
+    ] = kv
+    kv = inference_params.key_value_memory_dict[layer_idx][
+        batch_start:batch_end, :sequence_end, ...
+    ]
 
     return kv
 
@@ -624,13 +637,10 @@ class MHA(nn.Module):
         self.layer_idx = layer_idx
         self.return_residual = return_residual
         self.checkpointing = checkpointing
+        self._gradient_checkpointing_func = None
 
     def _forward_self_attn(
-        self,
-        x: torch.FloatTensor,
-        key_padding_mask: Optional[torch.BoolTensor],
-        cu_seqlens: Optional[torch.LongTensor] = None,
-        max_seqlen: Optional[int] = None,
+        self, x: torch.FloatTensor, key_padding_mask: Optional[torch.BoolTensor]
     ) -> torch.FloatTensor:
         qkv = self.Wqkv(x)
         qkv = rearrange(
@@ -643,20 +653,21 @@ class MHA(nn.Module):
         if self.flash_attn:
             batch_size, seqlen = qkv.shape[0], qkv.shape[1]
 
-            if (
-                key_padding_mask is not None
-                and cu_seqlens is None
-                and max_seqlen is None
-            ):
+            cu_seqlens, max_seqlen = None, None
+            if key_padding_mask is not None:
                 # If `key_padding_mask` is supplied, we need to unpad the input and retrieve
                 # the `cu_seqlens` and `max_seqlen` to be used by `flash-attn`
                 qkv, indices, cu_seqlens, max_seqlen = unpad_input(
                     qkv, key_padding_mask
                 )
 
-            if self.checkpointing:
-                attn_output = torch.utils.checkpoint.checkpoint(
-                    self.inner_attn, qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen
+            if self.checkpointing and self.training:
+                attn_output = self._gradient_checkpointing_func(
+                    self.inner_attn,
+                    qkv,
+                    cu_seqlens=cu_seqlens,
+                    max_seqlen=max_seqlen,
+                    use_reentrant=False,
                 )
             else:
                 attn_output = self.inner_attn(
@@ -670,9 +681,12 @@ class MHA(nn.Module):
                 else attn_output
             )
 
-        if self.checkpointing:
-            return torch.utils.checkpoint.checkpoint(
-                self.inner_attn, qkv, key_padding_mask=key_padding_mask
+        if self.checkpointing and self.training:
+            return self._gradient_checkpointing_func(
+                self.inner_attn,
+                qkv,
+                key_padding_mask=key_padding_mask,
+                use_reentrant=False,
             )
 
         return self.inner_attn(qkv, key_padding_mask=key_padding_mask)
@@ -725,8 +739,8 @@ class MHA(nn.Module):
                     q, key_padding_mask
                 )
 
-            if self.checkpointing:
-                attn_output = torch.utils.checkpoint.checkpoint(
+            if self.checkpointing and self.training:
+                attn_output = self._gradient_checkpointing_func(
                     self.inner_cross_attn,
                     q,
                     kv,
@@ -735,6 +749,7 @@ class MHA(nn.Module):
                     max_seqlen=max_seqlen_q,
                     cu_seqlens_k=cu_seqlens_k,
                     max_seqlen_k=max_seqlen_k,
+                    use_reentrant=False,
                 )
             else:
                 attn_output = self.inner_cross_attn(
@@ -753,13 +768,14 @@ class MHA(nn.Module):
                 else attn_output
             )
 
-        if self.checkpointing:
-            return torch.utils.checkpoint.checkpoint(
+        if self.checkpointing and self.training:
+            return self._gradient_checkpointing_func(
                 self.inner_cross_attn,
                 q,
                 kv,
                 key_padding_mask=key_padding_mask,
                 causal=causal,
+                use_reentrant=False,
             )
 
         return self.inner_cross_attn(
@@ -771,11 +787,8 @@ class MHA(nn.Module):
         x: torch.FloatTensor,
         past_key_values: Optional[InferenceParams] = None,
         attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
-        cu_seqlens: Optional[torch.LongTensor] = None,
-        max_seqlen: Optional[int] = None,
         **kwargs,
     ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
-        # TODO: Need an alternative way for dynamic control flow: torch.any(~attention_mask.bool())
         if attention_mask is not None:
             attention_mask = attention_mask.bool()
         else:
@@ -785,18 +798,12 @@ class MHA(nn.Module):
         if self.n_head == self.n_head_kv:
             if past_key_values is None:
                 # If `past_key_values` are not supplied, we run self-attention
-                attn_output = self._forward_self_attn(
-                    x, attention_mask, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen
-                )
+                attn_output = self._forward_self_attn(x, attention_mask)
             else:
                 # If `past_key_values` are supplied, it means that we might have cached values and
                 # could take advantage of cross-attention
                 attn_output = self._forward_cross_attn(
-                    x,
-                    past_key_values,
-                    attention_mask,
-                    cu_seqlens=cu_seqlens,
-                    max_seqlen=max_seqlen,
+                    x, past_key_values, attention_mask
                 )
         # MQA / GQA
         else:
@@ -830,6 +837,8 @@ class ParallelBlock(nn.Module):
 
         self.mixer = MHA(config, layer_idx=block_idx)
         self.mlp = MLP(config)
+        self.checkpointing = False
+        self._gradient_checkpointing_func = None
 
     def forward(
         self,
@@ -838,23 +847,52 @@ class ParallelBlock(nn.Module):
         attention_mask: Optional[torch.BoolTensor] = None,
         **kwargs,
     ) -> torch.FloatTensor:
-        residual = hidden_states
-        hidden_states = self.ln(hidden_states)
-
-        attn_outputs = self.mixer(
+        def _forward(
+            mixer,
+            resid_dropout,
+            mlp,
+            ln,
             hidden_states,
-            past_key_values=past_key_values,
-            attention_mask=attention_mask,
+            past_key_values,
+            attention_mask,
+        ):
+            residual = hidden_states
+            hidden_states = ln(hidden_states)
+
+            attn_outputs = mixer(
+                hidden_states,
+                past_key_values=past_key_values,
+                attention_mask=attention_mask,
+            )
+            if isinstance(attn_outputs, tuple):
+                attn_outputs = attn_outputs[0]
+
+            attn_outputs = resid_dropout(attn_outputs)
+            feed_forward_hidden_states = resid_dropout(mlp(hidden_states))
+
+            return attn_outputs + feed_forward_hidden_states + residual
+
+        if self.training and self.checkpointing:
+            return self._gradient_checkpointing_func(
+                _forward,
+                self.mixer,
+                self.resid_dropout,
+                self.mlp,
+                self.ln,
+                hidden_states,
+                past_key_values,
+                attention_mask,
+            )
+
+        return _forward(
+            self.mixer,
+            self.resid_dropout,
+            self.mlp,
+            self.ln,
+            hidden_states,
+            past_key_values,
+            attention_mask,
         )
-        if isinstance(attn_outputs, tuple):
-            attn_outputs = attn_outputs[0]
-
-        attn_outputs = self.resid_dropout(attn_outputs)
-        feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
-
-        hidden_states = attn_outputs + feed_forward_hidden_states + residual
-
-        return hidden_states
 
 
 class CausalLMHead(nn.Module):
@@ -911,7 +949,7 @@ class PhiPreTrainedModel(PreTrainedModel):
 
     config_class = PhiConfig
     base_model_prefix = "transformer"
-    supports_gradient_checkpointing = False
+    supports_gradient_checkpointing = True
     _no_split_modules = ["ParallelBlock"]
 
     def __init__(self, *inputs, **kwargs) -> None:
@@ -931,6 +969,14 @@ class PhiPreTrainedModel(PreTrainedModel):
                 module.bias.data.zero_()
             module.weight.data.fill_(1.0)
 
+    def _set_gradient_checkpointing(
+        self, enable: bool = True, gradient_checkpointing_func: Callable = checkpoint
+    ):
+        for module in self.modules():
+            if hasattr(module, "checkpointing"):
+                module._gradient_checkpointing_func = gradient_checkpointing_func
+                module.checkpointing = enable
+
     def prepare_inputs_for_generation(
         self,
         input_ids: torch.LongTensor,
@@ -951,7 +997,7 @@ class PhiPreTrainedModel(PreTrainedModel):
             )
         else:
             # Assume that `past_key_values` has cached all tokens up to the last token in `input_ids`
-            past_key_values.seqlen_offset = len(input_ids[0]) - 1
+            past_key_values.seqlen_offset = input_ids.shape[1] - 1
             input_ids = input_ids[:, -1].unsqueeze(-1)
 
         return {
@@ -988,8 +1034,6 @@ class PhiModel(PhiPreTrainedModel):
         input_ids: torch.LongTensor,
         past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
         attention_mask: Optional[torch.BoolTensor] = None,
-        cu_seqlens: Optional[torch.LongTensor] = None,
-        max_seqlen: Optional[int] = None,
     ) -> torch.FloatTensor:
         hidden_states = self.embd(input_ids)
 
