fix: missing key in enum

* feat: update handling for mistraltokenizer decode

* fix: update mistral common package version

* fix: to use correct release

* fix triton path

---------

Co-authored-by: Wing Lian <wing@axolotl.ai>

.github/workflows/main.yml

@@ -20,12 +20,11 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras: vllm
-            is_latest: true
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
@@ -88,8 +87,8 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras:
@@ -146,8 +145,8 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras:

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

@@ -26,11 +26,11 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.6.0
-            axolotl_extras: vllm
+            axolotl_extras:
             num_gpus: 2
             nightly_build: "true"
           - cuda: 124

.github/workflows/tests.yml

@@ -195,12 +195,12 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.6.0
             num_gpus: 1
-            axolotl_extras: vllm
+            axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
@@ -247,8 +247,8 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.6.0
             num_gpus: 1
@@ -311,7 +311,7 @@ jobs:
             python_version: "3.11"
             pytorch: 2.6.0
             num_gpus: 1
-            axolotl_extras: vllm
+            axolotl_extras:
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.pre-commit-config.yaml

@@ -19,7 +19,7 @@ repos:
     hooks:
       - id: isort
 -   repo: https://github.com/PyCQA/flake8
-    rev: 7.2.0
+    rev: 7.3.0
     hooks:
     - id: flake8
 -   repo: https://github.com/pylint-dev/pylint
@@ -27,7 +27,7 @@ repos:
     hooks:
     - id: pylint
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.16.0
+    rev: v1.16.1
     hooks:
     - id: mypy
       additional_dependencies:
@@ -36,7 +36,7 @@ repos:
             'pydantic>=2.5.3',
         ]
 -   repo: https://github.com/PyCQA/bandit
-    rev: 1.8.3
+    rev: 1.8.5
     hooks:
     -   id: bandit
         args: [

README.md

@@ -43,7 +43,7 @@ Features:
 - **Multiple Model Support**: Train various models like LLaMA, Mistral, Mixtral, Pythia, and more. We are compatible with HuggingFace transformers causal language models.
 - **Training Methods**: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), Multimodal, and Reward Modelling (RM) / Process Reward Modelling (PRM).
 - **Easy Configuration**: Re-use a single YAML file between dataset preprocess, training, evaluation, quantization, and inference.
-- **Performance Optimizations**: [Multipacking](https://docs.axolotl.ai/docs/multipack.html), [Flash Attention](https://github.com/Dao-AILab/flash-attention), [Xformers](https://github.com/facebookresearch/xformers), [Flex Attention](https://pytorch.org/blog/flexattention/), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), [Cut Cross Entropy](https://github.com/apple/ml-cross-entropy/tree/main), Sequence Parallelism (SP), LoRA optimizations, Multi-GPU training (FSDP1, FSDP2, DeepSpeed), Multi-node training (Torchrun, Ray), and many more!
+- **Performance Optimizations**: [Multipacking](https://docs.axolotl.ai/docs/multipack.html), [Flash Attention](https://github.com/Dao-AILab/flash-attention), [Xformers](https://github.com/facebookresearch/xformers), [Flex Attention](https://pytorch.org/blog/flexattention/), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), [Cut Cross Entropy](https://github.com/apple/ml-cross-entropy/tree/main), [Sequence Parallelism (SP)](https://docs.axolotl.ai/docs/sequence_parallelism.html), [LoRA optimizations](https://docs.axolotl.ai/docs/lora_optims.html), [Multi-GPU training (FSDP1, FSDP2, DeepSpeed)](https://docs.axolotl.ai/docs/multi-gpu.html), [Multi-node training (Torchrun, Ray)](https://docs.axolotl.ai/docs/multi-node.html), and many more!
 - **Flexible Dataset Handling**: Load from local, HuggingFace, and cloud (S3, Azure, GCP, OCI) datasets.
 - **Cloud Ready**: We ship [Docker images](https://hub.docker.com/u/axolotlai) and also [PyPI packages](https://pypi.org/project/axolotl/) for use on cloud platforms and local hardware.
 
