fix: update bradleyterry to use new chat_template

Feat: merge latest, update docs, fix dropped config bug, added unit test

.github/workflows/base.yml

@@ -36,12 +36,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.4.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "124"
-            cuda_version: 12.4.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.5.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout
         uses: actions/checkout@v3

.github/workflows/main.yml

@@ -29,11 +29,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.4.1
             axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -91,11 +86,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.4.1
             axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

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

@@ -21,17 +21,10 @@ jobs:
             pytorch: 2.3.1
             axolotl_extras:
             num_gpus: 2
-          - cuda: 124
+          - cuda: 121
-            cuda_version: 12.4.1
+            cuda_version: 12.1.1
             python_version: "3.11"
-            pytorch: 2.4.1
+            pytorch: 2.3.1
-            axolotl_extras:
-            num_gpus: 2
-            nightly_build: "true"
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.0
             axolotl_extras:
             num_gpus: 2
             nightly_build: "true"

.github/workflows/nightlies.yml

@@ -28,11 +28,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.4.1
             axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -90,11 +85,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.4.1
             axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/pypi.yml

@@ -27,7 +27,7 @@ jobs:
         run: |
           pip3 install wheel packaging
           pip3 install -e .
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
+          pip3 install -r requirements-tests.txt
 
       - name: Extract tag name
         id: tag

.github/workflows/tests-nightly.yml

@@ -25,7 +25,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.1", "2.5.0"]
+        pytorch_version: ["2.3.1", "2.4.1"]
     timeout-minutes: 20
 
     steps:
@@ -47,14 +47,13 @@ jobs:
           sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt
           sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt
           sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt
-          sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt
 
       - name: Install dependencies
         run: |
           pip3 install --upgrade pip
           pip3 install --upgrade packaging
           pip3 install -U -e .
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
+          pip3 install -r requirements-tests.txt
 
       - name: Run tests
         run: |
@@ -82,17 +81,17 @@ jobs:
             num_gpus: 1
             axolotl_extras: mamba-ssm
             nightly_build: "true"
-          - cuda: 124
+          - cuda: 121
-            cuda_version: 12.4.1
+            cuda_version: 12.1.1
             python_version: "3.11"
-            pytorch: 2.4.1
+            pytorch: 2.3.1
             num_gpus: 1
-            axolotl_extras:
+            axolotl_extras: mamba-ssm
             nightly_build: "true"
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.0
+            pytorch: 2.4.1
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"

.github/workflows/tests.yml

@@ -36,7 +36,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.1", "2.5.0"]
+        pytorch_version: ["2.3.1", "2.4.1"]
     timeout-minutes: 20
 
     steps:
@@ -49,20 +49,16 @@ jobs:
           python-version: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
 
-      - name: upgrade pip
-        run: |
-          pip3 install --upgrade pip
-          pip3 install --upgrade packaging setuptools wheel
-
       - name: Install PyTorch
         run: |
-          pip3 install torch==${{ matrix.pytorch_version }}
+          pip3 install torch==${{ matrix.pytorch_version }} --index-url https://download.pytorch.org/whl/cpu
 
       - name: Install dependencies
         run: |
-          pip3 show torch
+          pip3 install --upgrade pip
+          pip3 install --upgrade packaging
           pip3 install -U -e .
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
+          pip3 install -r requirements-tests.txt
 
       - name: Run tests
         run: |
@@ -72,17 +68,29 @@ jobs:
         run: |
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
-  docker-e2e-tests-1st:
+  docker-e2e-tests:
     if: github.repository_owner == 'axolotl-ai-cloud'
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
-    timeout-minutes: 90
+    timeout-minutes: 60
     needs: [pre-commit, pytest]
 
     strategy:
       fail-fast: false
       matrix:
         include:
+          - cuda: 121
+            cuda_version: 12.1.1
+            python_version: "3.10"
+            pytorch: 2.3.1
+            num_gpus: 1
+            axolotl_extras: mamba-ssm
+          - cuda: 121
+            cuda_version: 12.1.1
+            python_version: "3.11"
+            pytorch: 2.3.1
+            num_gpus: 1
+            axolotl_extras: mamba-ssm
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -111,49 +119,3 @@ jobs:
       - name: Run tests job on Modal
         run: |
           modal run cicd.tests
-
-  docker-e2e-tests:
-    if: github.repository_owner == 'axolotl-ai-cloud'
-    # this job needs to be run on self-hosted GPU runners...
-    runs-on: [self-hosted, modal]
-    timeout-minutes: 90
-    needs: [pre-commit, pytest, docker-e2e-tests-1st]
-
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.10"
-            pytorch: 2.3.1
-            num_gpus: 1
-            axolotl_extras: mamba-ssm
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.0
-            num_gpus: 1
-            axolotl_extras:
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-      - name: Install Modal
-        run: |
-          python -m pip install --upgrade pip
-          pip install modal==0.63.64 jinja2
-      - name: Update env vars
-        run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
-          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
-          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
-          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
-          echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
-          echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
-      - name: Run tests job on Modal
-        run: |
-          modal run cicd.tests

README.md

@@ -121,7 +121,7 @@ Features:
 
 Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.
 
-**Requirements**: Nvidia GPU (Ampere architecture or newer for `bf16` and Flash Attention), Python >=3.10 and PyTorch >=2.3.1.
+**Requirements**: Python >=3.10 and Pytorch >=2.1.1.
 
 ```bash
 git clone https://github.com/axolotl-ai-cloud/axolotl
@@ -562,8 +562,7 @@ plugins:
   - axolotl.integrations.liger.LigerPlugin
 liger_rope: true
 liger_rms_norm: true
-liger_glu_activation: true
+liger_swiglu: true
-liger_layer_norm: true
 liger_fused_linear_cross_entropy: true
 ```
 

cicd/Dockerfile.jinja

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

cicd/cicd.sh

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

cicd/multigpu.py

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

cicd/tests.py

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

deepspeed_configs/zero3_bf16.json

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

deepspeed_configs/zero3_bf16_cpuoffload_all.json

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

deepspeed_configs/zero3_bf16_cpuoffload_params.json

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

devtools/dev_sharegpt.yml

@@ -7,8 +7,8 @@ load_in_8bit: true
 load_in_4bit: false
 
 datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
+  - path: philschmid/guanaco-sharegpt-style
-    type: chat_template
+    type: sharegpt
     shards: 10
 val_set_size: 0
 output_dir: temp_debug/axolotl_outputs/model

docker/Dockerfile

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

docs/debugging.qmd

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

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

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

examples/gemma2/qlora.yml

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

examples/jamba/qlora_fsdp_large.yaml

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

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

@@ -4,7 +4,7 @@ plugins:
   - axolotl.integrations.liger.LigerPlugin
 liger_rope: true
 liger_rms_norm: true
-liger_glu_activation: true
+liger_swiglu: true
 liger_fused_linear_cross_entropy: true
 
 strict: false
@@ -14,10 +14,6 @@ datasets:
   - path: mlabonne/FineTome-100k
     type: chat_template
     split: train[:20%]
-    field_messages: conversations
-    message_field_role: from
-    message_field_content: value
-
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.02
 output_dir: ./outputs/out

