precommit

.github/workflows/base.yml

@@ -40,24 +40,12 @@ jobs:
             python_version: "3.11"
             pytorch: 2.6.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "126"
-            cuda_version: 12.6.3
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.6.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
             python_version: "3.11"
             pytorch: nightly
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "128"
-            cuda_version: 12.8.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: next
-            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@v4
@@ -79,7 +67,7 @@ jobs:
         uses: docker/build-push-action@v4
         with:
           context: .
-          file: ${{ matrix.pytorch == 'nightly' && './docker/Dockerfile-base-nightly' || matrix.pytorch == 'next' && './docker/Dockerfile-base-next' || './docker/Dockerfile-base' }}
+          file: ${{ matrix.pytorch == 'nightly' && './docker/Dockerfile-base-nightly' || './docker/Dockerfile-base' }}
           push: ${{ github.event_name != 'pull_request' }}
           tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}

.github/workflows/docs.yml

@@ -23,7 +23,6 @@ jobs:
         - name: Install dependencies
           run: |
             python3 -m pip install jupyter quartodoc
-            python3 -m pip install -e . --no-deps
         - name: Build autodoc
           run: quartodoc build
         - name: Publish to GitHub Pages (and render)

.github/workflows/main.yml

@@ -25,12 +25,12 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras: vllm
+            is_latest: true
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras:
-            is_latest: true
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -87,12 +87,12 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras:
+            is_latest: true
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras:
-            is_latest: true
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

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

@@ -42,7 +42,8 @@ jobs:
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.6.0
-            axolotl_extras: vllm
+            # awaiting vllm#12721
+            axolotl_extras:
             num_gpus: 2
             nightly_build: "true"
     runs-on: [self-hosted, modal]

.github/workflows/tests-nightly.yml

@@ -33,15 +33,6 @@ jobs:
       - name: Check out repository code
         uses: actions/checkout@v4
 
-      - name: Restore HF cache
-        id: hf-cache-restore
-        uses: actions/cache/restore@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
-
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
@@ -55,7 +46,7 @@ jobs:
 
       - 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: Update requirements.txt
         run: |
@@ -67,7 +58,8 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip3 show torch
+          pip3 install --upgrade pip
+          pip3 install --upgrade packaging==23.2
           pip3 install --no-build-isolation -U -e .
           python scripts/unsloth_install.py | sh
           python scripts/cutcrossentropy_install.py | sh
@@ -81,15 +73,10 @@ jobs:
         run: |
           axolotl --help
 
-      - name: Pre-Download dataset fixture
-        run: |
-          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
-
       - name: Run tests
         run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
-          pytest -v tests/patched/
-          pytest -v tests/cli/
+          pytest -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
+          pytest tests/patched/
 
       - name: cleanup pip cache
         run: |
@@ -149,4 +136,4 @@ jobs:
           echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
-          modal run cicd.e2e_tests
+          modal run cicd.tests

.github/workflows/tests.yml

@@ -63,7 +63,7 @@ jobs:
           path: |
             /home/runner/.cache/huggingface/hub/datasets--*
             /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
+          key: ${{ runner.os }}-hf-hub-cache-${{ hashFiles('**/conftest.py') }}
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -96,10 +96,6 @@ jobs:
         run: |
           axolotl --help
 
-      - name: Pre-Download dataset fixture
-        run: |
-          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
-
       - name: Run tests
         run: |
           pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
@@ -141,7 +137,7 @@ jobs:
           path: |
             /home/runner/.cache/huggingface/hub/datasets--*
             /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
+          key: ${{ runner.os }}-hf-hub-cache-${{ hashFiles('**/conftest.py') }}
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -175,9 +171,6 @@ jobs:
         run: |
           axolotl --help
 
-      - name: Show HF cache
-        run: huggingface-cli scan-cache
-
       - name: Run tests
         run: |
           pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
@@ -236,7 +229,7 @@ jobs:
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
-          modal run cicd.e2e_tests
+          modal run cicd.tests
 
   docker-e2e-tests:
     if: github.repository_owner == 'axolotl-ai-cloud'
@@ -260,7 +253,7 @@ jobs:
             python_version: "3.11"
             pytorch: 2.6.0
             num_gpus: 1
-            axolotl_extras: vllm
+            axolotl_extras:
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -283,4 +276,4 @@ jobs:
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
-          modal run cicd.e2e_tests
+          modal run cicd.tests

.isort.cfg

@@ -1,4 +1,3 @@
 [settings]
 profile=black
 known_third_party=wandb,comet_ml
-known_local_folder=src,tests

_quarto.yml

@@ -40,7 +40,6 @@ quartodoc:
         - cli.preprocess
         - cli.sweeps
         - cli.utils
-        - cli.vllm_serve
         - cli.cloud.base
         - cli.cloud.modal_
     - title: Trainers
@@ -134,7 +133,6 @@ quartodoc:
         - utils.schemas.datasets
         - utils.schemas.peft
         - utils.schemas.trl
-        - utils.schemas.multimodal
         - utils.schemas.integrations
         - utils.schemas.enums
         - utils.schemas.utils
@@ -244,7 +242,6 @@ website:
             - docs/unsloth.qmd
             - docs/torchao.qmd
             - docs/custom_integrations.qmd
-            - docs/sequence_parallelism.qmd
 
         - section: "Troubleshooting"
           contents:

cicd/multigpu.sh

@@ -2,5 +2,4 @@
 set -e
 
 # only run one test at a time so as not to OOM the GPU
-pytest -v -n2 /workspace/axolotl/tests/e2e/multigpu/ --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/
-pytest -v -n1 /workspace/axolotl/tests/e2e/multigpu/solo/
+pytest -v -n2 /workspace/axolotl/tests/e2e/multigpu/

docker/Dockerfile

@@ -20,9 +20,9 @@ WORKDIR /workspace/axolotl
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

docker/Dockerfile-base-next

@@ -1,38 +0,0 @@
-ARG CUDA_VERSION="12.8.1"
-ARG CUDNN_VERSION="8"
-ARG UBUNTU_VERSION="22.04"
-ARG MAX_JOBS=4
-
-FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
-
-ENV PATH="/root/miniconda3/bin:${PATH}"
-
-ARG PYTHON_VERSION="3.11"
-ARG PYTORCH_VERSION="next"
-ARG CUDA="128"
-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
-
-ENV PYTHON_VERSION=$PYTHON_VERSION
-ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
-
-RUN apt-get update \
-    && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config && rm -rf /var/lib/apt/lists/* \
-    && wget \
-    https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
-    && mkdir /root/.conda \
-    && bash Miniconda3-latest-Linux-x86_64.sh -b \
-    && rm -f Miniconda3-latest-Linux-x86_64.sh \
-    && conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
-
-ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
-
-WORKDIR /workspace
-
-RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
-    python3 -m pip install --no-cache-dir -U torch==2.7.0 --extra-index-url https://download.pytorch.org/whl/test/cu$CUDA && \
-    python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
-    python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
-
-RUN git lfs install --skip-repo && \
-    pip3 install awscli && \
-    pip3 install -U --no-cache-dir pydantic==2.10.6

docs/cli.qmd

@@ -170,7 +170,7 @@ axolotl merge-sharded-fsdp-weights config.yml
 
 ### evaluate
 
-Evaluates a model's performance (loss etc) on the train and eval datasets.
+Evaluates a model's performance using metrics specified in the config.
 
 ```bash
 # Basic evaluation
@@ -197,8 +197,6 @@ lm_eval_batch_size: # Batch size for evaluation
 output_dir: # Directory to save evaluation results
 ```
 
-See [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) for more details.
-
 ## Legacy CLI Usage
 
 While the new Click-based CLI is preferred, Axolotl still supports the legacy module-based CLI:
@@ -237,7 +235,7 @@ Create a cloud config YAML with your Modal settings:
 ```yaml
 # cloud_config.yml
 provider: modal
-gpu: a100       # Supported: l40s, a100-40gb, a100-80gb, a10g, h100, t4, l4
+gpu: a100  # Supported: l40s, a100-40gb, a100-80gb, a10g, h100, t4, l4
 gpu_count: 1    # Number of GPUs to use
 timeout: 86400  # Maximum runtime in seconds (24 hours)
 branch: main    # Git branch to use (optional)
@@ -250,7 +248,7 @@ volumes:        # Persistent storage volumes
   - name: axolotl-artifacts
     mount: /workspace/artifacts
 
-secrets:        # Secrets to inject
+env:            # Environment variables
   - WANDB_API_KEY
   - HF_TOKEN
 ```
@@ -276,27 +274,15 @@ axolotl lm-eval config.yml --cloud cloud_config.yml
 ### Cloud Configuration Options
 
 ```yaml
-provider:    # compute provider, currently only `modal` is supported
-gpu:         # GPU type to use
-gpu_count:   # Number of GPUs (default: 1)
-memory:      # RAM in GB (default: 128)
-timeout:     # Maximum runtime in seconds
+provider: # compute provider, currently only `modal` is supported
+gpu: # GPU type to use
+gpu_count: # Number of GPUs (default: 1)
+memory: # RAM in GB (default: 128)
+timeout: # Maximum runtime in seconds
 timeout_preprocess: # Preprocessing timeout
-branch:      # Git branch to use
-docker_tag:  # Custom Docker image tag
-volumes:     # List of persistent storage volumes
-
-# Environment variables to pass. Can be specified in two ways:
-# 1. As a string: Will load the value from the host computer's environment variables
-# 2. As a key-value pair: Will use the specified value directly
-# Example:
-# env:
-#   - CUSTOM_VAR  # Loads from host's $CUSTOM_VAR
-#   - {CUSTOM_VAR: "value"}  # Uses "value" directly
-env:
-
-# Secrets to inject. Same input format as `env` but for sensitive data.
-secrets:
-  # - HF_TOKEN
-  # - WANDB_API_KEY
+branch: # Git branch to use
+docker_tag: # Custom Docker image tag
+volumes: # List of persistent storage volumes
+env: # Environment variables to pass
+secrets: # Secrets to inject
 ```

docs/config.qmd

@@ -238,10 +238,10 @@ simpo_gamma: 0.5  # Target reward margin for the SimPO loss
 # grpo
 trl:
   use_vllm: # Optional[bool]. Whether to use VLLM for RL training.
-  vllm_server_host: # Optional[str]. Host of the vLLM server to connect to.
-  vllm_server_port: # Optional[int]. Port of the vLLM server to connect to.
-  vllm_server_timeout: # Optional[int]. Total timeout (in seconds) to wait for the vLLM server to respond.
-  vllm_guided_decoding_regex: # Optional[str]. Regex for vLLM guided decoding.
+  vllm_device: # Optional[str]. Device to use for VLLM.
+  vllm_gpu_memory_utilization: # Optional[float]. GPU memory utilization for VLLM.
+  vllm_max_model_len: # Optional[int]. Maximum length of the model for VLLM.
+  vllm_dtype: # Optional[str]. Data type for VLLM.
 
   beta: # Optional[float]. Beta parameter for the RL training. Same as `rl_beta`. Use
   max_completion_length: # Optional[int]. Maximum length of the completion for RL training.
@@ -320,13 +320,9 @@ total_num_tokens:
 sample_packing_group_size: 100000
 # The number of samples which can be packed into one sequence. Increase if using a large sequence_len with many short samples.
 sample_packing_bin_size: 200
-sample_pack_sequentially: # Optional[bool]. Whether to pack samples sequentially.
-
 # whether to concatenate samples during pretraining
 pretraining_sample_concatenation:
 
-curriculum_sampling: # Optional[bool]. Whether to use sequential sampling for curriculum learning
-
 # Use batch flattening for speedups when not using sample_packing
 batch_flattening:
 
@@ -358,27 +354,7 @@ lora_target_modules:
 #  - down_proj
 #  - up_proj
 lora_target_linear: # If true, will target all linear modules
-
-# List[int] | int. # The layer indices to transform, otherwise, apply to all layers
-# https://huggingface.co/docs/peft/v0.15.0/en/package_reference/lora#peft.LoraConfig.layers_to_transform
-peft_layers_to_transform:
-
-# Optional[bool]. Whether to use DoRA.
-# https://huggingface.co/docs/peft/v0.15.0/en/developer_guides/lora#weight-decomposed-low-rank-adaptation-dora
-peft_use_dora:
-
-# Optional[bool]. Whether to use RSLoRA.
-# https://huggingface.co/docs/peft/v0.15.0/en/developer_guides/lora#rank-stabilized-lora
-peft_use_rslora:
-
-# Optional[list[tuple[int, int]]]. List of layer indices to replicate.
-# https://huggingface.co/docs/peft/v0.15.0/en/developer_guides/lora#memory-efficient-layer-replication-with-lora
-peft_layer_replication:
-
-# bool | Literal["gaussian", "eva", "olora", "pissa", "pissa_niter_[number of iters]", "corda", "loftq"]
-# How to initialize LoRA weights. Default to True which is MS original implementation.
-# https://huggingface.co/docs/peft/v0.15.0/en/developer_guides/lora#initialization
-peft_init_lora_weights:
+peft_layers_to_transform: # The layer indices to transform, otherwise, apply to all layers
 
 # If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.
 # For LLaMA and Mistral, you need to save `embed_tokens` and `lm_head`. It may vary for other models.
@@ -490,7 +466,6 @@ auto_find_batch_size: # Optional[bool]
 
 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
-do_causal_lm_eval: # Whether to run causal language model evaluation for metrics in `eval_causal_lm_metrics`.
 eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", "chrf", "perplexity"]
 
 profiler_steps: # enable the pytorch profiler to capture the first N steps of training to the output_dir.
@@ -510,8 +485,7 @@ train_on_inputs: false
 # Note that training loss may have an oscillating pattern with this enabled.
 group_by_length: false
 
-# Whether to use gradient checkpointing. Available options are: true, false, "offload".
-# https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
+# Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
 gradient_checkpointing: false
 # additional kwargs to pass to the trainer for gradient checkpointing
 # gradient_checkpointing_kwargs:
@@ -532,58 +506,36 @@ lr_div_factor: # Learning rate div factor
 
 # Specify optimizer
 # Valid values are driven by the Transformers OptimizerNames class, see:
-# https://github.com/huggingface/transformers/blob/cbf924b76c03828101a34069a96d209314114fd5/src/transformers/training_args.py#L144-L189
+# https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134
 #
 # Note that not all optimizers may be available in your environment, ex: 'adamw_anyprecision' is part of
 # torchdistx, 'adamw_bnb_8bit' is part of bnb.optim.Adam8bit, etc. When in doubt, it is recommended to start with the optimizer used
 # in the examples/ for your model and fine-tuning use case.
 #
 # Valid values for 'optimizer' include:
+# - adamw_hf
 # - adamw_torch
 # - adamw_torch_fused
 # - adamw_torch_xla
-# - adamw_torch_npu_fused
 # - adamw_apex_fused
-# - adopt_adamw  (an EXPERIMENTAL optimizer, only for torch version >= 2.5.1)
+# - adopt_adamw (an EXPERIMENTAL optimizer, only for torch version >= 2.5.1)
 # - adafactor
 # - adamw_anyprecision
-# - adamw_torch_4bit
-# - ademamix
 # - sgd
 # - adagrad
 # - adamw_bnb_8bit
-# - adamw_8bit   # alias for adamw_bnb_8bit
-# - ademamix_8bit
 # - lion_8bit
 # - lion_32bit
 # - paged_adamw_32bit
 # - paged_adamw_8bit
-# - paged_ademamix_32bit
-# - paged_ademamix_8bit
 # - paged_lion_32bit
 # - paged_lion_8bit
-# - rmsprop
-# - rmsprop_bnb
-# - rmsprop_bnb_8bit
-# - rmsprop_bnb_32bit
 # - galore_adamw
 # - galore_adamw_8bit
 # - galore_adafactor
 # - galore_adamw_layerwise
 # - galore_adamw_8bit_layerwise
 # - galore_adafactor_layerwise
-# - lomo
-# - adalomo
-# - grokadamw
-# - schedule_free_adamw
-# - schedule_free_sgd
-# - apollo_adamw
-# - apollo_adamw_layerwise
-#
-# Additional custom optimizers include:
-# - optimi_adamw
-# - ao_adamw_8bit
-# - ao_adamw_fp8
 optimizer:
 # Dictionary of arguments to pass to the optimizer
 optim_args:
@@ -612,42 +564,29 @@ max_grad_norm:
 # currently only supported on Llama and Mistral
 neftune_noise_alpha:
 
-# Optional[bool]. Whether to bettertransformers
+# Whether to bettertransformers
 flash_optimum:
-
-# Note: Only one of the following attention patches can be used at a time.
-# For example, if you set `xformers_attention` to `true`, do not set `flash_attention` to `true`.
-
-# Optional[bool]. Whether to use xformers attention patch https://github.com/facebookresearch/xformers:
+# Whether to use xformers attention patch https://github.com/facebookresearch/xformers:
 xformers_attention:
-# Optional[bool]. Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention:
+# Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention:
 flash_attention:
-flash_attn_cross_entropy:  # Optional[bool]. Whether to use flash-attention cross entropy implementation - advanced use only
-flash_attn_rms_norm:  # Optional[bool]. Whether to use flash-attention rms norm implementation - advanced use only
-flash_attn_fuse_qkv: # Optional[bool]. Whether to fuse QKV into a single operation
-flash_attn_fuse_mlp: # Optional[bool]. Whether to fuse part of the MLP into a single operation
-# Optional[bool]. Whether to use scaled-dot-product attention
+flash_attn_cross_entropy:  # Whether to use flash-attention cross entropy implementation - advanced use only
+flash_attn_rms_norm:  # Whether to use flash-attention rms norm implementation - advanced use only
+flash_attn_fuse_qkv: # Whether to fuse QKV into a single operation
+flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
+# Whether to use scaled-dot-product attention
 # https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
 sdp_attention:
-# Optional[bool]. Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf
+# Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf
 s2_attention:
-
 # Optional[bool]. Whether to use low_cpu_mem_usage
 low_cpu_mem_usage:
-# Optional[str]. Resume from a specific checkpoint dir
+# Resume from a specific checkpoint dir
 resume_from_checkpoint:
-# Optional[bool]. If resume_from_checkpoint isn't set and you simply want it to start where it left off.
+# If resume_from_checkpoint isn't set and you simply want it to start where it left off.
 # Be careful with this being turned on between different models.
 auto_resume_from_checkpoints: false
 
-## Multimodal section
-# int | tuple[int, int] | None . Size to resize images to, width x height.
-# Will read from model/processor config if not set.
-image_size:
-# str. Algorithm to use for image resizing. "bilinear", "bicubic", "lanczos". Default is "bilinear".
-image_resize_algorithm: 'bilinear'
-## End of multimodal section
-
 # Don't mess with this, it's here for accelerate and torchrun
 local_rank:
 
@@ -688,9 +627,6 @@ ddp_broadcast_buffers:
 # subsequences, or set to 4 to split into four equal-sized subsequences.
 # See https://axolotl-ai-cloud.github.io/axolotl/docs/sequence_parallelism.html for more details.
 sequence_parallel_degree:
-# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
-# Must evenly divide the number of KV heads in your model.
-heads_k_stride: 1
 
 # Path to torch distx for optim 'adamw_anyprecision'
 torchdistx_path:

docs/docker.qmd

@@ -103,7 +103,8 @@ This uses the same tags as the [`main` image](#sec-main-tags).
 
 - `JUPYTER_DISABLE`: Disable Jupyter lab.
 - `JUPYTER_PASSWORD`: Set a password for the Jupyter lab.
-- `PUBLIC_KEY` / `SSH_KEY`: Add a public key for the SSH service.
+- `PUBLIC_KEY`: Add a public key for the SSH service.
+- `SSH_KEY`: Add a private key for the SSH service.
 
 #### Volume mounts
 

docs/faq.qmd

@@ -35,22 +35,12 @@ description: Frequently asked questions
 
 **Q: How to call Axolotl via custom python scripts?**
 
-> A: Since Axolotl is just Python, please see `src/axolotl/cli/main.py` on how each command is called.
+> A: Yes, since Axolotl is just Python, please see `src/axolotl/cli/main.py` on how each command is called.
 
 **Q: How to know the value to use for `fsdp_transformer_layer_cls_to_wrap`?**
 
 > A: This is the class name of the transformer layer to wrap with FSDP. For example, for `LlamaForCausalLM`, the value is `LlamaDecoderLayer`. To find this for a specific model, check the model's `PreTrainedModel` definition and look for `_no_split_modules` variable in the `modeling_<model_name>.py` file within `transformers` library.
 
-**Q: ValueError: Asking to pad but the tokenizer does not have a padding token. Please select a token to use as pad_token**
-
-> A: This is because the tokenizer does not have a padding token. Please add a padding token to the tokenizer via:
-
-> ```yaml
-> special_tokens:
->   # str. If you're not sure, set to same as `eos_token`.
->   pad_token: "..."
-> ```
-
 ### Chat templates
 
 **Q: `jinja2.exceptions.UndefinedError: 'dict object' has no attribute 'content' / 'role' / ____`**

docs/lora_optims.qmd

@@ -17,7 +17,6 @@ We currently support several common model architectures, including (but not limi
 - `qwen2`
 - `gemma`
 - `gemma2`
-- `gemma3`
 
 <details>
 

docs/multi-gpu.qmd

@@ -18,7 +18,6 @@ Axolotl supports several methods for multi-GPU training:
 
 - DeepSpeed (recommended)
 - FSDP (Fully Sharded Data Parallel)
-- Sequence parallelism
 - FSDP + QLoRA
 
 ## DeepSpeed {#sec-deepspeed}
@@ -67,28 +66,6 @@ fsdp_config:
   fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
 ```
 
-## Sequence parallelism {#sec-sequence-parallelism}
-
-We support sequence parallelism (SP) via the
-[ring-flash-attention](https://github.com/zhuzilin/ring-flash-attention) project. This
-allows one to split up sequences across GPUs, which is useful in the event that a
-single sequence causes OOM errors during model training.
-
-First, install `ring-flash-attn`, recommended via `pip install axolotl[ring-flash-attn]`,
-or from source with `pip install .[ring-flash-attn]`.
-
-Your Axolotl YAML config should contain the following lines:
-
-```{.yaml}
-sequence_parallel_degree: 4  # Split each sequence into 4 parts, one per GPU
-flash_attention: true  # Required with sequence parallelism
-
-# Optional; strides across the key dimension. Larger values use more memory but will make training faster.
-heads_k_stride: 1
-```
-
-See our [dedicated guide](sequence_parallelism.qmd) for more details.
-
 ### FSDP + QLoRA {#sec-fsdp-qlora}
 
 For combining FSDP with QLoRA, see our [dedicated guide](fsdp_qlora.qmd).

docs/multimodal.qmd

@@ -1,171 +1,28 @@
----
-title: MultiModal / Vision Language Models (BETA)
-format:
-  html:
-    toc: true
-    toc-depth: 3
----
+# MultiModal / Vision Language Models (BETA)
 
-## Supported Models
+### Supported Models
 
-- [Mllama](#sec-mllama)
-- [Pixtral](#sec-pixtral)
-- [Llava-1.5](#sec-llava-15)
-- [Mistral-Small-3.1](#sec-mistral-small-31)
-- [Gemma-3](#sec-gemma-3)
-- [Qwen2-VL](#sec-qwen2-vl)
-- [Qwen2.5-VL](#sec-qwen25-vl)
+- Mllama, i.e. llama with vision models
 
-## Usage
+### Usage
 
-Multimodal support is limited and doesn't have full feature parity.
-
-Here are the hyperparams you'll need to use to finetune a multimodal model.
+Currently multimodal support is limited and doesn't have full feature parity. To finetune a multimodal Llama w/ LoRA,
+you'll need to use the following in YAML in combination with the rest of the required hyperparams.
 