@@ -998,8 +1042,6 @@ class PhiModel(PhiPreTrainedModel):
                 hidden_states,
                 past_key_values=past_key_values,
                 attention_mask=attention_mask,
-                cu_seqlens=cu_seqlens,
-                max_seqlen=max_seqlen,
             )
 
         return hidden_states
@@ -1034,23 +1076,10 @@ class PhiForCausalLM(PhiPreTrainedModel):
         past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
         attention_mask: Optional[torch.BoolTensor] = None,
         labels: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
         **kwargs,
     ) -> CausalLMOutputWithPast:
-        cu_seqlens: Optional[torch.LongTensor] = None
-        max_seqlen: Optional[int] = None
-        if position_ids is not None:
-            batch_size, seq_length = input_ids.shape
-            position_ids = position_ids.view(-1, seq_length).long()
-            cu_seqlens, max_seqlen = get_cu_seqlens_from_pos_ids(position_ids)
-            cu_seqlens = cu_seqlens.squeeze()
-
         hidden_states = self.transformer(
-            input_ids,
-            past_key_values=past_key_values,
-            attention_mask=attention_mask,
-            cu_seqlens=cu_seqlens,
-            max_seqlen=max_seqlen,
+            input_ids, past_key_values=past_key_values, attention_mask=attention_mask
         )
         lm_logits = self.lm_head(hidden_states)
 

src/axolotl/monkeypatch/fastchat_conversation_turns.py

@@ -82,15 +82,44 @@ def get_turns(  # pylint: disable=too-many-return-statements
             else:
                 yield role + ":", ""
         return
-    if self.sep_style == SeparatorStyle.LLAMA2:
-        seps = [self.sep, self.sep2]
+    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
-        else:
-            yield "", "[INST] "
-        for i, (role, message) in enumerate(self.messages[1:]):
+        for i, (role, message) in enumerate(self.messages):
             if message:
-                yield role + " ", message + seps[i % 2]
+                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 seperated 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
@@ -118,6 +147,15 @@ def get_turns(  # pylint: disable=too-many-return-statements
             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]

src/axolotl/monkeypatch/mixtral/__init__.py

@@ -0,0 +1,22 @@
+"""
+Patches to support multipack for mixtral
+"""
+import transformers
+
+
+def replace_mixtral_attn_with_multipack_flash_attn():
+    from .modeling_mixtral import (
+        MixtralMultipackFlashAttention2,
+        mixtral_decoder_layer_forward,
+        mixtral_model_forward,
+    )
+
+    transformers.models.mixtral.modeling_mixtral.MixtralDecoderLayer.forward = (
+        mixtral_decoder_layer_forward
+    )
+    transformers.models.mixtral.modeling_mixtral.MixtralModel.forward = (
+        mixtral_model_forward
+    )
+    transformers.models.mixtral.modeling_mixtral.MIXTRAL_ATTENTION_CLASSES[
+        "flash_attention_2"
+    ] = MixtralMultipackFlashAttention2

src/axolotl/monkeypatch/mixtral/modeling_mixtral.py

@@ -0,0 +1,383 @@
+"""
+Mixtral modeling for multipack
+"""
+# pylint: disable=missing-module-docstring,unused-argument,protected-access,pointless-string-statement,duplicate-code
+import logging
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+from einops import rearrange
+from flash_attn import flash_attn_varlen_qkvpacked_func
+from transformers import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
+from transformers.modeling_outputs import MoeModelOutputWithPast
+from transformers.models.mixtral.modeling_mixtral import (
+    MixtralFlashAttention2,
+    apply_rotary_pos_emb,
+    repeat_kv,
+)
+
+from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
+
+LOG = logging.getLogger("axolotl.monkeypatch.mixtral")
+
+
+class MixtralMultipackFlashAttention2(MixtralFlashAttention2):
+    """
+    Custom multipack implementation w flash attention 2
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._flash_attn_uses_top_left_mask = True
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        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)
+
+        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)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin, position_ids
+        )
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        if cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
+            # special handling using sample packing
+            qkv = torch.stack(
+                [query_states, key_states, value_states], dim=2
+            )  # [bsz, nh, 3, q_len, hd]
+            qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
+            qkv = rearrange(qkv, "b s ... -> (b s) ...")
+
+            attn_output = flash_attn_varlen_qkvpacked_func(
+                qkv,
+                cu_seqlens,
+                max_seqlen,
+                dropout_p=self.attention_dropout,
+                softmax_scale=None,
+                causal=True,
+            )
+            attn_output = rearrange(attn_output, "(b s) ... -> b s ...", b=bsz)
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+def mixtral_decoder_layer_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: Optional[bool] = False,
+    output_router_logits: Optional[bool] = False,
+    use_cache: Optional[bool] = False,
+    cu_seqlens: Optional[torch.Tensor] = None,
+    max_seqlen: Optional[torch.Tensor] = None,
+    **kwargs,
+) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+    if "padding_mask" in kwargs:
+        warnings.warn(
+            "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+        )
+    """
+    Args:
+        hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+        attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+            `(batch, sequence_length)` where padding elements are indicated by 0.
+        past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+            returned tensors for more detail.
+        output_router_logits (`bool`, *optional*):
+            Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+            should not be returned during inference.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+            (see `past_key_values`).
+    """
+
+    residual = hidden_states
+
+    hidden_states = self.input_layernorm(hidden_states)
+
+    # Self Attention
+    hidden_states, self_attn_weights, present_key_value = self.self_attn(
+        hidden_states=hidden_states,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_value=past_key_value,
+        output_attentions=output_attentions,
+        use_cache=use_cache,
+        cu_seqlens=cu_seqlens,
+        max_seqlen=max_seqlen,
+    )
+    hidden_states = residual + hidden_states
+
+    # Fully Connected
+    residual = hidden_states
+    hidden_states = self.post_attention_layernorm(hidden_states)
+    hidden_states, router_logits = self.block_sparse_moe(hidden_states)
+    hidden_states = residual + hidden_states
+
+    outputs = (hidden_states,)
+
+    if output_attentions:
+        outputs += (self_attn_weights,)
+
+    if use_cache:
+        outputs += (present_key_value,)
+
+    if output_router_logits:
+        outputs += (router_logits,)
+
+    return outputs
+
+
+def mixtral_model_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    output_router_logits: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+) -> Union[Tuple, MoeModelOutputWithPast]:
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_router_logits = (
+        output_router_logits
+        if output_router_logits is not None
+        else self.config.output_router_logits
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # retrieve input_ids and inputs_embeds
+    if input_ids is not None and inputs_embeds is not None:
+        raise ValueError(
+            "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
+        )
+    if input_ids is not None:
+        batch_size, seq_length = input_ids.shape
+    elif inputs_embeds is not None:
+        batch_size, seq_length, _ = inputs_embeds.shape
+    else:
+        raise ValueError(
+            "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+        )
+
+    past_key_values_length = 0
+
+    if use_cache:
+        use_legacy_cache = not isinstance(past_key_values, Cache)
+        if use_legacy_cache:
+            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+        past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+    cu_seqlens = None
+    max_seqlen = None
+    if position_ids is None:
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+        position_ids = torch.arange(
+            past_key_values_length,
+            seq_length + past_key_values_length,
+            dtype=torch.long,
+            device=device,
+        )
+        position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+    else:
+        position_ids = position_ids.view(-1, seq_length).long()
+        cu_seqlens, max_seqlen = get_cu_seqlens_from_pos_ids(position_ids)
+        cu_seqlens = cu_seqlens.squeeze()
+
+    if inputs_embeds is None:
+        inputs_embeds = self.embed_tokens(input_ids)
+
+    if (
+        attention_mask is not None
+        and self._attn_implementation == "flash_attention_2"
+        and use_cache
+    ):
+        is_padding_right = attention_mask[:, -1].sum().item() != batch_size
+        if is_padding_right:
+            raise ValueError(
+                "You are attempting to perform batched generation with padding_side='right'"
+                " this may lead to unexpected behaviour for Flash Attention version of Mixtral. Make sure to "
+                " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+            )
+
+    if self._attn_implementation == "flash_attention_2":
+        # 2d mask is passed through the layers
+        attention_mask = (
+            attention_mask
+            if (attention_mask is not None and 0 in attention_mask)
+            else None
+        )
+    else:
+        # 4d mask is passed through the layers
+        attention_mask = _prepare_4d_causal_attention_mask(
+            attention_mask,
+            (batch_size, seq_length),
+            inputs_embeds,
+            past_key_values_length,
+            sliding_window=self.config.sliding_window,
+        )
+
+    hidden_states = inputs_embeds
+
+    if self.gradient_checkpointing and self.training:
+        if use_cache:
+            LOG.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
+            use_cache = False
+
+    # decoder layers
+    all_hidden_states = () if output_hidden_states else None
+    all_self_attns = () if output_attentions else None
+    all_router_logits = () if output_router_logits else None
+    next_decoder_cache = None
+
+    for decoder_layer in self.layers:
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        if self.gradient_checkpointing and self.training:
+            layer_outputs = self._gradient_checkpointing_func(
+                decoder_layer.__call__,
+                hidden_states,
+                attention_mask,
+                position_ids,
+                past_key_values,
+                output_attentions,
+                output_router_logits,
+                use_cache,
+                cu_seqlens,
+                max_seqlen,
+            )
+        else:
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=output_attentions,
+                output_router_logits=output_router_logits,
+                use_cache=use_cache,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+            )
+
+        hidden_states = layer_outputs[0]
+
+        if use_cache:
+            next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+        if output_attentions:
+            all_self_attns += (layer_outputs[1],)
+
+        if output_router_logits:
+            all_router_logits += (layer_outputs[-1],)
+
+    hidden_states = self.norm(hidden_states)
+
+    # add hidden states from the last decoder layer
+    if output_hidden_states:
+        all_hidden_states += (hidden_states,)
+
+    next_cache = None
+    if use_cache:
+        next_cache = (
+            next_decoder_cache.to_legacy_cache()
+            if use_legacy_cache
+            else next_decoder_cache
+        )
+
+    if not return_dict:
+        return tuple(
+            v
+            for v in [
+                hidden_states,
+                next_cache,
+                all_hidden_states,
+                all_self_attns,
+                all_router_logits,
+            ]
+            if v is not None
+        )
+
+    return MoeModelOutputWithPast(
+        last_hidden_state=hidden_states,
+        past_key_values=next_cache,
+        hidden_states=all_hidden_states,
+        attentions=all_self_attns,
+        router_logits=all_router_logits,
+    )