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

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

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

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

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

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

examples/phi/lora-3.5.yaml

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

requirements-dev.txt

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

requirements.txt

@@ -1,12 +1,12 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
 peft==0.13.2
-transformers==4.46.2
+transformers==4.45.2
 tokenizers>=0.20.1
 bitsandbytes==0.44.1
-accelerate==1.1.0
+accelerate==1.0.1
 datasets==3.0.1
-deepspeed==0.15.3
+deepspeed==0.14.4
 pydantic==2.6.3
 addict
 fire
@@ -16,7 +16,7 @@ flash-attn==2.6.3
 sentencepiece
 wandb
 einops
-xformers>=0.0.23.post1
+xformers==0.0.28.post1
 optimum==1.16.2
 hf_transfer
 colorama
@@ -34,7 +34,7 @@ tensorboard
 python-dotenv==1.0.1
 autoawq>=0.2.5
 triton>=2.3.0
-liger-kernel==0.4.0
+liger-kernel==0.3.0
 
 mamba-ssm==1.2.0.post1
 
@@ -43,7 +43,7 @@ s3fs>=2024.5.0
 gcsfs>=2024.5.0
 # adlfs
 
-trl @ git+https://github.com/huggingface/trl.git@31d02cfb795284591a084416b9dcb7bef5d08924
+trl==0.9.6
 zstandard==0.22.0
 fastcore
 

setup.py

@@ -31,8 +31,6 @@ def parse_requirements():
     try:
         xformers_version = [req for req in _install_requires if "xformers" in req][0]
         torchao_version = [req for req in _install_requires if "torchao" in req][0]
-        autoawq_version = [req for req in _install_requires if "autoawq" in req][0]
-
         if "Darwin" in platform.system():
             # don't install xformers on MacOS
             _install_requires.pop(_install_requires.index(xformers_version))
@@ -52,16 +50,10 @@ def parse_requirements():
             else:
                 raise ValueError("Invalid version format")
 
-            if (major, minor) >= (2, 5):
+            if (major, minor) >= (2, 4):
-                _install_requires.pop(_install_requires.index(xformers_version))
-                _install_requires.pop(_install_requires.index(autoawq_version))
-            elif (major, minor) >= (2, 4):
                 if patch == 0:
                     _install_requires.pop(_install_requires.index(xformers_version))
                     _install_requires.append("xformers>=0.0.27")
-                else:
-                    _install_requires.pop(_install_requires.index(xformers_version))
-                    _install_requires.append("xformers==0.0.28.post1")
             elif (major, minor) >= (2, 3):
                 _install_requires.pop(_install_requires.index(torchao_version))
                 if patch == 0:
@@ -81,6 +73,7 @@ def parse_requirements():
 
     except PackageNotFoundError:
         pass
+
     return _install_requires, _dependency_links
 
 
@@ -109,7 +102,6 @@ setup(
         ],
         "mamba-ssm": [
             "mamba-ssm==1.2.0.post1",
-            "causal_conv1d",
         ],
         "auto-gptq": [
             "auto-gptq==0.5.1",

src/axolotl/cli/__init__.py

@@ -272,7 +272,7 @@ def do_inference_gradio(
             importlib.import_module("axolotl.prompters"), prompter
         )
     elif cfg.chat_template:
-        chat_template_str = get_chat_template(cfg.chat_template, tokenizer=tokenizer)
+        chat_template_str = get_chat_template(cfg.chat_template)
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -462,12 +462,7 @@ def load_datasets(
         processor=processor,
     )
 
-    if (
+    if cli_args.debug or cfg.debug:
-        cli_args.debug
-        or cfg.debug
-        or cli_args.debug_text_only
-        or int(cli_args.debug_num_examples) > 0
-    ):
         LOG.info("check_dataset_labels...")
         check_dataset_labels(
             train_dataset.select(

src/axolotl/common/cli.py

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

src/axolotl/core/trainer_builder.py

@@ -7,7 +7,6 @@ import abc
 import gc
 import importlib
 import importlib.util
-import inspect
 import logging
 import math
 import os
@@ -28,6 +27,7 @@ from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import (
     EarlyStoppingCallback,
+    PreTrainedModel,
     Trainer,
     TrainerCallback,
     TrainingArguments,
@@ -48,7 +48,6 @@ from trl import (
 )
 from trl.trainer.utils import RewardDataCollatorWithPadding, pad_to_length
 
-from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils import is_comet_available, is_mlflow_available
@@ -667,9 +666,7 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         return DataLoader(bench_dataset, **dataloader_params)
         # return self.accelerator.prepare(DataLoader(bench_dataset, **dataloader_params))
 
-    def compute_loss(
+    def compute_loss(self, model, inputs, return_outputs=False):
-        self, model, inputs, return_outputs=False, num_items_in_batch=None
-    ):
         # use one's weighted cross entropy loss calc
         # if self.args.sample_packing:
         #     labels = inputs.pop("labels")
@@ -677,18 +674,8 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         #     loss = trainer_weighted_loss(outputs, labels, shift_labels=True)
         #     return (loss, outputs) if return_outputs else loss
         if self.args.orpo_alpha:
-            return self.orpo_compute_loss(
+            return self.orpo_compute_loss(model, inputs, return_outputs=return_outputs)
-                model,
+        return super().compute_loss(model, inputs, return_outputs=return_outputs)
-                inputs,
-                return_outputs=return_outputs,
-                num_items_in_batch=num_items_in_batch,
-            )
-        return super().compute_loss(
-            model,
-            inputs,
-            return_outputs=return_outputs,
-            num_items_in_batch=num_items_in_batch,
-        )
 
     @staticmethod
     def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
@@ -784,13 +771,7 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         ).squeeze(2)
         return torch.mul(per_token_logps, mask).sum(dim=1) / mask.sum(dim=1)
 