 ```yaml
+base_model: alpindale/Llama-3.2-11B-Vision-Instruct
 processor_type: AutoProcessor
-
 skip_prepare_dataset: true
-remove_unused_columns: false  # leave columns in place as they are needed to handle image embeddings during training
-sample_packing: false  # not yet supported with multimodal
 
-chat_template:  # see in next section
-
-# example dataset
+chat_template: llama3_2_vision
 datasets:
   - path: HuggingFaceH4/llava-instruct-mix-vsft
     type: chat_template
     split: train[:1%]
     field_messages: messages
+remove_unused_columns: false
+sample_packing: false
 
-# (optional) if doing lora, only finetune the Language model,
-# leave the vision model and vision tower frozen
-# load_in_8bit: true
-adapter: lora
+# only finetune the Language model, leave the vision model and vision tower frozen
 lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-# (optional) if you want to resize images to a set size
-image_size: 512
-image_resize_algorithm: bilinear
-```
-
-Please see [examples](https://github.com/axolotl-ai/axolotl/tree/main/examples) folder for full configs.
-
-::: {.callout-warning}
-Some of our chat_templates have been extended to support broader dataset types. This should not break any existing configs.
-:::
-
-### Mllama {#sec-mllama}
-
-```yaml
-base_model: meta-llama/Llama-3.2-11B-Vision-Instruct
-
-chat_template: llama3_2_vision
-```
-
-### Pixtral {#sec-pixtral}
-
-```yaml
-base_model: mistralai/Pixtral-12B-2409
-
-chat_template: pixtral
-```
-
-### Llava-1.5 {#sec-llava-15}
-
-```yaml
-base_model: llava-hf/llava-1.5-7b-hf
-
-chat_template: llava
-```
-
-### Mistral-Small-3.1 {#sec-mistral-small-31}
-
-```yaml
-base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
-
-chat_template: mistral_v7_tekken
-```
-
-### Gemma-3 {#sec-gemma-3}
-
-::: {.callout-tip}
-The Gemma3-1B model is a text-only model, so please train as regular text model.
-:::
-
-For multi-modal 4B/12B/27B models, use the following config:
-
-```yaml
-base_model: google/gemma-3-4b-it
-
-chat_template: gemma3
-```
-
-### Qwen2-VL {#sec-qwen2-vl}
-
-```yaml
-base_model: Qwen/Qwen2-VL-7B-Instruct
-
-chat_template: qwen2_vl
-```
-
-### Qwen2.5-VL {#sec-qwen25-vl}
-
-```yaml
-base_model: Qwen/Qwen2.5-VL-7B-Instruct
-
-chat_template: qwen2_vl  # same as qwen2-vl
-```
-
-## Dataset Format
-
-For multi-modal datasets, we adopt an extended `chat_template` format similar to OpenAI's Message format.
-
-- A message is a list of `role` and `content`.
-- `role` can be `system`, `user`, `assistant`, etc.
-- `content` is a list of `type` and (`text` or `image` or `path` or `url` or `base64`).
-
-::: {.callout-note}
-For backwards compatibility:
-
-- If the dataset has a `images` or `image` column of `list[Image]`, it will be appended to the first `content` list as `{"type": "image", "image": ...}`. However, if the content already has a `{"type": "image"}` but no `image` key, it will be set the `image` key.
-- If `content` is a string, it will be converted to a list with `type` as `text`.
-:::
-
-::: {.callout-tip}
-For image loading, you can use the following keys within `content` alongside `"type": "image"`:
-
-- `"path": "/path/to/image.jpg"`
-- `"url": "https://example.com/image.jpg"`
-- `"base64": "..."`
-- `"image": PIL.Image`
-:::
-
-Here is an example of a multi-modal dataset:
-```json
-[
-  {
-    "messages": [
-        {
-            "role": "system",
-            "content": [
-              {"type": "text", "text": "You are a helpful assistant."}
-              ]
-        },
-        {
-            "role": "user",
-            "content": [
-                {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
-                {"type": "text", "text": "Describe this image in detail."}
-            ]
-        },
-        {
-            "role": "assistant",
-            "content": [
-              {"type": "text", "text": "The image is a bee."}
-            ]
-        }
-    ]
-  }
-]
 ```

docs/rlhf.qmd

@@ -502,48 +502,9 @@ The input format is a simple JSON input with customizable fields based on the ab
 Check out our [GRPO cookbook](https://github.com/axolotl-ai-cloud/axolotl-cookbook/tree/main/grpo#training-an-r1-style-large-language-model-using-grpo).
 :::
 
-If you have multiple GPUs available, we reccomend using `vLLM` with the `GRPOTrainer` to significantly speedup trajectory generation during training.
-First, launch a `vLLM` server using `trl vllm-serve` - you may use a config file or CLI overrides to configure your vLLM server. In this example, we're
-using 4 GPUs - 2 for training, and 2 for vLLM:
-
-::: {.callout-important}
-Make sure you've installed the correct version of vLLM by including it as an extra when installing axolotl, e.g. `pip install axolotl[vllm]`.
-:::
-
-```yaml
-base_model: Qwen/Qwen2.5-1.5B-Instruct
-
-vllm:
-    host: 0.0.0.0
-    port: 8000
-    tensor_parallel_size: 2
-    gpu_memory_utilization: 0.85
-    dtype: auto
-    # max_model_len: # you may find it useful to set the vLLM model context length if you know this beforehand
-
-rl: grpo
-trl:
-    use_vllm: true
-    vllm_server_host: 0.0.0.0
-    vllm_server_port: 8000
-    vllm_server_timeout: 300
-```
-
-```bash
-CUDA_VISIBLE_DEVICES=2,3 axolotl vllm_serve grpo.yaml
-```
-
-Your `vLLM` instance will now attempt to spin up, and it's time to kick off training utilizing our remaining two GPUs. In another terminal, execute:
-
-```bash
-CUDA_VISIBLE_DEVICES=0,1 axolotl train grpo.yaml --num-processes 2
-```
-
-#### Reward functions
-
 GRPO uses custom reward functions and transformations. Please have them ready locally.
 
-For example, to load OpenAI's GSM8K and use a random reward for completions:
+For ex, to load OpenAI's GSM8K and use a random reward for completions:
 
 ```python
 # rewards.py
@@ -569,6 +530,8 @@ trl:
     beta: 0.001
     max_completion_length: 256
     use_vllm: True
+    vllm_device: auto
+    vllm_gpu_memory_utilization: 0.15
     num_generations: 4
     reward_funcs: ["rewards.rand_reward_func"]    # format: '{file_name}.{fn_name}'
     reward_weights: [1.0]

docs/sequence_parallelism.qmd

@@ -25,8 +25,6 @@ To enable sequence parallelism, add the following to your configuration file:
 ```yaml
 # Set to a divisor (> 1) of the number of GPUs available
 sequence_parallel_degree: 4  # Split sequences across 4 GPUs
-# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
-heads_k_stride: 1
 ```
 
 The `sequence_parallel_degree` should be a divisor of the total number of GPUs. For example:
@@ -60,16 +58,11 @@ To use sequence parallelism, you need:
 ## Example
 
 ```yaml
+# Example config with sequence parallelism
 base_model: meta-llama/Llama-3-8B-Instruct
 sequence_len: 8192
-
-...
-
-sequence_parallel_degree: 4  # Split each sequence into 4 parts, one per GPU
+sequence_parallel_degree: 2  # Split each sequence into 4 parts
 flash_attention: true  # Required with sequence parallelism
-# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
-heads_k_stride: 1
-
 ...
 ```
 

examples/cohere/command-r-7b-qlora.yml

@@ -1,71 +0,0 @@
-base_model: CohereForAI/c4ai-command-r7b-12-2024
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-# huggingface repo
-chat_template: cohere
-datasets:
-  - path: cgato/SlimOrcaDedupCleaned
-    type: chat_template
-    field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
-
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-adapter: qlora
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-
-sequence_len: 2048
-sample_packing: true
-eval_sample_packing: false
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch:
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:

examples/gemma3/gemma-3-1b-qlora.yml

@@ -1,79 +0,0 @@
-base_model: google/gemma-3-1b-it
-# optionally might have model_type or tokenizer_type
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-# gemma3 doesn't seem to play nice with ddp
-ddp_find_unused_parameters: true
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-# huggingface repo
-chat_template: gemma3
-datasets:
-  - path: cgato/SlimOrcaDedupCleaned
-    type: chat_template
-    field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
-
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-adapter: qlora
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-
-sequence_len: 2048
-sample_packing: true
-eval_sample_packing: false
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch:
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:

examples/gemma3/gemma-3-4b-qlora.yml

@@ -1,66 +0,0 @@
-base_model: google/gemma-3-4b-it
-strict: false
-
-# gemma3 doesn't seem to play nice with ddp
-ddp_find_unused_parameters: true
-
-chat_template: gemma3
-datasets:
-  - path: cgato/SlimOrcaDedupCleaned
-    type: chat_template
-    field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-output_dir: ./outputs/out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-local_rank:
-logging_steps: 1
-flash_attention: true
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:

examples/gemma3/gemma-3-4b-vision-qlora.yml

@@ -1,68 +0,0 @@
-base_model: google/gemma-3-4b-it
-processor_type: AutoProcessor
-strict: false
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-# gemma3 doesn't seem to play nice with ddp
-ddp_find_unused_parameters: true
-
-chat_template: gemma3
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-    field_messages: messages
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-output_dir: ./outputs/out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-local_rank:
-logging_steps: 1
-flash_attention: true
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:

examples/llama-3/lora-1b-deduplicate-sft.yml

@@ -19,6 +19,7 @@ val_set_size: 0.0
 output_dir: ./outputs/lora-out
 
 dataset_exact_deduplication: true
+test_value: true
 
 sequence_len: 4096
 sample_packing: true

examples/llama-3/lora-1b-sample-packing-sequentially.yml

@@ -1,80 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-# optionally might have model_type or tokenizer_type
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.0
-output_dir: ./outputs/lora-out
-
-test_value: true
-
-sequence_len: 4096
-sample_packing: true
-sample_packing_sequentially: true
-curriculum_sampling: true
-eval_sample_packing: false
-pad_to_sequence_len: true
-
-adapter: lora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-lora_modules_to_save:
-  - embed_tokens
-  - lm_head
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-s2_attention:
-
-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/llava/lora-7b.yaml

@@ -1,63 +0,0 @@
-base_model: llava-hf/llava-1.5-7b-hf
-processor_type: AutoProcessor
-strict: false
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-chat_template: llava
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-    field_messages: messages
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 8192
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-local_rank:
-logging_steps: 1
-flash_attention: true
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:

examples/mistral/mistral-small-3.1-24B-lora.yml

@@ -1,66 +0,0 @@
-base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
-processor_type: AutoProcessor
-strict: false
-
-load_in_8bit: true
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-chat_template: mistral_v7_tekken
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-    field_messages: messages
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-output_dir: ./outputs/out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 2048
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-local_rank:
-logging_steps: 1
-flash_attention: false # PixtralVisionModel does not support Flash Attention 2.0 yet.
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:

examples/pixtral/lora-12b.yml

@@ -1,65 +0,0 @@
-base_model: mistral-community/pixtral-12b
-processor_type: AutoProcessor
-strict: false
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-chat_template: pixtral
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-    field_messages: messages
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 8192
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-local_rank:
-logging_steps: 1
-flash_attention: false # PixtralVisionModel does not support Flash Attention 2.0 yet
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  pad_token: <pad>

examples/qwen2-vl/lora-7b.yaml

@@ -1,63 +0,0 @@
-base_model: Qwen/Qwen2-VL-7B-Instruct
-processor_type: AutoProcessor
-strict: false
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-chat_template: qwen2_vl
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-    field_messages: messages
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 8192
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-local_rank:
-logging_steps: 1
-flash_attention: true
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:

requirements.txt

@@ -1,23 +1,23 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 
 # START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.45.4
+bitsandbytes==0.45.3
 triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
-liger-kernel==0.5.5
+liger-kernel==0.5.3
 # END section
 
 packaging==23.2
 
 peft==0.15.0
-transformers==4.50.3
+transformers==4.49.0
 tokenizers>=0.21.1
 accelerate==1.5.2
-datasets==3.5.0
-deepspeed==0.15.4
-trl==0.16.0
+datasets==3.4.1
+deepspeed==0.16.4
+trl==0.15.1
 
 optimum==1.16.2
 hf_transfer

requirements_env.txt

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

setup.py

@@ -10,13 +10,15 @@ from pathlib import Path
 from setuptools import find_packages, setup
 
 
-def parse_requirements(extras_require_map):
+def parse_requirements():
     _install_requires = []
     _dependency_links = []
     with open("./requirements.txt", encoding="utf-8") as requirements_file:
         lines = [r.strip() for r in requirements_file.readlines()]
         for line in lines:
-            is_extras = "deepspeed" in line or "mamba-ssm" in line
+            is_extras = (
+                "deepspeed" in line or "mamba-ssm" in line or "lion-pytorch" in line
+            )
             if line.startswith("--extra-index-url"):
                 # Handle custom index URLs
                 _, url = line.split()
@@ -67,7 +69,6 @@ def parse_requirements(extras_require_map):
             if (major, minor) >= (2, 6):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append("xformers==0.0.29.post2")
-                extras_require_map["vllm"] = ["vllm==0.8.1"]
             elif (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:
@@ -87,7 +88,7 @@ def parse_requirements(extras_require_map):
 
     except PackageNotFoundError:
         pass
-    return _install_requires, _dependency_links, extras_require_map
+    return _install_requires, _dependency_links
 
 
 def get_package_version():
@@ -104,50 +105,7 @@ def get_package_version():
     return version_
 
 
-extras_require = {
-    "flash-attn": ["flash-attn==2.7.4.post1"],
-    "ring-flash-attn": [
-        "flash-attn==2.7.4.post1",
-        "ring-flash-attn>=0.1.4",
-        "yunchang==0.6.0",
-    ],
-    "deepspeed": [
-        "deepspeed==0.15.4",
-        "deepspeed-kernels",
-    ],
-    "mamba-ssm": [
-        "mamba-ssm==1.2.0.post1",
-        "causal_conv1d",
-    ],
-    "auto-gptq": [
-        "auto-gptq==0.5.1",
-    ],
-    "mlflow": [
-        "mlflow",
-    ],
-    "galore": [
-        "galore_torch",
-    ],
-    "apollo": [
-        "apollo-torch",
-    ],
-    "optimizers": [
-        "galore_torch",
-        "apollo-torch",
-        "lomo-optim==0.1.1",
-        "torch-optimi==0.2.1",
-    ],
-    "ray": [
-        "ray[train]",
-    ],
-    "vllm": [
-        "vllm==0.7.2",
-    ],
-}
-
-install_requires, dependency_links, extras_require_build = parse_requirements(
-    extras_require
-)
+install_requires, dependency_links = parse_requirements()
 
 setup(
     version=get_package_version(),
@@ -160,5 +118,40 @@ setup(
             "axolotl=axolotl.cli.main:main",
         ],
     },
-    extras_require=extras_require_build,
+    extras_require={
+        "flash-attn": ["flash-attn==2.7.4.post1"],
+        "ring-flash-attn": ["ring-flash-attn>=0.1.4", "yunchang==0.6.0"],
+        "deepspeed": [
+            "deepspeed==0.16.4",
+            "deepspeed-kernels",
+        ],
+        "mamba-ssm": [
+            "mamba-ssm==1.2.0.post1",
+            "causal_conv1d",
+        ],
+        "auto-gptq": [
+            "auto-gptq==0.5.1",
+        ],
+        "mlflow": [
+            "mlflow",
+        ],
+        "lion-pytorch": [
+            "lion-pytorch==0.1.2",
+        ],
+        "galore": [
+            "galore_torch",
+        ],
+        "optimizers": [
+            "galore_torch",
+            "lion-pytorch==0.1.2",
+            "lomo-optim==0.1.1",
+            "torch-optimi==0.2.1",
+        ],
+        "ray": [
+            "ray[train]",
+        ],
+        "vllm": [
+            "vllm==0.7.2",
+        ],
+    },
 )

src/axolotl/__init__.py

@@ -4,4 +4,4 @@ import pkgutil
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-__version__ = "0.8.0"
+__version__ = "0.8.0.dev0"

src/axolotl/cli/args.py

@@ -35,55 +35,6 @@ class TrainerCliArgs:
     num_processes: Optional[int] = field(default=None)
 
 
-@dataclass
-class VllmServeCliArgs:
-    """Dataclass with CLI arguments for `axolotl vllm-serve` command."""
-
-    tensor_parallel_size: int = field(
-        default=1,
-        metadata={"help": "Number of tensor parallel workers to use."},
-    )
-    host: str = field(
-        default="0.0.0.0",  # nosec B104
-        metadata={"help": "Host address to run the server on."},
-    )
-    port: int = field(
-        default=8000,
-        metadata={"help": "Port to run the server on."},
-    )
-    gpu_memory_utilization: Optional[float] = field(
-        default=None,
-        metadata={
-            "help": "Ratio (between 0 and 1) of GPU memory to reserve for the model weights, activations, and KV "
-            "cache on the device dedicated to generation powered by vLLM. Higher values will increase the KV cache "
-            "size and thus improve the model's throughput. However, if the value is too high, it may cause "
-            "out-of-memory (OOM) errors during initialization."
-        },
-    )
-    dtype: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "Data type to use for vLLM generation. If set to 'auto', the data type will be automatically "
-            "determined based on the model configuration. Find the supported values in the vLLM documentation."
-        },
-    )
-    max_model_len: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "If set, the `max_model_len` to use for vLLM. This can be useful when running with reduced "
-            "`vllm_gpu_memory_utilization`, leading to a reduced KV cache size. If not set, vLLM will use the model "
-            "context size, which might be much larger than the KV cache, leading to inefficiencies."
-        },
-    )
-    enable_prefix_caching: Optional[bool] = field(
-        default=None,
-        metadata={
-            "help": "Whether to enable prefix caching in vLLM. If set to `True`, ensure that the model and the "
-            "hardware support this feature."
-        },
-    )
-
-
 @dataclass
 class EvaluateCliArgs:
     """Dataclass with CLI arguments for `axolotl evaluate` command."""

src/axolotl/cli/inference.py

@@ -56,7 +56,7 @@ def do_inference(
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: Inference-specific CLI arguments.
     """
-    model, tokenizer, _ = load_model_and_tokenizer(cfg=cfg, inference=True)
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg, inference=True)
     prompter = cli_args.prompter
 
     prompter_module = None
@@ -151,7 +151,7 @@ def do_inference_gradio(
     """
     import gradio as gr
 
-    model, tokenizer, _ = load_model_and_tokenizer(cfg=cfg, inference=True)
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg, inference=True)
     prompter = cli_args.prompter
 
     prompter_module = None
@@ -256,7 +256,7 @@ def do_cli(
     """
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
-    parsed_cfg = load_cfg(config, inference=True, rl=None, **kwargs)
+    parsed_cfg = load_cfg(config, inference=True, **kwargs)
     parsed_cfg.sample_packing = False
     parser = transformers.HfArgumentParser(InferenceCliArgs)
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(

src/axolotl/cli/main.py

@@ -14,12 +14,7 @@ import yaml
 from dotenv import load_dotenv
 
 import axolotl
-from axolotl.cli.args import (
-    EvaluateCliArgs,
-    PreprocessCliArgs,
-    TrainerCliArgs,
-    VllmServeCliArgs,
-)
+from axolotl.cli.args import EvaluateCliArgs, PreprocessCliArgs, TrainerCliArgs
 from axolotl.cli.sweeps import generate_sweep_configs
 from axolotl.cli.utils import (
     add_options_from_config,
@@ -28,7 +23,6 @@ from axolotl.cli.utils import (
     fetch_from_github,
     filter_none_kwargs,
 )
-from axolotl.cli.vllm_serve import do_vllm_serve
 from axolotl.integrations.lm_eval.cli import lm_eval
 from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.schemas.config import AxolotlInputConfig
@@ -322,14 +316,6 @@ def fetch(directory: str, dest: Optional[str]) -> None:
     fetch_from_github(f"{directory}/", dest)
 
 
-@cli.command()
-@click.argument("config", type=click.Path(exists=True, path_type=str))
-@add_options_from_dataclass(VllmServeCliArgs)
-@filter_none_kwargs
-def vllm_serve(config: str, **cli_args: VllmServeCliArgs):
-    do_vllm_serve(config, cli_args)
-
-
 cli.add_command(lm_eval)
 
 

src/axolotl/cli/merge_lora.py

@@ -27,7 +27,7 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
     """
     print_axolotl_text_art()
 
-    model, tokenizer, processor = load_model_and_tokenizer(cfg=cfg)
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg)
     safe_serialization = cfg.save_safetensors is True
 
     LOG.info("Running merge of LoRA with base model...")
@@ -44,9 +44,6 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
         )
         tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
 
-        if processor:
-            processor.save_pretrained(str(Path(cfg.output_dir) / "merged"))
-
 
 def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
     """
@@ -74,10 +71,8 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
         load_in_8bit=False,
         load_in_4bit=False,
         flash_attention=False,
-        sequence_parallel_degree=None,
         deepspeed=None,
         fsdp=None,
-        fsdp_config=None,
         **kwargs,
     )
 
@@ -88,6 +83,13 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
             f"Target directory for merge: `{parsed_cfg.lora_model_dir}` does not exist."
         )
 
+    parsed_cfg.load_in_4bit = False
+    parsed_cfg.load_in_8bit = False
+    parsed_cfg.flash_attention = False
+    parsed_cfg.deepspeed = None
+    parsed_cfg.fsdp = None
+    parsed_cfg.fsdp_config = None
+
     do_merge_lora(cfg=parsed_cfg)
 
 

src/axolotl/cli/train.py

@@ -17,7 +17,6 @@ from axolotl.cli.config import load_cfg
 from axolotl.common.datasets import load_datasets, load_preference_datasets
 from axolotl.integrations.base import PluginManager
 from axolotl.train import train
-from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.config import normalize_config, resolve_dtype
 from axolotl.utils.dict import DictDefault
 
@@ -34,9 +33,6 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: Training-specific CLI arguments.
     """
-    # Enable expandable segments for cuda allocation to improve VRAM usage
-    set_pytorch_cuda_alloc_conf()
-
     print_axolotl_text_art()
     check_accelerate_default_config()
     if int(os.getenv("LOCAL_RANK", "0")) == 0:

src/axolotl/cli/utils.py

@@ -13,16 +13,11 @@ from typing import Any, Callable, Type, Union, get_args, get_origin
 import click
 import requests
 from pydantic import BaseModel
-from transformers import (
-    PreTrainedModel,
-    PreTrainedTokenizer,
-    PreTrainedTokenizerFast,
-    ProcessorMixin,
-)
+from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
 
 from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_model, load_processor, load_tokenizer
+from axolotl.utils.models import load_model, load_tokenizer
 
 configure_logging()
 LOG = logging.getLogger(__name__)
@@ -300,13 +295,9 @@ def load_model_and_tokenizer(
     *,
     cfg: DictDefault,
     inference: bool = False,
-) -> tuple[
-    PreTrainedModel,
-    PreTrainedTokenizer | PreTrainedTokenizerFast | Any,
-    ProcessorMixin | None,
-]:
+) -> tuple[PreTrainedModel, PreTrainedTokenizer | PreTrainedTokenizerFast | Any]:
     """
-    Helper function for loading a model, tokenizer, and processor specified in the given `axolotl`
+    Helper function for loading a model and tokenizer specified in the given `axolotl`
     config.
 
     Args:
@@ -314,7 +305,7 @@ def load_model_and_tokenizer(
         inference: Boolean denoting inference mode.
 
     Returns:
-        Tuple of (PreTrainedModel, PreTrainedTokenizer, ProcessorMixin).
+        `transformers` model and tokenizer.
     """
     LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
     tokenizer = load_tokenizer(cfg)
@@ -322,9 +313,4 @@ def load_model_and_tokenizer(
     LOG.info("loading model...")
     model, _ = load_model(cfg, tokenizer, inference=inference)
 
-    processor = None
-    if cfg.is_multimodal:
-        LOG.info("loading processor...")
-        processor = load_processor(cfg, tokenizer)
-
-    return model, tokenizer, processor
+    return model, tokenizer

src/axolotl/cli/vllm_serve.py

@@ -1,55 +0,0 @@
-"""
-CLI to start the vllm server for online RL
-"""
-
-from pathlib import Path
-from typing import Union
-
-from trl.scripts.vllm_serve import ScriptArguments
-from trl.scripts.vllm_serve import main as vllm_serve_main
-
-from axolotl.cli.config import load_cfg
-
-
-def do_vllm_serve(
-    config: Union[Path, str],
-    cli_args: dict,
-):
-    """
-    Starts the VLLM server for serving LLM models used for online RL
-
-    Args
-        :param cfg: Parsed doct of the YAML config
-        :param cli_args: dict of additional command-line arguments of type VllmServeCliArgs
-
-    Returns:
-        process_id: the process id of the started VLLM server
-    """
-    cfg = load_cfg(config)
-    model = cfg.base_model
-
-    tensor_parallel_size = (
-        cli_args.get("tensor_parallel_size") or cfg.vllm.tensor_parallel_size
-    )
-    host = cli_args.get("host") or cfg.vllm.host
-    port = cli_args.get("port") or cfg.vllm.port
-    gpu_memory_utilization = (
-        cli_args.get("gpu_memory_utilization") or cfg.vllm.gpu_memory_utilization
-    )
-    dtype = cli_args.get("dtype") or cfg.vllm.dtype
-    max_model_len = cli_args.get("max_model_len") or cfg.vllm.max_model_len
-    enable_prefix_caching = (
-        cli_args.get("enable_prefix_caching") or cfg.vllm.enable_prefix_caching
-    )
-
-    vllm_script_args = ScriptArguments(
-        model,
-        tensor_parallel_size=tensor_parallel_size,
-        host=host,
-        port=port,
-        gpu_memory_utilization=gpu_memory_utilization,
-        dtype=dtype,
-        max_model_len=max_model_len,
-        enable_prefix_caching=enable_prefix_caching,
-    )
-    vllm_serve_main(vllm_script_args)

src/axolotl/core/trainer_builder.py

@@ -60,7 +60,6 @@ from axolotl.core.training_args import (
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback
-from axolotl.processing_strategies import get_processing_strategy
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
@@ -69,6 +68,7 @@ from axolotl.utils.callbacks import (
     LossWatchDogCallback,
     SaveAxolotlConfigtoWandBCallback,
     SaveBetterTransformerModelCallback,
+    SaveModelCallback,
     bench_eval_callback_factory,
     causal_lm_bench_eval_callback_factory,
     log_prediction_callback_factory,
@@ -248,6 +248,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         if self.cfg.gc_steps:
             callbacks.append(GCCallback(gc_steps=self.cfg.gc_steps))
+        callbacks.append(SaveModelCallback())
 
         return callbacks
 
@@ -524,15 +525,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             and self.cfg.eval_steps
             and self.cfg.save_steps % self.cfg.eval_steps == 0
         ) or False
-
-        # handle ddp
-        ddp_find_unused_parameters = None
-        if self.cfg.ddp:
-            ddp_find_unused_parameters = bool(self.cfg.ddp_find_unused_parameters)
         training_arguments_kwargs["ddp_find_unused_parameters"] = (
-            ddp_find_unused_parameters
+            False if self.cfg.ddp else None
         )
-
         training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
         training_arguments_kwargs["curriculum_sampling"] = self.cfg.curriculum_sampling
         report_to = []
@@ -752,12 +747,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 self.cfg.accelerator_config
             )
 
-        if self.cfg.image_size:
-            training_arguments_kwargs["image_size"] = self.cfg.image_size
-        if self.cfg.image_resize_algorithm:
-            training_arguments_kwargs["image_resize_algorithm"] = (
-                self.cfg.image_resize_algorithm
-            )
         if self.cfg.kd_ce_alpha is not None:
             training_arguments_kwargs["kd_ce_alpha"] = self.cfg.kd_ce_alpha
         if self.cfg.kd_alpha is not None:
@@ -901,13 +890,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         else:
             if self.cfg.processor_type and self.processor:
                 collator = MultiModalChatDataCollator
-                kwargs["processing_strategy"] = get_processing_strategy(
-                    self.processor,
-                    training_args.chat_template,
-                    self.cfg.chat_template,
-                    image_size=training_args.image_size,
-                    image_resize_algorithm=training_args.image_resize_algorithm,
-                )
+                kwargs["processor"] = self.processor
+                kwargs["chat_template"] = training_args.chat_template
             elif self.cfg.batch_flattening:
                 collator = DataCollatorWithFlattening
                 collator_args.pop(0)
@@ -941,6 +925,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
 
     def get_callbacks(self):
         callbacks = super().get_callbacks()
+        callbacks.append(SaveModelCallback())
 
         return callbacks
 

src/axolotl/core/trainers/base.py

@@ -8,11 +8,12 @@ import logging
 import os
 from collections import defaultdict
 from functools import wraps
-from typing import Literal
+from typing import Any, Literal
 
 import datasets
 import torch
 from datasets import Dataset
+from torch import nn
 from torch.utils.data import (
     BatchSampler,
     DataLoader,
@@ -27,7 +28,6 @@ from typing_extensions import override
 
 from axolotl.core.trainers.mixins import (
     OptimizerMixin,
-    RngLoaderMixin,
     SchedulerMixin,
     SequenceParallelMixin,
 )
@@ -40,9 +40,7 @@ from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 LOG = logging.getLogger(__name__)
 