src/axolotl/monkeypatch/neft_embeddings.py

@@ -1,65 +0,0 @@
-"""
-patches implemented through the trainer hooks to enable NEFT/noisy embeddings per https://arxiv.org/abs/2310.05914
-"""
-import torch
-from peft import PeftModel
-from transformers import PreTrainedModel
-
-
-def patch_neft(alpha, model):
-    embeddings = None
-    if isinstance(model, PreTrainedModel):
-        embeddings = model.get_input_embeddings()
-    if isinstance(model, PeftModel):
-        embeddings = model.base_model.get_input_embeddings()
-    if not embeddings:
-        raise ValueError(f"unhandled model class for neft: {model.__class__.__name__}")
-    embeddings.noisy_embedding_alpha = alpha
-    old_forward = embeddings.forward
-
-    # This hack seems to be needed to properly use a custom forward pass
-    # all credits to: https://discuss.pytorch.org/t/how-can-i-replace-the-forward-method-of-a-predefined-torchvision-model-with-my-customized-forward-function/54224/11
-    bound_method = neft_forward.__get__(  # pylint: disable=no-value-for-parameter
-        embeddings, embeddings.__class__
-    )
-    setattr(embeddings, "forward", bound_method)
-
-    embeddings._old_forward = old_forward  # pylint: disable=protected-access
-    return model
-
-
-def unpatch_neft(model):
-    embeddings = None
-    if isinstance(model, PreTrainedModel):
-        embeddings = model.get_input_embeddings()
-    if isinstance(model, PeftModel):
-        embeddings = model.base_model.get_input_embeddings()
-    if not embeddings:
-        raise ValueError(f"unhandled model class for neft: {model.__class__.__name__}")
-    if hasattr(embeddings, "_old_forward"):
-        embeddings.forward = embeddings._old_forward  # pylint: disable=protected-access
-        del embeddings._old_forward  # pylint: disable=protected-access
-        del embeddings.noisy_embedding_alpha
-
-
-def neft_forward(self, inputs: torch.Tensor):
-    embeddings = self._old_forward(inputs)  # pylint: disable=protected-access
-
-    if self.training:
-        dims = torch.tensor(embeddings.size(1) * embeddings.size(2))
-        mag_norm = self.noisy_embedding_alpha / torch.sqrt(dims)
-        embeddings = embeddings + torch.zeros_like(embeddings).uniform_(
-            -mag_norm, mag_norm
-        )
-
-    return embeddings
-
-
-def pretrain_hook(cfg, trainer):
-    if cfg.noisy_embedding_alpha:
-        trainer.model = patch_neft(cfg.noisy_embedding_alpha, trainer.model)
-
-
-def post_train_hook(cfg, trainer):
-    if cfg.noisy_embedding_alpha:
-        unpatch_neft(trainer.model)

src/axolotl/monkeypatch/utils.py

@@ -55,6 +55,7 @@ def get_cu_seqlens(attn_mask):
     return torch.stack(results).to(dtype=torch.int32), torch.stack(max_seq_lens)
 
 
+@torch.jit.script
 def get_cu_seqlens_from_pos_ids(position_ids):
     """generate a cumulative sequence length mask for flash attention using pos ids"""
     if len(position_ids.shape) == 1:
@@ -81,7 +82,7 @@ def get_cu_seqlens_from_pos_ids(position_ids):
         # Get the indices where the sequence starts
         start_indices = torch.cat(
             [
-                (seq_starts).nonzero(as_tuple=True)[0],
+                torch.nonzero(seq_starts).unbind(dim=1)[0],
                 torch.tensor([len(adjusted_row)], dtype=torch.int32, device=device),
             ]
         )

src/axolotl/monkeypatch/xpos_rope_llama_monkey_patch.py

@@ -1,94 +0,0 @@
-# pylint: skip-file
-"""
-Copied from https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
-"""
-import torch
-import transformers
-import transformers.models.llama.modeling_llama
-from einops import rearrange
-
-
-class XposRotaryEmbedding(torch.nn.Module):
-    def __init__(
-        self,
-        dim,
-        max_position_embeddings=2048,
-        base=10000,
-        device=None,
-        scale_base=2048,
-        use_xpos=True,
-    ):
-        super().__init__()
-        self.max_seq_len_cached = max_position_embeddings
-        self.scale_base = scale_base
-
-        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
-        t = torch.arange(self.max_seq_len_cached, device=device).type_as(inv_freq)
-        freqs = torch.einsum("i , j -> i j", t, inv_freq)
-        freqs = torch.cat((freqs, freqs), dim=-1)
-
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("freqs_cached", freqs, persistent=False)
-
-        if not use_xpos:
-            self.register_buffer("scale", None)
-            self.register_buffer("scale_cached", torch.ones(1))
-            return
-
-        scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
-        power = (t - (self.max_seq_len_cached // 2)) / self.scale_base
-        scale_cached = scale ** rearrange(power, "n -> n 1")
-        scale_cached = torch.cat((scale_cached, scale_cached), dim=-1)
-
-        self.register_buffer("scale", scale, persistent=False)
-        self.register_buffer("scale_cached", scale_cached, persistent=False)
-
-    def forward(
-        self,
-        x,
-        seq_len,
-    ):
-        if seq_len > self.max_seq_len_cached:
-            self.max_seq_len_cached = seq_len
-            t = torch.arange(self.max_seq_len_cached, device=x.device).type_as(
-                self.inv_freq
-            )
-            freqs = torch.einsum("i , j -> i j", t, self.inv_freq)
-            freqs = torch.cat((freqs, freqs), dim=-1).to(dtype=x.dtype)
-
-            self.register_buffer("freqs_cached", freqs)
-
-            if self.scale is None:
-                self.register_buffer(
-                    "scale_cached", torch.ones(1, device=x.device).to(dtype=x.dtype)
-                )
-
-                return self.freqs_cached.to(dtype=x.dtype), self.scale_cached
-
-            power = (t - (seq_len // 2)) / self.scale_base
-            scale = self.scale ** rearrange(power, "n -> n 1")
-            scale = torch.cat((scale, scale), dim=-1).to(dtype=x.dtype)
-            self.register_buffer("scale_cached", scale)
-
-        return self.freqs_cached.to(dtype=x.dtype), self.scale_cached.to(dtype=x.dtype)
-
-
-def rotate_half(x):
-    x1, x2 = x.chunk(2, dim=-1)
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, freqs, scale=1, position_ids=None):
-    freqs = freqs[position_ids, :]
-    if scale.shape[-1] != 1:
-        scale = scale[position_ids, :]
-
-    q_embed = (q * freqs.cos() * scale) + (rotate_half(q) * freqs.sin() * scale)
-    k_embed = (k * freqs.cos() * 1 / scale) + (rotate_half(k) * freqs.sin() * 1 / scale)
-
-    return q_embed, k_embed
-
-
-def replace_llama_rope_with_xpos_rope():
-    transformers.models.llama.modeling_llama.LlamaRotaryEmbedding = XposRotaryEmbedding
-    transformers.models.llama.modeling_llama.apply_rotary_pos_emb = apply_rotary_pos_emb