-    def orpo_compute_loss(
+    def orpo_compute_loss(self, model, inputs, return_outputs=False):
-        self,
-        model,
-        inputs,
-        return_outputs=False,
-        num_items_in_batch=None,  # pylint: disable=unused-argument
-    ):
         concat_inputs = AxolotlTrainer.orpo_concatenate_inputs(
             inputs,
             label_pad_token=-100,
@@ -896,13 +877,13 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
         for key, value in metrics.items():
             self._stored_metrics[train_eval][key].append(value)
 
-    def _save_checkpoint(self, model, trial, **kwargs):
+    def _save_checkpoint(self, model, trial, metrics=None):
         # make sure the checkpoint dir exists, since trainer is flakey
         checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
         run_dir = self._get_output_dir(trial=trial)
         output_dir = os.path.join(run_dir, checkpoint_folder)
         os.makedirs(output_dir, exist_ok=True)
-        return super()._save_checkpoint(model, trial, **kwargs)
+        return super()._save_checkpoint(model, trial, metrics=metrics)
 
 
 class AxolotlMambaTrainer(AxolotlTrainer):
@@ -917,7 +898,6 @@ class AxolotlMambaTrainer(AxolotlTrainer):
         model,
         inputs,
         return_outputs=False,  # pylint: disable=unused-argument
-        num_items_in_batch=None,  # pylint: disable=unused-argument
     ):
         input_ids = inputs.pop("input_ids")
         lm_logits = model(input_ids).logits
@@ -1025,32 +1005,18 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
         return super().push_to_hub(*args, **kwargs)
 
     def tokenize_row(
-        self,
+        self, feature, model: Optional[Union[PreTrainedModel, torch.nn.Module]] = None
-        features,
-        processing_class,
-        max_prompt_length,
-        max_completion_length,
-        add_special_tokens,
     ) -> Dict:
-        res = super().tokenize_row(
+        res = super().tokenize_row(feature, model=model)
-            features,
+        if self.tokenizer.bos_token_id is None and res["prompt_input_ids"][0] is None:
-            processing_class,
-            max_prompt_length,
-            max_completion_length,
-            add_special_tokens,
-        )
-        if processing_class.bos_token_id is None and res["prompt_input_ids"][0] is None:
             for key in res.keys():
                 res[key] = res[key][1:]
         return res
 
     def training_step(
-        self,
+        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
-        model: nn.Module,
-        inputs: Dict[str, Union[torch.Tensor, Any]],
-        num_items_in_batch=None,
     ) -> torch.Tensor:
-        loss: torch.Tensor = super().training_step(model, inputs, num_items_in_batch)
+        loss: torch.Tensor = super().training_step(model, inputs)
         gc.collect()
         torch.cuda.empty_cache()
         return loss
@@ -1148,28 +1114,17 @@ class TrainerBuilderBase(abc.ABC):
 
     def get_callbacks(self) -> List[TrainerCallback]:
         callbacks = []
-
-        plugin_manager = PluginManager.get_instance()
-        callbacks.extend(
-            plugin_manager.add_callbacks_pre_trainer(cfg=self.cfg, model=self.model)
-        )
-
         if self.cfg.use_wandb:
             callbacks.append(
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
             )
         if self.cfg.use_mlflow and is_mlflow_available():
-            from transformers.integrations.integration_utils import MLflowCallback
-
             from axolotl.utils.callbacks.mlflow_ import (
                 SaveAxolotlConfigtoMlflowCallback,
             )
 
-            callbacks.extend(
+            callbacks.append(
-                [
+                SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
-                    SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path),
-                    MLflowCallback,
-                ]
             )
         if self.cfg.use_comet and is_comet_available():
             from axolotl.utils.callbacks.comet_ import SaveAxolotlConfigtoCometCallback
@@ -1180,17 +1135,11 @@ class TrainerBuilderBase(abc.ABC):
 
         return callbacks
 
+    @abstractmethod
     def get_post_trainer_create_callbacks(self, trainer):
         """
         Callbacks added after the trainer is created, usually b/c these need access to the trainer
         """
-        callbacks = []
-
-        plugin_manager = PluginManager.get_instance()
-        callbacks.extend(
-            plugin_manager.add_callbacks_post_trainer(cfg=self.cfg, trainer=trainer)
-        )
-        return callbacks
 
     def hook_pre_create_training_args(self, training_arguments_kwargs):
         # TODO
@@ -1236,7 +1185,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
+        callbacks = []
         if self.cfg.use_wandb and self.cfg.eval_table_size > 0:
             LogPredictionCallback = log_prediction_callback_factory(
                 trainer, self.tokenizer, "wandb"
@@ -1608,8 +1557,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs["pretraining"] = bool(self.cfg.pretraining_dataset)
         if self.cfg.chat_template:
             training_arguments_kwargs["chat_template"] = get_chat_template(
-                self.cfg.chat_template,
+                self.cfg.chat_template
-                tokenizer=self.tokenizer,
             )
 
         if self.cfg.rl == "orpo":
@@ -1714,17 +1662,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 return_tensors="pt",
                 **data_collator_kwargs,
             )
-        sig = inspect.signature(trainer_cls)
-        if "processing_class" in sig.parameters.keys():
-            trainer_kwargs["processing_class"] = self.tokenizer
-        else:
-            trainer_kwargs["tokenizer"] = self.tokenizer
-
         trainer = trainer_cls(
             model=self.model,
             train_dataset=self.train_dataset,
             eval_dataset=self.eval_dataset,
             args=training_args,
+            tokenizer=self.tokenizer,
             data_collator=self.build_collator(training_args, **data_collator_kwargs),
             callbacks=self.get_callbacks(),
             **trainer_kwargs,
@@ -1765,8 +1708,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         ]
         if self.cfg.reward_model:
             collator = RewardDataCollatorWithPadding
-            if "max_length" in kwargs:
-                kwargs.pop("max_length")
         elif use_batch_sampler_collator:
             if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES:
                 collator = V2BatchSamplerDataCollatorForSeq2Seq
@@ -1804,7 +1745,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
+        callbacks = []
         return callbacks
 
     def build_training_arguments(self, total_num_steps):
@@ -1969,7 +1910,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             dpo_trainer_kwargs["max_length"] = self.cfg.sequence_len
             dpo_trainer_kwargs["max_target_length"] = None
             dpo_trainer_kwargs["max_prompt_length"] = self.cfg.sequence_len
-            dpo_trainer_kwargs["generate_during_eval"] = self.cfg.use_wandb
+            dpo_trainer_kwargs["generate_during_eval"] = True
         elif self.cfg.rl == "orpo":
             trainer_cls = AxolotlORPOTrainer
             trainer_cls_args = [self.model]
@@ -1981,17 +1922,11 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             trainer_cls_args = [self.model]
         else:
             raise ValueError(f"Unsupported RL: {self.cfg.rl}")
-
-        sig = inspect.signature(trainer_cls)
-        if "processing_class" in sig.parameters.keys():
-            dpo_trainer_kwargs["processing_class"] = self.tokenizer
-        else:
-            dpo_trainer_kwargs["tokenizer"] = self.tokenizer
-
         dpo_trainer = trainer_cls(
             *trainer_cls_args,
             args=training_args,
             train_dataset=self.train_dataset,
+            tokenizer=self.tokenizer,
             callbacks=self.get_callbacks(),
             **dpo_trainer_kwargs,
         )
@@ -2013,11 +1948,11 @@ class HFPPOTrainerBuilder(TrainerBuilderBase):
     """
 