 
-class AxolotlTrainer(
-    SchedulerMixin, OptimizerMixin, RngLoaderMixin, SequenceParallelMixin, Trainer
-):
+class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trainer):
     """Extend the base Trainer for axolotl helpers"""
 
     args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
@@ -114,7 +112,6 @@ class AxolotlTrainer(
             packing_efficiency_estimate=self.args.sample_packing_efficiency,
             batch_max_len=batch_max_len,
             batch_size=batch_size,
-            sequential=self.args.sample_packing_sequentially,
             drop_last=True,
         )
 
@@ -592,3 +589,27 @@ class AxolotlTrainer(
         output_dir = os.path.join(run_dir, checkpoint_folder)
         os.makedirs(output_dir, exist_ok=True)
         return super()._save_checkpoint(model, trial, **kwargs)
+
+    def training_step(
+        self,
+        model: nn.Module,
+        inputs: dict[str, torch.Tensor | Any],
+        num_items_in_batch: int | None = None,
+    ) -> torch.Tensor:
+        """
+        Perform a training step on a batch of inputs. Overrides the
+        `transformers.trainer.Trainer` method to handle sequence parallelism if
+        enabled.
+
+        Args:
+            model: Model to perform training step for.
+            inputs: Dictionary mapping.
+        """
+        # Set up sequence parallelism for this step if enabled
+        if self.args.sequence_parallel_degree > 1:
+            self._update_ring_flash_attn_params(inputs)
+
+        # Proceed with normal training step
+        loss = super().training_step(model, inputs, num_items_in_batch)
+
+        return loss

src/axolotl/core/trainers/dpo/trainer.py

@@ -13,7 +13,7 @@ from transformers import Trainer
 from transformers.utils import is_sagemaker_mp_enabled
 from trl import DPOTrainer
 
-from axolotl.core.trainers.mixins import RngLoaderMixin, SchedulerMixin
+from axolotl.core.trainers.mixins import SchedulerMixin
 from axolotl.core.trainers.utils import (
     sanitize_kwargs_for_ds_tagging,
     sanitize_kwargs_for_tagging,
@@ -23,7 +23,7 @@ if is_sagemaker_mp_enabled():
     import smdistributed.modelparallel.torch as smp
 
 
-class AxolotlDPOTrainer(RngLoaderMixin, SchedulerMixin, DPOTrainer):
+class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
     """
     Extend the base DPOTrainer for axolotl helpers
     """

src/axolotl/core/trainers/grpo/__init__.py

@@ -40,15 +40,18 @@ class GRPOStrategy:
 
         if trl.use_vllm:
             grpo_args_kwargs["use_vllm"] = trl.use_vllm
-            grpo_args_kwargs["vllm_server_host"] = trl.vllm_server_host
-            grpo_args_kwargs["vllm_server_port"] = trl.vllm_server_port
-            if trl.vllm_server_timeout:
-                grpo_args_kwargs["vllm_server_timeout"] = trl.vllm_server_timeout
-            if trl.vllm_guided_decoding_regex:
-                grpo_args_kwargs["vllm_guided_decoding_regex"] = (
-                    trl.vllm_guided_decoding_regex
+            grpo_args_kwargs["vllm_device"] = (
+                trl.vllm_device if trl.vllm_device else "auto"
+            )
+
+            if trl.vllm_gpu_memory_utilization:
+                grpo_args_kwargs["vllm_gpu_memory_utilization"] = (
+                    trl.vllm_gpu_memory_utilization
                 )
 
+            if trl.vllm_max_model_len:
+                grpo_args_kwargs["vllm_max_model_len"] = trl.vllm_max_model_len
+
         if trl.num_generations:
             grpo_args_kwargs["num_generations"] = trl.num_generations
 
@@ -67,25 +70,6 @@ class GRPOStrategy:
         if trl.reward_weights:
             grpo_args_kwargs["reward_weights"] = trl.reward_weights
 
-        if trl.scale_rewards is not None:
-            grpo_args_kwargs["scale_rewards"] = trl.scale_rewards
-
-        if trl.temperature is not None:
-            grpo_args_kwargs["temperature"] = trl.temperature
-        if trl.top_p is not None:
-            grpo_args_kwargs["top_p"] = trl.top_p
-        if trl.top_k is not None:
-            grpo_args_kwargs["top_k"] = trl.top_k
-        if trl.min_p is not None:
-            grpo_args_kwargs["min_p"] = trl.min_p
-        if trl.repetition_penalty is not None:
-            grpo_args_kwargs["repetition_penalty"] = trl.repetition_penalty
-
-        if trl.num_iterations is not None:
-            grpo_args_kwargs["num_iterations"] = trl.num_iterations
-        if trl.epsilon is not None:
-            grpo_args_kwargs["epsilon"] = trl.epsilon
-
         return grpo_args_kwargs
 
     @classmethod

src/axolotl/core/trainers/grpo/trainer.py

@@ -2,68 +2,108 @@
 Axolotl GRPO trainer
 """
 
-from contextlib import nullcontext
+from accelerate.utils import is_peft_model
+from accelerate.utils.other import is_compiled_module
+from transformers import PreTrainedModel
+from trl import GRPOConfig, GRPOTrainer
+from trl.models import unwrap_model_for_generation
 
-from accelerate.utils import is_deepspeed_available, is_peft_model
-from trl import GRPOTrainer
-from trl.extras.profiling import profiling_decorator
-
-from axolotl.core.trainers.mixins import RngLoaderMixin, SchedulerMixin
-
-if is_deepspeed_available():
-    import deepspeed
+from axolotl.core.trainers.base import SchedulerMixin
 
 
-class AxolotlGRPOTrainer(RngLoaderMixin, SchedulerMixin, GRPOTrainer):
+# mypy: ignore-errors
+class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
     """
     Extend the base GRPOTrainer for axolotl helpers
     """
 
     _tag_names = ["trl", "grpo", "axolotl"]
 
-    @profiling_decorator
-    def _move_model_to_vllm(self):
-        # For DeepSpeed ZeRO-3, we need to gather all parameters before operations
-        deepspeed_plugin = self.accelerator.state.deepspeed_plugin
-        zero_stage_3 = deepspeed_plugin is not None and deepspeed_plugin.zero_stage == 3
-        gather_if_zero3 = (
-            deepspeed.zero.GatheredParameters if zero_stage_3 else nullcontext
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # pylint: disable=access-member-before-definition
+        # Enable gradient checkpointing if requested
+        if kwargs["args"].gradient_checkpointing:
+            # Ensure use_cache is disabled
+            if hasattr(self.model, "config"):
+                self.model.config.use_cache = False
+
+            # Enable gradient checkpointing on the base model for PEFT
+            if is_peft_model(self.model) and hasattr(
+                self.model.base_model, "gradient_checkpointing_enable"
+            ):
+                self.model.base_model.gradient_checkpointing_enable()
+            # Enable gradient checkpointing for non-PEFT models
+            elif hasattr(self.model, "gradient_checkpointing_enable"):
+                self.model.gradient_checkpointing_enable()
+            self.model = self._enable_gradient_checkpointing(self.model, kwargs["args"])
+        # pylint: enable=access-member-before-definition
+
+    def _enable_gradient_checkpointing(
+        self, model: PreTrainedModel, args: GRPOConfig
+    ) -> PreTrainedModel:
+        """Enables gradient checkpointing for the model."""
+        # pylint: disable=unused-argument,redefined-builtin
+        gradient_checkpointing_kwargs = args.gradient_checkpointing_kwargs or {}
+        use_reentrant = (
+            "use_reentrant" not in gradient_checkpointing_kwargs
+            or gradient_checkpointing_kwargs["use_reentrant"]
         )
 
-        if is_peft_model(self.model):
-            # With PEFT and DeepSpeed ZeRO Stage 3, we must gather the full model at once before merging, as merging
-            # adapters in a sharded manner is not supported.
-            with gather_if_zero3(list(self.model.parameters())):
-                self.model.merge_adapter()
+        if use_reentrant:
+            if hasattr(model, "enable_input_require_grads"):
+                model.enable_input_require_grads()
+            else:
 
-                # Update vLLM weights while parameters are gathered
-                for name, param in self.model.named_parameters():
-                    # When using PEFT, we need to recover the original parameter name and discard some parameters
-                    name = (
-                        name.removeprefix("base_model.model.")
-                        .removeprefix("base_model.model.")
-                        .replace(".base_layer", "")
-                    )
-                    if self.model.prefix in name:
-                        continue
-                    # When module to save, remove its prefix and discard the original module
-                    if "original_module" in name:
-                        continue
-                    name = name.replace("modules_to_save.default.", "")
+                def make_inputs_require_grad(module, input, output):
+                    output.requires_grad_(True)
 
-                    if self.accelerator.is_main_process:
-                        self.vllm_client.update_named_param(name, param.data)
+                model.get_input_embeddings().register_forward_hook(
+                    make_inputs_require_grad
+                )
 
-                # Unmerge adapters while parameters are still gathered
-                self.model.unmerge_adapter()
-                # Parameters will automatically be repartitioned when exiting the context
-        else:
-            # For non-PEFT models, simply gather and update each parameter individually.
-            for name, param in self.model.named_parameters():
-                with gather_if_zero3([param]):
-                    if self.accelerator.is_main_process:
-                        self.vllm_client.update_named_param(name, param.data)
+        return model
+        # pylint: enable=unused-argument,redefined-builtin
 
-        # Reset cache on main process
-        if self.accelerator.is_main_process:
-            self.vllm_client.reset_prefix_cache()
+    def _move_model_to_vllm(self):
+        with unwrap_model_for_generation(
+            self.model,
+            self.accelerator,
+            gather_deepspeed3_params=self.args.ds3_gather_for_generation,
+        ) as unwrapped_model:
+            if is_compiled_module(unwrapped_model):
+                unwrapped_model = (
+                    unwrapped_model._orig_mod  # pylint: disable=protected-access
+                )
+            if is_peft_model(unwrapped_model):
+                unwrapped_model.merge_adapter()
+                state_dict = unwrapped_model.state_dict()
+                # Remove base_model and base_layer prefixes
+                state_dict = {
+                    k.removeprefix("base_model.model.")
+                    .removeprefix("base_model.model.")
+                    .replace(".base_layer", ""): v
+                    for k, v in state_dict.items()
+                }
+                # Remove values with adapter prefix (example: "_lora")
+                state_dict = {
+                    k: v
+                    for k, v in state_dict.items()
+                    if unwrapped_model.prefix not in k
+                }
+                # When module to save, remove its prefix and discard the original module
+                state_dict = {
+                    k.replace("modules_to_save.default.", ""): v
+                    for k, v in state_dict.items()
+                    if "original_module" not in k
+                }
+            else:
+                state_dict = unwrapped_model.state_dict()
+            if self.accelerator.is_main_process:
+                llm_model = (
+                    self.llm.llm_engine.model_executor.driver_worker.model_runner.model
+                )
+                llm_model.load_weights(state_dict.items())
+            if is_peft_model(unwrapped_model):
+                unwrapped_model.unmerge_adapter()

src/axolotl/core/trainers/mixins/__init__.py

@@ -4,6 +4,5 @@
 # flake8: noqa
 
 from .optimizer import OptimizerMixin
-from .rng_state_loader import RngLoaderMixin
 from .scheduler import SchedulerMixin
 from .sequence_parallel import SequenceParallelMixin

src/axolotl/core/trainers/mixins/rng_state_loader.py

@@ -1,67 +0,0 @@
-"""
-Temporary fix/override for bug in resume from checkpoint
-
-See https://github.com/huggingface/transformers/pull/37162
-
-TODO: Remove when upstream added PR to release
-"""
-
-import logging
-import os
-import random
-
-import numpy as np
-import torch
-from transformers import Trainer, is_torch_npu_available
-from transformers.trainer import safe_globals
-from transformers.trainer_pt_utils import set_rng_state_for_device
-from transformers.training_args import ParallelMode
-
-LOG = logging.getLogger(__name__)
-
-
-class RngLoaderMixin(Trainer):
-    """
-    mixin for method override to load RNG states from a checkpoint
-    """
-
-    def _load_rng_state(self, checkpoint):
-        # Load RNG states from `checkpoint`
-        if checkpoint is None:
-            return
-
-        if self.args.world_size > 1:
-            process_index = self.args.process_index
-            rng_file = os.path.join(checkpoint, f"rng_state_{process_index}.pth")
-            if not os.path.isfile(rng_file):
-                LOG.info(
-                    f"Didn't find an RNG file for process {process_index}, if you are resuming a training that "
-                    "wasn't launched in a distributed fashion, reproducibility is not guaranteed."
-                )
-                return
-        else:
-            rng_file = os.path.join(checkpoint, "rng_state.pth")
-            if not os.path.isfile(rng_file):
-                LOG.info(
-                    "Didn't find an RNG file, if you are resuming a training that was launched in a distributed "
-                    "fashion, reproducibility is not guaranteed."
-                )
-                return
-
-        # Use safe_globals to ensure numpy RNG states can be deserialized safely under PyTorch 2.6+,
-        # which requires allowlisted classes when loading with weights_only=True.
-        with safe_globals():
-            checkpoint_rng_state = torch.load(rng_file)  # nosec B614
-        random.setstate(checkpoint_rng_state["python"])
-        np.random.set_state(checkpoint_rng_state["numpy"])
-        torch.random.set_rng_state(checkpoint_rng_state["cpu"])
-
-        is_distributed = self.args.parallel_mode == ParallelMode.DISTRIBUTED
-        if torch.cuda.is_available():
-            set_rng_state_for_device(
-                "CUDA", torch.cuda, checkpoint_rng_state, is_distributed
-            )
-        if is_torch_npu_available():
-            set_rng_state_for_device(
-                "NPU", torch.npu, checkpoint_rng_state, is_distributed
-            )

src/axolotl/core/trainers/mixins/sequence_parallel.py

@@ -7,7 +7,6 @@ import torch
 import torch.distributed as dist
 import torch.nn.functional as F
 from datasets import Dataset
-from torch import nn
 from torch.utils.data import DistributedSampler, Sampler
 
 from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group
@@ -130,53 +129,3 @@ class SequenceParallelMixin:
         )
 
         update_ring_flash_attn_params(cu_seqlens, self.ring_attn_group)
-
-    def training_step(
-        self,
-        model: nn.Module,
-        inputs: dict[str, torch.Tensor | Any],
-        num_items_in_batch: int | None = None,
-    ) -> torch.Tensor:
-        """
-        Perform a training step on a batch of inputs. Overrides the
-        `transformers.trainer.Trainer` method to handle sequence parallelism if
-        enabled.
-
-        Args:
-            model: Model to perform training step for.
-            inputs: Dictionary mapping.
-        """
-        # Set up sequence parallelism for this step if enabled
-        if self.args.sequence_parallel_degree > 1:
-            self._update_ring_flash_attn_params(inputs)
-
-        # Proceed with normal training step
-        return super().training_step(model, inputs, num_items_in_batch)  # type: ignore
-
-    def prediction_step(
-        self,
-        model: nn.Module,
-        inputs: dict[str, torch.Tensor | Any],
-        prediction_loss_only: bool,
-        ignore_keys: list[str] | None = None,
-    ) -> tuple[torch.Tensor | None, torch.Tensor | None, torch.Tensor | None]:
-        """
-        Perform a prediction step on a batch of inputs. Overrides the
-        `transformers.trainer.Trainer` method to handle sequence parallelism if
-        enabled.
-
-        Args:
-            model: Model to perform prediction step for.
-            inputs: Dictionary mapping of inputs.
-            prediction_loss_only: Whether to return only the loss.
-            ignore_keys: Keys to ignore in the inputs.
-
-        Returns:
-            Tuple of (loss, logits, labels).
-        """
-        # Set up sequence parallelism for this prediction step if enabled
-        if self.args.sequence_parallel_degree > 1:
-            self._update_ring_flash_attn_params(inputs)
-
-        # Proceed with normal prediction step
-        return super().prediction_step(model, inputs, prediction_loss_only, ignore_keys)  # type: ignore

src/axolotl/core/trainers/trl.py

@@ -1,7 +1,5 @@
 """Module for TRL PPO trainer"""
 
-from typing import Literal, Union
-
 import torch
 from tqdm import tqdm
 from trl import (
@@ -13,7 +11,6 @@ from trl import (
     RewardTrainer,
 )
 
-from axolotl.core.trainers.mixins import RngLoaderMixin
 from axolotl.core.trainers.mixins.scheduler import SchedulerMixin
 
 
@@ -75,87 +72,15 @@ class TRLPPOTrainer(PPOTrainer):
             )
 
 
-class AxolotlORPOTrainer(RngLoaderMixin, SchedulerMixin, ORPOTrainer):
+class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
     """
     Extend the base ORPOTrainer for axolotl helpers
     """
 
     tag_names = ["axolotl", "orpo"]
 
-    def get_batch_loss_metrics(
-        self,
-        model,
-        batch: dict[str, Union[list, torch.LongTensor]],
-        train_eval: Literal["train", "eval"] = "train",
-    ):
-        """Compute the ORPO loss and other metrics for the given batch of inputs for train or test."""
 
-        # TODO remove once https://github.com/huggingface/trl/pull/3069 is included in a trl release
-
-        metrics = {}
-
-        forward_output = self.concatenated_forward(model, batch)
-        (
-            policy_chosen_logps,
-            policy_rejected_logps,
-            policy_chosen_logits,
-            policy_rejected_logits,
-            policy_nll_loss,
-        ) = forward_output[:5]
-        if self.aux_loss_enabled:
-            aux_loss = forward_output[5]
-
-        losses, chosen_rewards, rejected_rewards, log_odds_ratio, log_odds_chosen = (
-            self.odds_ratio_loss(policy_chosen_logps, policy_rejected_logps)
-        )
-        # full ORPO loss
-        loss = policy_nll_loss - losses.mean()
-
-        reward_accuracies = (chosen_rewards > rejected_rewards).float()
-
-        prefix = "eval_" if train_eval == "eval" else ""
-        metrics[f"{prefix}rewards/chosen"] = self.accelerator.gather_for_metrics(
-            chosen_rewards
-        ).mean()
-        metrics[f"{prefix}rewards/rejected"] = self.accelerator.gather_for_metrics(
-            rejected_rewards
-        ).mean()
-        metrics[f"{prefix}rewards/accuracies"] = self.accelerator.gather_for_metrics(
-            reward_accuracies
-        ).mean()
-        metrics[f"{prefix}rewards/margins"] = self.accelerator.gather_for_metrics(
-            chosen_rewards - rejected_rewards
-        ).mean()
-        metrics[f"{prefix}logps/rejected"] = (
-            self.accelerator.gather_for_metrics(policy_rejected_logps).detach().mean()
-        )
-        metrics[f"{prefix}logps/chosen"] = (
-            self.accelerator.gather_for_metrics(policy_chosen_logps).detach().mean()
-        )
-        metrics[f"{prefix}logits/rejected"] = self.accelerator.gather_for_metrics(
-            policy_rejected_logits.detach().mean()
-        ).mean()
-        metrics[f"{prefix}logits/chosen"] = self.accelerator.gather_for_metrics(
-            policy_chosen_logits.detach().mean()
-        ).mean()
-        metrics[f"{prefix}nll_loss"] = (
-            self.accelerator.gather_for_metrics(policy_nll_loss).detach().mean()
-        )
-        metrics[f"{prefix}log_odds_ratio"] = (
-            self.accelerator.gather_for_metrics(log_odds_ratio).detach().mean()
-        )
-        metrics[f"{prefix}log_odds_chosen"] = (
-            self.accelerator.gather_for_metrics(log_odds_chosen).detach().mean()
-        )
-        for k, v in metrics.items():
-            metrics[k] = v.item()
-        if self.aux_loss_enabled:
-            loss += self.aux_loss_coef * aux_loss
-
-        return loss, metrics
-
-
-class AxolotlKTOTrainer(RngLoaderMixin, SchedulerMixin, KTOTrainer):
+class AxolotlKTOTrainer(SchedulerMixin, KTOTrainer):
     """
     Extend the base KTOTrainer for axolotl helpers
     """
@@ -163,89 +88,15 @@ class AxolotlKTOTrainer(RngLoaderMixin, SchedulerMixin, KTOTrainer):
     tag_names = ["axolotl", "kto"]
 
 
-class AxolotlCPOTrainer(RngLoaderMixin, SchedulerMixin, CPOTrainer):
+class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
     """
     Extend the base CPOTrainer for axolotl helpers
     """
 
     tag_names = ["axolotl", "cpo"]
 
-    def get_batch_loss_metrics(
-        self,
-        model,
-        batch: dict[str, Union[list, torch.LongTensor]],
-        train_eval: Literal["train", "eval"] = "train",
-    ):
-        """Compute the CPO loss and other metrics for the given batch of inputs for train or test."""
-        metrics = {}
 
-        forward_output = self.concatenated_forward(model, batch)
-        (
-            policy_chosen_logps,
-            policy_rejected_logps,
-            policy_chosen_logits,
-            policy_rejected_logits,
-            policy_nll_loss,
-        ) = forward_output[:5]
-        if self.aux_loss_enabled:
-            aux_loss = forward_output[5]
-
-        losses, chosen_rewards, rejected_rewards = self.cpo_loss(
-            policy_chosen_logps,
-            policy_rejected_logps,
-        )
-
-        loss = losses.mean() + self.cpo_alpha * policy_nll_loss
-        reward_accuracies = (chosen_rewards > rejected_rewards).float()
-
-        prefix = "eval_" if train_eval == "eval" else ""
-        metrics[f"{prefix}rewards/chosen"] = (
-            self.accelerator.gather_for_metrics(chosen_rewards).mean().item()
-        )
-        metrics[f"{prefix}rewards/rejected"] = (
-            self.accelerator.gather_for_metrics(rejected_rewards).mean().item()
-        )
-        metrics[f"{prefix}rewards/accuracies"] = (
-            self.accelerator.gather_for_metrics(reward_accuracies).mean().item()
-        )
-        metrics[f"{prefix}rewards/margins"] = (
-            self.accelerator.gather_for_metrics(chosen_rewards - rejected_rewards)
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}logps/rejected"] = (
-            self.accelerator.gather_for_metrics(policy_rejected_logps)
-            .detach()
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}logps/chosen"] = (
-            self.accelerator.gather_for_metrics(policy_chosen_logps)
-            .detach()
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}logits/rejected"] = (
-            self.accelerator.gather_for_metrics(policy_rejected_logits.detach().mean())
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}logits/chosen"] = (
-            self.accelerator.gather_for_metrics(policy_chosen_logits.detach().mean())
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}nll_loss"] = (
-            self.accelerator.gather_for_metrics(policy_nll_loss).detach().mean().item()
-        )
-
-        if self.aux_loss_enabled:
-            loss += self.aux_loss_coef * aux_loss
-
-        return loss, metrics
-
-
-class AxolotlRewardTrainer(RngLoaderMixin, SchedulerMixin, RewardTrainer):
+class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
     """
     Extend the base RewardTrainer for axolotl helpers
     """
@@ -253,7 +104,7 @@ class AxolotlRewardTrainer(RngLoaderMixin, SchedulerMixin, RewardTrainer):
     tag_names = ["axolotl", "reward"]
 
 
-class AxolotlPRMTrainer(RngLoaderMixin, SchedulerMixin, PRMTrainer):
+class AxolotlPRMTrainer(SchedulerMixin, PRMTrainer):
     """
     Extend the base trl.PRMTrainer for axolotl helpers
     """

src/axolotl/core/training_args.py

@@ -5,7 +5,6 @@ extra axolotl specific training args
 from dataclasses import dataclass, field
 from typing import Optional
 
-from PIL.Image import Resampling
 from transformers import TrainingArguments
 from trl import CPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig
 
@@ -34,12 +33,6 @@ class AxolotlTrainingMixins:
         default=False,
         metadata={"help": "Use sample packing for efficient training."},
     )
-    sample_packing_sequentially: bool = field(
-        default=False,
-        metadata={
-            "help": "Use next-fit sample packing that preserves the order of samples coming from the sampler. Use in combination with curriculum_sampling for fully sequential packing."
-        },
-    )
     multipack_real_batches: bool = field(
         default=False,
         metadata={"help": "Use real batches for efficient training."},
@@ -219,20 +212,6 @@ class AxolotlTrainingMixins:
         metadata={"help": "The number of workers to use in sequence parallelism"},
     )
 
-    # multi-modal section
-
-    image_size: int | tuple[int, int] | None = field(
-        default=None,
-        metadata={"help": "The size of the image to resize to"},
-    )
-
-    image_resize_algorithm: Resampling | None = field(
-        default=None,
-        metadata={"help": "The algorithm to use for image resizing"},
-    )
-
-    # end of multi-modal section
-
 
 @dataclass
 class AxolotlTrainingArguments(AxolotlTrainingMixins, TrainingArguments):

src/axolotl/evaluate.py

@@ -15,7 +15,6 @@ from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
 from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.distributed import cleanup_distributed
 from axolotl.utils.models import load_model, load_processor, load_tokenizer
 from axolotl.utils.trainer import setup_trainer
 
@@ -160,6 +159,4 @@ def evaluate(*, cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> Dict[str, f
     del model
     del tokenizer
 
-    cleanup_distributed()
-
     return all_metrics

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -1,6 +1,6 @@
 # Cut Cross Entropy
 
-Cut Cross Entropy (CCE) reduces VRAM usage through optimization on the cross-entropy operation during loss calculation.
+Cut Cross Entropy reduces VRAM usage through optimization on the cross-entropy operation during loss calculation.
 