@@ -59,6 +59,8 @@ Features:
 
 ### Installation
 
+#### Using pip
+
 ```bash
 pip3 install -U packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
@@ -68,6 +70,13 @@ axolotl fetch examples
 axolotl fetch deepspeed_configs  # OPTIONAL
 ```
 
+#### Using Docker
+
+Installing with Docker can be less error prone than installing in your own environment.
+```bash
+docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
+```
+
 Other installation approaches are described [here](https://docs.axolotl.ai/docs/installation.html).
 
 ### Your First Fine-tune

cicd/single_gpu.py

@@ -32,6 +32,8 @@ df_args = {
     "NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
     "CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
     "HF_HOME": "/workspace/data/huggingface-cache/hub",
+    "PYTHONUNBUFFERED": os.environ.get("PYTHONUNBUFFERED", "1"),
+    "DEEPSPEED_LOG_LEVEL": os.environ.get("DEEPSPEED_LOG_LEVEL", "WARNING"),
 }
 
 dockerfile_contents = df_template.render(**df_args)

docker/Dockerfile-base

@@ -37,7 +37,3 @@ RUN git lfs install --skip-repo && \
     pip3 install awscli && \
     # The base image ships with `pydantic==1.8.2` which is not working
     pip3 install -U --no-cache-dir pydantic==1.10.10
-
-RUN if [ "$PYTORCH_VERSION" = "2.7.1" ] ; then \
-        pip3 install flash-attn==2.7.4.post1; \
-    fi

docker/Dockerfile-uv-base

@@ -34,7 +34,3 @@ RUN uv pip install packaging setuptools wheel psutil \
     && uv pip install --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" \
     && uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" \
     && uv pip install awscli pydantic
-
-RUN if [ "$PYTORCH_VERSION" = "2.7.1" ] ; then \
-        uv pip install --no-build-isolation flash-attn==2.7.4.post1; \
-    fi

docs/dataset-formats/conversation.qmd

@@ -9,7 +9,7 @@ order: 3
 Chat Template strategy uses a jinja2 template that converts a list of messages into a prompt. Support using tokenizer's template, a supported template, or custom jinja2.
 
 ```{.json filename="data.jsonl"}
-{"conversations": [{"role": "...", "content": "..."}]}
+{"messages": [{"role": "...", "content": "..."}, {"role": "...", "content": "..."}, ...]}
 ```
 
 See [configs](../config-reference.qmd) for full configs and supported templates.

docs/docker.qmd

@@ -9,7 +9,7 @@ format:
 This section describes the different Docker images that are released by AxolotlAI at [Docker Hub](https://hub.docker.com/u/axolotlai).
 
 ::: {.callout-important}
-For Blackwell GPUs, please use the tags with Pytorch 2.7.1 and CUDA 12.8.
+For Blackwell GPUs, please use the tags with PyTorch 2.7.1 and CUDA 12.8.
 :::
 
 ## Base
@@ -34,6 +34,7 @@ Tags examples:
 
 - `main-base-py3.11-cu128-2.7.1`
 - `main-base-py3.11-cu126-2.7.1`
+- `main-base-py3.11-cu126-2.6.0`
 - `main-base-py3.11-cu124-2.6.0`
 - `main-base-py3.11-cu124-2.5.1`
 
@@ -73,13 +74,15 @@ There may be some extra tags appended to the image, like `-vllm` which installs
 
 Tags examples:
 
-- `main-py3.11-cu126-2.7.0`
+- `main-py3.11-cu128-2.7.1`
+- `main-py3.11-cu126-2.7.1`
+- `main-py3.11-cu126-2.6.0`
 - `main-py3.11-cu124-2.6.0`
 - `main-py3.11-cu124-2.5.1`
 - `main-latest`
 - `main-20250303-py3.11-cu124-2.6.0`
 - `main-20250303-py3.11-cu124-2.5.1`
-- `0.9.2`
+- `0.10.1`
 
 ## Cloud
 

docs/multimodal.qmd

@@ -16,6 +16,7 @@ format:
 - [Gemma-3](#sec-gemma-3)
 - [Qwen2-VL](#sec-qwen2-vl)
 - [Qwen2.5-VL](#sec-qwen25-vl)
+- [Phi3-V](#sec-phi3-v)
 