     def get_callbacks(self):
-        callbacks = super().get_callbacks()
+        callbacks = []
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
+        callbacks = []
         return callbacks
 
     def build(self, total_num_steps):

src/axolotl/integrations/base.py

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

src/axolotl/integrations/liger/__init__.py

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

src/axolotl/integrations/liger/args.py

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

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

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

src/axolotl/monkeypatch/unsloth_.py

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

src/axolotl/monkeypatch/xformers_/__init__.py

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

src/axolotl/train.py

@@ -260,10 +260,8 @@ def train(
 
     if not cfg.hub_model_id:
         try:
-            trainer.create_model_card(
+            trainer.create_model_card(model_name=cfg.output_dir.lstrip("./"))
-                model_name=cfg.output_dir.lstrip("./").encode("utf-8").decode("utf-8")
+        except AttributeError:
-            )
-        except (AttributeError, UnicodeDecodeError):
             pass
     elif cfg.hub_model_id:
         # defensively push to the hub to ensure the model card is updated

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

@@ -57,7 +57,6 @@ class ChatTemplate(str, Enum):
     jinja = "jinja"  # pylint: disable=invalid-name
     qwen_25 = "qwen_25"  # pylint: disable=invalid-name
     tokenizer_default = "tokenizer_default"  # pylint: disable=invalid-name
-    exaone = "exaone"  # pylint: disable=invalid-name
 
 
 class DeprecatedParameters(BaseModel):
@@ -584,7 +583,6 @@ class AxolotlInputConfig(
     resume_from_checkpoint: Optional[str] = None
     auto_resume_from_checkpoints: Optional[bool] = None
     resize_token_embeddings_to_32x: Optional[bool] = None
-    mean_resizing_embeddings: Optional[bool] = False
 
     rl: Optional[RLType] = None
     reward_model: Optional[bool] = None

src/axolotl/utils/data/sft.py

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

src/axolotl/utils/mlflow_.py

@@ -16,7 +16,3 @@ def setup_mlflow_env_vars(cfg: DictDefault):
     # Enable mlflow if experiment name is present
     if cfg.mlflow_experiment_name and len(cfg.mlflow_experiment_name) > 0:
         cfg.use_mlflow = True
-
-    # Enable logging hf artifacts in mlflow if value is truthy
-    if cfg.hf_mlflow_log_artifacts is True:
-        os.environ["HF_MLFLOW_LOG_ARTIFACTS"] = "true"

src/axolotl/utils/samplers/multipack.py

@@ -133,8 +133,6 @@ class MultipackBatchSampler(BatchSampler):
         self.eff_total_used = 0
         self.eff_total_slots = 0
 
-        self.len_across_ranks = None
-
     def set_epoch(self, epoch: int):
         self.epoch = epoch
 
@@ -197,14 +195,15 @@ class MultipackBatchSampler(BatchSampler):
             LOG.info(f"gather_len_batches: {repr(estimates)}")
             return math.floor(0.998 * min(estimates))
 
-        min_len_batches = reduce_and_broadcast(lambda: num, calc_min_len)
+        min_len_batches = reduce_and_broadcast(
+            lambda: num,
+            calc_min_len,
+        )
         return min_len_batches
 
     def __len__(self):
-        if not self.len_across_ranks:
+        len_batches = self.num_batches()
-            len_batches = self.num_batches()
+        return self.gather_len_batches(len_batches)
-            self.len_across_ranks = self.gather_len_batches(len_batches)
-        return self.len_across_ranks
 
     def _len_est(self):
         efficiency = (

test.yml

@@ -1,76 +0,0 @@
-base_model: JackFram/llama-68m
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-liger_rope: true
-liger_rms_norm: true
-liger_glu_activation: true
-liger_fused_linear_cross_entropy: true
-
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.5
-output_dir: ./outputs/out
-
-sequence_len: 1024
-sample_packing: true
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_torch
-lr_scheduler: cosine
-learning_rate: 2e-5
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-early_stopping_patience:
-resume_from_checkpoint:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 100
-evals_per_epoch: 2
-eval_table_size:
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-
-fsdp:
-  - full_shard
-  - auto_wrap
-fsdp_config:
-  fsdp_limit_all_gathers: true
-  fsdp_sync_module_states: true
-  fsdp_offload_params: true
-  fsdp_use_orig_params: false
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_sharding_strategy: FULL_SHARD
-  fsdp_backward_prefetch: BACKWARD_PRE
-special_tokens:
-  pad_token: <|finetune_right_pad_id|>
-  eos_token: <|eot_id|>

tests/e2e/integrations/liger.py

@@ -1,6 +1,7 @@
 """
 Simple end-to-end test for Liger integration
 """
+
 import unittest
 from pathlib import Path
 
@@ -63,51 +64,6 @@ class LigerIntegrationTestCase(unittest.TestCase):
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "model.safetensors").exists()
 
-    @with_temp_dir
-    def test_llama_wo_flce2(self, temp_dir):
-        cfg = DictDefault(
-            {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
-                "plugins": [
-                    "axolotl.integrations.liger.LigerPlugin",
-                ],
-                "liger_rope": True,
-                "liger_rms_norm": True,
-                "liger_swiglu": True,
-                "liger_cross_entropy": True,
-                "liger_fused_linear_cross_entropy": False,
-                "sequence_len": 1024,
-                "val_set_size": 0.1,
-                "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
-                },
-                "datasets": [
-                    {
-                        "path": "mhenrichsen/alpaca_2k_test",
-                        "type": "alpaca",
-                    },
-                ],
-                "num_epochs": 1,
-                "micro_batch_size": 8,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch",
-                "lr_scheduler": "cosine",
-                "save_safetensors": True,
-                "bf16": "auto",
-            }
-        )
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "model.safetensors").exists()
-    
     @with_temp_dir
     def test_llama_w_flce(self, temp_dir):
         cfg = DictDefault(