src/axolotl/prompt_strategies/llama2_chat.py

@@ -81,8 +81,9 @@ class LLama2ChatTokenizingStrategy(PromptTokenizingStrategy):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.sequence_len = 4096
-        self.tokenizer.add_special_tokens({"pad_token": "<pad>"})
+        self.tokenizer.add_special_tokens(
+            {"pad_token": getattr(self.tokenizer, "pad_token", "<pad>")}
+        )
         # https://huggingface.co/meta-llama/Llama-2-7b-chat-hf/blob/main/added_tokens.json
 
     def tokenize_prompt(self, prompt):

src/axolotl/prompt_strategies/sharegpt.py

@@ -39,6 +39,23 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
     return strategy
 
 
+def load_ultrachat(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+    conversation = (
+        ds_cfg["conversation"] if ds_cfg and "conversation" in ds_cfg else None
+    )
+    strategy = UltrachatShareGPTPromptTokenizingStrategy(
+        ShareGPTPrompterV2(
+            conversation=conversation,
+        ),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+    if ds_cfg and "strict" in ds_cfg:
+        strategy.strict = ds_cfg["strict"]
+    return strategy
+
+
 def load_role(tokenizer, cfg):
     return SimpleRoleShareGPTPromptTokenizingStrategy(
         ShareGPTPrompterV2(),
@@ -109,3 +126,17 @@ class GuanacoShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
             {"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

src/axolotl/prompt_tokenizers.py

@@ -379,10 +379,12 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
                         add_eos_token=False,
                         strip_bos_token=True,
                     )
-                    # everything from this is masked out from the labels
-                    labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
+                    if self.train_on_inputs:
+                        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 assistant in role:
-                    # TODO label assistant token/tokens w/ IGNORE_TOKEN_ID
                     role = (
                         role.replace(role_remap[1]["from"], role_remap[1]["to"])
                         if role_remap
@@ -392,9 +394,13 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
                     # 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=True,
+                        add_eos_token=add_eos_token,
                         strip_bos_token=True,
                     )
                     role_res = self._tokenize(
@@ -402,18 +408,24 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
                         add_eos_token=False,
                         strip_bos_token=True,
                     )
-                    # not masked out from labels
                     labels = copy.deepcopy(res["input_ids"])
-                    len_role = len(role_res["input_ids"])
-                    labels[:len_role] = [IGNORE_TOKEN_ID] * min(len_role, len(labels))
+                    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)
+                        )
                 elif 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
                     )
-                    # everything from this is masked out from the labels
-                    labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
+                    if self.train_on_inputs:
+                        labels = copy.deepcopy(res["input_ids"])
+                    else:
+                        # everything from this is masked out from the labels
+                        labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
                 else:
                     LOG.warning(f"unhandled role: {role}")
                     continue

src/axolotl/prompters.py

@@ -33,8 +33,8 @@ class AlpacaPrompter(Prompter):
     Base class for alpaca prompters
     """
 
-    system_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
-    system_no_input_prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
+    system_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request."
+    system_no_input_prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
     system_format: str = "{system}"
     turn_format: str
     turn_no_input_format: str

src/axolotl/train.py

@@ -5,19 +5,22 @@ import signal
 import sys
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Optional
+from typing import Optional, Tuple, Union
 
 import torch
 import transformers.modelcard
 from accelerate.logging import get_logger
 from datasets import Dataset
 from optimum.bettertransformer import BetterTransformer
+from peft import PeftModel
+from pkg_resources import get_distribution  # type: ignore
+from transformers import PreTrainedModel, PreTrainedTokenizer
 from transformers.deepspeed import is_deepspeed_zero3_enabled
 
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.logging_config import configure_logging
-from axolotl.monkeypatch import neft_embeddings
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.freeze import freeze_parameters_except
 from axolotl.utils.models import load_model, load_tokenizer
 from axolotl.utils.trainer import setup_trainer
 
@@ -42,7 +45,7 @@ class TrainDatasetMeta:
 
 def train(
     *, cfg: DictDefault, cli_args: TrainerCliArgs, dataset_meta: TrainDatasetMeta
-):
+) -> Tuple[Union[PeftModel, PreTrainedModel], PreTrainedTokenizer]:
     # load the tokenizer first
     LOG.debug(
         f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",
@@ -60,6 +63,17 @@ def train(
         msg += " and peft_config..."
     LOG.debug(msg)
     model, peft_config = load_model(cfg, tokenizer, inference=cli_args.inference)
+    model_ref = None
+    if cfg.rl:
+        if cfg.adapter and not cfg.rl_adapter_ref_model:
+            # use built-in trl autounwrap
+            LOG.debug("Passing model_ref: None to RL trainer")
+            model_ref = None  # explicit setting to None
+        else:
+            # load the model again for model_ref/baseline
+            model_ref, _ = load_model(
+                cfg, tokenizer, inference=cli_args.inference, reference_model=True
+            )
 
     safe_serialization = cfg.save_safetensors is True
 
@@ -78,8 +92,11 @@ def train(
             )
     resume_from_checkpoint = cfg.resume_from_checkpoint
 
+    if cfg.unfrozen_parameters:
+        freeze_parameters_except(model, cfg.unfrozen_parameters)
+
     trainer = setup_trainer(
-        cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps
+        cfg, train_dataset, eval_dataset, (model, model_ref), tokenizer, total_num_steps
     )
 
     if hasattr(model, "config"):
@@ -112,6 +129,12 @@ def train(
     badge_markdown = """[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)"""
     transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n{badge_markdown}"
 
+    if getattr(cfg, "axolotl_config_path"):
+        raw_axolotl_cfg = Path(cfg.axolotl_config_path)
+        version = get_distribution("axolotl").version
+        if raw_axolotl_cfg.is_file():
+            transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n<details><summary>See axolotl config</summary>\n\naxolotl version: `{version}`\n```yaml\n{raw_axolotl_cfg.read_text(encoding='utf-8')}\n```\n\n</details><br>\n"
+
     LOG.info("Starting trainer...")
     if cfg.group_by_length:
         LOG.info("hang tight... sorting dataset for group_by_length")
@@ -172,25 +195,26 @@ def train(
 
     if not cfg.hub_model_id:
         trainer.create_model_card(model_name=cfg.output_dir.lstrip("./"))
+    elif cfg.hub_model_id:
+        # defensively push to the hub to ensure the model card is updated
+        trainer.push_to_hub()
 
     return model, tokenizer
 
 
-def pretrain_hooks(cfg, trainer):
+def pretrain_hooks(_cfg, _trainer):
     """
     Run hooks right before kicking off the training
     :param cfg:
     :param trainer:
     :return:
     """
-    neft_embeddings.pretrain_hook(cfg, trainer)
 