 See https://github.com/apple/ml-cross-entropy
 
@@ -29,20 +29,6 @@ plugins:
 cut_cross_entropy: true
 ```
 
-## Supported Models
-
-- llama
-- phi3
-- gemma
-- gemma2
-- gemma3
-- gemma3_text
-- mistral
-- mistral3
-- qwen2
-- cohere
-- cohere2
-
 ## Citation
 
 ```bib

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -25,8 +25,8 @@ import torch
 
 from axolotl.integrations.base import BasePlugin
 from axolotl.utils import get_pytorch_version
-from axolotl.utils.distributed import zero_only
 
+from ...utils.distributed import zero_only
 from .args import CutCrossEntropyArgs  # pylint: disable=unused-import. # noqa: F401
 
 LOG = logging.getLogger("axolotl.integrations.cut_cross_entropy")
@@ -72,9 +72,7 @@ class CutCrossEntropyPlugin(BasePlugin):
         if cfg.cut_cross_entropy:
             self._check_requirements()
 
-            from axolotl.integrations.cut_cross_entropy.monkeypatch.patch import (
-                cce_patch,
-            )
+            from cut_cross_entropy.transformers import cce_patch
 
             with zero_only():
                 LOG.info(

src/axolotl/integrations/cut_cross_entropy/monkeypatch/cohere.py

@@ -1,201 +0,0 @@
-"""Cohere and Cohere2 CCE patch."""
-
-# This patch is based off transformers 4.50.0.
-# It patches the forward function for CohereForCausalLM and Cohere2ForCausalLM.
-# It scales the hidden states by the logit scale in advance instead of the logits as the
-# operation is done internally and should be mathematically equivalent.
-
-# pylint: disable=duplicate-code
-
-from types import MethodType
-from typing import Optional, Tuple, Union
-
-import torch
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-    apply_lce,
-)
-from transformers.cache_utils import Cache
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.models.cohere.modeling_cohere import (
-    _CONFIG_FOR_DOC,
-    COHERE_INPUTS_DOCSTRING,
-    KwargsForCausalLM,
-)
-from transformers.processing_utils import Unpack
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
-from transformers.utils.deprecation import deprecate_kwarg
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(COHERE_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
-def cce_forward(
-    self,
-    input_ids: torch.LongTensor | None = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **kwargs: Unpack[KwargsForCausalLM],
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-    Returns:
-
-    Example:
-
-    ```python
-    >> from transformers import AutoTokenizer, CohereForCausalLM
-
-    >> model = CohereForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01")
-    >> tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01")
-
-    >> prompt = "Hey, are you conscious? Can you talk to me?"
-    >> inputs = tokenizer(prompt, return_tensors="pt")
-
-    >> # Generate
-    >> generate_ids = model.generate(inputs.input_ids, max_length=30)
-    >> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
-    ```"""
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        **kwargs,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-    slice_indices = (
-        slice(-logits_to_keep, None)
-        if isinstance(logits_to_keep, int)
-        else logits_to_keep
-    )
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        # scale weight by logit_scale in-place of logits
-        loss = apply_lce(
-            hidden_states[:, slice_indices, :],
-            self.lm_head.weight * self.logit_scale,
-            labels,
-            _PATCH_OPTS,
-            **kwargs,
-        )
-    else:
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-        logits = logits * self.logit_scale  # main diff from Llama
-
-        if labels is not None:
-            loss = self.loss_function(
-                logits=logits,
-                labels=labels,
-                vocab_size=self.config.vocab_size,
-                **kwargs,
-            )
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-def patch_cohere(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.cohere import modeling_cohere
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_cohere.CohereForCausalLM
-        ), f"Expected a CohereForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_cohere.CohereForCausalLM.forward = cce_forward
-    return None
-
-
-def patch_cohere2(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.cohere2 import modeling_cohere2
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_cohere2.Cohere2ForCausalLM
-        ), f"Expected a Cohere2ForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_cohere2.Cohere2ForCausalLM.forward = cce_forward
-    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/gemma.py

@@ -1,175 +0,0 @@
-"""Gemma CCE patch"""
-
-# This patch is based off transformers 4.50.0.
-
-# pylint: disable=duplicate-code
-
-from types import MethodType
-from typing import Optional, Tuple, Union
-
-import torch
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-    apply_lce,
-)
-from transformers.cache_utils import Cache
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.models.gemma.modeling_gemma import (
-    _CONFIG_FOR_DOC,
-    GEMMA_INPUTS_DOCSTRING,
-    KwargsForCausalLM,
-)
-from transformers.processing_utils import Unpack
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
-from transformers.utils.deprecation import deprecate_kwarg
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
-def cce_forward(
-    self,
-    input_ids: torch.LongTensor | None = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **kwargs: Unpack[KwargsForCausalLM],
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from transformers import AutoTokenizer, GemmaForCausalLM
-
-    >>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b")
-    >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
-
-    >>> prompt = "What is your favorite condiment?"
-    >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-    >>> # Generate
-    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    "What is your favorite condiment?"
-    ```"""
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        **kwargs,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-    slice_indices = (
-        slice(-logits_to_keep, None)
-        if isinstance(logits_to_keep, int)
-        else logits_to_keep
-    )
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        loss = apply_lce(
-            hidden_states[:, slice_indices, :],
-            self.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-            **kwargs,
-        )
-    else:
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-        if labels is not None:
-            loss = self.loss_function(
-                logits=logits,
-                labels=labels,
-                vocab_size=self.config.vocab_size,
-                **kwargs,
-            )
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-def patch_gemma(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.gemma import modeling_gemma
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_gemma.GemmaForCausalLM
-        ), f"Expected a GemmaForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_gemma.GemmaForCausalLM.forward = cce_forward
-    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/gemma3.py

@@ -1,459 +0,0 @@
-"""Gemma2 and Gemma3 (text and multimodal) CCE patch."""
-
-# Implementation originally adapted from https://github.com/apple/ml-cross-entropy/pull/29
-# and updated for transformers 4.50.0.
-# This is a modified version of the patch that allows for deferred logits calculation for gemma3 and works
-# with both gemma3 (text and multimodal) models.
-
-# pylint: disable=duplicate-code
-
-from types import MethodType
-from typing import Optional, Tuple, Union
-
-import torch
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-)
-from torch import nn
-from transformers.cache_utils import Cache, HybridCache
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.models.gemma3.modeling_gemma3 import (
-    _CONFIG_FOR_DOC,
-    GEMMA3_INPUTS_DOCSTRING,
-    Gemma3CausalLMOutputWithPast,
-    logger,
-)
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    is_torchdynamo_compiling,
-    replace_return_docstrings,
-)
-from transformers.utils.deprecation import deprecate_kwarg
-
-from axolotl.integrations.cut_cross_entropy.monkeypatch.utils import apply_lce
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
-def cce_forward(
-    self,
-    input_ids: torch.LongTensor | None = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[HybridCache] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    defer_logits_calculation: bool = False,
-    **loss_kwargs,
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-        defer_logits_calculation (`bool`, *optional*):
-            If `True`, defer logits calculation to the ConditionalGeneration forward. This is used to avoid the
-            memory overhead of calculating logits using regular lm_head forward pass and to use CCE.
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from transformers import AutoTokenizer, Gemma3ForCausalLM
-
-    >>> model = Gemma3ForCausalLM.from_pretrained("google/gemma-2-9b")
-    >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
-
-    >>> prompt = "What is your favorite condiment?"
-    >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-    >>> # Generate
-    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    "What is your favorite condiment?"
-    ```"""
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        **loss_kwargs,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-    slice_indices = (
-        slice(-logits_to_keep, None)
-        if isinstance(logits_to_keep, int)
-        else logits_to_keep
-    )
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        loss = apply_lce(
-            hidden_states[:, slice_indices, :],
-            self.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-            softcap=getattr(self.config, "final_logit_softcapping", None),
-            **loss_kwargs,
-        )
-    elif _PATCH_OPTS is not None and defer_logits_calculation:
-        # defer logits calculation to the ConditionalGeneration forward
-        logits = hidden_states[:, slice_indices, :]
-    else:
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-        if self.config.final_logit_softcapping is not None:
-            logits = logits / self.config.final_logit_softcapping
-            logits = torch.tanh(logits)
-            logits = logits * self.config.final_logit_softcapping
-
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=Gemma3CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
-def cce_forward_multimodal(
-    self,
-    input_ids: torch.LongTensor | None = None,
-    pixel_values: torch.FloatTensor | None = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
-    token_type_ids: Optional[torch.LongTensor] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **lm_kwargs,
-) -> Union[Tuple, Gemma3CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from PIL import Image
-    >>> import requests
-    >>> from transformers import AutoProcessor, Gemma3ForConditionalGeneration
-
-    >>> model = Gemma3ForConditionalGeneration.from_pretrained("google/Gemma3-test-224px-hf")
-    >>> processor = AutoProcessor.from_pretrained("google/Gemma3-test-224px-hf")
-
-    >>> prompt = "answer en Where is the cow standing?"
-    >>> url = "https://huggingface.co/gv-hf/Gemma3-test-224px-hf/resolve/main/cow_beach_1.png"
-    >>> image = Image.open(requests.get(url, stream=True).raw)
-
-    >>> inputs = processor(images=image, text=prompt,  return_tensors="pt")
-
-    >>> # Generate
-    >>> generate_ids = model.generate(**inputs, max_length=30)
-    >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    "answer en Where is the cow standing?\nbeach"
-    ```"""
-
-    if (input_ids is None) ^ (inputs_embeds is not None):
-        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    is_training = token_type_ids is not None and labels is not None
-
-    # Replace image id woth PAD if the image token if OOV, to avoid index-errors
-    if input_ids is not None and self.config.image_token_index >= self.vocab_size:
-        special_image_mask = input_ids == self.config.image_token_index
-        llm_input_ids = input_ids.clone()
-        llm_input_ids[special_image_mask] = 0
-    else:
-        llm_input_ids = input_ids  # type: ignore
-
-    if inputs_embeds is None:
-        inputs_embeds = self.get_input_embeddings()(llm_input_ids)
-
-    if cache_position is None:
-        past_seen_tokens = (
-            past_key_values.get_seq_length() if past_key_values is not None else 0  # type: ignore
-        )
-        cache_position = torch.arange(  # type: ignore
-            past_seen_tokens,
-            past_seen_tokens + inputs_embeds.shape[1],
-            device=inputs_embeds.device,
-        )
-
-    # Merge text and images
-    if pixel_values is not None:
-        image_features = self.get_image_features(pixel_values)
-
-        if input_ids is None:
-            special_image_mask = inputs_embeds == self.get_input_embeddings()(
-                torch.tensor(
-                    self.config.image_token_index,
-                    dtype=torch.long,
-                    device=inputs_embeds.device,
-                )
-            )
-        else:
-            special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(
-                -1
-            )
-            special_image_mask = special_image_mask.expand_as(inputs_embeds).to(
-                inputs_embeds.device
-            )
-
-        if (
-            not is_torchdynamo_compiling()
-            and inputs_embeds[special_image_mask].numel() != image_features.numel()
-        ):
-            image_tokens_in_text = (special_image_mask).sum(dim=1).sum(dim=0)[0]
-            raise ValueError(
-                f"Number of images does not match number of special image tokens in the input text. "
-                f"Got {image_tokens_in_text} image tokens in the text but {image_features.shape[0] * image_features.shape[1]} "
-                "tokens from image embeddings."
-            )
-        image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
-        inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)  # type: ignore
-
-    # mask out pad-token-ids in labels for BC
-    if labels is not None and self.pad_token_id in labels:
-        logger.warning_once(
-            "`labels` contains `pad_token_id` which will be masked with `config.ignore_index`. "
-            "You have to mask out `pad_token_id` when preparing `labels`, this behavior will be removed in v.4.46.",
-        )
-        labels = torch.where(  # type: ignore
-            input_ids == self.pad_token_id, self.config.ignore_index, labels
-        )
-
-    causal_mask = self._update_causal_mask(  # pylint: disable=protected-access
-        attention_mask,
-        token_type_ids,
-        past_key_values,
-        cache_position,
-        inputs_embeds,
-        is_training,
-    )
-    outputs = self.language_model(
-        attention_mask=causal_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        logits_to_keep=logits_to_keep,
-        defer_logits_calculation=True,  # enable deferred logits calculation
-        **lm_kwargs,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        loss = apply_lce(
-            hidden_states,
-            self.language_model.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-            softcap=getattr(self.config, "final_logit_softcapping", None),
-            **lm_kwargs,
-        )
-    else:
-        logits = hidden_states
-        if labels is not None:
-            # Upcast to float if we need to compute the loss to avoid potential precision issues
-            logits = logits.float()
-            shift_logits = logits[..., :-1, :]
-            shift_labels = labels[..., 1:]
-            if attention_mask is not None:
-                # we use the input attention mask to shift the logits and labels, because it is 2D.
-                # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
-                shift_attention_mask = attention_mask[:, -shift_logits.shape[1] :].to(
-                    logits.device
-                )
-                shift_logits = shift_logits[
-                    shift_attention_mask.to(logits.device) != 0
-                ].contiguous()
-                shift_labels = shift_labels[
-                    shift_attention_mask.to(shift_labels.device) != 0
-                ].contiguous()
-            else:
-                shift_logits = shift_logits.contiguous()
-                shift_labels = shift_labels.contiguous()
-            # Flatten the tokens
-            loss_fct = nn.CrossEntropyLoss()
-
-            flat_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
-            flat_labels = shift_labels.view(-1).to(shift_logits.device)
-            loss = loss_fct(flat_logits, flat_labels)
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return Gemma3CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-        image_hidden_states=image_features if pixel_values is not None else None,
-    )
-
-
-def patch_gemma2(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.gemma2 import modeling_gemma2
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_gemma2.Gemma2ForCausalLM
-        ), f"Expected a Gemma2ForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_gemma2.Gemma2ForCausalLM.forward = cce_forward
-    return None
-
-
-def patch_gemma3_text(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.gemma3 import modeling_gemma3
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_gemma3.Gemma3ForCausalLM
-        ), f"Expected a Gemma3ForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_gemma3.Gemma3ForCausalLM.forward = cce_forward
-    return None
-
-
-def patch_gemma3(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.gemma3 import modeling_gemma3
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_gemma3.Gemma3ForConditionalGeneration
-        ), f"Expected a Gemma3ForConditionalGeneration model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward_multimodal, maybe_model)
-
-        # patch the causal model to enable deferred logits calculation
-        maybe_model.language_model.forward = MethodType(
-            cce_forward, maybe_model.language_model
-        )
-        return maybe_model
-
-    modeling_gemma3.Gemma3ForConditionalGeneration.forward = cce_forward_multimodal
-    # patch the causal model to enable deferred logits calculation
-    modeling_gemma3.Gemma3ForCausalLM.forward = cce_forward
-    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/mistral3.py

@@ -1,392 +0,0 @@
-"""Mistral and Mistral3 CCE patch."""
-
-# pylint: disable=duplicate-code
-
-from types import MethodType
-from typing import Optional, Tuple, Union
-
-import torch
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-    apply_lce,
-)
-from torch import nn
-from transformers.cache_utils import Cache
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.models.mistral3.modeling_mistral3 import (
-    Mistral3CausalLMOutputWithPast,
-)
-from transformers.models.mistral.modeling_mistral import (
-    _CONFIG_FOR_DOC,
-    MISTRAL_INPUTS_DOCSTRING,
-    KwargsForCausalLM,
-)
-from transformers.processing_utils import Unpack
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    is_torchdynamo_compiling,
-    replace_return_docstrings,
-)
-from transformers.utils.deprecation import deprecate_kwarg
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
-def cce_forward(
-    self,
-    input_ids: torch.LongTensor | None = None,
-    attention_mask: Optional[torch.Tensor] | None = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    defer_logits_calculation: bool = False,
-    **kwargs: Unpack[KwargsForCausalLM],
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-        defer_logits_calculation (`bool`, *optional*):
-            If `True`, defer logits calculation to the ConditionalGeneration forward. This is used to avoid the
-            memory overhead of calculating logits using regular lm_head forward pass and to use CCE.
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from transformers import AutoTokenizer, MistralForCausalLM
-
-    >>> model = MistralForCausalLM.from_pretrained("meta-mistral/Mistral-2-7b-hf")
-    >>> tokenizer = AutoTokenizer.from_pretrained("meta-mistral/Mistral-2-7b-hf")
-
-    >>> prompt = "Hey, are you conscious? Can you talk to me?"
-    >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-    >>> # Generate
-    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
-    ```"""
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        **kwargs,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-    slice_indices = (
-        slice(-logits_to_keep, None)
-        if isinstance(logits_to_keep, int)
-        else logits_to_keep
-    )
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        loss = apply_lce(
-            hidden_states[:, slice_indices, :],
-            self.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-            **kwargs,
-        )
-    elif _PATCH_OPTS is not None and defer_logits_calculation:
-        # defer logits calculation to the ConditionalGeneration forward
-        logits = hidden_states[:, slice_indices, :]
-    else:
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-        if labels is not None:
-            loss = self.loss_function(
-                logits=logits,
-                labels=labels,
-                vocab_size=self.config.vocab_size,
-                **kwargs,
-            )
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-def cce_forward_multimodal(
-    self,
-    input_ids: torch.LongTensor | None = None,
-    pixel_values: torch.FloatTensor | None = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[list[torch.FloatTensor]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    vision_feature_layer: Optional[Union[int, list[int]]] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    image_sizes: torch.Tensor | None = None,
-    **lm_kwargs,
-) -> Union[Tuple, Mistral3CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from PIL import Image
-    >>> import requests
-    >>> from transformers import AutoProcessor, Mistral3ForConditionalGeneration
-
-    >>> model = Mistral3ForConditionalGeneration.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
-    >>> processor = AutoProcessor.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
-
-    >>> prompt = "<s>[INST][IMG]What is the image?[/INST]"
-    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    >>> image = Image.open(requests.get(url, stream=True).raw)
-
-    >>> inputs = processor(images=image, text=prompt, return_tensors="pt")
-
-    >>> # Generate
-    >>> generate_ids = model.generate(**inputs, max_new_tokens=15)
-    >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    "What is the image?The image depicts two cats lying on a pink blanket."
-    ```"""
-
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-    vision_feature_layer = (
-        vision_feature_layer
-        if vision_feature_layer is not None
-        else self.config.vision_feature_layer
-    )
-
-    if (input_ids is None) ^ (inputs_embeds is not None):
-        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-    if pixel_values is not None and inputs_embeds is not None:
-        raise ValueError(
-            "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
-        )
-
-    if inputs_embeds is None:
-        inputs_embeds = self.get_input_embeddings()(input_ids)
-
-    if pixel_values is not None:
-        image_features = self.get_image_features(
-            pixel_values=pixel_values,
-            vision_feature_layer=vision_feature_layer,
-            image_sizes=image_sizes,
-        )
-
-        special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1)
-        special_image_mask = special_image_mask.expand_as(inputs_embeds).to(
-            inputs_embeds.device
-        )
-        if (
-            not is_torchdynamo_compiling()
-            and inputs_embeds[special_image_mask].numel() != image_features.numel()
-        ):
-            n_image_tokens = (input_ids == self.config.image_token_index).sum()
-            n_image_features = image_features.shape[0] * image_features.shape[1]
-            raise ValueError(
-                f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
-            )
-        image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
-        inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)  # type: ignore
-
-    outputs = self.language_model(
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        logits_to_keep=logits_to_keep,
-        defer_logits_calculation=True,  # enable deferred logits calculation
-        **lm_kwargs,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        loss = apply_lce(
-            hidden_states,
-            self.language_model.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-            **lm_kwargs,
-        )
-    else:
-        logits = hidden_states
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            if attention_mask is not None:
-                # we use the input attention mask to shift the logits and labels, because it is 2D.
-                # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
-                shift_attention_mask = attention_mask[:, -(logits.shape[1] - 1) :].to(
-                    logits.device
-                )
-                shift_logits = logits[..., :-1, :][
-                    shift_attention_mask.to(logits.device) != 0
-                ].contiguous()
-                shift_labels = labels[..., 1:][
-                    shift_attention_mask.to(labels.device) != 0
-                ].contiguous()
-            else:
-                shift_logits = logits[..., :-1, :].contiguous()
-                shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = nn.CrossEntropyLoss()
-            loss = loss_fct(
-                shift_logits.view(-1, shift_logits.size(-1)),
-                shift_labels.view(-1).to(shift_logits.device),
-            )
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return Mistral3CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-        image_hidden_states=image_features if pixel_values is not None else None,
-    )
-
-
-def patch_mistral(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.mistral import modeling_mistral
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_mistral.MistralForCausalLM
-        ), f"Expected a MistralForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_mistral.MistralForCausalLM.forward = cce_forward
-    return None
-
-
-def patch_mistral3(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.mistral import modeling_mistral
-    from transformers.models.mistral3 import modeling_mistral3
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_mistral3.Mistral3ForConditionalGeneration
-        ), f"Expected a Mistral3ForConditionalGeneration model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward_multimodal, maybe_model)
-
-        # patch the causal model to enable deferred logits calculation
-        maybe_model.language_model.forward = MethodType(
-            cce_forward, maybe_model.language_model
-        )
-        return maybe_model
-
-    modeling_mistral3.Mistral3ForConditionalGeneration.forward = cce_forward_multimodal
-    # patch the causal model to enable deferred logits calculation
-    modeling_mistral.MistralForCausalLM.forward = cce_forward
-    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/mllama.py

@@ -1,379 +0,0 @@
-"""Mllama CCE patch."""
-
-# pylint: disable=duplicate-code
-
-from types import MethodType
-from typing import Optional, Tuple, Union
-
-import torch
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-    apply_lce,
-)
-from transformers.cache_utils import Cache
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.models.mllama.modeling_mllama import (
-    MLLAMA_INPUTS_DOCSTRING,
-    _prepare_cross_attention_mask,
-)
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
-from transformers.utils.deprecation import deprecate_kwarg
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(MLLAMA_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class="MllamaTextConfig"
-)
-def cce_forward(
-    self,
-    input_ids: torch.LongTensor | None = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    cross_attention_states: Optional[torch.LongTensor] = None,
-    cross_attention_mask: Optional[torch.LongTensor] = None,
-    full_text_row_masked_out_mask: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    defer_logits_calculation: bool = False,
-    **loss_kwargs,
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-        defer_logits_calculation (`bool`, *optional*):
-            If `True`, defer logits calculation to the ConditionalGeneration forward. This is used to avoid the
-            memory overhead of calculating logits using regular lm_head forward pass and to use CCE.
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from transformers import AutoTokenizer, MllamaForCausalLM
-
-    >>> model = MllamaForCausalLM.from_pretrained("Llama-3.2-11B-Vision")
-    >>> tokenizer = AutoTokenizer.from_pretrained("Llama-3.2-11B-Vision")
-
-    >>> prompt = "If I had to write a haiku, it would be:"
-    >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-    >>> # Generate
-    >>> generate_ids = model.generate(inputs.input_ids, max_length=40, do_sample=True, temperature=0.6)
-    >>> result = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    >>> print(result)
-    If I had to write a haiku, it would be: "Snowflakes gently fall" - simple, yet peaceful.
-    I love the idea of snowflakes gently falling, each one
-    ```
-    """
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        cross_attention_states=cross_attention_states,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        cross_attention_mask=cross_attention_mask,
-        full_text_row_masked_out_mask=full_text_row_masked_out_mask,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    slice_indices = (
-        slice(-logits_to_keep, None)
-        if isinstance(logits_to_keep, int)
-        else logits_to_keep
-    )
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        loss = apply_lce(
-            hidden_states[:, slice_indices, :],
-            self.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-            **loss_kwargs,
-        )
-    elif _PATCH_OPTS is not None and defer_logits_calculation:
-        # defer logits calculation to the ConditionalGeneration forward
-        logits = hidden_states[:, slice_indices, :]
-    else:
-        logits = self.lm_head(hidden_states[:, slice_indices, :]).float()
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(MLLAMA_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class="MllamaConfig"
-)
-def cce_forward_multimodal(
-    self,
-    input_ids: Optional[torch.LongTensor] = None,
-    pixel_values: Optional[torch.FloatTensor] = None,
-    aspect_ratio_mask: Optional[torch.Tensor] = None,
-    aspect_ratio_ids: Optional[torch.Tensor] = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    cross_attention_mask: Optional[torch.Tensor] = None,
-    cross_attention_states: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[list[torch.FloatTensor]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **loss_kwargs,
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from PIL import Image
-    >>> import requests
-    >>> from transformers import AutoProcessor, MllamaForConditionalGeneration
-
-    >>> checkpoint = "meta-llama/Llama-3.2-11B-Vision"
-    >>> model = MllamaForConditionalGeneration.from_pretrained(checkpoint)
-    >>> processor = AutoProcessor.from_pretrained(checkpoint)
-
-    >>> prompt = "<|image|>If I had to write a haiku for this one"
-    >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
-    >>> image = Image.open(requests.get(url, stream=True).raw)
-
-    >>> inputs = processor(text=prompt, images=image, return_tensors="pt")
-
-    >>> # Generate
-    >>> output = model.generate(**inputs, max_new_tokens=15)
-
-    >>> prompt_len = inputs.input_ids.shape[-1]
-    >>> generated_ids = output[:, prompt_len:]
-    >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
-    >>> print(generated_text)
-    [', it would be:.\\nA stop sign in Chinatown.\\n']
-    ```
-    """
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    if (input_ids is None) ^ (inputs_embeds is not None):
-        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-    if pixel_values is not None and inputs_embeds is not None:
-        raise ValueError(
-            "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
-        )
-
-    if pixel_values is not None and cross_attention_states is not None:
-        raise ValueError(
-            "`pixel_values` and `cross_attention_states` cannot be provided simultaneously"
-        )
-
-    if pixel_values is not None:
-        if aspect_ratio_ids is None:
-            raise ValueError(
-                "`aspect_ratio_ids` must be provided if `pixel_values` is provided"
-            )
-        # get vision tokens from vision model
-        vision_outputs = self.vision_model(
-            pixel_values=pixel_values,
-            aspect_ratio_ids=aspect_ratio_ids,
-            aspect_ratio_mask=aspect_ratio_mask,
-            output_hidden_states=output_hidden_states,
-            output_attentions=output_attentions,
-            return_dict=return_dict,
-        )
-        cross_attention_states = vision_outputs[0]
-        cross_attention_states = self.multi_modal_projector(
-            cross_attention_states
-        ).reshape(
-            -1, cross_attention_states.shape[-2], self.hidden_size  # type: ignore
-        )
-
-    if cross_attention_mask is not None:
-        cross_attention_mask, full_text_row_masked_out_mask = (
-            _prepare_cross_attention_mask(
-                cross_attention_mask,
-                num_vision_tokens=self.vision_model.num_patches,
-                dtype=self.dtype,
-            )
-        )
-    else:
-        full_text_row_masked_out_mask = None
-
-    if cross_attention_mask is not None and cache_position is not None:
-        cross_attention_mask = cross_attention_mask[:, :, cache_position]
-        full_text_row_masked_out_mask = full_text_row_masked_out_mask[
-            :, :, cache_position
-        ]
-
-    outputs = self.language_model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        cross_attention_states=cross_attention_states,
-        cross_attention_mask=cross_attention_mask,
-        full_text_row_masked_out_mask=full_text_row_masked_out_mask,
-        past_key_values=past_key_values,
-        use_cache=use_cache,
-        inputs_embeds=inputs_embeds,
-        output_hidden_states=output_hidden_states,
-        output_attentions=output_attentions,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        logits_to_keep=logits_to_keep,
-        defer_logits_calculation=True,  # enable deferred logits calculation
-        **loss_kwargs,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        loss = apply_lce(
-            hidden_states,
-            self.language_model.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-            **loss_kwargs,
-        )
-    else:
-        # Temporary fix to calculate the loss in main class, as the model's vocab size may be resized
-        logits = hidden_states
-
-        if labels is not None:
-            loss = self.loss_function(
-                logits, labels, self.config.get_text_config().vocab_size, **loss_kwargs
-            )
-
-    if not return_dict:
-        return (loss,) + outputs if loss is not None else outputs
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=outputs.logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-def patch_mllama(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.mllama import modeling_mllama
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_mllama.MllamaForConditionalGeneration
-        ), f"Expected a MllamaForConditionalGeneration model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward_multimodal, maybe_model)
-
-        # patch the language model
-        maybe_model.language_model.forward = MethodType(
-            cce_forward, maybe_model.language_model
-        )
-        return maybe_model
-
-    modeling_mllama.MllamaForConditionalGeneration.forward = cce_forward_multimodal
-
-    # patch the causal language model
-    modeling_mllama.MllamaForCausalLM.forward = cce_forward
-    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/patch.py

@@ -1,85 +0,0 @@
-# Copyright (C) 2024 Apple Inc. All Rights Reserved.
-
-"""Cut Cross Entropy patcher"""
-
-import transformers
-from cut_cross_entropy.cce_utils import LinearCrossEntropyImpl
-from cut_cross_entropy.linear_cross_entropy import LCE_IMPL_DEFAULT
-from cut_cross_entropy.transformers.llama import patch_llama
-from cut_cross_entropy.transformers.phi3 import patch_phi3
-from cut_cross_entropy.transformers.qwen2 import patch_qwen2
-from cut_cross_entropy.transformers.utils import PatchOptions, TransformersModelT
-
-from axolotl.integrations.cut_cross_entropy.monkeypatch.cohere import (
-    patch_cohere,
-    patch_cohere2,
-)
-from axolotl.integrations.cut_cross_entropy.monkeypatch.gemma import patch_gemma
-from axolotl.integrations.cut_cross_entropy.monkeypatch.gemma3 import (
-    patch_gemma2,
-    patch_gemma3,
-    patch_gemma3_text,
-)
-from axolotl.integrations.cut_cross_entropy.monkeypatch.mistral3 import (
-    patch_mistral,
-    patch_mistral3,
-)
-from axolotl.integrations.cut_cross_entropy.monkeypatch.mllama import patch_mllama
-
-CUT_CROSS_ENTROPY_MODEL_MAPPING = {
-    "llama": patch_llama,
-    "mllama": patch_mllama,
-    "phi3": patch_phi3,
-    "gemma": patch_gemma,
-    "gemma2": patch_gemma2,
-    "gemma3": patch_gemma3,
-    "gemma3_text": patch_gemma3_text,
-    "mistral": patch_mistral,
-    "mistral3": patch_mistral3,
-    "qwen2": patch_qwen2,
-    "cohere": patch_cohere,
-    "cohere2": patch_cohere2,
-}
-
-
-def cce_patch(
-    model_type_or_model: str | TransformersModelT | transformers.PretrainedConfig,
-    impl: str | LinearCrossEntropyImpl = LCE_IMPL_DEFAULT,
-    reduction: str = "mean",
-    filter_eps: float | str | None = "auto",
-    accum_e_fp32: bool = False,
-    accum_c_fp32: bool = False,
-    filter_e_grad: bool = True,
-    filter_c_grad: bool = True,
-    train_only: bool = False,
-) -> TransformersModelT | None:
-    if isinstance(impl, LinearCrossEntropyImpl):
-        impl = impl.name.lower()
-
-    if impl not in (v.name.lower() for v in LinearCrossEntropyImpl):
-        raise ValueError(f"Unknown {impl=}")
-
-    if isinstance(model_type_or_model, transformers.PreTrainedModel):
-        model_type = model_type_or_model.config.model_type
-    elif isinstance(model_type_or_model, transformers.PretrainedConfig):
-        model_type = model_type_or_model.model_type
-    else:
-        model_type = model_type_or_model
-
-    patch_options = PatchOptions(
-        impl=impl,
-        reduction=reduction,
-        filter_eps=filter_eps,
-        accum_e_fp32=accum_e_fp32,
-        accum_c_fp32=accum_c_fp32,
-        filter_e_grad=filter_e_grad,
-        filter_c_grad=filter_c_grad,
-        train_only=train_only,
-    )
-
-    if model_type in CUT_CROSS_ENTROPY_MODEL_MAPPING:
-        return CUT_CROSS_ENTROPY_MODEL_MAPPING[model_type](
-            model_type_or_model, patch_options
-        )
-
-    raise RuntimeError(f"Unknown model type {model_type}")

src/axolotl/integrations/cut_cross_entropy/monkeypatch/utils.py

@@ -1,40 +0,0 @@
-# Copyright (C) 2024 Apple Inc. All Rights Reserved.
-
-"""Monkeypatch for apply_lce to add softcap."""
-
-import torch
-from cut_cross_entropy import linear_cross_entropy
-from cut_cross_entropy.transformers.utils import PatchOptions
-
-
-def apply_lce(
-    e: torch.Tensor,
-    c: torch.Tensor,
-    labels: torch.Tensor,
-    opts: PatchOptions,
-    bias: torch.Tensor | None = None,
-    softcap: float | None = None,
-    **loss_kwargs,
-) -> torch.Tensor:
-    """Monkey patch for apply_lce to support softcap kwarg."""
-    num_items_in_batch = loss_kwargs.get("num_items_in_batch", None)
-    cce_kwargs = opts.to_kwargs()
-    if num_items_in_batch is not None and cce_kwargs["reduction"] == "mean":
-        cce_kwargs["reduction"] = "sum"
-    else:
-        num_items_in_batch = None
-
-    loss = linear_cross_entropy(
-        e,
-        c,
-        labels.to(e.device),
-        bias=bias,
-        shift=True,
-        softcap=softcap,
-        **cce_kwargs,
-    )
-
-    if num_items_in_batch is not None:
-        loss = loss / num_items_in_batch
-
-    return loss

src/axolotl/integrations/liger/README.md

@@ -20,26 +20,6 @@ liger_layer_norm: true
 liger_fused_linear_cross_entropy: true
 ```
 