tests/e2e/multigpu/test_eval.py

@@ -1,155 +0,0 @@
-"""
-E2E tests for multigpu eval
-"""
-import logging
-import os
-import unittest
-from pathlib import Path
-
-import yaml
-from accelerate.test_utils import execute_subprocess_async
-
-from axolotl.utils.dict import DictDefault
-
-from ..utils import with_temp_dir
-
-LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
-os.environ["WANDB_DISABLED"] = "true"
-
-AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
-
-
-class TestMultiGPUEval(unittest.TestCase):
-    """
-    Test case for MultiGPU Eval Sample Packing
-    """
-
-    @with_temp_dir
-    def test_eval_sample_packing(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "JackFram/llama-68m",
-                "load_in_8bit": False,
-                "load_in_4bit": True,
-                "strict": False,
-                "sequence_len": 2048,
-                "adapter": "qlora",
-                "sample_packing": True,
-                "eval_sample_packing": True,
-                "pad_to_sequence_len": True,
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "lora_modules_to_save": ["embed_tokens", "lm_head"],
-                "val_set_size": 0.1,
-                "special_tokens": {"pad_token": "<|end_of_text|>"},
-                "datasets": [
-                    {
-                        "path": "teknium/GPT4-LLM-Cleaned",
-                        "type": "alpaca",
-                    },
-                ],
-                "num_epochs": 1,
-                "max_steps": 5,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 4,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_8bit",
-                "lr_scheduler": "cosine",
-                "flash_attention": True,
-                "loss_watchdog_threshold": 5.0,
-                "loss_watchdog_patience": 3,
-                "bf16": "auto",
-                "warmup_steps": 1,
-                "evals_per_epoch": 2,
-                "eval_max_new_tokens": 128,
-                "saves_per_epoch": 1,
-                "logging_steps": 1,
-                "weight_decay": 0.0,
-            }
-        )
-
-        # write cfg to yaml file
-        Path(temp_dir).mkdir(parents=True, exist_ok=True)
-        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
-            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
-
-        execute_subprocess_async(
-            [
-                "accelerate",
-                "launch",
-                "--num-processes",
-                "2",
-                "-m",
-                "axolotl.cli.train",
-                str(Path(temp_dir) / "config.yaml"),
-            ]
-        )
-
-    @with_temp_dir
-    def test_eval(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "JackFram/llama-68m",
-                "load_in_8bit": False,
-                "load_in_4bit": True,
-                "strict": False,
-                "sequence_len": 2048,
-                "adapter": "qlora",
-                "sample_packing": True,
-                "eval_sample_packing": False,
-                "pad_to_sequence_len": True,
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "lora_modules_to_save": ["embed_tokens", "lm_head"],
-                "val_set_size": 0.1,
-                "special_tokens": {"pad_token": "<|end_of_text|>"},
-                "datasets": [
-                    {
-                        "path": "teknium/GPT4-LLM-Cleaned",
-                        "type": "alpaca",
-                    },
-                ],
-                "num_epochs": 1,
-                "max_steps": 5,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 4,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_8bit",
-                "lr_scheduler": "cosine",
-                "flash_attention": True,
-                "loss_watchdog_threshold": 5.0,
-                "loss_watchdog_patience": 3,
-                "bf16": "auto",
-                "warmup_steps": 1,
-                "evals_per_epoch": 2,
-                "eval_max_new_tokens": 128,
-                "saves_per_epoch": 1,
-                "logging_steps": 1,
-                "weight_decay": 0.0,
-            }
-        )
-
-        # write cfg to yaml file
-        Path(temp_dir).mkdir(parents=True, exist_ok=True)
-        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
-            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
-
-        execute_subprocess_async(
-            [
-                "accelerate",
-                "launch",
-                "--num-processes",
-                "2",
-                "-m",
-                "axolotl.cli.train",
-                str(Path(temp_dir) / "config.yaml"),
-            ]
-        )

tests/e2e/multigpu/test_llama.py

@@ -14,7 +14,7 @@ from huggingface_hub import snapshot_download
 
 from axolotl.utils.dict import DictDefault
 
-from ..utils import is_hopper, with_temp_dir
+from ..utils import with_temp_dir
 
 LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
 os.environ["WANDB_DISABLED"] = "true"
@@ -59,7 +59,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
+                "max_steps": 100,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -116,7 +116,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
+                "max_steps": 50,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -144,146 +144,6 @@ class TestMultiGPULlama(unittest.TestCase):
             ]
         )
 
-    @pytest.mark.skipif(is_hopper(), reason="h100 doesn't support 8-bit lora")
-    @with_temp_dir
-    def test_dpo_lora_ddp(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "TinyLlama/TinyLlama_v1.1",
-                "tokenizer_type": "LlamaTokenizer",
-                "sequence_len": 2048,
-                "sample_packing": False,
-                "eval_sample_packing": False,
-                "pad_to_sequence_len": True,
-                "load_in_8bit": True,
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0.05,
-                "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
-                },
-                "rl": "dpo",
-                "chat_template": "llama3",
-                "datasets": [
-                    {
-                        "path": "fozziethebeat/alpaca_messages_2k_dpo_test",
-                        "type": "chat_template.default",
-                        "field_messages": "conversation",
-                        "field_chosen": "chosen",
-                        "field_rejected": "rejected",
-                        "message_field_role": "role",
-                        "message_field_content": "content",
-                        "roles": {
-                            "system": ["system"],
-                            "user": ["user"],
-                            "assistant": ["assistant"],
-                        },
-                    },
-                ],
-                "num_epochs": 1,
-                "max_steps": 15,
-                "micro_batch_size": 4,
-                "gradient_accumulation_steps": 4,
-                "output_dir": temp_dir,
-                "warmup_steps": 0,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_8bit",
-                "lr_scheduler": "cosine",
-                "flash_attention": True,
-            }
-        )
-
-        # write cfg to yaml file
-        Path(temp_dir).mkdir(parents=True, exist_ok=True)
-        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
-            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
-
-        execute_subprocess_async(
-            [
-                "accelerate",
-                "launch",
-                "--num-processes",
-                "2",
-                "-m",
-                "axolotl.cli.train",
-                str(Path(temp_dir) / "config.yaml"),
-            ]
-        )
-
-    @with_temp_dir
-    def test_dpo_qlora_ddp(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM-135M",
-                "sequence_len": 2048,
-                "sample_packing": False,
-                "eval_sample_packing": False,
-                "pad_to_sequence_len": True,
-                "load_in_4bit": True,
-                "adapter": "qlora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0.05,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                },
-                "rl": "dpo",
-                "chat_template": "chatml",
-                "datasets": [
-                    {
-                        "path": "fozziethebeat/alpaca_messages_2k_dpo_test",
-                        "type": "chat_template.default",
-                        "field_messages": "conversation",
-                        "field_chosen": "chosen",
-                        "field_rejected": "rejected",
-                        "message_field_role": "role",
-                        "message_field_content": "content",
-                        "roles": {
-                            "system": ["system"],
-                            "user": ["user"],
-                            "assistant": ["assistant"],
-                        },
-                    },
-                ],
-                "num_epochs": 1,
-                "max_steps": 15,
-                "micro_batch_size": 4,
-                "gradient_accumulation_steps": 4,
-                "output_dir": temp_dir,
-                "warmup_steps": 0,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_8bit",
-                "lr_scheduler": "cosine",
-                "flash_attention": True,
-            }
-        )
-
-        # write cfg to yaml file
-        Path(temp_dir).mkdir(parents=True, exist_ok=True)
-        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
-            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
-
-        execute_subprocess_async(
-            [
-                "accelerate",
-                "launch",
-                "--num-processes",
-                "2",
-                "-m",
-                "axolotl.cli.train",
-                str(Path(temp_dir) / "config.yaml"),
-            ]
-        )
-
     @with_temp_dir
     def test_fsdp(self, temp_dir):
         # pylint: disable=duplicate-code
@@ -305,7 +165,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
+                "max_steps": 100,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -371,7 +231,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
+                "max_steps": 100,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -413,6 +273,7 @@ class TestMultiGPULlama(unittest.TestCase):
             ]
         )
 