 
-def post_train_hooks(cfg, trainer):
+def post_train_hooks(_cfg, _trainer):
     """
     Run hooks right after training completes
     :param cfg:
     :param trainer:
     :return:
     """
-    neft_embeddings.post_train_hook(cfg, trainer)

src/axolotl/utils/callbacks.py

@@ -4,6 +4,8 @@ from __future__ import annotations
 
 import logging
 import os
+from shutil import copyfile
+from tempfile import NamedTemporaryFile
 from typing import TYPE_CHECKING, Dict, List
 
 import evaluate
@@ -561,10 +563,15 @@ class SaveAxolotlConfigtoWandBCallback(TrainerCallback):
     ):
         if is_main_process():
             try:
-                artifact = wandb.Artifact(name="axolotl-config", type="config")
-                artifact.add_file(local_path=self.axolotl_config_path)
-                wandb.run.log_artifact(artifact)
-                LOG.info("Axolotl config has been saved to WandB as an artifact.")
+                # sync config to top level in run, cannot delete file right away because wandb schedules it to be synced even w/policy = 'now', so let OS delete it later.
+                with NamedTemporaryFile(
+                    mode="w", delete=False, suffix=".yml", prefix="axolotl_config_"
+                ) as temp_file:
+                    copyfile(self.axolotl_config_path, temp_file.name)
+                    wandb.save(temp_file.name)
+                LOG.info(
+                    "The Axolotl config has been saved to the WandB run under files."
+                )
             except (FileNotFoundError, ConnectionError) as err:
                 LOG.warning(f"Error while saving Axolotl config to WandB: {err}")
         return control

src/axolotl/utils/chat_templates.py

@@ -0,0 +1,29 @@
+"""
+This module provides functionality for selecting chat templates based on user choices.
+These templates are used for formatting messages in a conversation.
+"""
+
+
+def chat_templates(user_choice: str):
+    """
+    Finds the correct chat_template for the tokenizer_config.
+
+    Args:
+        user_choice (str): The user's choice of template.
+
+    Returns:
+        str: The chosen template string.
+
+    Raises:
+        ValueError: If the user_choice is not found in the templates.
+    """
+
+    templates = {
+        "inst": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",  # I don't know what this one is called. Used by Mistral/Mixtral.
+        "chatml": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+    }
+
+    if user_choice in templates:
+        return templates[user_choice]
+
+    raise ValueError(f"Template '{user_choice}' not found.")

src/axolotl/utils/collators.py

@@ -178,3 +178,24 @@ class MambaDataCollator:
             "input_ids": input_ids,
             "labels": labels,
         }
+
+
+@dataclass
+class PretrainingBatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
+    """
+    Collator for multipack specific to the using the BatchSampler
+    """
+
+    def __call__(self, features, return_tensors=None):
+        chunked_data = {}
+        for feature in features.keys():
+            if feature == "length":
+                continue
+            if feature == "attention_mask":
+                arrays = [(1) * np.array(item) for item in features[feature]]
+                chunked_data[feature] = np.concatenate(arrays)
+            else:
+                arrays = [np.array(item) for item in features[feature]]
+                chunked_data[feature] = np.concatenate(arrays)
+        features = [chunked_data]
+        return super().__call__(features, return_tensors=return_tensors)

src/axolotl/utils/config.py

@@ -61,6 +61,14 @@ def normalize_config(cfg):
         cfg.device_map = {"": int(os.environ.get("LOCAL_RANK", 0))}
         cfg.batch_size = cfg.batch_size * cfg.world_size
 
+    if cfg.bf16 == "auto":
+        if is_torch_bf16_gpu_available():
+            LOG.debug("bf16 support detected, enabling for this configuration.")
+            cfg.bf16 = True
+        else:
+            LOG.debug("bf16 support not detected, disabling for this configuration.")
+            cfg.bf16 = False
+
     if cfg.device == "mps":
         cfg.load_in_8bit = False
         cfg.tf32 = False
@@ -77,6 +85,15 @@ def normalize_config(cfg):
     else:
         cfg.torch_dtype = torch.float32
 
+    if cfg.saves_per_epoch:
+        save_steps = 1.0 / (cfg.saves_per_epoch * cfg.num_epochs)
+        if save_steps < 1.0:  # prevent saves on every step
+            cfg.save_steps = save_steps
+    if cfg.evals_per_epoch:
+        eval_steps = 1.0 / (cfg.evals_per_epoch * cfg.num_epochs)
+        if eval_steps < 1.0:  # prevent evals on every step
+            cfg.eval_steps = eval_steps
+
     cfg.dataset_processes = cfg.dataset_processes or os.cpu_count()
 
     if not cfg.base_model_config:
@@ -141,7 +158,26 @@ def normalize_config(cfg):
     log_gpu_memory_usage(LOG, "baseline", cfg.device)
 
 
+def normalize_cfg_datasets(cfg):
+    """
+    helpers for mapping chat_template to various dataset configurations as necessary
+    """
+
+    if cfg.chat_template and cfg.chat_template == "chatml":
+        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: chatml` to match your chat_template"
+                    )
+                    cfg.datasets[idx].conversation = "chatml"
+
+
 def validate_config(cfg):
+    """
+    This is a "pre-validation" step that handles the yaml configuration before we have any
+    information about the model architecture
+    """
     if is_torch_bf16_gpu_available():
         if not cfg.bf16 and not cfg.bfloat16:
             LOG.info("bf16 support detected, but not enabled for this configuration.")
@@ -221,6 +257,11 @@ def validate_config(cfg):
     if cfg.adapter == "lora" and (cfg.flash_attn_fuse_qkv or cfg.flash_attn_fuse_mlp):
         raise ValueError("Fused modules are not supported with LoRA")
 
+    if cfg.adapter and cfg.peft_layers_to_transform and cfg.unfrozen_parameters:
+        raise ValueError(
+            "`unfrozen_parameters` used with `peft_layers_to_transform` can have unexpected behavior."
+        )
+
     if cfg.relora_steps:
         if cfg.adapter not in ("lora", "qlora"):
             raise ValueError("cfg.adapter must be lora or qlora to use ReLoRA")
@@ -352,6 +393,27 @@ def validate_config(cfg):
                 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."
+        )
+    if cfg.saves_per_epoch and cfg.save_strategy and cfg.save_strategy != "steps":
+        raise ValueError(
+            "save_strategy must be empty or set to `steps` when used with saves_per_epoch."
+        )
+    if cfg.evals_per_epoch and cfg.eval_steps:
+        raise ValueError(
+            "eval_steps and evals_per_epoch are mutually exclusive and cannot be used together."
+        )
+    if (
+        cfg.evals_per_epoch
+        and cfg.evaluation_strategy
+        and cfg.evaluation_strategy != "steps"
+    ):
+        raise ValueError(
+            "evaluation_strategy must be empty or set to `steps` when used with evals_per_epoch."
+        )
     if cfg.save_strategy and cfg.save_steps and cfg.save_strategy != "steps":
         raise ValueError(
             "save_strategy and save_steps mismatch. Please set save_strategy to 'steps' or remove save_steps."
@@ -392,11 +454,6 @@ def validate_config(cfg):
     if cfg.warmup_steps and cfg.warmup_ratio:
         raise ValueError("warmup_steps and warmup_ratio are mutually exclusive")
 
-    if cfg.is_qwen_derived_model and cfg.gradient_checkpointing:
-        LOG.warning(
-            "Gradient checkpointing is broken for Qwen models for transformers>=4.35.0, except main branch."
-        )
-
     if cfg.wandb_run_id and not cfg.wandb_name:
         cfg.wandb_name = cfg.wandb_run_id
 
@@ -404,6 +461,25 @@ def validate_config(cfg):
             "wandb_run_id sets the ID of the run. If you would like to set the name, please use wandb_name instead."
         )
 
+    if cfg.noisy_embedding_alpha is not None:
+        # Deprecated, use neftune_noise_alpha
+        LOG.warning("noisy_embedding_alpha is deprecated, use neftune_noise_alpha")
+        if cfg.neftune_noise_alpha is None:
+            cfg.neftune_noise_alpha = cfg.noisy_embedding_alpha
+        else:
+            # User is providing both; bail and have them sort out their settings
+            raise ValueError(
+                "noisy_embedding_alpha is deprecated, use neftune_noise_alpha; both are set, please remove the deprecated noisy_embedding_alpha setting"
+            )
+
+    if cfg.neftune_noise_alpha is not None and cfg.neftune_noise_alpha <= 0.0:
+        raise ValueError("neftune_noise_alpha must be > 0.0")
+
+    if cfg.max_memory is not None and cfg.gpu_memory_limit is not None:
+        raise ValueError(
+            "max_memory and gpu_memory_limit are mutually exclusive and cannot be used together."
+        )
+
     # TODO
     # MPT 7b
     # https://github.com/facebookresearch/bitsandbytes/issues/25

src/axolotl/utils/data.py

@@ -2,6 +2,7 @@
 import functools
 import hashlib
 import logging
+from collections import defaultdict
 from pathlib import Path
 from typing import Dict, List, Tuple, Union
 