-## Supported Models
-
-- deepseek_v2
-- gemma
-- gemma2
-- gemma3 (partial support, no support for FLCE yet)
-- granite
-- jamba
-- llama
-- mistral
-- mixtral
-- mllama
-- mllama_text_model
-- olmo2
-- paligemma
-- phi3
-- qwen2
-- qwen2_5_vl
-- qwen2_vl
-
 ## Citation
 
 ```bib

src/axolotl/integrations/liger/__init__.py

@@ -21,7 +21,6 @@ It is designed to be performant, correct, and light-weight.
 import inspect
 import logging
 import sys
-from functools import partial
 
 from axolotl.integrations.base import BasePlugin
 
@@ -42,18 +41,11 @@ class LigerPlugin(BasePlugin):
     def pre_model_load(self, cfg):
         from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
         from liger_kernel.transformers.functional import liger_cross_entropy
-        from liger_kernel.transformers.geglu import LigerGEGLUMLP
-        from liger_kernel.transformers.layer_norm import LigerLayerNorm
         from liger_kernel.transformers.monkey_patch import MODEL_TYPE_TO_APPLY_LIGER_FN
         from liger_kernel.transformers.rms_norm import LigerRMSNorm
         from liger_kernel.transformers.rope import liger_rotary_pos_emb
         from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
 
-        if cfg.liger_cross_entropy and cfg.liger_fused_linear_cross_entropy:
-            raise ValueError(
-                "Cannot have both `liger_cross_entropy` and `liger_fused_linear_cross_entropy` set."
-            )
-
         if cfg.model_config_type in MODEL_TYPE_TO_APPLY_LIGER_FN:
             apply_liger_fn = MODEL_TYPE_TO_APPLY_LIGER_FN[cfg.model_config_type]
             liger_fn_sig = inspect.signature(apply_liger_fn)
@@ -90,8 +82,6 @@ class LigerPlugin(BasePlugin):
                 modeling_jamba.JambaRMSNorm = LigerRMSNorm
             if cfg.liger_glu_activation:
                 modeling_jamba.JambaMLP = LigerSwiGLUMLP
-            if cfg.liger_layer_norm:
-                modeling_jamba.nn.LayerNorm = LigerLayerNorm
             if cfg.liger_cross_entropy:
                 from transformers.loss.loss_utils import nn
 
@@ -114,51 +104,13 @@ class LigerPlugin(BasePlugin):
                 # The DeepseekV2 version of RoPE is different than upstream LLaMA.
                 # See https://github.com/linkedin/Liger-Kernel/issues/129#issuecomment-2313763528
                 logging.warning("Fused liger_rope is not supported for DeepseekV2.")
-            if cfg.liger_glu_activation:
-                logging.warning("liger_glu_activation is not supported for DeepseekV2.")
             if cfg.liger_rms_norm:
                 modeling_mod.DeepseekV2RMSNorm = LigerRMSNorm
             if cfg.liger_glu_activation:
                 modeling_mod.DeepseekV2MLP.forward = LigerSwiGLUMLP.forward
-            if cfg.liger_layer_norm:
-                modeling_mod.DeepseekV2MLP.forward = LigerLayerNorm.forward
             if cfg.liger_cross_entropy:
                 # We do not patch `nn.functional.cross_entropy` for DeepseekV2 as it still uses
                 # nn.CrossEntropyLoss in the forward method.
                 modeling_mod.CrossEntropyLoss = LigerCrossEntropyLoss
             if cfg.liger_fused_linear_cross_entropy:
                 modeling_mod.DeepseekV2ForCausalLM.forward = deepseekv2_lce_forward
-        elif cfg.model_config_type in ["gemma3", "gemma3_text"]:
-            from transformers.models.gemma3 import modeling_gemma3
-
-            if cfg.liger_rope:
-                modeling_gemma3.apply_rotary_pos_emb = liger_rotary_pos_emb
-            if cfg.liger_rms_norm:
-
-                def _liger_rms_norm_wrapper(dim, **kwargs):
-                    "Convert 'dim' keyword to 'hidden_size' to pass to LigerRMSNorm"
-                    return LigerRMSNorm(hidden_size=dim, **kwargs)
-
-                modeling_gemma3.Gemma3RMSNorm = partial(
-                    _liger_rms_norm_wrapper,
-                    offset=1.0,
-                    casting_mode="gemma",
-                    init_fn="zeros",
-                    in_place=False,
-                )
-            if cfg.liger_glu_activation:
-                modeling_gemma3.Gemma3MLP = LigerGEGLUMLP
-            if cfg.liger_layer_norm:
-                modeling_gemma3.nn.LayerNorm = LigerLayerNorm
-
-            if cfg.liger_cross_entropy:
-                from transformers.loss.loss_utils import nn
-
-                nn.functional.cross_entropy = liger_cross_entropy
-
-            if cfg.liger_fused_linear_cross_entropy:
-                raise NotImplementedError(
-                    "Fused linear cross entropy is not yet supported for Gemma3."
-                )
-        elif cfg.model_config_type in ["deepseek_v3"]:
-            raise ValueError(f"Unsupported model config type: {cfg.model_config_type}")

src/axolotl/monkeypatch/attention/ring_attn.py

@@ -27,7 +27,7 @@ def get_ring_attn_group() -> dist.ProcessGroup:
     return RING_ATTN_GROUP
 
 
-def set_ring_attn_group(ring_attn_group: dist.ProcessGroup | None):
+def set_ring_attn_group(ring_attn_group: dist.ProcessGroup):
     """
     Setter for ring attention group on this rank.
 
@@ -38,19 +38,13 @@ def set_ring_attn_group(ring_attn_group: dist.ProcessGroup | None):
     RING_ATTN_GROUP = ring_attn_group
 
 
-def register_ring_attn(sequence_parallel_degree: int, heads_k_stride: int | None):
+def register_ring_attn(sequence_parallel_degree: int):
     """
     Create ring attention group and substitute flash attn with ring flash attn.
 
     Args:
         sequence_parallel_degree: Sequence parallelism factor.
-        heads_k_stride: Sequence parallelism K head stride size. Passed
-            through to `ring_flash_attn.substitute_hf_flash_attn`.
     """
-    if get_ring_attn_group() is not None:
-        LOG.info("Ring attention already registered, exiting early...")
-        return
-
     LOG.info(
         "Enabling ring attention sequence parallelism: "
         f"each sequence will be processed across {sequence_parallel_degree} GPUs"
@@ -90,11 +84,6 @@ def register_ring_attn(sequence_parallel_degree: int, heads_k_stride: int | None
     if rank == 0:
         LOG.info(f"Sequence parallel group assignments: {group_assignments}")
 
-    if heads_k_stride is None:
-        heads_k_stride = 1
-
     from ring_flash_attn import substitute_hf_flash_attn
 
-    substitute_hf_flash_attn(
-        process_group=get_ring_attn_group(), heads_k_stride=heads_k_stride
-    )
+    substitute_hf_flash_attn(get_ring_attn_group(), sequence_parallel_degree)

src/axolotl/monkeypatch/gemma3.py

@@ -1,238 +0,0 @@
-"""Monkeypatch for gemma3 conditional generation forward to fix loss exploding"""
-
-# pylint: disable=duplicate-code
-
-from typing import Optional, Tuple, Union
-
-import torch
-from transformers.cache_utils import Cache
-from transformers.models.gemma3.modeling_gemma3 import (
-    _CONFIG_FOR_DOC,
-    GEMMA3_INPUTS_DOCSTRING,
-    Gemma3CausalLMOutputWithPast,
-    logger,
-)
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    is_torchdynamo_compiling,
-    replace_return_docstrings,
-)
-from transformers.utils.deprecation import deprecate_kwarg
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=Gemma3CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
-def new_forward(
-    self,
-    input_ids: torch.LongTensor = None,
-    pixel_values: torch.FloatTensor = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
-    token_type_ids: Optional[torch.LongTensor] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **lm_kwargs,
-) -> Union[Tuple, Gemma3CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from PIL import Image
-    >>> import requests
-    >>> from transformers import AutoProcessor, Gemma3ForConditionalGeneration
-
-    >>> model = Gemma3ForConditionalGeneration.from_pretrained("google/Gemma3-test-224px-hf")
-    >>> processor = AutoProcessor.from_pretrained("google/Gemma3-test-224px-hf")
-
-    >>> prompt = "answer en Where is the cow standing?"
-    >>> url = "https://huggingface.co/gv-hf/Gemma3-test-224px-hf/resolve/main/cow_beach_1.png"
-    >>> image = Image.open(requests.get(url, stream=True).raw)
-
-    >>> inputs = processor(images=image, text=prompt,  return_tensors="pt")
-
-    >>> # Generate
-    >>> generate_ids = model.generate(**inputs, max_length=30)
-    >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    "answer en Where is the cow standing?\nbeach"
-    ```"""
-
-    if (input_ids is None) ^ (inputs_embeds is not None):
-        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    is_training = token_type_ids is not None and labels is not None
-
-    # Replace image id with PAD if the image token is OOV, to avoid index-errors
-    if input_ids is not None and self.config.image_token_index >= self.vocab_size:
-        special_image_mask = input_ids == self.config.image_token_index
-        llm_input_ids = input_ids.clone()
-        llm_input_ids[special_image_mask] = 0
-    else:
-        llm_input_ids = input_ids
-
-    if inputs_embeds is None:
-        inputs_embeds = self.get_input_embeddings()(llm_input_ids)
-
-    if cache_position is None:
-        past_seen_tokens = (
-            past_key_values.get_seq_length() if past_key_values is not None else 0
-        )
-        cache_position = torch.arange(
-            past_seen_tokens,
-            past_seen_tokens + inputs_embeds.shape[1],
-            device=inputs_embeds.device,
-        )
-
-    # Merge text and images
-    if pixel_values is not None:
-        image_features = self.get_image_features(pixel_values)
-
-        if input_ids is None:
-            special_image_mask = inputs_embeds == self.get_input_embeddings()(
-                torch.tensor(
-                    self.config.image_token_index,
-                    dtype=torch.long,
-                    device=inputs_embeds.device,
-                )
-            )
-        else:
-            special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(
-                -1
-            )
-            special_image_mask = special_image_mask.expand_as(inputs_embeds).to(
-                inputs_embeds.device
-            )
-
-        if (
-            not is_torchdynamo_compiling()
-            and inputs_embeds[special_image_mask].numel() != image_features.numel()
-        ):
-            image_tokens_in_text = (special_image_mask).sum(dim=1).sum(dim=0)[0]
-            raise ValueError(
-                f"Number of images does not match number of special image tokens in the input text. "
-                f"Got {image_tokens_in_text} image tokens in the text but {image_features.shape[0] * image_features.shape[1]} "
-                "tokens from image embeddings."
-            )
-        image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
-        inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
-
-    # mask out pad-token-ids in labels for BC
-    if labels is not None and self.pad_token_id in labels:
-        logger.warning_once(
-            "`labels` contains `pad_token_id` which will be masked with `config.ignore_index`. "
-            "You have to mask out `pad_token_id` when preparing `labels`, this behavior will be removed in v.4.46.",
-        )
-        labels = torch.where(
-            input_ids == self.pad_token_id, self.config.ignore_index, labels
-        )
-
-    causal_mask = self._update_causal_mask(  # pylint: disable=protected-access
-        attention_mask,
-        token_type_ids,
-        past_key_values,
-        cache_position,
-        inputs_embeds,
-        is_training,
-    )
-    outputs = self.language_model(
-        attention_mask=causal_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        logits_to_keep=logits_to_keep,
-        **lm_kwargs,
-    )
-
-    logits = outputs[0]
-    loss = None
-    if labels is not None:
-        if attention_mask is not None:
-            # Get the shifted attention mask
-            shift_attention_mask = attention_mask[:, -logits.shape[1] + 1 :].to(
-                logits.device
-            )  # +1 for shift
-
-            # Filter logits and labels based on attention mask
-            valid_indices = shift_attention_mask != 0
-            filtered_logits = logits[..., :-1, :][valid_indices]
-            filtered_labels = labels[..., 1:][valid_indices.to(labels.device)]
-
-            # TODO: do we need to handle num_items_in_batch given we filter the logits and labels?
-
-            loss = self.loss_function(
-                logits=filtered_logits,
-                labels=None,  # we pass shift_labels
-                shift_labels=filtered_labels,
-                vocab_size=self.config.text_config.vocab_size,
-                **lm_kwargs,
-            )
-        else:
-            # Standard case without filtering
-            loss = self.loss_function(
-                logits=logits,
-                labels=labels,
-                vocab_size=self.config.text_config.vocab_size,
-                **lm_kwargs,
-            )
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return Gemma3CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-        image_hidden_states=image_features if pixel_values is not None else None,
-    )
-
-
-def patch_gemma3conditionalgeneration_forward():
-    from transformers.models.gemma3.modeling_gemma3 import (
-        Gemma3ForConditionalGeneration,
-    )
-
-    Gemma3ForConditionalGeneration.forward = new_forward

src/axolotl/monkeypatch/lora_kernels.py

@@ -252,38 +252,12 @@ def apply_lora_kernel_patches(
     LOG.setLevel(logging.INFO)
 
     # Choose activation based on model type
-    activation = None
-    text_config = (
-        model.config.get_text_config()
-        if hasattr(model.config, "get_text_config")
-        else model.config
-    )
-    if hasattr(text_config, "hidden_act"):
-        activation = text_config.hidden_act
-    elif hasattr(text_config, "hidden_activation"):
-        activation = text_config.hidden_activation
-
-    # map activation to supported activation
-    if "gelu" in activation:
-        # gemma3 uses gelu_pytorch_tanh
-        activation = "gelu"
-
+    activation = model.config.hidden_act
     if activation not in SUPPORTED_ACTIVATIONS:
         raise NotImplementedError(f"Activation {activation} is not supported")
 
-    layers = []
-    # check for multimodal models first
-    if hasattr(model, "language_model"):
-        layers = model.language_model.model.layers
-    elif hasattr(model, "model"):
-        layers = model.model.model.layers
-    else:
-        raise NotImplementedError(
-            f"Model type {model.config.model_type} is not supported yet. Please create an Issue."
-        )
-
     # Patch each layer
-    for layer in layers:
+    for layer in model.model.model.layers:
         # Add QKV, O fallback implementations to start
         # These will be overwritten later (if some conditions apply)
         layer.self_attn.apply_qkv = types.MethodType(

src/axolotl/monkeypatch/multipack.py

@@ -22,10 +22,6 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
     "phi3",
     "gemma",
     "gemma2",
-    "gemma3",
-    "gemma3_text",
-    "cohere",
-    "cohere2",
     "gemmoe",
     "starcoder2",
     "deepseek_v2",

src/axolotl/processing_strategies.py

@@ -1,278 +0,0 @@
-"""Module containing ProcessingStrategy classes and its derivative for different MultiModal Model types"""
-
-from copy import deepcopy
-from typing import Optional
-
-from PIL import Image, ImageOps
-from PIL.Image import Resampling
-from torch import Tensor
-from transformers import ProcessorMixin
-from transformers.image_utils import load_image
-
-
-class ProcessingStrategy:
-    """Base Processing Strategy class"""
-
-    def __init__(
-        self,
-        processor: ProcessorMixin,
-        chat_template: Optional[str] = None,
-        image_size: int | tuple[int, int] | None = None,
-        image_resize_algorithm: Resampling | None = None,
-    ):
-        self.processor = processor
-        self.chat_template = chat_template
-        self.image_token = None
-        self.image_token_id = None
-
-        self.image_size = image_size
-        self.image_resize_algorithm = (
-            image_resize_algorithm or Image.Resampling.BILINEAR
-        )
-
-        if hasattr(processor, "image_token"):
-            self.image_token = processor.image_token
-            self.image_token_id = processor.tokenizer.convert_tokens_to_ids(
-                self.image_token
-            )
-
-    def __call__(self, examples: list[dict]) -> list[dict]:
-        """
-        Preprocess conversation examples to ensure consistent format.
-        Converts different conversation formats to OpenAI format with 'messages'.
-        Supports two formats:
-        1. OpenAI format with 'messages'
-        2. Legacy format with 'conversations'
-
-        Args:
-            examples: list of conversation dictionaries
-
-        Returns:
-            list of dicts in OpenAI format with 'messages' key
-
-        Raises:
-            ValueError: If the conversation format is not supported
-        """
-        role_mapping = {
-            "human": "user",
-            "gpt": "assistant",
-        }
-
-        def normalize_role(role: str) -> str:
-            """Normalize role names to OpenAI format. Default to original role if not found."""
-            return role_mapping.get(role, role)
-
-        def convert_legacy_format(example: dict) -> dict:
-            """Convert legacy 'conversations' format to OpenAI 'messages' format."""
-            messages = [
-                {"role": normalize_role(convo["from"]), "content": convo["value"]}
-                for convo in example["conversations"]
-            ]
-
-            # Create new dict without 'conversations' key
-            result = deepcopy(example)
-            result.pop("conversations")
-            result["messages"] = messages
-            return result
-
-        def convert_messages_to_multimedia_messages(messages: list[dict]) -> list[dict]:
-            """Convert regular messages format to Messages format with content type"""
-
-            new_messages = []
-            for message in messages:
-                if isinstance(message["content"], str):
-                    new_messages.append(
-                        {
-                            "role": message["role"],
-                            "content": [
-                                {
-                                    "type": "text",
-                                    "text": message["content"],
-                                }
-                            ],
-                        }
-                    )
-                elif isinstance(message["content"], list):
-                    content = message["content"]
-
-                    new_messages.append(
-                        {
-                            "role": message["role"],
-                            "content": content,
-                        }
-                    )
-
-            return new_messages
-
-        processed_examples = []
-        for example in examples:
-            if not ("messages" in example or "conversations" in example):
-                raise ValueError(
-                    "Only `messages` and `conversations` message keys are currently supported."
-                )
-
-            processed_example = None
-            if "messages" in example:  # OpenAI format
-                processed_example = example
-            else:  # Legacy format
-                processed_example = convert_legacy_format(example)
-
-            # convert regular messages format to Messages format with content type
-            # for compatibility with apply_chat_template
-            processed_example["messages"] = convert_messages_to_multimedia_messages(
-                processed_example["messages"]
-            )
-
-            # find the image key if it exists
-            possible_image_keys = ["images", "image"]
-            image_key = None
-            for key in possible_image_keys:
-                if key in processed_example:
-                    image_key = key
-                    break
-
-            # if the image key exists, add the image to the first message
-            if image_key is not None:
-                # TODO: check if it's normal to be single image only for common datasets
-                # From observation, it's usually a list of single image but some datasets may have several columns for images
-                # Temporary solution: take the first image and suggest people convert their datasets to use multi-content Messages
-                image_value = processed_example[image_key][0]
-
-                # Handle image loading (Image, url, path, base64)
-                image_value = load_image(image_value)
-
-                if self.image_size is not None:
-                    assert hasattr(
-                        image_value, "resize"
-                    ), "Image does not have a resize method"
-
-                    if isinstance(self.image_size, tuple):
-                        image_value = image_value.resize(
-                            self.image_size, self.image_resize_algorithm
-                        )
-                    else:
-                        # Set the padding value; here we use black (0, 0, 0) for RGB images
-                        padding_color = (0, 0, 0)
-
-                        # When image_size is an int (square target), preserve aspect ratio then pad
-                        # This is to prevent aspect ratio distortion when resizing to square
-                        image_value = ImageOps.pad(
-                            image_value,
-                            (self.image_size, self.image_size),
-                            method=self.image_resize_algorithm,
-                            color=padding_color,
-                        )
-
-                # Look for any image type in the first message
-                # some dataset have an {type: "image"} in the first message
-                ind_to_add = None
-
-                for i, content in enumerate(
-                    processed_example["messages"][0]["content"]
-                ):
-                    # Usually datasets created with image columns, don't have it in the messages itself
-                    if content["type"] == "image" and all(
-                        k not in content for k in ["image", "url", "path", "base64"]
-                    ):
-                        ind_to_add = i
-                        break
-
-                # If an image type is found, add the image to that index
-                if ind_to_add is not None:
-                    processed_example["messages"][0]["content"][ind_to_add][
-                        "image"
-                    ] = image_value
-                else:
-                    # if no image type is found, add it to end of the first message
-                    processed_example["messages"][0]["content"].append(
-                        {
-                            "type": "image",
-                            "image": image_value,
-                        }
-                    )
-
-            processed_examples.append(processed_example)
-
-        return processed_examples
-
-    def process_labels(self, input_ids: Tensor) -> Tensor:
-        labels = input_ids.clone()
-
-        # The labels are the input_ids, and we mask the padding tokens in the loss computation
-        labels[labels == self.processor.tokenizer.pad_token_id] = -100
-
-        # Ignore the image token index in the loss computation (model specific)
-        labels[labels == self.image_token_id] = -100
-
-        return labels
-
-
-class Qwen2VLProcessingStrategy(ProcessingStrategy):
-    """Processing Strategy class for Qwen2-VL"""
-
-    def __init__(
-        self,
-        processor: ProcessorMixin,
-        chat_template: Optional[str] = None,
-        image_size: int | tuple[int, int] | None = None,
-        image_resize_algorithm: Resampling | None = None,
-    ):
-        super().__init__(processor, chat_template, image_size, image_resize_algorithm)
-        self.image_token = "<|image_pad|>"  # nosec
-        self.image_token_id = processor.tokenizer.convert_tokens_to_ids(
-            self.image_token
-        )
-
-
-class Gemma3ProcessingStrategy(ProcessingStrategy):
-    """Processing Strategy class for Gemma3"""
-
-    def __init__(
-        self,
-        processor: ProcessorMixin,
-        chat_template: Optional[str] = None,
-        image_size: int | tuple[int, int] | None = None,
-        image_resize_algorithm: Resampling | None = None,
-    ):
-        super().__init__(processor, chat_template, image_size, image_resize_algorithm)
-        self.image_token = processor.tokenizer.special_tokens_map["boi_token"]
-        self.image_token_id = processor.tokenizer.convert_tokens_to_ids(
-            self.image_token
-        )
-
-    def process_labels(self, input_ids):
-        labels = input_ids.clone()
-
-        # Follows https://ai.google.dev/gemma/docs/core/huggingface_vision_finetune_qlora
-        labels[labels == self.processor.tokenizer.pad_token_id] = -100
-        labels[labels == self.image_token_id] = -100
-        labels[labels == 262144] = -100  # corresponds to <image_soft_token>
-
-        return labels
-
-
-def get_processing_strategy(
-    processor: ProcessorMixin,
-    chat_template,
-    chat_template_type,
-    image_size: int | tuple[int, int] | None = None,
-    image_resize_algorithm: Resampling | None = None,
-):
-    if chat_template_type == "qwen2_vl":
-        return Qwen2VLProcessingStrategy(
-            processor, chat_template, image_size, image_resize_algorithm
-        )
-    if chat_template_type == "gemma3":
-        return Gemma3ProcessingStrategy(
-            processor, chat_template, image_size, image_resize_algorithm
-        )
-    if chat_template_type in [
-        "llama3_2_vision",
-        "llava",
-        "mistral_v7_tekken",
-        "pixtral",
-    ]:
-        return ProcessingStrategy(
-            processor, chat_template, image_size, image_resize_algorithm
-        )
-    raise ValueError(f"Unsupported chat template type: {chat_template_type}")

src/axolotl/prompt_strategies/chat_template.py

@@ -411,15 +411,11 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         if turn_idx >= len(turns):
             raise ValueError(f"Turn index {turn_idx} out of range")
 