+    @pytest.mark.skip("disabled due to upstream issue")
     @with_temp_dir
     def test_fsdp_qlora_prequant_packed(self, temp_dir):
         # pylint: disable=duplicate-code
@@ -421,7 +282,6 @@ class TestMultiGPULlama(unittest.TestCase):
                 "base_model": "axolotl-ai-co/TinyLlama_v1.1-bnb-nf4-bf16",
                 "tokenizer_type": "AutoTokenizer",
                 "adapter": "qlora",
-                "mean_resizing_embeddings": True,
                 "load_in_4bit": True,
                 "lora_r": 8,
                 "lora_alpha": 16,
@@ -437,7 +297,7 @@ class TestMultiGPULlama(unittest.TestCase):
                 "sequence_len": 2048,
                 "val_set_size": 0.05,
                 "special_tokens": {
-                    "pad_token": "</s>",
+                    "pad_token": "<|end_of_text|>",
                 },
                 "datasets": [
                     {
@@ -447,7 +307,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
+                "max_steps": 100,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -513,7 +373,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
+                "max_steps": 100,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
@@ -572,7 +432,7 @@ class TestMultiGPULlama(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
+                "max_steps": 100,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,

tests/e2e/multigpu/test_qwen2.py

@@ -47,7 +47,7 @@ class TestMultiGPUQwen2(unittest.TestCase):
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 15,
+                "max_steps": 100,
                 "warmup_steps": 20,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 2,

tests/e2e/patched/test_4d_multipack_llama.py

@@ -13,7 +13,7 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
-from ..utils import require_torch_2_3_1, with_temp_dir
+from ..utils import require_torch_2_1_1, with_temp_dir
 
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
@@ -24,7 +24,7 @@ class Test4dMultipackLlama(unittest.TestCase):
     Test case for Llama models using 4d attention with multipack
     """
 
-    @require_torch_2_3_1
+    @require_torch_2_1_1
     @with_temp_dir
     def test_sdp_lora_packing(self, temp_dir):
         # pylint: disable=duplicate-code

tests/e2e/patched/test_unsloth_integration.py

@@ -1,12 +1,22 @@
 """Test module for checking whether the integration of Unsloth with Hugging Face Transformers is working as expected."""
 import unittest
 
-from axolotl.monkeypatch.unsloth_ import check_self_attn_is_patchable
+from axolotl.monkeypatch.unsloth_ import (
+    check_cel_is_patchable,
+    check_self_attn_is_patchable,
+)
 
 
 class TestUnslothIntegration(unittest.TestCase):
     """Unsloth monkeypatch integration tests."""
 
+    def test_is_cel_patchable(self):
+        # ensures the current version of transformers has loss code that matches our patching code
+        self.assertTrue(
+            check_cel_is_patchable(),
+            "HF transformers loss code has changed and isn't patchable",
+        )
+
     def test_is_self_attn_patchable(self):
         # ensures the current version of transformers has loss code that matches our patching code
         self.assertTrue(

tests/e2e/test_load_model.py

@@ -1,95 +0,0 @@
-"""Module for testing ModelLoader."""
-
-import shutil
-import tempfile
-
-import pytest
-import torch
-
-from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import ModelLoader, load_model, load_tokenizer
-
-
-@pytest.fixture(name="temp_dir")
-def fixture_temp_dir():
-    temp_dir = tempfile.mkdtemp()
-    yield temp_dir
-    shutil.rmtree(temp_dir)
-
-
-class TestLoadModelUtils:
-    """
-    Testing module testing ModelLoader.
-    """
-
-    def setup_method(self):
-        # load config
-        self.cfg = DictDefault(
-            {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
-                "tokenizer_config": "JackFram/llama-68m",
-                "sequence_len": 1024,
-                "load_in_8bit": False,
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0.1,
-                "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
-                },
-                "datasets": [
-                    {
-                        "path": "mhenrichsen/alpaca_2k_test",
-                        "type": "alpaca",
-                    },
-                ],
-                "num_epochs": 1,
-                "micro_batch_size": 8,
-                "gradient_accumulation_steps": 1,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch",
-                "lr_scheduler": "cosine",
-            }
-        )
-        self.model_loader = (  # pylint: disable=attribute-defined-outside-init
-            ModelLoader(
-                cfg=self.cfg,
-                tokenizer="",
-            )
-        )
-
-    @pytest.mark.parametrize("embedding_modules", ["embed_tokens", "lm_head"])
-    @pytest.mark.parametrize(
-        "dist_dtype", [torch.bfloat16, torch.float16, torch.float32]
-    )
-    @pytest.mark.parametrize("before_kbit_train_or_finetune", [True, False])
-    def test_convert_embedding_modules_dtype(
-        self, temp_dir, embedding_modules, dist_dtype, before_kbit_train_or_finetune
-    ):
-        self.cfg.output_dir = temp_dir
-        self.model_loader.tokenizer = load_tokenizer(self.cfg)  # pylint: disable=all
-        self.model_loader.model, _ = load_model(
-            self.cfg,
-            self.model_loader.tokenizer,
-            inference=False,
-            reference_model=True,
-        )
-        self.model_loader.convert_embedding_modules_dtype(
-            embedding_modules, dist_dtype, before_kbit_train_or_finetune
-        )
-        for name, module in self.model_loader.model.named_modules():
-            if (
-                "norm" in name
-                or (before_kbit_train_or_finetune and name.endswith(".gate"))
-                or (
-                    any(m in name for m in embedding_modules)
-                    and hasattr(module, "weight")
-                )
-            ):
-                for _, param in module.named_parameters():
-                    assert param.dtype == dist_dtype