@@ -14,6 +15,7 @@ from datasets import (
     load_from_disk,
 )
 from huggingface_hub import hf_hub_download
+from torch.utils.data import RandomSampler
 from transformers import PreTrainedTokenizerBase
 
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
@@ -39,11 +41,14 @@ from axolotl.prompters import (
     SummarizeTLDRPrompter,
     UnsupportedPrompter,
 )
+from axolotl.utils.collators import PretrainingBatchSamplerDataCollatorForSeq2Seq
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process, zero_first
+from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 from axolotl.utils.trainer import (
     calculate_total_num_steps,
     process_datasets_for_packing,
+    process_pretraining_datasets_for_packing,
 )
 
 LOG = logging.getLogger("axolotl")
@@ -64,9 +69,17 @@ def prepare_dataset(cfg, tokenizer):
                 tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
             )
     else:
+        path = cfg.pretraining_dataset
+        name = None
+        if isinstance(cfg.pretraining_dataset, dict):
+            path = cfg.pretraining_dataset["path"]
+            name = cfg.pretraining_dataset["name"]
+
         train_dataset = load_pretraining_dataset(
-            cfg.pretraining_dataset,
+            path,
             tokenizer,
+            cfg,
+            name=name,
             max_tokens=cfg.sequence_len,
             seed=cfg.seed or 42,
         )
@@ -806,9 +819,27 @@ def encode_pretraining(
     return ret
 
 
-def load_pretraining_dataset(path, tokenizer, max_tokens=2048, seed=42):
-    encode = functools.partial(encode_pretraining, tokenizer, max_tokens)
-    dataset = load_dataset(path, streaming=True, split="train")
+def load_pretraining_dataset(path, tokenizer, cfg, name=None, max_tokens=2048, seed=42):
+    if cfg.sample_packing:
+        collate_fn = PretrainingBatchSamplerDataCollatorForSeq2Seq(
+            tokenizer,
+            return_tensors="pt",
+            padding=True,
+            pad_to_multiple_of=max_tokens * cfg.micro_batch_size,
+        )
+        encode = functools.partial(
+            encode_packed_pretraining,
+            tokenizer,
+            collate_fn,
+            max_seq_length=max_tokens,
+            batch_size=cfg.micro_batch_size,
+        )
+        # set this to 1 so downstream data_loader doesn't try to increase the batch again
+        cfg.micro_batch_size = 1
+    else:
+        encode = functools.partial(encode_pretraining, tokenizer, max_tokens)
+
+    dataset = load_dataset(path, streaming=True, split="train", name=name)
     dataset = dataset.shuffle(seed=seed, buffer_size=10_000)
     dataset = dataset.map(
         encode,
@@ -819,3 +850,58 @@ def load_pretraining_dataset(path, tokenizer, max_tokens=2048, seed=42):
         remove_columns=dataset.features.keys(),
     )
     return dataset
+
+
+def encode_packed_pretraining(
+    tokenizer: PreTrainedTokenizerBase,
+    collate_fn,
+    examples: List[str],
+    max_seq_length: int = 2048,
+    batch_size: int = 4,
+) -> Dict[str, List]:
+    # pylint: disable=duplicate-code
+    # tokenize all the examples
+    # rows get split with stride (overlap)
+    res = tokenizer(
+        examples,
+        truncation=True,
+        max_length=max_seq_length - 1,
+        add_special_tokens=True,
+        return_overflowing_tokens=True,
+        stride=256,
+    )
+
+    input_ids = [seq + [tokenizer.eos_token_id] for seq in res["input_ids"]]
+    attention_mask = [seq + [1] for seq in res["attention_mask"]]
+
+    tokenized_examples = {
+        "input_ids": input_ids,
+        "attention_mask": attention_mask,
+    }
+
+    train_dataset = Dataset.from_dict(tokenized_examples)
+    train_dataset = process_pretraining_datasets_for_packing(
+        train_dataset, max_seq_length
+    )
+
+    sampler = MultipackBatchSampler(
+        RandomSampler(train_dataset),
+        batch_size=batch_size,
+        drop_last=True,
+        batch_max_len=batch_size * max_seq_length,
+        lengths=get_dataset_lengths(train_dataset),
+    )
+
+    chunked_data = defaultdict(list)
+
+    for data in sampler:
+        features = train_dataset[data]
+        features["labels"] = features["input_ids"].copy()
+        collated_features = collate_fn(features)
+
+        for feature in features.keys():
+            if feature == "length":
+                continue
+            chunked_data[feature].append(collated_features[feature].squeeze(0))
+
+    return chunked_data

src/axolotl/utils/freeze.py

@@ -0,0 +1,38 @@
+"""
+module to freeze/unfreeze parameters by name
+"""
+import logging
+import re
+
+from axolotl.utils.distributed import is_main_process
+
+LOG = logging.getLogger("axolotl.utils.freeze")
+
+
+def freeze_parameters_except(model, regex_patterns):
+    """
+    Freezes all layers of the given model except for the layers that match given regex patterns.
+    Periods in the patterns are treated as literal periods, not as wildcard characters.
+
+    Parameters:
+    - model (nn.Module): The PyTorch model to be modified.
+    - regex_patterns (list of str): List of regex patterns to match layer names to keep unfrozen.
+
+    Returns:
+    None; the model is modified in place.
+    """
+    # Escape periods and compile the regex patterns
+    compiled_patterns = [
+        re.compile(pattern.replace(".", "\\.")) for pattern in regex_patterns
+    ]
+
+    # First, freeze all parameters in the model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # Unfreeze layers that match the regex patterns
+    for name, param in model.named_parameters():
+        if any(pattern.match(name) for pattern in compiled_patterns):
+            if is_main_process():
+                LOG.debug(f"unfreezing {name}")
+            param.requires_grad = True

src/axolotl/utils/lora_embeddings.py

@@ -0,0 +1,14 @@
+"""
+helpers for lora embeddings
+"""
+
+
+def get_linear_embedding_layers(model_type):
+    """
+    returns the linear embedding layers needed for loras, dependent on the model arch
+    """
+    if model_type == "phi-msft":
+        return ["embd.wte", "lm_head.linear"]
+    if model_type == "gpt_neox":
+        return ["embed_in", "embed_out"]
+    return ["embed_tokens", "lm_head"]

src/axolotl/utils/mlflow_.py

@@ -0,0 +1,18 @@
+"""Module for mlflow utilities"""
+
+import os
+
+from axolotl.utils.dict import DictDefault
+
+
+def setup_mlflow_env_vars(cfg: DictDefault):
+    for key in cfg.keys():
+        if key.startswith("mlflow_"):
+            value = cfg.get(key, "")
+
+            if value and isinstance(value, str) and len(value) > 0:
+                os.environ[key.upper()] = value
+
+    # Enable mlflow if experiment name is present
+    if cfg.mlflow_experiment_name and len(cfg.mlflow_experiment_name) > 0:
+        cfg.use_mlflow = True

src/axolotl/utils/models.py

@@ -2,7 +2,7 @@
 import logging
 import math
 import os
-from typing import Optional, Tuple  # noqa: F401
+from typing import Any, Optional, Tuple, Union  # noqa: F401
 
 import addict
 import bitsandbytes as bnb
@@ -21,17 +21,23 @@ from transformers import (  # noqa: F401
     PreTrainedModel,
     PreTrainedTokenizerBase,
 )
+from transformers.deepspeed import is_deepspeed_zero3_enabled
 
 from axolotl.models.mamba import fix_mamba_attn_for_loss
 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_EOS_TOKEN
 from axolotl.utils.bench import log_gpu_memory_usage
+from axolotl.utils.chat_templates import chat_templates
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.lora_embeddings import get_linear_embedding_layers
 
 LOG = logging.getLogger("axolotl")
 
 
-def check_model_config(cfg: DictDefault, model_config: AutoConfig):
-    quant_config_exists = hasattr(model_config, "quantization_config")
+def check_model_config(cfg: DictDefault, model_config: Union[AutoConfig, DictDefault]):
+    quant_config_exists = (
+        hasattr(model_config, "quantization_config")
+        and model_config.quantization_config
+    )
     quant_config_method_is_gptq = (
         quant_config_exists
         and "quant_method" in model_config.quantization_config
@@ -50,29 +56,39 @@ def check_model_config(cfg: DictDefault, model_config: AutoConfig):
             "Please use the `gptq` flag to train quantized model or point to a non-quantized model."
         )
 