-        # mistral/gemma3 does not output message if it contains only system message
+        # mistral does not output message if it contains only system message
         if (
             turn_idx == 0
             and turns[0].get("role") == "system"
-            and (
-                "mistral" in self.tokenizer.name_or_path.lower()
-                # gemma3 uses gemma tokenizer
-                or "gemma" in self.tokenizer.name_or_path.lower()
-            )
+            and "mistral" in self.tokenizer.name_or_path.lower()
         ):
             return -1, -1
 

src/axolotl/train.py

@@ -14,7 +14,6 @@ import transformers.modelcard
 from accelerate.logging import get_logger
 from accelerate.utils import save_fsdp_model
 from datasets import Dataset
-from huggingface_hub.errors import OfflineModeIsEnabled
 from peft import PeftConfig, PeftModel
 from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
 from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
@@ -27,7 +26,6 @@ from axolotl.contribs.lgpl import (  # pylint: disable = no-name-in-module
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
 from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.distributed import cleanup_distributed
 from axolotl.utils.freeze import freeze_layers_except
 from axolotl.utils.models import load_model, load_processor, load_tokenizer
 from axolotl.utils.trainer import setup_trainer
@@ -158,8 +156,6 @@ def setup_signal_handler(
                 _model.save_pretrained(
                     cfg.output_dir, safe_serialization=safe_serialization
                 )
-
-            cleanup_distributed()
             sys.exit(0)
 
         _model_weakref = weakref.ref(model)
@@ -306,7 +302,7 @@ def create_model_card(cfg: DictDefault, trainer: Trainer):
                     model_card_kwarg["dataset_tags"] = dataset_tags
 
             trainer.create_model_card(**model_card_kwarg)
-        except (AttributeError, UnicodeDecodeError, OfflineModeIsEnabled):
+        except (AttributeError, UnicodeDecodeError):
             pass
     elif cfg.hub_model_id:
         # Defensively push to the hub to ensure the model card is updated
@@ -318,7 +314,6 @@ def save_initial_configs(
     tokenizer: PreTrainedTokenizer,
     model: PreTrainedModel,
     peft_config: PeftConfig | None,
-    processor: ProcessorMixin | None,
 ):
     """
     Save initial configurations before training.
@@ -346,10 +341,6 @@ def save_initial_configs(
         LOG.info(f"Pre-saving model config to {cfg.output_dir}...")
         model.config.save_pretrained(str(output_dir))
 
-    if processor:
-        LOG.info(f"Pre-saving processor to {cfg.output_dir}...")
-        processor.save_pretrained(str(output_dir))
-
 
 def setup_model_card(cfg: DictDefault):
     """
@@ -417,7 +408,6 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
     PeftModel | PreTrainedModel,
     PreTrainedTokenizer,
     PeftConfig | None,
-    ProcessorMixin | None,
 ]:
     """
     Load model, tokenizer, trainer, etc. Helper function to encapsulate the full
@@ -433,7 +423,6 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
             - Model
             - Tokenizer
             - PEFT config
-            - Processor
     """
     # Load tokenizer, processor and model
     model, tokenizer, peft_config, processor = setup_model_and_tokenizer(cfg)
@@ -464,7 +453,6 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
         model,
         tokenizer,
         peft_config,
-        processor,
     )
 
 
@@ -481,35 +469,42 @@ def train(
     Returns:
         Tuple of (model, tokenizer) after training
     """
-    # Setup model, tokenizer, (causal or RLHF) trainer, etc.
+    # Setup model, tokenizer, (causal or RLHF) trainer etc.
     (
         trainer,
         model,
         tokenizer,
         peft_config,
-        processor,
     ) = setup_model_and_trainer(cfg, dataset_meta)
 
-    # Handle untrained tokens if configured
+    # Determine if we need to resume from a checkpoint
+    resume_from_checkpoint = determine_resume_checkpoint(cfg)
+
+    # Configuration for saving
     safe_serialization = cfg.save_safetensors is True
+
+    # Handle untrained tokens if configured
     train_dataset = dataset_meta.train_dataset
     handle_untrained_tokens_fix(
         cfg, model, tokenizer, train_dataset, safe_serialization
     )
 
-    # Additional setup
-    save_initial_configs(cfg, tokenizer, model, peft_config, processor)
+    # Save initial configs
+    save_initial_configs(cfg, tokenizer, model, peft_config)
+
+    # Set up signal handler for graceful termination
     setup_signal_handler(cfg, model, safe_serialization)
+
+    # Set up badges and config info for model card
     setup_model_card(cfg)
 
     # Execute the training
-    resume_from_checkpoint = determine_resume_checkpoint(cfg)
     execute_training(cfg, trainer, resume_from_checkpoint)
 
-    # Save the trained model and cleanup
+    # Save the trained model
     save_trained_model(cfg, trainer, model, safe_serialization)
+
+    # Create model card
     create_model_card(cfg, trainer)
-    if not cfg.use_ray:
-        cleanup_distributed()
 
     return model, tokenizer, trainer

src/axolotl/utils/callbacks/__init__.py

@@ -816,6 +816,27 @@ class SaveAxolotlConfigtoWandBCallback(TrainerCallback):
         return control
 
 
+class SaveModelCallback(TrainerCallback):
+    """Callback to save model on train end"""
+
+    def on_step_end(  # pylint: disable=unused-argument
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        # Save
+        if state.global_step >= state.max_steps:
+            control.should_save = True
+
+    def on_train_end(  # pylint: disable=unused-argument
+        self, args, state, control, **kwargs
+    ):
+        control.should_save = True
+        return control
+
+
 class GCCallback(TrainerCallback):
     """Callback to garbage collect torch cache"""
 

src/axolotl/utils/collators/batching.py

@@ -112,7 +112,6 @@ class DataCollatorForSeq2Seq:
             self.local_world_size = dist.get_world_size(group=sp_group)
 
     def __call__(self, features, return_tensors=None):
-        has_attn_mask = "attention_mask" in features[0].keys()
         labels = None
         if return_tensors is None:
             return_tensors = self.return_tensors
@@ -165,8 +164,6 @@ class DataCollatorForSeq2Seq:
             pad_to_multiple_of=self.pad_to_multiple_of,
             return_tensors=return_tensors,
         )
-        if not has_attn_mask:
-            del features["attention_mask"]
 
         # prepare decoder_input_ids
         if (

src/axolotl/utils/collators/mm_chat.py

@@ -2,17 +2,15 @@
 Collators for multi-modal chat messages and packing
 """
 
+from copy import deepcopy
 from dataclasses import dataclass
 from typing import Any, Optional, Union
 
-import torch
-from torch import Tensor
-from transformers import PreTrainedTokenizerBase
+from PIL import Image
+from transformers import PreTrainedTokenizerBase, ProcessorMixin
 from transformers.data.data_collator import DataCollatorMixin
 from transformers.utils import PaddingStrategy
 
-from axolotl.processing_strategies import ProcessingStrategy
-
 
 @dataclass
 class MultiModalChatDataCollator(DataCollatorMixin):
@@ -21,9 +19,11 @@ class MultiModalChatDataCollator(DataCollatorMixin):
     """
 
     tokenizer: PreTrainedTokenizerBase
-    processing_strategy: ProcessingStrategy
-    packing: bool = False
+    processor: ProcessorMixin
     return_tensors: str = "pt"
+    chat_template: Optional[str] = None
+    packing: bool = False
+    max_images: int = -1
     padding: Union[bool, str, PaddingStrategy] = True
     pad_to_multiple_of: Optional[int] = None
 
@@ -31,62 +31,162 @@ class MultiModalChatDataCollator(DataCollatorMixin):
         if self.packing:
             raise ValueError("Packing is currently not supported.")
 
-    def torch_call(self, examples: list[dict]) -> dict[str, Any]:
-        return self.process_rows(examples)
+    def torch_call(
+        self, examples: list[Union[list[int], Any, dict[str, Any]]]
+    ) -> dict[str, Any]:
+        # Handle dict or lists with proper padding and conversion to tensor.
+
+        return self.__class__.process_rows(
+            examples, self.processor, self.chat_template, self.max_images
+        )
+
+    @staticmethod
+    def process_rows(examples, processor, chat_template, max_images, length_only=False):
+        # HINT: use `_torch_collate_batch` to stack and pad tensors
+        # see also DataCollatorWithFlattening and DefaultDataCollator
+
+        # *** This is COPIED from the trl example sft_vlm.py code ***
+        # use this as a starting point
+
+        def _preprocess(examples: list[dict]) -> list[dict]:
+            """
+            Preprocess conversation examples to ensure consistent format.
+
+            Converts different conversation formats to OpenAI format with 'messages'.
+            Supports two formats:
+            1. OpenAI format with 'messages'
+            2. Legacy format with 'conversations'
+
+            Args:
+                examples: list of conversation dictionaries
+
+            Returns:
+                dict in OpenAI format with 'messages' key
+
+            Raises:
+                ValueError: If the conversation format is not supported
+            """
+            role_mapping = {
+                "human": "user",
+                "gpt": "assistant",
+            }
+
+            def normalize_role(role: str) -> str:
+                """Normalize role names to OpenAI format. Default to original role if not found."""
+                return role_mapping.get(role, role)
+
+            def convert_legacy_format(example: dict) -> dict:
+                """Convert legacy 'conversations' format to OpenAI 'messages' format."""
+                messages = [
+                    {
+                        "role": normalize_role(convo["from"]),
+                        "content": convo["value"],
+                    }
+                    for convo in example["conversations"]
+                ]
+
+                # Create new dict without 'conversations' key
+                result = deepcopy(example)
+                result.pop("conversations")
+                return {"messages": messages, **result}
+
+            processed_examples = []
+            for example in examples:
+                # OpenAI format
+                if "messages" in example:
+                    processed_examples.append(example)
+
+                # Legacy format
+                elif "conversations" in example:
+                    processed_examples.append(convert_legacy_format(example))
+
+                else:
+                    raise ValueError(
+                        "Only `messages` and `conversations` message keys are currently supported."
+                    )
+
+            return processed_examples
+
+        def _process_images(examples, max_images):
+            """
+            Process images from examples, ensuring consistency in image presence and applying max_images limit.
+
+            Args:
+                examples: List of dictionaries that may contain 'images' key
+                max_images: Maximum number of images to keep per example (0 means no limit)
+
+            Returns:
+                Either None (if no images) or List[Image objects] (if all examples have images)
+
+            Raises:
+                ValueError: If there's a mix of None and non-None images
+            """
+
+            def get_image(example):
+                if "images" not in example:
+                    return None
+                images = example["images"]
+                if isinstance(images, str):
+                    return Image.open(images)
+                return images
+
+            images = [get_image(example) for example in examples]
+
+            # Count None and non-None images
+            none_count = sum(1 for img in images if img is None)
+
+            # All images are None
+            if none_count == len(images):
+                return None
+
+            # Mix of None and non-None images
+            if none_count > 0:
+                raise ValueError(
+                    "All images should be either None or not None. "
+                    "Please provide images for all examples or None."
+                )
+
+            # Apply max_images limit if specified
+            if max_images > 0:
+                images = [
+                    (
+                        img_batch[:max_images]
+                        if isinstance(img_batch, (list, tuple))
+                        else img_batch
+                    )
+                    for img_batch in images
+                ]
+
+            return images
 
-    def process_rows(
-        self,
-        examples: list[dict],
-    ) -> dict[str, Tensor]:
         # Preprocess the examples
-        examples = self.processing_strategy(examples)
+        examples = _preprocess(examples)
 
-        # Initialize batch
-        batch: dict[str, Any] = {}
-
-        # Process each example
-        for example in examples:
-            # Apply chat template to process the example
-            # This method requires transformers>=4.49.0
-            result = self.processing_strategy.processor.apply_chat_template(
-                example["messages"],
-                add_generation_prompt=True,
-                tokenize=True,
-                return_tensors="pt",
-                padding=True,
-                return_dict=True,
-                chat_template=self.processing_strategy.chat_template,
+        # Get the texts and images, and apply the chat template
+        texts = [
+            processor.apply_chat_template(
+                example["messages"], chat_template=chat_template, tokenize=False
             )
+            for example in examples
+        ]
 
-            # TODO: Check if need handling for len(input_ids) > sequence_len
+        images = _process_images(examples, max_images=max_images)
 
-            # Add the processed tensors to our batch
-            for key in result.keys():
-                if key not in batch:
-                    batch[key] = []
+        # Tokenize the texts and process the images
+        batch = processor(text=texts, images=images, return_tensors="pt", padding=True)
 
-                batch[key].append(result[key].squeeze(0))
-
-        # Pad sequences to the same length
-        input_ids = torch.nn.utils.rnn.pad_sequence(
-            batch["input_ids"],
-            batch_first=True,
-            padding_value=self.tokenizer.pad_token_id,
+        # The labels are the input_ids, and we mask the padding tokens in the loss computation
+        labels = batch["input_ids"].clone()
+        labels[labels == processor.tokenizer.pad_token_id] = -100  #
+        # Ignore the image token index in the loss computation (model specific)
+        image_token_id = processor.tokenizer.convert_tokens_to_ids(
+            processor.image_token
         )
+        labels[labels == image_token_id] = -100
+        batch["labels"] = labels
 
-        attention_mask = torch.nn.utils.rnn.pad_sequence(
-            batch["attention_mask"], batch_first=True, padding_value=0
-        )
-
-        # Create the final batch
-        final_batch = {
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-        }
-
-        # Process the labels
-        final_batch["labels"] = self.processing_strategy.process_labels(
-            final_batch["input_ids"]
-        )
-
-        return final_batch
+        if length_only:
+            return {
+                "length": [len(sample["input_ids"]) for sample in batch["input_ids"]]
+            }
+        return batch

src/axolotl/utils/config/__init__.py

@@ -13,7 +13,7 @@ from axolotl.integrations.base import PluginManager
 from axolotl.integrations.config import merge_input_args
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import MULTIMODAL_AUTO_MODEL_MAPPING, load_model_config
+from axolotl.utils.models import load_model_config
 from axolotl.utils.schemas.config import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
 )
@@ -158,7 +158,7 @@ def normalize_config(cfg):
 
     cfg.is_multimodal = (
         hasattr(model_config, "model_type")
-        and model_config.model_type in MULTIMODAL_AUTO_MODEL_MAPPING
+        and model_config.model_type in ["llava", "mllama"]
         or any(
             multimodal_name in cfg.base_model.lower()
             for multimodal_name in [
@@ -171,6 +171,7 @@ def normalize_config(cfg):
         cfg.processor_config = (
             cfg.processor_config or cfg.base_model_config or cfg.base_model
         )
+        model_config = model_config.text_config
 
     cfg.model_config_type = model_config.model_type
 

src/axolotl/utils/data/shared.py

@@ -6,12 +6,8 @@ from pathlib import Path
 from typing import Optional, Union
 
 from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
-from huggingface_hub import hf_hub_download, snapshot_download
-from huggingface_hub.errors import (
-    HFValidationError,
-    RepositoryNotFoundError,
-    RevisionNotFoundError,
-)
+from huggingface_hub import hf_hub_download
+from huggingface_hub.errors import HFValidationError
 
 from axolotl.utils.dict import DictDefault
 
@@ -74,25 +70,20 @@ def load_dataset_w_config(
     # pylint: disable=invalid-name
     ds: Optional[Union[Dataset, DatasetDict]] = None  # pylint: disable=invalid-name
     ds_from_hub = False
+    ds_trust_remote_code = config_dataset.trust_remote_code
     try:
         # this is just a basic check to see if the path is a
         # valid HF dataset that's loadable
-        snapshot_download(
-            repo_id=config_dataset.path,
-            repo_type="dataset",
+        load_dataset(
+            config_dataset.path,
+            name=config_dataset.name,
+            streaming=True,
             token=use_auth_token,
             revision=config_dataset.revision,
-            ignore_patterns=["*"],
+            trust_remote_code=ds_trust_remote_code,
         )
         ds_from_hub = True
-    except (
-        RepositoryNotFoundError,
-        RevisionNotFoundError,
-        FileNotFoundError,
-        ConnectionError,
-        HFValidationError,
-        ValueError,
-    ):
+    except (FileNotFoundError, ConnectionError, HFValidationError, ValueError):
         pass
 
     ds_from_cloud = False
@@ -238,8 +229,7 @@ def load_dataset_w_config(
             trust_remote_code=config_dataset.trust_remote_code,
             **load_ds_kwargs,
         )
-    elif config_dataset.data_files:
-        fp: str | list[str] | None = None
+    else:
         if isinstance(config_dataset.data_files, str):
             fp = hf_hub_download(
                 repo_id=config_dataset.path,

src/axolotl/utils/distributed.py

@@ -71,8 +71,8 @@ def barrier():
 
 def is_main_process():
     """
-    Check if the current process is the main process. If not in distributed mode,
-    always return `True`.
+    Check if the current process is the main process.
+    If not in distributed mode, always return True.
     """
     if not is_distributed():
         return True
@@ -87,18 +87,6 @@ def get_world_size():
     return int(os.getenv("WORLD_SIZE", "1"))
 
 
-def cleanup_distributed():
-    """
-    Destroy process group if torch distributed is initialized. Called in training early
-    termination or when training successfully completes.
-    """
-    # Ensure that all operations are completed before destroying the process group
-    torch.cuda.synchronize()
-    # Destroy the process group
-    if torch.distributed.is_initialized():
-        torch.distributed.destroy_process_group()
-
-
 @contextmanager
 def zero_only():
     """

src/axolotl/utils/models.py

@@ -8,7 +8,7 @@ import math
 import os
 import types
 from functools import cached_property
-from typing import Any, Dict, Optional, Tuple
+from typing import Any, Dict, Optional, Tuple, Union  # noqa: F401
 
 import addict
 import bitsandbytes as bnb
@@ -25,7 +25,7 @@ from peft import (
     prepare_model_for_kbit_training,
 )
 from torch import nn
-from transformers import (
+from transformers import (  # noqa: F401
     AddedToken,
     AutoConfig,
     AutoModelForCausalLM,
@@ -34,17 +34,12 @@ from transformers import (
     AutoTokenizer,
     AwqConfig,
     BitsAndBytesConfig,
-    Gemma3ForConditionalGeneration,
     GPTQConfig,
     LlavaForConditionalGeneration,
-    Mistral3ForConditionalGeneration,
     MllamaForConditionalGeneration,
-    PretrainedConfig,
     PreTrainedModel,
     PreTrainedTokenizerBase,
     ProcessorMixin,
-    Qwen2_5_VLForConditionalGeneration,
-    Qwen2VLForConditionalGeneration,
 )
 from transformers.integrations.deepspeed import (
     HfTrainerDeepSpeedConfig,
@@ -74,13 +69,9 @@ from axolotl.utils.model_shard_quant import load_sharded_model, load_sharded_mod
 
 LOG = logging.getLogger(__name__)
 
-MULTIMODAL_AUTO_MODEL_MAPPING = {
-    "mllama": MllamaForConditionalGeneration,
+MULTIMODEL_AUTO_MODEL_MAPPING = {
     "llava": LlavaForConditionalGeneration,
-    "qwen2_vl": Qwen2VLForConditionalGeneration,
-    "qwen2_5_vl": Qwen2_5_VLForConditionalGeneration,
-    "mistral3": Mistral3ForConditionalGeneration,
-    "gemma3": Gemma3ForConditionalGeneration,
+    "mllama": MllamaForConditionalGeneration,
 }
 
 
@@ -108,30 +99,9 @@ def get_module_class_from_name(module, name):
     return None
 
 
-def check_model_config(cfg: DictDefault, model_config: PretrainedConfig):
-    # Set use_cache to False
-    if hasattr(model_config, "use_cache"):
-        model_config.use_cache = False
-
+def check_model_config(cfg: DictDefault, model_config: Union[AutoConfig, DictDefault]):
     if cfg.is_multimodal:
-        # For multimodal configs, use_cache is set in the text_config
-        if hasattr(model_config, "get_text_config"):
-            text_config = model_config.get_text_config()
-            if hasattr(text_config, "use_cache"):
-                text_config.use_cache = False
-        else:
-            raise ValueError(
-                "No text config found for multimodal model. Please raise an Issue with model details."
-            )
-
-        # check if image_size is not set and load image size from model config if available
-        if (
-            cfg.image_size is None
-            and hasattr(model_config, "vision_config")
-            and hasattr(model_config.vision_config, "image_size")
-        ):
-            cfg.image_size = model_config.vision_config.image_size
-            LOG.debug(f"Loaded image size: {cfg.image_size} from model config")
+        model_config = model_config.text_config
 
     quant_config_exists = (
         hasattr(model_config, "quantization_config")
@@ -470,31 +440,6 @@ def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
         **processor_kwargs,
     )
 
-    # Attempt to load image size from processor if available
-    if (
-        cfg.image_size is None
-        and hasattr(processor, "size")
-        and any(dim in processor.size for dim in ["width", "height"])
-    ):
-        im_width = None
-        im_height = None
-        if "width" in processor.size:
-            im_width = processor.size["width"]
-        if "height" in processor.size:
-            im_height = processor.size["height"]
-
-        # If both width and height are set, use a tuple
-        if im_width is not None and im_height is not None:
-            cfg.image_size = (im_width, im_height)
-        # If only width is set, use as integer
-        elif im_width is not None:
-            cfg.image_size = im_width
-        # If only height is set, use as integer
-        elif im_height is not None:
-            cfg.image_size = im_height
-
-        LOG.debug(f"Loaded image size: {cfg.image_size} from processor")
-
     return processor
 
 
@@ -531,19 +476,14 @@ class ModelLoader:
 
         # init model config
         self.model_config = load_model_config(cfg)
+        if cfg.is_multimodal:
+            self.text_model_config = self.model_config.text_config
+        else:
+            self.text_model_config = self.model_config
 
         self.auto_model_loader = AutoModelForCausalLM  # pylint: disable=invalid-name
 
     def apply_patches(self) -> None:
-        # patch gemma3 conditional generation forward before loading plugins
-        # as it could be overridden by plugins
-        if self.cfg.model_config_type == "gemma3":
-            from axolotl.monkeypatch.gemma3 import (
-                patch_gemma3conditionalgeneration_forward,
-            )
-
-            patch_gemma3conditionalgeneration_forward()
-
         # load any patches from plugins
         from axolotl.integrations.base import PluginManager
 
@@ -618,10 +558,7 @@ class ModelLoader:
             # Initialize ring attn for sequence parallelism. This must be done after
             # model init but before the first forward pass, since it modifies flash
             # attn to use ring comm for SP training across multiple GPUs.
-            register_ring_attn(
-                sequence_parallel_degree=self.cfg.sequence_parallel_degree,
-                heads_k_stride=self.cfg.heads_k_stride,
-            )
+            register_ring_attn(self.cfg.sequence_parallel_degree)
 
     def patch_attention(self) -> None:
         if hasattr(self.model_config, "model_type"):
@@ -736,7 +673,7 @@ class ModelLoader:
         should be set according to the type of the model.
         """
         if self.cfg.is_multimodal:
-            self.auto_model_loader = MULTIMODAL_AUTO_MODEL_MAPPING.get(
+            self.auto_model_loader = MULTIMODEL_AUTO_MODEL_MAPPING.get(
                 self.model_config.model_type, AutoModelForVision2Seq
             )
 
@@ -959,6 +896,8 @@ class ModelLoader:
             quantization_config = (
                 quantization_config or self.model_kwargs["quantization_config"]
             )
+            if self.cfg.is_multimodal:
+                self.model_config.text_config = self.text_model_config
             self.model = load_sharded_model_quant(
                 self.base_model,
                 self.model_config,
@@ -979,6 +918,9 @@ class ModelLoader:
 
             _ = _configure_zero3_memory_efficient_loading()
 