tests/e2e/test_packing_loss.py

@@ -1,74 +0,0 @@
-"""
-E2E tests for packed training
-"""
-
-import logging
-import os
-import unittest
-
-from tbparse import SummaryReader
-from transformers.utils import is_torch_bf16_gpu_available
-
-from axolotl.cli import load_datasets
-from axolotl.common.cli import TrainerCliArgs
-from axolotl.train import train
-from axolotl.utils.config import normalize_config
-from axolotl.utils.dict import DictDefault
-
-from .utils import most_recent_subdir, with_temp_dir
-
-LOG = logging.getLogger("axolotl.tests.e2e")
-os.environ["WANDB_DISABLED"] = "true"
-
-
-class TestPackedLlama(unittest.TestCase):
-    """
-    Test case for Packed training of llama models
-    """
-
-    @with_temp_dir
-    def test_loss_packed(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM-135M",
-                "sequence_len": 1024,
-                "sample_packing": True,
-                "flash_attention": True,
-                "val_set_size": 0.0,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                },
-                "datasets": [
-                    {
-                        "path": "vicgalle/alpaca-gpt4",
-                        "type": "alpaca",
-                    },
-                ],
-                "num_epochs": 1,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 4,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch",
-                "lr_scheduler": "cosine",
-                "max_steps": 5,
-                "use_tensorboard": True,
-            }
-        )
-        if is_torch_bf16_gpu_available():
-            cfg.bf16 = True
-        else:
-            cfg.fp16 = True
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-
-        tb_log_path = most_recent_subdir(temp_dir + "/runs")
-        event_file = os.path.join(tb_log_path, sorted(os.listdir(tb_log_path))[0])
-        reader = SummaryReader(event_file)
-        df = reader.scalars  # pylint: disable=invalid-name
-        df = df[(df.tag == "train/train_loss")]  # pylint: disable=invalid-name
-        assert df.value.values[-1] < 2.0, "Loss is too high"

tests/e2e/utils.py

@@ -9,8 +9,6 @@ from functools import wraps
 from importlib.metadata import version
 from pathlib import Path
 
-import torch
-
 
 def with_temp_dir(test_func):
     @wraps(test_func)
@@ -37,18 +35,13 @@ def most_recent_subdir(path):
     return subdir
 
 
-def require_torch_2_3_1(test_case):
+def require_torch_2_1_1(test_case):
     """
-    Decorator marking a test that requires torch >= 2.3.1
+    Decorator marking a test that requires torch >= 2.1.1
     """
 
-    def is_min_2_3_1():
+    def is_min_2_1_1():
         torch_version = version("torch")
-        return torch_version >= "2.3.1"
+        return torch_version >= "2.1.1"
 
-    return unittest.skipUnless(is_min_2_3_1(), "test torch 2.3.1")(test_case)
+    return unittest.skipUnless(is_min_2_1_1(), "test torch 2.1.1")(test_case)
-
-
-def is_hopper():
-    compute_capability = torch.cuda.get_device_capability()
-    return compute_capability == (9, 0)

tests/integrations/liger.py

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

tests/test_datasets.py

@@ -306,10 +306,6 @@ class TestDatasetPreparation(unittest.TestCase):
         """Verify that processing data from the hub works with a specific revision"""
         with tempfile.TemporaryDirectory() as tmp_dir:
             prepared_path = Path(tmp_dir) / "prepared"
-
-            # make sure prepared_path is empty
-            shutil.rmtree(prepared_path, ignore_errors=True)
-
             cfg = DictDefault(
                 {
                     "tokenizer_config": "huggyllama/llama-7b",
@@ -371,44 +367,43 @@ class TestDatasetPreparation(unittest.TestCase):
     def test_load_local_hub_with_revision(self):
         """Verify that a local copy of a hub dataset can be loaded with a specific revision"""
         with tempfile.TemporaryDirectory() as tmp_dir:
-            with tempfile.TemporaryDirectory() as tmp_dir2:
+            tmp_ds_path = Path("mhenrichsen/alpaca_2k_test")
-                tmp_ds_path = Path(tmp_dir2) / "mhenrichsen/alpaca_2k_test"
+            tmp_ds_path.mkdir(parents=True, exist_ok=True)
-                tmp_ds_path.mkdir(parents=True, exist_ok=True)
+            snapshot_download(
-                snapshot_download(
+                repo_id="mhenrichsen/alpaca_2k_test",
-                    repo_id="mhenrichsen/alpaca_2k_test",
+                repo_type="dataset",
-                    repo_type="dataset",
+                local_dir=tmp_ds_path,
-                    local_dir=tmp_ds_path,
+                revision="d05c1cb",
-                    revision="d05c1cb",
+            )
-                )
 
-                prepared_path = Path(tmp_dir) / "prepared"
+            prepared_path = Path(tmp_dir) / "prepared"
-                cfg = DictDefault(
+            cfg = DictDefault(
-                    {
+                {
-                        "tokenizer_config": "huggyllama/llama-7b",
+                    "tokenizer_config": "huggyllama/llama-7b",
-                        "sequence_len": 1024,
+                    "sequence_len": 1024,
-                        "datasets": [
+                    "datasets": [
-                            {
+                        {
-                                "path": "mhenrichsen/alpaca_2k_test",
+                            "path": "mhenrichsen/alpaca_2k_test",
-                                "ds_type": "parquet",
+                            "ds_type": "parquet",
-                                "type": "alpaca",
+                            "type": "alpaca",
-                                "data_files": [
+                            "data_files": [
-                                    f"{tmp_ds_path}/alpaca_2000.parquet",
+                                "mhenrichsen/alpaca_2k_test/alpaca_2000.parquet",
-                                ],
+                            ],
-                                "revision": "d05c1cb",
+                            "revision": "d05c1cb",
-                            },
+                        },
-                        ],
+                    ],
-                    }
+                }
-                )
+            )
 
-                dataset, _ = load_tokenized_prepared_datasets(
+            dataset, _ = load_tokenized_prepared_datasets(
-                    self.tokenizer, cfg, prepared_path
+                self.tokenizer, cfg, prepared_path
-                )
+            )
 