+    lora_modules_to_save = get_linear_embedding_layers(model_config.model_type)
+    if (
+        cfg.adapter
+        and cfg.tokens
+        and (
+            not cfg.lora_modules_to_save
+            or not all(x in cfg.lora_modules_to_save for x in lora_modules_to_save)
+        )
+    ):
+        lora_modules_to_save = ", ".join(map(lambda x: f"`{x}`", lora_modules_to_save))
+        raise ValueError(
+            f"`lora_modules_to_save` not properly set when adding new tokens. Please include {lora_modules_to_save} in `lora_modules_to_save`."
+        )
+
 
 def load_model_config(cfg):
     model_config_name = cfg.base_model_config or cfg.base_model
+    if not model_config_name and cfg.tokenizer_config:
+        model_config_name = cfg.tokenizer_config
     trust_remote_code = cfg.trust_remote_code is True
-    model_type = cfg.model_type
 
-    if model_type == "MixtralForCausalLM":
-        from axolotl.models.mixtral.configuration_moe_mistral import MixtralConfig
-
-        model_config = MixtralConfig.from_pretrained(model_config_name)
-    else:
-        try:
-            model_config = AutoConfig.from_pretrained(
-                model_config_name, trust_remote_code=trust_remote_code
+    try:
+        model_config = AutoConfig.from_pretrained(
+            model_config_name, trust_remote_code=trust_remote_code
+        )
+    except ValueError as err:
+        if "mamba" in model_config_name:
+            return addict.Dict(
+                {
+                    "model_type": "mamba",
+                }
             )
-        except ValueError as err:
-            if "mamba" in model_config_name:
-                return addict.Dict(
-                    {
-                        "model_type": "mamba",
-                    }
-                )
-            raise err
+        raise err
 
     if cfg.model_config:
         for key, val in cfg.model_config.items():
@@ -84,6 +100,7 @@ def load_model_config(cfg):
 
 
 def load_tokenizer(cfg):
+    model_config = load_model_config(cfg)
     tokenizer_kwargs = {}
     use_fast = True  # this is the default
 
@@ -140,7 +157,28 @@ def load_tokenizer(cfg):
                 setattr(tokenizer, attr_name, "<|endoftext|>")
 
     if cfg.special_tokens:
+        lora_modules_to_save = get_linear_embedding_layers(model_config.model_type)
         for k, val in cfg.special_tokens.items():
+            # check if new special token is not already in tokenizer and
+            # is adapter training to make sure lora_modules_to_save is set
+            # pylint: disable=too-many-boolean-expressions
+            if (
+                (getattr(tokenizer, k) is None or getattr(tokenizer, k) != val)
+                and cfg.adapter
+                and (
+                    not cfg.lora_modules_to_save
+                    or not all(
+                        x in cfg.lora_modules_to_save for x in lora_modules_to_save
+                    )
+                )
+            ):
+                lora_modules_to_save = ", ".join(
+                    [f"`{x}`" for x in lora_modules_to_save]
+                )
+                raise ValueError(
+                    f"Please set lora_modules_to_save to {lora_modules_to_save} when using an adapter and changing the special tokens."
+                )
+
             tokenizer.add_special_tokens(
                 {k: AddedToken(val, rstrip=False, lstrip=False, normalized=False)}
             )
@@ -174,6 +212,12 @@ def load_tokenizer(cfg):
     LOG.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
     LOG.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
 
+    if cfg.chat_template:
+        tokenizer.chat_template = chat_templates(cfg.chat_template)
+    else:
+        LOG.info(
+            "No Chat template selected. Consider adding a chat template for easier inference."
+        )
     return tokenizer
 
 
@@ -181,6 +225,7 @@ def load_model(
     cfg: DictDefault,
     tokenizer: PreTrainedTokenizerBase,
     inference: bool = False,
+    reference_model: bool = False,
 ) -> Tuple[PreTrainedModel, Optional[PeftConfig]]:
     """
     Load a model for a given configuration and tokenizer.
@@ -235,17 +280,6 @@ def load_model(
 
         LOG.info("patching with sdp attention")
         hijack_llama_sdp_attention()
-    elif cfg.is_llama_derived_model and cfg.landmark_attention:
-        from axolotl.monkeypatch.llama_landmark_attn import (
-            MEM_TOKEN,
-            patch_llama_with_landmark_attn,
-        )
-
-        LOG.info("patching with landmark attention")
-        patch_llama_with_landmark_attn()
-
-        # Note: This might overwrite previous additional_special_tokens
-        tokenizer.add_special_tokens({"additional_special_tokens": [MEM_TOKEN]})
 
     if cfg.is_mistral_derived_model and cfg.flash_attention and cfg.sample_packing:
         from axolotl.monkeypatch.mistral_attn_hijack_flash import (
@@ -255,13 +289,17 @@ def load_model(
         LOG.info("patching with flash attention")
         replace_mistral_attn_with_flash_attn(packed=cfg.sample_packing)
 
-    if cfg.is_llama_derived_model and cfg.xpos_rope:
-        from axolotl.monkeypatch.xpos_rope_llama_monkey_patch import (
-            replace_llama_rope_with_xpos_rope,
+    if (
+        cfg.model_config_type == "mixtral"
+        and cfg.flash_attention
+        and cfg.sample_packing
+    ):
+        from axolotl.monkeypatch.mixtral import (
+            replace_mixtral_attn_with_multipack_flash_attn,
         )
 
-        LOG.info("patching with xpos rope")
-        replace_llama_rope_with_xpos_rope()
+        LOG.info("patching with flash attention")
+        replace_mixtral_attn_with_multipack_flash_attn()
 
     if (
         cfg.is_llama_derived_model
@@ -275,9 +313,53 @@ def load_model(
 
     model_kwargs = {}
 
-    model_kwargs["device_map"] = cfg.device_map
-    model_kwargs["max_memory"] = cfg.max_memory
+    max_memory = cfg.max_memory
+    device_map = cfg.device_map
+
+    if cfg.gpu_memory_limit:
+        gpu_memory_limit = (
+            str(cfg.gpu_memory_limit) + "GiB"
+            if isinstance(cfg.gpu_memory_limit, int)
+            else cfg.gpu_memory_limit
+        )
+
+        max_memory = {}
+        for i in range(torch.cuda.device_count()):
+            max_memory[i] = gpu_memory_limit
+        max_memory["cpu"] = "256GiB"  # something sufficiently large to fit anything
+
+    if max_memory is not None:
+        # Based on https://github.com/togethercomputer/OpenChatKit/blob/main/inference/bot.py
+        from accelerate import infer_auto_device_map, init_empty_weights
+
+        with init_empty_weights():
+            model_canvas = AutoModelForCausalLM.from_config(model_config)
+        model_canvas.tie_weights()
+        device_map = infer_auto_device_map(
+            model_canvas,
+            max_memory=max_memory,
+            dtype=cfg.torch_dtype,
+        )
+        # We can discard max_memory now as we have a device map set up for us
+        max_memory = None
+
+    model_kwargs["device_map"] = device_map
     model_kwargs["torch_dtype"] = cfg.torch_dtype
+    # TODO can we put the reference model on it's own gpu? I think we have to move logits around to calculate loss
+    # if cfg.rl:
+    #     if torch.cuda.device_count() > 1:
+    #         if reference_model:
+    #             model_kwargs["device_map"] = "cuda:" + str(
+    #                 torch.cuda.current_device() + 1
+    #             )
+    #         else:
+    #             model_kwargs["device_map"] = "cuda:" + str(torch.cuda.current_device())
+
+    if is_deepspeed_zero3_enabled():
+        del model_kwargs["device_map"]
+    elif cfg.deepspeed:
+        del model_kwargs["device_map"]
+        model_kwargs["low_cpu_mem_usage"] = True
 