+            if self.cfg.is_multimodal:
+                self.model_config.text_config = self.text_model_config
+
             # Load model with random initialization if specified
             if self.cfg.random_init_weights:
                 # AutoModel classes support the from_config method
@@ -1033,6 +975,8 @@ class ModelLoader:
             and self.model_type != "AutoModelForCausalLM"
             and not self.cfg.trust_remote_code
         ):
+            if self.cfg.is_multimodal:
+                self.model_config.text_config = self.text_model_config
             if self.cfg.gptq:
                 self.model = self.auto_model_loader.from_pretrained(
                     self.base_model,
@@ -1048,7 +992,25 @@ class ModelLoader:
                     **self.model_kwargs,
                 )
         else:
+            # Shouldn't be a problem most of the time. will obviously error if the model doesn't support this
+            # when training starts
+            if (
+                hasattr(self.text_model_config, "max_seq_len")
+                and self.text_model_config.max_seq_len
+                and self.cfg.sequence_len > self.text_model_config.max_seq_len
+            ):
+                self.text_model_config.max_seq_len = self.cfg.sequence_len
+                LOG.warning(f"increasing context length to {self.cfg.sequence_len}")
+            elif (
+                hasattr(self.text_model_config, "max_sequence_length")
+                and self.text_model_config.max_sequence_length
+                and self.cfg.sequence_len > self.text_model_config.max_sequence_length
+            ):
+                self.text_model_config.max_sequence_length = self.cfg.sequence_len
+                LOG.warning(f"increasing context length to {self.cfg.sequence_len}")
             if self.cfg.gptq:
+                if self.cfg.is_multimodal:
+                    self.model_config.text_config = self.text_model_config
                 self.model = self.auto_model_loader.from_pretrained(
                     self.base_model,
                     config=self.model_config,
@@ -1067,6 +1029,8 @@ class ModelLoader:
 
                 _ = _configure_zero3_memory_efficient_loading()
 
+                if self.cfg.is_multimodal:
+                    self.model_config.text_config = self.text_model_config
                 self.model = self.auto_model_loader.from_pretrained(
                     self.base_model,
                     config=self.model_config,
@@ -1230,9 +1194,7 @@ class ModelLoader:
             )
         ):
             resize_kwargs = {}
-            if self.cfg.mean_resizing_embeddings is not None and not (
-                self.model_config.model_type == "llava"
-            ):
+            if self.cfg.mean_resizing_embeddings is not None:
                 resize_kwargs["mean_resizing"] = self.cfg.mean_resizing_embeddings
             self.model.resize_token_embeddings(embeddings_len, **resize_kwargs)
         else:
@@ -1331,6 +1293,8 @@ class ModelLoader:
                 requires_grad.append(f"{name}: {param.requires_grad}")
         if len(requires_grad) == 0:
             LOG.warning("there are no parameters that require gradient updates")
+        if hasattr(self.model, "config"):
+            self.model.config.use_cache = False
 
         if self.cfg.flash_optimum:
             from optimum.bettertransformer import BetterTransformer

src/axolotl/utils/samplers/multipack.py

@@ -8,13 +8,11 @@ from typing import Any, Iterable, List, Union
 
 import numba
 import numpy as np
-from torch.utils.data import BatchSampler, Sampler, SequentialSampler
+from torch.utils.data import BatchSampler, Sampler
 
 from axolotl.utils.distributed import reduce_and_broadcast
 
-LOG = logging.getLogger(__name__)
-
-LOG.setLevel(logging.INFO)
+LOG = logging.getLogger("axolotl.utils.samplers.multipack")
 
 
 @numba.njit
@@ -105,55 +103,6 @@ def allocate(
     return result, s, len(result) * c * n
 
 
-@numba.njit
-def allocate_sequentially(lengths: np.ndarray, rank: int, c: int, n: int):
-    """
-    Sequential allocator that preserves example order
-
-    Parameters:
-    - lengths: The lengths of all examples
-    - rank: The current rank (for distributed training)
-    - c: The capacity of each bin (maximum sequence length)
-    - n: Number of ranks
-
-    Returns:
-    - result: List of batches for the current rank
-    - total_used: Number of actual example tokens
-    - total_slots: Maximum theoretical number of example tokens (number of bins * bin capacity)
-    """
-    result = []
-    total_used = 0
-
-    # First, do sequential packing into bins
-    all_bins = []
-    current_bin = [0 for i in range(0)]  # numba hint
-    remaining_capacity = c
-
-    for idx, size in enumerate(lengths):
-        if size <= remaining_capacity:
-            # Example fits in current bin
-            current_bin.append(idx)
-            remaining_capacity -= size
-            total_used += size
-        else:
-            # Example doesn't fit, start a new bin
-            if current_bin:  # Add non-empty bin to all_bins
-                all_bins.append(current_bin)
-            current_bin = [idx]
-            remaining_capacity = c - size
-            total_used += size
-
-    # Add the last bin if not empty
-    if current_bin:
-        all_bins.append(current_bin)
-
-    # Assign bins to ranks - each rank gets every n-th bin
-    for bin_idx in range(rank, len(all_bins), n):
-        result.append(all_bins[bin_idx])
-
-    return result, total_used, len(all_bins) * c
-
-
 class MultipackBatchSampler(BatchSampler):
     """Batch sampler class for multipack"""
 
@@ -166,7 +115,6 @@ class MultipackBatchSampler(BatchSampler):
         packing_efficiency_estimate: float = 1.0,
         drop_last: bool = False,
         num_count_samples: int = 16,
-        sequential: bool = False,
         **kwargs,
     ):
         super().__init__(sampler, batch_size, drop_last)
@@ -174,7 +122,6 @@ class MultipackBatchSampler(BatchSampler):
         self.batch_max_len = batch_max_len
         self.lengths: np.ndarray = lengths
         self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
-        self.sequential = sequential
 
         assert isinstance(self.lengths, np.ndarray)
 
@@ -189,11 +136,6 @@ class MultipackBatchSampler(BatchSampler):
         # the minimum packed dataset length across all ranks determined by a gather/broadcast
         self.len_across_ranks = None
 
-        if self.sequential and not isinstance(sampler, SequentialSampler):
-            LOG.warn(
-                "using sequential sample packing with non-sequential sampler, did you want to also enable curriculum_sampling?"
-            )
-
     def set_epoch(self, epoch: int):
         self.epoch = epoch
 
@@ -203,21 +145,13 @@ class MultipackBatchSampler(BatchSampler):
         lengths = self.lengths[indices]
         lengths_cumsum = np.cumsum(lengths)
 
-        if self.sequential:
-            batches, total_used, total_slots = allocate_sequentially(
-                lengths=lengths,
-                rank=0,
-                c=self.batch_max_len,
-                n=1,
-            )
-        else:
-            batches, total_used, total_slots = allocate(
-                lengths=lengths,
-                lengths_cumsum=lengths_cumsum,
-                rank=0,
-                c=self.batch_max_len,
-                n=1,
-            )
+        batches, total_used, total_slots = allocate(
+            lengths=lengths,
+            lengths_cumsum=lengths_cumsum,
+            rank=0,
+            c=self.batch_max_len,
+            n=1,
+        )
 
         batches = [
             [

src/axolotl/utils/schemas/config.py

@@ -42,11 +42,9 @@ from axolotl.utils.schemas.model import (
     ModelOutputConfig,
     SpecialTokensConfig,
 )
-from axolotl.utils.schemas.multimodal import MultiModalConfig
 from axolotl.utils.schemas.peft import LoraConfig, ReLoRAConfig
 from axolotl.utils.schemas.training import HyperparametersConfig
 from axolotl.utils.schemas.trl import TRLConfig
-from axolotl.utils.schemas.vllm import VllmConfig
 
 LOG = logging.getLogger(__name__)
 
@@ -66,7 +64,6 @@ class AxolotlInputConfig(
     LISAConfig,
     GradioConfig,
     RayConfig,
-    MultiModalConfig,
     RemappedParameters,
     DeprecatedParameters,
     BaseModel,
@@ -87,9 +84,6 @@ class AxolotlInputConfig(
     trl: TRLConfig | None = Field(
         default_factory=lambda: TRLConfig(),  # pylint: disable=unnecessary-lambda
     )
-    vllm: VllmConfig | None = Field(
-        default_factory=lambda: VllmConfig(),  # pylint: disable=unnecessary-lambda
-    )
     reward_model: bool | None = None
     process_reward_model: bool | None = None
     num_labels: int | None = None
@@ -192,7 +186,6 @@ class AxolotlInputConfig(
     sample_packing: bool | None = None
     sample_packing_group_size: int | None = 100_000
     sample_packing_bin_size: int | None = 200
-    sample_packing_sequentially: bool | None = None
     eval_sample_packing: bool | None = None
     pad_to_sequence_len: bool | None = None
     curriculum_sampling: bool | None = None
@@ -253,7 +246,6 @@ class AxolotlInputConfig(
     val_set_size: float | None = Field(default=0.0)
 
     sequence_parallel_degree: int | None = None
-    heads_k_stride: int | None = None
 
     special_tokens: SpecialTokensConfig | None = None
     tokens: list[str] | None = None
@@ -1114,7 +1106,7 @@ class AxolotlInputConfig(
 
     @field_validator("sequence_parallel_degree", mode="before")
     @classmethod
-    def check_sequence_parallel_degree(cls, value, info):
+    def check_sequence_parallel_config(cls, value, info):
         if not value:
             value = 1
 
@@ -1135,17 +1127,6 @@ class AxolotlInputConfig(
 
         return value
 
-    @model_validator(mode="before")
-    @classmethod
-    def check_muon_deepspeed_fsdp(cls, data):
-        if data.get("optimizer") == "muon" and (
-            data.get("deepspeed") or data.get("fsdp") or data.get("fsdp_config")
-        ):
-            raise ValueError(
-                "Muon optimizer is currently incompatible with DeepSpeed and FSDP"
-            )
-        return data
-
 
 class AxolotlConfigWCapabilities(AxolotlInputConfig):
     """wrapper to valdiate gpu capabilities with the configured options"""
@@ -1281,14 +1262,3 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
             if data["beta"] != data["trl"]["beta"]:
                 raise ValueError("beta and trl.beta must match or one must be removed")
         return data
-
-    @model_validator(mode="after")
-    def check_min_torch_version(self):
-        if self.env_capabilities and self.env_capabilities.torch_version:
-            torch_version = self.env_capabilities.torch_version
-            if version.parse(torch_version) < version.parse("2.5.1"):
-                LOG.warning(
-                    f"torch=={torch_version} may not be supported in future versions. Please consider upgrading to torch>=2.5.1."
-                )
-
-        return self

src/axolotl/utils/schemas/enums.py

@@ -22,7 +22,6 @@ class ChatTemplate(str, Enum):
     mistral_v1 = "mistral_v1"  # pylint: disable=invalid-name
     mistral_v2v3 = "mistral_v2v3"  # pylint: disable=invalid-name
     mistral_v3_tekken = "mistral_v3_tekken"  # pylint: disable=invalid-name
-    mistral_v7_tekken = "mistral_v7_tekken"  # pylint: disable=invalid-name
     gemma = "gemma"  # pylint: disable=invalid-name
     cohere = "cohere"  # pylint: disable=invalid-name
     llama3 = "llama3"  # pylint: disable=invalid-name
@@ -37,10 +36,6 @@ class ChatTemplate(str, Enum):
     tokenizer_default = "tokenizer_default"  # pylint: disable=invalid-name
     exaone = "exaone"  # pylint: disable=invalid-name
     metharme = "metharme"  # pylint: disable=invalid-name
-    pixtral = "pixtral"  # pylint: disable=invalid-name
-    llava = "llava"  # pylint: disable=invalid-name
-    qwen2_vl = "qwen2_vl"  # pylint: disable=invalid-name
-    gemma3 = "gemma3"  # pylint: disable=invalid-name
 
 
 class CustomSupportedOptimizers(str, Enum):

src/axolotl/utils/schemas/multimodal.py

@@ -1,48 +0,0 @@
-"""Pydantic models for multimodal-related configuration"""
-
-from typing import Literal
-
-from PIL.Image import Resampling
-from pydantic import BaseModel, Field, field_validator
-
-
-class MultiModalConfig(BaseModel):
-    """Multi-modal configuration subset"""
-
-    image_size: int | tuple[int, int] | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": (
-                "The size of the image to resize to. It can be an integer (resized into padded-square image) or a tuple (width, height)."
-                "If not provided, we will attempt to load from preprocessor.size, otherwise, images won't be resized."
-            )
-        },
-    )
-    image_resize_algorithm: (
-        Literal["bilinear", "bicubic", "lanczos"] | Resampling | None
-    ) = Field(
-        default=None,
-        json_schema_extra={
-            "description": "The resampling algorithm to use for image resizing. Default is bilinear. Please refer to PIL.Image.Resampling for more details."
-        },
-    )
-
-    @field_validator("image_resize_algorithm", mode="before")
-    @classmethod
-    def convert_image_resize_algorithm(cls, image_resize_algorithm):
-        """
-        Convert the image resize algorithm to a PIL.Image.Resampling enum.
-        """
-        if isinstance(image_resize_algorithm, str):
-            image_resize_algorithm = image_resize_algorithm.lower()
-            if image_resize_algorithm == "bilinear":
-                image_resize_algorithm = Resampling.BILINEAR
-            elif image_resize_algorithm == "bicubic":
-                image_resize_algorithm = Resampling.BICUBIC
-            elif image_resize_algorithm == "lanczos":
-                image_resize_algorithm = Resampling.LANCZOS
-            else:
-                raise ValueError(
-                    f"Invalid image resize algorithm: {image_resize_algorithm}"
-                )
-        return image_resize_algorithm

src/axolotl/utils/schemas/trl.py

@@ -20,30 +20,27 @@ class TRLConfig(BaseModel):
     )
 
     # GRPO specific args
-    # Ref: https://github.com/huggingface/trl/blob/26d86757a7c7e24e397ea44f57ecce6031dfac01/trl/trainer/grpo_config.py#L23
-    use_vllm: bool = Field(
+    # Ref: https://github.com/huggingface/trl/blob/e3244d2d096ff1e2e248c931d06d39e165e20623/trl/trainer/grpo_config.py#L22
+    use_vllm: bool | None = Field(
         default=False,
         json_schema_extra={"description": "Whether to use VLLM for RL training"},
     )
-    vllm_server_host: str | None = Field(
-        default="0.0.0.0",  # nosec B104
-        json_schema_extra={"description": "Host of the vLLM server to connect to"},
+    vllm_device: str | None = Field(
+        default="auto",
+        json_schema_extra={"description": "Device to use for VLLM"},
     )
-    vllm_server_port: int | None = Field(
-        default=8000,
-        json_schema_extra={"description": "Port of the vLLM server to connect to"},
+    vllm_gpu_memory_utilization: float | None = Field(
+        default=0.9,
+        json_schema_extra={"description": "GPU memory utilization for VLLM"},
     )
-    vllm_server_timeout: int | None = Field(
+    vllm_dtype: str | None = Field(
+        default="auto",
+        json_schema_extra={"description": "Data type for VLLM"},
+    )
+    vllm_max_model_len: int | None = Field(
         default=None,
         json_schema_extra={
-            "description": "Total timeout duration in seconds to wait for the vLLM server to be up. If the server is not up "
-            "after the timeout, a `ConnectionError` is raised."
-        },
-    )
-    vllm_guided_decoding_regex: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Regex for vLLM guided decoding. If `None` (default), guided decoding is disabled."
+            "description": "Maximum length of the model context for VLLM"
         },
     )
 
@@ -88,48 +85,3 @@ class TRLConfig(BaseModel):
             "description": "Sync steps for the reference model. Requires `sync_ref_model=True`."
         },
     )
-    scale_rewards: bool = Field(
-        default=True,
-        json_schema_extra={
-            "description": "Whether to scale the rewards for GRPO by dividing them by their standard deviation."
-        },
-    )
-
-    temperature: float | None = Field(
-        default=None,
-        json_schema_extra={"description": "Sampling temperature for the GRPO policy."},
-    )
-    top_p: float | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Top-p sampling probability for the generation policy."
-        },
-    )
-    top_k: int | None = Field(
-        default=None,
-        json_schema_extra={"description": "Top-k sampling for the generation policy."},
-    )
-    min_p: float | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Minimum probability for the generation policy."
-        },
-    )
-    repetition_penalty: float | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far."
-        },
-    )
-    num_iterations: int | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Number of iterations per batch (denoted as μ in the algorithm) for GRPO."
-        },
-    )
-    epsilon: float | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Epsilon value for clipping in the GRPO algorithm."
-        },
-    )

src/axolotl/utils/schemas/vllm.py

@@ -1,38 +0,0 @@
-"""
-Pydantic models for VLLM configuration, used primarily for RL training with TRL + grpo
-"""
-
-from pydantic import BaseModel, Field
-
-
-class VllmConfig(BaseModel):
-    """
-    Configuration for VLLM server
-    """
-
-    device: str | None = Field(
-        default="auto",
-        json_schema_extra={"description": "Device to use for VLLM"},
-    )
-    tensor_parallel_size: int | None = Field(
-        default=None,
-        json_schema_extra={"description": "Tensor parallel size for VLLM"},
-    )
-    gpu_memory_utilization: float | None = Field(
-        default=0.9,
-        json_schema_extra={"description": "GPU memory utilization for VLLM"},
-    )
-    dtype: str | None = Field(
-        default="auto",
-        json_schema_extra={"description": "Data type for VLLM"},
-    )
-    max_model_len: int | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Maximum length of the model context for VLLM"
-        },
-    )
-    enable_prefix_caching: bool | None = Field(
-        default=None,
-        json_schema_extra={"description": "Enable prefix caching for VLLM"},
-    )

src/axolotl/utils/trainer.py

@@ -13,7 +13,7 @@ import torch
 import torch.cuda
 from accelerate.logging import get_logger
 from datasets import IterableDataset, disable_caching, enable_caching
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
+from torch.utils.data import DataLoader, RandomSampler
 from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
@@ -235,7 +235,7 @@ def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2):
 
 
 def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
-    if cfg.model_config_type in ["mamba", "gemma3"]:
+    if cfg.model_config_type == "mamba":
         LOG.info("dropping attention_mask column")
         train_dataset = train_dataset.remove_columns("attention_mask")
         if eval_dataset:
@@ -456,18 +456,13 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             else:
                 sampler_batch_size = cfg.micro_batch_size
                 batch_max_len = cfg.sequence_len
-            if cfg.curriculum_sampling:
-                sampler = SequentialSampler(train_dataset)
-            else:
-                sampler = RandomSampler(train_dataset)
             sampler = MultipackBatchSampler(
-                sampler=sampler,
+                sampler=RandomSampler(train_dataset),
                 lengths=get_dataset_lengths(train_dataset),
                 batch_size=sampler_batch_size,
                 batch_max_len=batch_max_len,
                 group_size=cfg.sample_packing_group_size,
                 bin_size=cfg.sample_packing_bin_size,
-                sequential=cfg.sample_packing_sequentially,
                 drop_last=True,
             )
 

tests/conftest.py

@@ -8,16 +8,10 @@ import shutil
 import sys
 import tempfile
 import time
-from pathlib import Path
 
-import datasets
 import pytest
 import requests
 from huggingface_hub import snapshot_download
-from tokenizers import AddedToken
-from transformers import AutoTokenizer
-
-from tests.hf_offline_utils import disable_hf_offline, enable_hf_offline
 
 
 def retry_on_request_exceptions(max_retries=3, delay=1):
@@ -31,11 +25,9 @@ def retry_on_request_exceptions(max_retries=3, delay=1):
                 except (
                     requests.exceptions.ReadTimeout,
                     requests.exceptions.ConnectionError,
-                    requests.exceptions.HTTPError,
                 ) as exc:
                     if attempt < max_retries - 1:
-                        wait = 2**attempt * delay  # in seconds
-                        time.sleep(wait)
+                        time.sleep(delay)
                     else:
                         raise exc
 
@@ -45,35 +37,26 @@ def retry_on_request_exceptions(max_retries=3, delay=1):
 
 
 @retry_on_request_exceptions(max_retries=3, delay=5)
-@disable_hf_offline
 def snapshot_download_w_retry(*args, **kwargs):
     return snapshot_download(*args, **kwargs)
 
 
-@pytest.fixture(scope="session", autouse=True)
-def download_ds_fixture_bundle():
-    ds_dir = snapshot_download_w_retry(
-        "axolotl-ai-internal/axolotl-oss-dataset-fixtures", repo_type="dataset"
-    )
-    return Path(ds_dir)
-
-
 @pytest.fixture(scope="session", autouse=True)
 def download_smollm2_135m_model():
     # download the model
-    snapshot_download_w_retry("HuggingFaceTB/SmolLM2-135M", repo_type="model")
+    snapshot_download_w_retry("HuggingFaceTB/SmolLM2-135M")
 
 
 @pytest.fixture(scope="session", autouse=True)
 def download_llama_68m_random_model():
     # download the model
-    snapshot_download_w_retry("JackFram/llama-68m", repo_type="model")
+    snapshot_download_w_retry("JackFram/llama-68m")
 
 
 @pytest.fixture(scope="session", autouse=True)
 def download_qwen_2_5_half_billion_model():
     # download the model
-    snapshot_download_w_retry("Qwen/Qwen2.5-0.5B", repo_type="model")
+    snapshot_download_w_retry("Qwen/Qwen2.5-0.5B")
 
 
 @pytest.fixture(scope="session", autouse=True)
@@ -111,52 +94,13 @@ def download_argilla_distilabel_capybara_dpo_7k_binarized_dataset():
 
 
 @pytest.fixture(scope="session", autouse=True)
-def download_argilla_distilabel_intel_orca_dpo_dataset():
+def download_argilla_ultrafeedback_binarized_preferences_cleaned_dataset():
     # download the dataset
     snapshot_download_w_retry(
-        "argilla/distilabel-intel-orca-dpo-pairs", repo_type="dataset"
+        "argilla/ultrafeedback-binarized-preferences-cleaned", repo_type="dataset"
     )
 
 
-# @pytest.fixture(scope="session", autouse=True)
-# def download_argilla_ultrafeedback_binarized_preferences_cleaned_dataset():
-#     # download the dataset
-#     snapshot_download_w_retry(
-#         "argilla/ultrafeedback-binarized-preferences-cleaned", repo_type="dataset"
-#     )
-
-
-# @pytest.fixture(scope="session", autouse=True)
-# def download_fozzie_alpaca_dpo_dataset():
-#     # download the dataset
-#     snapshot_download_w_retry(
-#         "fozziethebeat/alpaca_messages_2k_dpo_test", repo_type="dataset"
-#     )
-#     snapshot_download_w_retry(
-#         "fozziethebeat/alpaca_messages_2k_dpo_test",
-#         repo_type="dataset",
-#         revision="ea82cff",
-#     )
-
-
-# @pytest.fixture(scope="session")
-# @disable_hf_offline
-# def dataset_fozzie_alpaca_dpo_dataset(
-#     download_fozzie_alpaca_dpo_dataset,
-# ):  # pylint: disable=unused-argument,redefined-outer-name
-#     return load_dataset("fozziethebeat/alpaca_messages_2k_dpo_test", split="train")
-#
-#
-# @pytest.fixture(scope="session")
-# @disable_hf_offline
-# def dataset_fozzie_alpaca_dpo_dataset_rev_ea82cff(
-#     download_fozzie_alpaca_dpo_dataset,
-# ):  # pylint: disable=unused-argument,redefined-outer-name
-#     return load_dataset(
-#         "fozziethebeat/alpaca_messages_2k_dpo_test", split="train", revision="ea82cff"
-#     )
-
-
 @pytest.fixture(scope="session", autouse=True)
 def download_arcee_ai_distilabel_intel_orca_dpo_pairs_dataset():
     # download the dataset
@@ -165,192 +109,10 @@ def download_arcee_ai_distilabel_intel_orca_dpo_pairs_dataset():
     )
 
 
-@pytest.fixture(scope="session", autouse=True)
-def download_argilla_dpo_pairs_dataset():
-    # download the dataset
-    snapshot_download_w_retry(
-        "argilla/distilabel-intel-orca-dpo-pairs", repo_type="dataset"
-    )
-
-
 @pytest.fixture(scope="session", autouse=True)
 def download_tiny_shakespeare_dataset():
     # download the dataset
-    snapshot_download_w_retry("winglian/tiny-shakespeare", repo_type="dataset")
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_deepseek_model_fixture():
-    snapshot_download_w_retry("axolotl-ai-co/DeepSeek-V3-11M", repo_type="model")
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_huggyllama_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "huggyllama/llama-7b",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_llama_1b_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "NousResearch/Llama-3.2-1B",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_llama3_8b_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "NousResearch/Meta-Llama-3-8B", repo_type="model", allow_patterns=["*token*"]
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_llama3_8b_instruct_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "NousResearch/Meta-Llama-3-8B-Instruct",
-        repo_type="model",
-        allow_patterns=["*token*"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_phi_35_mini_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "microsoft/Phi-3.5-mini-instruct", repo_type="model", allow_patterns=["*token*"]
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_phi_3_medium_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "microsoft/Phi-3-medium-128k-instruct",
-        repo_type="model",
-        allow_patterns=["*token*"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_mistral_7b_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "casperhansen/mistral-7b-instruct-v0.1-awq",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_gemma_2b_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "unsloth/gemma-2b-it",
-        revision="703fb4a",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_gemma2_9b_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "mlx-community/gemma-2-9b-it-4bit",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_mlx_mistral_7b_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "mlx-community/Mistral-7B-Instruct-v0.3-4bit",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture
-def download_llama2_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "NousResearch/Llama-2-7b-hf",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture
-@enable_hf_offline
-def tokenizer_huggyllama(
-    download_huggyllama_model_fixture,
-):  # pylint: disable=unused-argument,redefined-outer-name
-    tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
-    tokenizer.pad_token = "</s>"
-
-    return tokenizer
-
-
-@pytest.fixture
-@enable_hf_offline
-def tokenizer_huggyllama_w_special_tokens(
-    tokenizer_huggyllama,
-):  # pylint: disable=redefined-outer-name
-    tokenizer_huggyllama.add_special_tokens(
-        {
-            "bos_token": "<s>",
-            "eos_token": "</s>",
-            "unk_token": "<unk>",
-        }
-    )
-
-    return tokenizer_huggyllama
-
-
-@pytest.fixture
-@enable_hf_offline
-def tokenizer_llama2_7b(
-    download_llama2_model_fixture,
-):  # pylint: disable=unused-argument,redefined-outer-name
-    tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-hf")
-
-    return tokenizer
-
-
-@pytest.fixture
-@enable_hf_offline
-def tokenizer_mistral_7b_instruct(
-    download_mlx_mistral_7b_model_fixture,
-):  # pylint: disable=unused-argument,redefined-outer-name
-    return AutoTokenizer.from_pretrained("casperhansen/mistral-7b-instruct-v0.1-awq")
-
-
-@pytest.fixture
-def tokenizer_mistral_7b_instruct_chatml(tokenizer_mistral_7b_instruct):
-    tokenizer_mistral_7b_instruct.add_special_tokens(
-        {
-            "eos_token": AddedToken(
-                "<|im_end|>", rstrip=False, lstrip=False, normalized=False
-            )
-        }
-    )
-    tokenizer_mistral_7b_instruct.add_tokens(
-        [
-            AddedToken("<|im_start|>", rstrip=False, lstrip=False, normalized=False),
-        ]
-    )
-    return tokenizer_mistral_7b_instruct
+    snapshot_download_w_retry("Trelis/tiny-shakespeare", repo_type="dataset")
 