 ## Usage
 
@@ -126,6 +127,15 @@ base_model: Qwen/Qwen2.5-VL-7B-Instruct
 chat_template: qwen2_vl  # same as qwen2-vl
 ```
 
+### Phi3-V {#sec-phi3-v}
+
+```yaml
+base_model: microsoft/Phi-3.5-vision-instruct
+
+trust_remote_code: true
+chat_template: phi_35_vl
+```
+
 ## Dataset Format
 
 For multi-modal datasets, we adopt an extended `chat_template` format similar to OpenAI's Message format.

examples/falcon-h1/falcon-h1-1b-deep-qlora.yaml

@@ -0,0 +1,71 @@
+base_model: tiiuae/Falcon-H1-1.5B-Deep-Base
+# 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
+
+load_in_8bit: false
+load_in_4bit: true
+
+# huggingface repo
+chat_template: falcon_h1
+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_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+  - in_proj
+  - gate_proj
+  - up_proj
+  - down_proj
+
+sequence_len: 2048
+sample_packing: false
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch:
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/falcon-h1/falcon-h1-1b-qlora.yaml

@@ -0,0 +1,71 @@
+base_model: tiiuae/Falcon-H1-1.5B-Base
+# 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
+
+load_in_8bit: false
+load_in_4bit: true
+
+# huggingface repo
+chat_template: falcon_h1
+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_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+  - in_proj
+  - gate_proj
+  - up_proj
+  - down_proj
+
+sequence_len: 2048
+sample_packing: false
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch:
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/falcon-h1/falcon-h1-34b-qlora.yaml

@@ -0,0 +1,71 @@
+base_model: tiiuae/Falcon-H1-34B-Base
+# 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
+
+load_in_8bit: false
+load_in_4bit: true
+
+# huggingface repo
+chat_template: falcon_h1
+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_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+  - in_proj
+  - gate_proj
+  - up_proj
+  - down_proj
+
+sequence_len: 2048
+sample_packing: false
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch:
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/falcon-h1/falcon-h1-3b-qlora.yaml

@@ -0,0 +1,71 @@
+base_model: tiiuae/Falcon-H1-3B-Base
+# 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
+
+load_in_8bit: false
+load_in_4bit: true
+
+# huggingface repo
+chat_template: falcon_h1
+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_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+  - in_proj
+  - gate_proj
+  - up_proj
+  - down_proj
+
+sequence_len: 2048
+sample_packing: false
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/falcon-h1/falcon-h1-500m-qlora.yaml

@@ -0,0 +1,71 @@
+base_model: tiiuae/Falcon-H1-0.5B-Instruct
+# 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
+
+load_in_8bit: false
+load_in_4bit: true
+
+# huggingface repo
+chat_template: falcon_h1
+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_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+  - in_proj
+  - gate_proj
+  - up_proj
+  - down_proj
+
+sequence_len: 2048
+sample_packing: false
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch:
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/falcon-h1/falcon-h1-7b-qlora.yaml

@@ -0,0 +1,71 @@
+base_model: tiiuae/Falcon-H1-7B-Base
+# 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
+
+load_in_8bit: false
+load_in_4bit: true
+
+# huggingface repo
+chat_template: falcon_h1
+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_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+  - in_proj
+  - gate_proj
+  - up_proj
+  - down_proj
+
+sequence_len: 2048
+sample_packing: false
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

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

@@ -13,6 +13,8 @@ load_in_4bit: true
 
 # huggingface repo
 chat_template: gemma3
+eot_tokens:
+  - <end_of_turn>
 datasets:
   - path: cgato/SlimOrcaDedupCleaned
     type: chat_template