     if cfg.model_revision:
         model_kwargs["revision"] = cfg.model_revision
@@ -293,24 +375,49 @@ def load_model(
                 **model_config.quantization_config
             )
     if cfg.adapter == "qlora" and cfg.load_in_4bit:
+        bnb_config = {
+            "load_in_4bit": True,
+            "llm_int8_threshold": 6.0,
+            "llm_int8_has_fp16_weight": False,
+            "bnb_4bit_compute_dtype": cfg.torch_dtype,
+            "bnb_4bit_use_double_quant": True,
+            "bnb_4bit_quant_type": "nf4",
+        }
+
+        if cfg.bnb_config_kwargs:
+            bnb_config.update(cfg.bnb_config_kwargs)
+
         model_kwargs["quantization_config"] = BitsAndBytesConfig(
-            load_in_4bit=True,
-            llm_int8_threshold=6.0,
-            llm_int8_has_fp16_weight=False,
-            bnb_4bit_compute_dtype=cfg.torch_dtype,
-            bnb_4bit_use_double_quant=True,
-            bnb_4bit_quant_type="nf4",
+            **bnb_config,
         )
     # sample packing uses custom FA2 patch
-    if cfg.flash_attention and not cfg.sample_packing:
-        if (
-            cfg.is_llama_derived_model
-            or cfg.is_falcon_derived_model
-            or cfg.is_mistral_derived_model
-        ):
-            # TODO enable once properly supported in transformers
-            # model_kwargs["attn_implementation"] = "flash_attention_2"
-            model_kwargs["use_flash_attention_2"] = True  # legacy, to be deprecated
+    if cfg.flash_attention:
+        if not cfg.sample_packing:
+            if (
+                cfg.is_llama_derived_model
+                or cfg.is_falcon_derived_model
+                or cfg.is_mistral_derived_model
+                or model_config.model_type == "mixtral"
+            ):
+                model_kwargs["attn_implementation"] = "flash_attention_2"
+                model_config._attn_implementation = (  # pylint: disable=protected-access
+                    "flash_attention_2"
+                )
+        else:
+            if model_config.model_type == "mixtral":
+                model_kwargs["attn_implementation"] = "flash_attention_2"
+                model_config._attn_implementation = (  # pylint: disable=protected-access
+                    "flash_attention_2"
+                )
+            else:
+                model_kwargs["attn_implementation"] = "eager"
+                model_config._attn_implementation = (  # pylint: disable=protected-access
+                    "eager"
+                )
+        if model_config.model_type == "phi-msft":
+            model_config.flash_attn = True
+            model_config.flash_rotary = True
+            model_config.fused_dense = True
 
     try:
         if cfg.is_llama_derived_model and not cfg.trust_remote_code and not cfg.gptq:
@@ -363,20 +470,12 @@ def load_model(
         #         device=cfg.device,
         #     )
         #     model.train() # sets to train instead of eval mode
-        elif model_type == "PhiForCausalLM":
+        elif model_type == "PhiForCausalLM" or model_config.model_type == "phi-msft":
             from axolotl.models.phi import PhiForCausalLM
 
             model = PhiForCausalLM.from_pretrained(
                 base_model,
-                load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
-                load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
-                **model_kwargs,
-            )
-        elif model_type == "MixtralForCausalLM":
-            from axolotl.models.mixtral import MixtralForCausalLM
-
-            model = MixtralForCausalLM.from_pretrained(
-                base_model,
+                config=model_config,
                 load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                 load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
                 **model_kwargs,
@@ -389,7 +488,6 @@ def load_model(
             model_kwargs["device"] = torch.cuda.current_device()
             del model_kwargs["torch_dtype"]
             del model_kwargs["device_map"]
-            del model_kwargs["max_memory"]
 
             model = MambaLMHeadModel.from_pretrained(
                 base_model,
@@ -493,13 +591,14 @@ def load_model(
         log_gpu_memory_usage(LOG, "after model load", model.device)
 
     # make sure these are fp32 per Ramesh et al. (2021)
+    embedding_modules = get_linear_embedding_layers(cfg.model_config_type)
     for name, module in model.named_modules():
-        if "norm" in name:
+        if any(m in name for m in ["norm", "gate"]):
             module.to(torch.float32)
         if model_config.model_type == "btlm":
             # don't upcast lm_head for btlm
             continue
-        if "lm_head" in name or "embed_tokens" in name:
+        if any(m in name for m in embedding_modules):
             if hasattr(module, "weight"):
                 module.to(torch.float32)
 
@@ -524,18 +623,20 @@ def load_model(
 
     # LlamaRMSNorm layers are in fp32 after kbit_training or full finetune, so we need to
     # convert them back to fp16/bf16 for flash-attn compatibility.
-    if needs_fa2_dtype or (cfg.flash_attention and cfg.is_llama_derived_model):
+    if needs_fa2_dtype or cfg.flash_attention:
         LOG.info("converting modules to %s for flash attention", cfg.torch_dtype)
         for name, module in model.named_modules():
             if "norm" in name:
                 module.to(cfg.torch_dtype)
-            if "lm_head" in name or "embed_tokens" in name:
+            if any(m in name for m in embedding_modules):
                 if hasattr(module, "weight"):
                     module.to(cfg.torch_dtype)
 
-    model, lora_config = load_adapter(model, cfg, cfg.adapter)
+    lora_config = None
+    if not reference_model or cfg.lora_model_dir:
+        model, lora_config = load_adapter(model, cfg, cfg.adapter)
 
-    if cfg.ddp and not load_in_8bit:
+    if cfg.ddp and not load_in_8bit and not (cfg.rl and cfg.load_in_4bit):
         model.to(f"cuda:{cfg.local_rank}")
 
     if torch.cuda.device_count() > 1 and int(os.getenv("WORLD_SIZE", "1")) == 1:
@@ -635,6 +736,7 @@ def load_lora(model, cfg, inference=False):
         r=cfg.lora_r,
         lora_alpha=cfg.lora_alpha,
         target_modules=lora_target_modules,
+        layers_to_transform=cfg.peft_layers_to_transform,
         lora_dropout=cfg.lora_dropout,
         fan_in_fan_out=cfg.lora_fan_in_fan_out,
         modules_to_save=cfg.lora_modules_to_save if cfg.lora_modules_to_save else None,
@@ -644,10 +746,15 @@ def load_lora(model, cfg, inference=False):
 
     if cfg.lora_model_dir:
         LOG.debug("Loading pretained PEFT - LoRA")
+        model_kwargs: Any = {}
+        if cfg.lora_on_cpu:
+            model_kwargs["max_memory"] = {"cpu": "256GiB"}
+            model_kwargs["device_map"] = {"": "cpu"}
         model = PeftModel.from_pretrained(
             model,
             cfg.lora_model_dir,
             is_trainable=(not inference),
+            **model_kwargs,
         )
     else:
         model = get_peft_model(model, lora_config)

src/axolotl/utils/samplers/__init__.py

@@ -2,3 +2,4 @@
 axolotl samplers module
 """
 from .multipack import MultipackBatchSampler  # noqa: F401
+from .utils import get_dataset_lengths  # noqa: F401

src/axolotl/utils/samplers/utils.py

@@ -0,0 +1,17 @@
+"""
+helper util to calculate dataset lengths
+"""
+import numpy as np
+
+
+def get_dataset_lengths(dataset):
+    if "length" in dataset.data.column_names:
+        lengths = np.array(dataset.data.column("length"))
+    else:
+        lengths = (
+            dataset.data.column("position_ids")
+            .to_pandas()
+            .apply(lambda x: x[-1] + 1)
+            .values
+        )
+    return lengths

src/axolotl/utils/schedulers.py

@@ -100,3 +100,43 @@ def get_cosine_schedule_with_quadratic_warmup(
         num_cycles=num_cycles,
     )
     return LambdaLR(optimizer, lr_lambda, last_epoch)
+
+
+def _get_cosine_schedule_with_min_lr_lambda(
+    current_step: int,
+    *,
+    num_warmup_steps: int,
+    num_training_steps: int,
+    min_lr_ratio: float
+):
+    # Warm up
+    if current_step < num_warmup_steps:
+        return float(current_step) / float(max(1, num_warmup_steps))
+
+    # Cosine learning rate decay
+    progress = float(current_step - num_warmup_steps) / float(
+        max(1, num_training_steps - num_warmup_steps)
+    )
+    scaling = 0.5 * (1.0 + math.cos(math.pi * progress))
+    return (1 - min_lr_ratio) * scaling + min_lr_ratio
+
+
+def get_cosine_schedule_with_min_lr(
+    optimizer: Optimizer,
+    num_warmup_steps: int,
+    num_training_steps: int,
+    min_lr_ratio: float = 0.0,
+):
+    """
+    Create a learning rate schedule which has:
+        - linear warmup from 0 -> `max_lr` over `num_warmup_steps`
+        - cosine learning rate annealing from `max_lr` -> `min_lr` over `num_training_steps`
+    """
+
+    lr_lambda = partial(
+        _get_cosine_schedule_with_min_lr_lambda,
+        num_warmup_steps=num_warmup_steps,
+        num_training_steps=num_training_steps,
+        min_lr_ratio=min_lr_ratio,
+    )
+    return LambdaLR(optimizer, lr_lambda)