 
 @pytest.fixture
@@ -416,88 +178,3 @@ def cleanup_monkeypatches():
             module_globals = module_name_tuple[1]
             for module_global in module_globals:
                 globals().pop(module_global, None)
-
-
-@pytest.fixture
-def dataset_winglian_tiny_shakespeare(
-    download_ds_fixture_bundle: Path,
-):  # pylint: disable=redefined-outer-name
-    ds_path = download_ds_fixture_bundle / "winglian__tiny-shakespeare"
-    return datasets.load_from_disk(ds_path)
-
-
-@pytest.fixture
-def dataset_tatsu_lab_alpaca(
-    download_ds_fixture_bundle: Path,
-):  # pylint: disable=redefined-outer-name
-    ds_path = download_ds_fixture_bundle / "tatsu-lab__alpaca"
-    return datasets.load_from_disk(ds_path)["train"]
-
-
-@pytest.fixture
-def dataset_mhenrichsen_alpaca_2k_test(
-    download_ds_fixture_bundle: Path,
-):  # pylint: disable=redefined-outer-name
-    ds_path = download_ds_fixture_bundle / "mhenrichsen__alpaca_2k_test"
-    return datasets.load_from_disk(ds_path)["train"]
-
-
-@pytest.fixture
-def dataset_argilla_ultrafeedback_binarized_preferences_cleaned(
-    download_ds_fixture_bundle: Path,
-):  # pylint: disable=redefined-outer-name
-    ds_path = (
-        download_ds_fixture_bundle
-        / "argilla__ultrafeedback-binarized-preferences-cleaned"
-    )
-    return datasets.load_from_disk(ds_path)["train"]
-
-
-@pytest.fixture
-def dataset_fozziethebeat_alpaca_messages_2k_dpo_test(
-    download_ds_fixture_bundle: Path,
-):  # pylint: disable=redefined-outer-name
-    ds_path = download_ds_fixture_bundle / "fozziethebeat__alpaca_messages_2k_dpo_test"
-    return datasets.load_from_disk(ds_path)["train"]
-
-
-@pytest.fixture
-def dataset_fozziethebeat_alpaca_messages_2k_dpo_test_rev_ea82cff(
-    download_ds_fixture_bundle: Path,
-):  # pylint: disable=redefined-outer-name
-    ds_path = (
-        download_ds_fixture_bundle
-        / "fozziethebeat__alpaca_messages_2k_dpo_test__rev_ea82cff"
-    )
-    return datasets.load_from_disk(ds_path)["train"]
-
-
-# # pylint: disable=redefined-outer-name,unused-argument
-# def test_load_fixtures(
-#     download_smollm2_135m_model,
-#     download_llama_68m_random_model,
-#     download_qwen_2_5_half_billion_model,
-#     download_tatsu_lab_alpaca_dataset,
-#     download_mhenrichsen_alpaca_2k_dataset,
-#     download_mhenrichsen_alpaca_2k_w_revision_dataset,
-#     download_mlabonne_finetome_100k_dataset,
-#     download_argilla_distilabel_capybara_dpo_7k_binarized_dataset,
-#     download_argilla_ultrafeedback_binarized_preferences_cleaned_dataset,
-#     download_fozzie_alpaca_dpo_dataset,
-#     download_arcee_ai_distilabel_intel_orca_dpo_pairs_dataset,
-#     download_argilla_dpo_pairs_dataset,
-#     download_tiny_shakespeare_dataset,
-#     download_deepseek_model_fixture,
-#     download_huggyllama_model_fixture,
-#     download_llama_1b_model_fixture,
-#     download_llama3_8b_model_fixture,
-#     download_llama3_8b_instruct_model_fixture,
-#     download_phi_35_mini_model_fixture,
-#     download_phi_3_medium_model_fixture,
-#     download_mistral_7b_model_fixture,
-#     download_gemma_2b_model_fixture,
-#     download_gemma2_9b_model_fixture,
-#     download_mlx_mistral_7b_model_fixture,
-#     download_llama2_model_fixture,
-# ):
-#     pass

tests/core/chat/test_messages.py

@@ -10,13 +10,10 @@ from transformers import AddedToken, AutoTokenizer
 from axolotl.core.chat.format.chatml import format_message
 from axolotl.core.chat.messages import ChatFormattedChats, Chats
 
-from tests.hf_offline_utils import enable_hf_offline  # noqa
-
 
 @pytest.fixture(scope="session", name="llama_tokenizer")
-@enable_hf_offline
 def llama_tokenizer_fixture():
-    return AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B")
+    return AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3.1-8B")
 
 
 @pytest.fixture(scope="session", name="chatml_tokenizer")

tests/e2e/integrations/test_kd.py

@@ -5,6 +5,7 @@ e2e tests for kd trainer support in Axolotl
 from pathlib import Path
 
 import pytest
+from e2e.utils import check_tensorboard, require_torch_2_5_1
 
 from axolotl.cli.args import TrainerCliArgs
 from axolotl.common.datasets import load_datasets
@@ -12,8 +13,6 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
 from axolotl.utils.dict import DictDefault
 
-from tests.e2e.utils import check_tensorboard, require_torch_2_5_1
-
 
 @pytest.fixture(name="kd_min_cfg")
 def min_cfg(temp_dir):

tests/e2e/integrations/test_liger.py

@@ -2,13 +2,15 @@
 Simple end-to-end test for Liger integration
 """
 
+from e2e.utils import require_torch_2_4_1
+
 from axolotl.cli.args import TrainerCliArgs
 from axolotl.common.datasets import load_datasets
 from axolotl.train import train
 from axolotl.utils.config import normalize_config, prepare_plugins
 from axolotl.utils.dict import DictDefault
 
-from tests.e2e.utils import check_model_output_exists, require_torch_2_4_1
+from ..utils import check_model_output_exists
 
 
 class LigerIntegrationTestCase:

tests/e2e/multigpu/solo/test_grpo.py

@@ -1,294 +0,0 @@
-"""
-GRPO test suite
-"""
-
-import os
-import random
-import subprocess  # nosec B404
-import sys
-import time
-from pathlib import Path
-
-import pytest
-import requests
-import yaml
-from accelerate.test_utils import execute_subprocess_async
-from transformers.testing_utils import get_torch_dist_unique_port
-
-from axolotl.utils.dict import DictDefault
-
-from tests.e2e.utils import require_vllm
-
-
-def start_vllm(
-    model: str, env: dict | None = None, wait: int | None = None, quiet=False, **kwargs
-) -> int:
-    """
-    helper function to start the VLLM server in the background, mostly for testing purposes
-    """
-    cmd = [sys.executable, "-m", "trl.scripts.vllm_serve", "--model", model]
-
-    if tensor_parallel_size := kwargs.get("tensor_parallel_size"):
-        cmd.extend(["--tensor-parallel-size", str(tensor_parallel_size)])
-    if host := kwargs.get("host"):
-        cmd.extend(["--host", host])
-    if port := kwargs.get("port"):
-        cmd.extend(["--port", str(port)])
-    if gpu_memory_utilization := kwargs.get("gpu_memory_utilization"):
-        cmd.extend(["--gpu-memory-utilization", str(gpu_memory_utilization)])
-    if dtype := kwargs.get("dtype"):
-        cmd.extend(["--dtype", dtype])
-    if max_model_len := kwargs.get("max_model_len"):
-        cmd.extend(["--max-model-len", str(max_model_len)])
-    if kwargs.get("enable_prefix_caching"):
-        cmd.extend(["--enable-prefix-caching", "True"])
-
-    # print out the command to be executed
-    print(" ".join(cmd))
-
-    # start `trl vllm-serve` command in the background and capture the process id
-    process = subprocess.Popen(  # pylint: disable=consider-using-with
-        cmd,
-        env=env,
-        stdout=subprocess.DEVNULL if quiet else subprocess.PIPE,
-        stderr=subprocess.DEVNULL if quiet else subprocess.PIPE,
-    )  # nosec B603
-
-    # print out the process id so the user can easily kill it later
-    print(f"VLLM server process started (PID: {process.pid})")
-
-    # wait until the http server is ready, even if it 404s, but timeout after 60 seconds
-    started = False
-    if wait and host and port:
-        for _ in range(int(wait)):
-            try:
-                response = requests.get(f"http://{host}:{port}", timeout=1)
-                if int(response.status_code) in [200, 404]:
-                    started = True
-                    break
-            except requests.exceptions.RequestException:
-                pass
-
-            # also check if the process.pid is still running
-            if not process.poll() is None:
-                break
-
-            time.sleep(1)
-
-    if wait and not started:
-        print(
-            f"VLLM server process did not start within {wait} seconds. Please check your server logs."
-        )
-        process.kill()
-        raise RuntimeError(f"VLLM server process did not start within {wait} seconds.")
-
-    # return the process id
-    return process.pid
-
-
-class TestGRPO:
-    """
-    Test case for GRPO training using multilpe GPUs
-    """
-
-    def _utils_write_yaml_and_rewards(self, cfg, temp_dir, suffix=""):
-        # 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))
-        with open(f"rewards_{suffix}.py", "w", encoding="utf-8") as fout:
-            fout.write(
-                """import random
-def rand_reward_func(completions, **kwargs) -> list[float]:
-    return [random.uniform(0, 1) for _ in completions]
-
-def oai_gsm8k_transform(cfg, *args, **kwargs):
-    def transform_fn(example, tokenizer=None):
-        label = example["answer"].split("####")[-1].strip().replace(",", "")
-        return {
-            "prompt": [{"role": "user", "content": example["question"]},],
-            "answer": label,
-        }
-    return transform_fn, {"remove_columns": ["question"]}
-"""
-            )
-
-    @pytest.mark.parametrize(
-        "num_gpus",
-        [1, 2],
-    )
-    @require_vllm
-    def test_llama_dora(self, temp_dir, num_gpus):
-        rnd_reward_suffix = str(random.randint(1000, 9999))
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "chat_template": "llama3",
-                "rl": "grpo",
-                "trl": {
-                    "beta": 0.001,
-                    "max_completion_length": 256,
-                    "use_vllm": True,
-                    "num_generations": 4,
-                    "reward_funcs": [f"rewards_{rnd_reward_suffix}.rand_reward_func"],
-                },
-                "vllm": {
-                    "max_model_len": 800,
-                    "enable_prefix_caching": True,
-                },
-                "datasets": [
-                    {
-                        "path": "openai/gsm8k",
-                        "name": "main",
-                        "type": f"rewards_{rnd_reward_suffix}.oai_gsm8k_transform",
-                    },
-                ],
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "peft_use_dora": True,
-                "flash_attention": True,
-                "sequence_len": 1024,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                },
-                "max_steps": 3,
-                "num_epochs": 1,
-                "micro_batch_size": 4,
-                "gradient_accumulation_steps": 2,
-                "warmup_steps": 10,
-                "val_set_size": 0.0,
-                "output_dir": temp_dir,
-                "learning_rate": 0.0001,
-                "optimizer": "adamw_torch_fused",
-                "lr_scheduler": "cosine",
-                "save_safetensors": True,
-                "bf16": "auto",
-                "use_tensorboard": True,
-            }
-        )
-
-        self._utils_write_yaml_and_rewards(cfg, temp_dir, suffix=rnd_reward_suffix)
-
-        current_env = os.environ.copy()
-        env = {
-            "NCCL_P2P_LEVEL": "LOC",
-            **current_env,
-            "CUDA_VISIBLE_DEVICES": "1",
-        }
-        vllm_process_id = start_vllm(
-            cfg.base_model,
-            env=env,
-            quiet=True,
-            wait=120,
-            gpu_memory_utilization=0.15,
-            max_model_len=cfg.vllm.max_model_len,
-            enable_prefix_caching=cfg.vllm.enable_prefix_caching,
-            host="0.0.0.0",
-            port=8000,
-        )
-
-        try:
-            execute_subprocess_async(
-                [
-                    "axolotl",
-                    "train",
-                    str(Path(temp_dir) / "config.yaml"),
-                    "--num-processes",
-                    str(num_gpus),
-                    "--main-process-port",
-                    f"{get_torch_dist_unique_port()}",
-                ],
-                env={"NCCL_P2P_LEVEL": "LOC", "NCCL_DEBUG": "INFO", **current_env},
-            )
-        finally:
-            os.kill(vllm_process_id, 9)
-
-    @pytest.mark.parametrize(
-        "num_gpus",
-        [1, 2],
-    )
-    @require_vllm
-    def test_llama_fft(self, temp_dir, num_gpus):
-        rnd_reward_suffix = str(random.randint(1000, 9999))
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "chat_template": "llama3",
-                "rl": "grpo",
-                "trl": {
-                    "beta": 0.001,
-                    "max_completion_length": 256,
-                    "use_vllm": True,
-                    "num_generations": 4,
-                    "reward_funcs": [f"rewards_{rnd_reward_suffix}.rand_reward_func"],
-                },
-                "vllm": {
-                    "max_model_len": 800,
-                    "enable_prefix_caching": True,
-                },
-                "datasets": [
-                    {
-                        "path": "openai/gsm8k",
-                        "name": "main",
-                        "type": f"rewards_{rnd_reward_suffix}.oai_gsm8k_transform",
-                    },
-                ],
-                "flash_attention": True,
-                "sequence_len": 1024,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                },
-                "max_steps": 3,
-                "num_epochs": 1,
-                "micro_batch_size": 4,
-                "gradient_accumulation_steps": 2,
-                "warmup_steps": 10,
-                "val_set_size": 0.0,
-                "output_dir": temp_dir,
-                "learning_rate": 0.0001,
-                "optimizer": "adamw_torch_fused",
-                "lr_scheduler": "cosine",
-                "save_safetensors": True,
-                "bf16": "auto",
-                "use_tensorboard": True,
-            }
-        )
-
-        self._utils_write_yaml_and_rewards(cfg, temp_dir, suffix=rnd_reward_suffix)
-
-        current_env = os.environ.copy()
-        env = {
-            "NCCL_P2P_LEVEL": "LOC",  # nccl can be brittle, assume P2P isn't reliable
-            **current_env,
-            "CUDA_VISIBLE_DEVICES": "1",
-        }
-        vllm_process_id = start_vllm(
-            cfg.base_model,
-            env=env,
-            quiet=True,
-            wait=120,
-            gpu_memory_utilization=0.15,
-            max_model_len=cfg.vllm.max_model_len,
-            enable_prefix_caching=cfg.vllm.enable_prefix_caching,
-            host="0.0.0.0",
-            port=8000,
-        )
-
-        try:
-            execute_subprocess_async(
-                [
-                    "axolotl",
-                    "train",
-                    str(Path(temp_dir) / "config.yaml"),
-                    "--num-processes",
-                    str(num_gpus),
-                    "--main-process-port",
-                    f"{get_torch_dist_unique_port()}",
-                ],
-                env={"NCCL_P2P_LEVEL": "LOC", "NCCL_DEBUG": "INFO", **current_env},
-            )
-        finally:
-            os.kill(vllm_process_id, 9)

tests/e2e/multigpu/test_eval.py

@@ -52,9 +52,9 @@ class TestMultiGPUEval:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 2,
+                "max_steps": 5,
                 "micro_batch_size": 2,
-                "gradient_accumulation_steps": 2,
+                "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
@@ -121,9 +121,9 @@ class TestMultiGPUEval:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 2,
+                "max_steps": 5,
                 "micro_batch_size": 2,
-                "gradient_accumulation_steps": 2,
+                "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",

tests/e2e/multigpu/test_gemma3.py

@@ -1,100 +0,0 @@
-"""
-E2E tests for multigpu lora tinyllama
-"""
-
-import logging
-import os
-from pathlib import Path
-
-import pytest
-import yaml
-from accelerate.test_utils import execute_subprocess_async
-from huggingface_hub import snapshot_download
-from transformers.testing_utils import get_torch_dist_unique_port
-
-from axolotl.utils.dict import DictDefault
-
-from tests.e2e.utils import check_tensorboard
-
-LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
-os.environ["WANDB_DISABLED"] = "true"
-
-AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_model():
-    # download the model
-    snapshot_download("axolotl-mirrors/gemma-3-4b-pt", repo_type="model")
-
-
-class TestMultiGPUGemma3:
-    """
-    Test case for Gemma3 models using LoRA
-    """
-
-    def test_lora_ddp_packed(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "axolotl-mirrors/gemma-3-4b-pt",
-                "sequence_len": 2048,
-                "ddp_find_unused_parameters": True,
-                "sample_packing": True,
-                "eval_sample_packing": False,
-                "pad_to_sequence_len": True,
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0.0,
-                "chat_template": "gemma3",
-                "datasets": [
-                    {
-                        "path": "mlabonne/FineTome-100k",
-                        "type": "chat_template",
-                        "split": "train[:10%]",
-                        "field_messages": "conversations",
-                        "message_field_role": "from",
-                        "message_field_content": "value",
-                    },
-                ],
-                "num_epochs": 1,
-                "max_steps": 2,
-                "micro_batch_size": 4,
-                "gradient_checkpointing": True,
-                "gradient_checkpointing_kwargs": {
-                    "use_reentrant": False,
-                },
-                "gradient_accumulation_steps": 2,
-                "output_dir": temp_dir,
-                "learning_rate": 0.0001,
-                "optimizer": "adamw_8bit",
-                "lr_scheduler": "cosine",
-                "flash_attention": True,
-                "use_tensorboard": True,
-                "bf16": 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(
-            [
-                "axolotl",
-                "train",
-                str(Path(temp_dir) / "config.yaml"),
-                "--num-processes",
-                "2",
-                "--main-process-port",
-                f"{get_torch_dist_unique_port()}",
-            ]
-        )
-
-        check_tensorboard(
-            temp_dir + "/runs", "train/train_loss", 1.8, "Train Loss is too high"
-        )

tests/e2e/multigpu/test_grpo.py

@@ -0,0 +1,174 @@
+"""
+GRPO test suite
+"""
+
+import random
+from pathlib import Path
+
+import pytest
+import yaml
+from accelerate.test_utils import execute_subprocess_async
+from e2e.utils import require_vllm
+from transformers.testing_utils import get_torch_dist_unique_port
+
+from axolotl.utils.dict import DictDefault
+
+
+class TestGRPO:
+    """
+    Test case for GRPO training using multilpe GPUs
+    """
+
+    def _utils_write_yaml_and_rewards(self, cfg, temp_dir, suffix=""):
+        # 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))
+        with open(f"rewards_{suffix}.py", "w", encoding="utf-8") as fout:
+            fout.write(
+                """import random
+def rand_reward_func(completions, **kwargs) -> list[float]:
+    return [random.uniform(0, 1) for _ in completions]
+
+def oai_gsm8k_transform(cfg, *args, **kwargs):
+    def transform_fn(example, tokenizer=None):
+        label = example["answer"].split("####")[-1].strip().replace(",", "")
+        return {
+            "prompt": [{"role": "user", "content": example["question"]},],
+            "answer": label,
+        }
+    return transform_fn, {"remove_columns": ["question"]}
+"""
+            )
+
+    @pytest.mark.parametrize(
+        "num_gpus",
+        [1, 2],
+    )
+    @require_vllm
+    def test_llama_dora(self, temp_dir, num_gpus):
+        rnd_reward_suffix = str(random.randint(1000, 9999))
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "chat_template": "llama3",
+                "rl": "grpo",
+                "trl": {
+                    "beta": 0.001,
+                    "max_completion_length": 256,
+                    "use_vllm": True,
+                    "vllm_device": "auto" if num_gpus == 1 else "cuda:1",
+                    "vllm_gpu_memory_utilization": 0.15,
+                    "num_generations": 4,
+                    "reward_funcs": [f"rewards_{rnd_reward_suffix}.rand_reward_func"],
+                },
+                "datasets": [
+                    {
+                        "path": "openai/gsm8k",
+                        "name": "main",
+                        "type": f"rewards_{rnd_reward_suffix}.oai_gsm8k_transform",
+                    },
+                ],
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "peft_use_dora": True,
+                "flash_attention": True,
+                "sequence_len": 1024,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "max_steps": 5,
+                "num_epochs": 1,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 2,
+                "warmup_steps": 10,
+                "val_set_size": 0.0,
+                "output_dir": temp_dir,
+                "learning_rate": 0.0001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+                "use_tensorboard": True,
+            }
+        )
+
+        self._utils_write_yaml_and_rewards(cfg, temp_dir, suffix=rnd_reward_suffix)
+
+        execute_subprocess_async(
+            [
+                "axolotl",
+                "train",
+                str(Path(temp_dir) / "config.yaml"),
+                "--num-processes",
+                str(num_gpus),
+                "--main-process-port",
+                f"{get_torch_dist_unique_port()}",
+            ]
+        )
+
+    @pytest.mark.parametrize(
+        "num_gpus",
+        [1, 2],
+    )
+    @require_vllm
+    def test_llama_fft(self, temp_dir, num_gpus):
+        rnd_reward_suffix = str(random.randint(1000, 9999))
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "chat_template": "llama3",
+                "rl": "grpo",
+                "trl": {
+                    "beta": 0.001,
+                    "max_completion_length": 256,
+                    "use_vllm": True,
+                    "vllm_device": "auto" if num_gpus == 1 else "cuda:1",
+                    "vllm_gpu_memory_utilization": 0.15,
+                    "num_generations": 4,
+                    "reward_funcs": [f"rewards_{rnd_reward_suffix}.rand_reward_func"],
+                },
+                "datasets": [
+                    {
+                        "path": "openai/gsm8k",
+                        "name": "main",
+                        "type": f"rewards_{rnd_reward_suffix}.oai_gsm8k_transform",
+                    },
+                ],
+                "flash_attention": True,
+                "sequence_len": 1024,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "max_steps": 5,
+                "num_epochs": 1,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 2,
+                "warmup_steps": 10,
+                "val_set_size": 0.0,
+                "output_dir": temp_dir,
+                "learning_rate": 0.0001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+                "use_tensorboard": True,
+            }
+        )
+
+        self._utils_write_yaml_and_rewards(cfg, temp_dir, suffix=rnd_reward_suffix)
+
+        execute_subprocess_async(
+            [
+                "axolotl",
+                "train",
+                str(Path(temp_dir) / "config.yaml"),
+                "--num-processes",
+                str(num_gpus),
+                "--main-process-port",
+                f"{get_torch_dist_unique_port()}",
+            ]
+        )

tests/e2e/multigpu/test_llama.py

@@ -9,13 +9,12 @@ from pathlib import Path
 import pytest
 import yaml
 from accelerate.test_utils import execute_subprocess_async
+from e2e.utils import check_tensorboard
 from huggingface_hub import snapshot_download
 from transformers.testing_utils import get_torch_dist_unique_port
 
 from axolotl.utils.dict import DictDefault
 
-from tests.e2e.utils import check_tensorboard
-
 LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
 os.environ["WANDB_DISABLED"] = "true"
 
@@ -58,7 +57,6 @@ class TestMultiGPULlama:
                 "max_steps": 2,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
-                "gradient_checkpointing": True,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
@@ -122,7 +120,6 @@ class TestMultiGPULlama:
                 "max_steps": 2,
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": gradient_accumulation_steps,
-                "gradient_checkpointing": True,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
@@ -195,7 +192,6 @@ class TestMultiGPULlama:
                 "max_steps": 2,
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 4,
-                "gradient_checkpointing": True,
                 "output_dir": temp_dir,
                 "warmup_steps": 0,
                 "learning_rate": 0.00001,
@@ -273,7 +269,6 @@ class TestMultiGPULlama:
                 "max_steps": 2,
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 4,
-                "gradient_checkpointing": True,
                 "output_dir": temp_dir,
                 "warmup_steps": 0,
                 "learning_rate": 0.00001,
@@ -334,7 +329,6 @@ class TestMultiGPULlama:
                 "max_steps": 2,
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": gradient_accumulation_steps,
-                "gradient_checkpointing": True,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
@@ -404,8 +398,7 @@ class TestMultiGPULlama:
                 "num_epochs": 1,
                 "max_steps": 2,
                 "micro_batch_size": 4,
-                "gradient_accumulation_steps": 2,
-                "gradient_checkpointing": True,
+                "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
@@ -484,8 +477,7 @@ class TestMultiGPULlama:
                 "num_epochs": 1,
                 "max_steps": 2,
                 "micro_batch_size": 4,
-                "gradient_accumulation_steps": 2,
-                "gradient_checkpointing": True,
+                "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
@@ -785,10 +777,9 @@ class TestMultiGPULlama:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 2,
+                "max_steps": 5,
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": 1,
-                "gradient_checkpointing": True,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",

tests/e2e/multigpu/test_qwen2.py

@@ -46,7 +46,7 @@ class TestMultiGPUQwen2:
                     },
                 ],
                 "num_epochs": 1,
-                "max_steps": 2,
+                "max_steps": 5,
                 "warmup_steps": 20,
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 2,

tests/e2e/multigpu/test_ray.py

@@ -9,11 +9,10 @@ from pathlib import Path
 import pytest
 import yaml
 from accelerate.test_utils import execute_subprocess_async
+from e2e.utils import check_tensorboard, require_torch_lt_2_6_0
 
 from axolotl.utils.dict import DictDefault
 
-from tests.e2e.utils import check_tensorboard, require_torch_lt_2_6_0
-
 LOG = logging.getLogger(__name__)
 os.environ["WANDB_DISABLED"] = "true"
 
@@ -50,7 +49,7 @@ class TestMultiGPURay:
                 "num_epochs": 1,
                 "max_steps": 2,
                 "micro_batch_size": 4,
-                "gradient_accumulation_steps": 2,
+                "gradient_accumulation_steps": 4,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",

tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py

@@ -144,7 +144,7 @@ def test_swiglu_mlp_integration(small_llama_model):
 def test_geglu_model_integration():
     """Test GeGLU activation with Gemma model."""
     model = AutoModelForCausalLM.from_pretrained(
-        "mhenrichsen/gemma-2b", torch_dtype=torch.float16, device_map="auto"
+        "mhenrichsen/gemma-2b", torch_dtype=torch.float16, device_map="cuda"
     )
     peft_config = get_peft_config(
         {
@@ -347,7 +347,7 @@ def test_model_architecture(model_config):
     """Test LoRA kernel patches across different model architectures."""
     # Load model with appropriate dtype
     model = AutoModelForCausalLM.from_pretrained(
-        model_config["name"], torch_dtype=model_config["dtype"], device_map="auto"
+        model_config["name"], torch_dtype=model_config["dtype"], device_map="cuda"
     )
 
     # Apply LoRA configuration
@@ -408,7 +408,7 @@ def test_kernel_training_integration():
     )
 
     # Load model
-    model, _, _ = load_model_and_tokenizer(cfg=cfg)
+    model, _ = load_model_and_tokenizer(cfg=cfg)
 
     # Verify correct activation function
     layer = model.model.model.layers[0]