-                assert len(dataset) == 2000
+            assert len(dataset) == 2000
-                assert "input_ids" in dataset.features
+            assert "input_ids" in dataset.features
-                assert "attention_mask" in dataset.features
+            assert "attention_mask" in dataset.features
-                assert "labels" in dataset.features
+            assert "labels" in dataset.features
-                shutil.rmtree(tmp_ds_path)
+            shutil.rmtree(tmp_ds_path)
 
 
 if __name__ == "__main__":

tests/test_validation.py

@@ -13,7 +13,6 @@ from axolotl.utils import is_comet_available
 from axolotl.utils.config import validate_config
 from axolotl.utils.config.models.input.v0_4_1 import AxolotlConfigWCapabilities
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.mlflow_ import setup_mlflow_env_vars
 from axolotl.utils.models import check_model_config
 from axolotl.utils.wandb_ import setup_wandb_env_vars
 
@@ -1433,58 +1432,3 @@ class TestValidationComet(BaseValidation):
 
         for key in comet_env.keys():
             os.environ.pop(key, None)
-
-
-class TestValidationMLflow(BaseValidation):
-    """
-    Validation test for MLflow
-    """
-
-    def test_hf_mlflow_artifacts_config_sets_env(self, minimal_cfg):
-        cfg = (
-            DictDefault(
-                {
-                    "hf_mlflow_log_artifacts": True,
-                }
-            )
-            | minimal_cfg
-        )
-
-        new_cfg = validate_config(cfg)
-
-        assert new_cfg.hf_mlflow_log_artifacts is True
-
-        # Check it's not already present in env
-        assert "HF_MLFLOW_LOG_ARTIFACTS" not in os.environ
-
-        setup_mlflow_env_vars(new_cfg)
-
-        assert os.environ.get("HF_MLFLOW_LOG_ARTIFACTS") == "true"
-
-        os.environ.pop("HF_MLFLOW_LOG_ARTIFACTS", None)
-
-    def test_mlflow_not_used_by_default(self, minimal_cfg):
-        cfg = DictDefault({}) | minimal_cfg
-
-        new_cfg = validate_config(cfg)
-
-        setup_mlflow_env_vars(new_cfg)
-
-        assert cfg.use_mlflow is not True
-
-        cfg = (
-            DictDefault(
-                {
-                    "mlflow_experiment_name": "foo",
-                }
-            )
-            | minimal_cfg
-        )
-
-        new_cfg = validate_config(cfg)
-
-        setup_mlflow_env_vars(new_cfg)
-
-        assert new_cfg.use_mlflow is True
-
-        os.environ.pop("MLFLOW_EXPERIMENT_NAME", None)

tests/utils/test_models.py

@@ -1,64 +1,18 @@
 """Module for testing models utils file."""
 
-from unittest.mock import MagicMock, patch
+
+import unittest
+from unittest.mock import patch
 
 import pytest
-from transformers import BitsAndBytesConfig, PreTrainedTokenizerBase
-from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
-from transformers.utils.import_utils import is_torch_mps_available
 
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import ModelLoader, load_model
+from axolotl.utils.models import load_model
 
 
-class TestModelsUtils:
+class ModelsUtilsTest(unittest.TestCase):
     """Testing module for models utils."""
 
-    def setup_method(self) -> None:
-        # load config
-        self.cfg = DictDefault(  # pylint: disable=attribute-defined-outside-init
-            {
-                "base_model": "JackFram/llama-68m",
-                "model_type": "LlamaForCausalLM",
-                "tokenizer_type": "LlamaTokenizer",
-                "load_in_8bit": True,
-                "load_in_4bit": False,
-                "adapter": "lora",
-                "flash_attention": False,
-                "sample_packing": True,
-                "device_map": "auto",
-            }
-        )
-        self.tokenizer = MagicMock(  # pylint: disable=attribute-defined-outside-init
-            spec=PreTrainedTokenizerBase
-        )
-        self.inference = False  # pylint: disable=attribute-defined-outside-init
-        self.reference_model = True  # pylint: disable=attribute-defined-outside-init
-
-        # init ModelLoader
-        self.model_loader = (  # pylint: disable=attribute-defined-outside-init
-            ModelLoader(
-                cfg=self.cfg,
-                tokenizer=self.tokenizer,
-                inference=self.inference,
-                reference_model=self.reference_model,
-            )
-        )
-
-    def test_set_device_map_config(self):
-        # check device_map
-        device_map = self.cfg.device_map
-        if is_torch_mps_available():
-            device_map = "mps"
-        self.model_loader.set_device_map_config()
-        if is_deepspeed_zero3_enabled():
-            assert "device_map" not in self.model_loader.model_kwargs
-        else:
-            assert device_map in self.model_loader.model_kwargs["device_map"]
-
-        # check torch_dtype
-        assert self.cfg.torch_dtype == self.model_loader.model_kwargs["torch_dtype"]
-
     def test_cfg_throws_error_with_s2_attention_and_sample_packing(self):
         cfg = DictDefault(
             {
@@ -81,38 +35,3 @@ class TestModelsUtils:
                 "shifted-sparse attention does not currently support sample packing"
                 in str(exc.value)
             )
-
-    @pytest.mark.parametrize("adapter", ["lora", "qlora", None])
-    @pytest.mark.parametrize("load_in_8bit", [True, False])
-    @pytest.mark.parametrize("load_in_4bit", [True, False])
-    @pytest.mark.parametrize("gptq", [True, False])
-    def test_set_quantization_config(
-        self,
-        adapter,
-        load_in_8bit,
-        load_in_4bit,
-        gptq,
-    ):
-        # init cfg as args
-        self.cfg.load_in_8bit = load_in_8bit
-        self.cfg.load_in_4bit = load_in_4bit
-        self.cfg.gptq = gptq
-        self.cfg.adapter = adapter
-
-        self.model_loader.set_quantization_config()
-        if "quantization_config" in self.model_loader.model_kwargs or self.cfg.gptq:
-            assert not (
-                hasattr(self.model_loader.model_kwargs, "load_in_8bit")
-                and hasattr(self.model_loader.model_kwargs, "load_in_4bit")
-            )
-        elif load_in_8bit and self.cfg.adapter is not None:
-            assert self.model_loader.model_kwargs["load_in_8bit"]
-        elif load_in_4bit and self.cfg.adapter is not None:
-            assert self.model_loader.model_kwargs["load_in_4bit"]
-
-        if (self.cfg.adapter == "qlora" and load_in_4bit) or (
-            self.cfg.adapter == "lora" and load_in_8bit
-        ):
-            assert self.model_loader.model_kwargs.get(
-                "quantization_config", BitsAndBytesConfig
-            )