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

@@ -6,6 +6,8 @@ load_in_4bit: true
 ddp_find_unused_parameters: true
 
 chat_template: gemma3
+eot_tokens:
+  - <end_of_turn>
 datasets:
   - path: cgato/SlimOrcaDedupCleaned
     type: chat_template

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

@@ -12,6 +12,8 @@ sample_packing: false
 ddp_find_unused_parameters: true
 
 chat_template: gemma3
+eot_tokens:
+  - <end_of_turn>
 datasets:
   - path: HuggingFaceH4/llava-instruct-mix-vsft
     type: chat_template

examples/qwen2_5-vl/lora-7b.yaml

@@ -0,0 +1,55 @@
+base_model: Qwen/Qwen2.5-VL-7B-Instruct
+processor_type: AutoProcessor
+
+# 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.language_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
+
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0

requirements.txt

@@ -1,7 +1,7 @@
 --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==0.46.0
 triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
@@ -15,7 +15,7 @@ huggingface_hub==0.32.2
 peft==0.15.2
 transformers==4.52.4
 tokenizers>=0.21.1
-accelerate==1.7.0
+accelerate==1.8.1
 datasets==3.6.0
 deepspeed>=0.17.0
 trl==0.18.2
@@ -68,4 +68,4 @@ schedulefree==1.4.1
 axolotl-contribs-lgpl==0.0.6
 axolotl-contribs-mit==0.0.3
 
-mistral-common==1.6.0
+mistral-common==1.6.3

scripts/cutcrossentropy_install.py

@@ -29,5 +29,5 @@ UV_PREFIX = "uv " if USE_UV else ""
 
 print(
     UNINSTALL_PREFIX
-    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@a1174ca"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@78b2a45713a54c9bedf8b33f5e31cf07a1a57154"'
 )

setup.py

@@ -111,9 +111,9 @@ def get_package_version():
 
 
 extras_require = {
-    "flash-attn": ["flash-attn==2.7.4.post1"],
+    "flash-attn": ["flash-attn==2.8.0.post2"],
     "ring-flash-attn": [
-        "flash-attn==2.7.4.post1",
+        "flash-attn==2.8.0.post2",
         "ring-flash-attn>=0.1.4",
         "yunchang==0.6.0",
     ],

src/axolotl/cli/checks.py

@@ -6,6 +6,7 @@ from pathlib import Path
 from accelerate.commands.config import config_args
 from huggingface_hub import HfApi
 from huggingface_hub.utils import LocalTokenNotFoundError
+from requests import HTTPError
 
 from axolotl.utils.logging import get_logger
 
@@ -46,3 +47,8 @@ def check_user_token() -> bool:
             "Error verifying HuggingFace token. Remember to log in using `huggingface-cli login` and get your access token from https://huggingface.co/settings/tokens if you want to use gated models or datasets."
         )
         return False
+    except HTTPError:
+        LOG.warning(
+            "Error accessing HuggingFace. This may be due to a network issue or rate limiting."
+        )
+        return False

src/axolotl/cli/cloud/__init__.py

@@ -7,7 +7,6 @@ from typing import Union
 
 import yaml
 
-from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.cloud.modal_ import ModalCloud
 from axolotl.utils.dict import DictDefault
 
@@ -24,7 +23,6 @@ def do_cli_preprocess(
     cloud_config: Union[Path, str],
     config: Union[Path, str],
 ) -> None:
-    print_axolotl_text_art()
     cloud_cfg = load_cloud_cfg(cloud_config)
     cloud = ModalCloud(cloud_cfg)
     with open(config, "r", encoding="utf-8") as file:
@@ -39,7 +37,6 @@ def do_cli_train(
     cwd=None,
     **kwargs,
 ) -> None:
-    print_axolotl_text_art()
     cloud_cfg = load_cloud_cfg(cloud_config)
     cloud = ModalCloud(cloud_cfg)
     with open(config, "r", encoding="utf-8") as file:
@@ -54,7 +51,6 @@ def do_cli_lm_eval(
     cloud_config: Union[Path, str],
     config: Union[Path, str],
 ) -> None:
-    print_axolotl_text_art()
     cloud_cfg = load_cloud_cfg(cloud_config)
     cloud = ModalCloud(cloud_cfg)
     with open(config, "r", encoding="utf-8") as file:

src/axolotl/cli/config.py

@@ -28,6 +28,8 @@ from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 LOG = get_logger(__name__)
 
+API_KEY_FIELDS = {"comet_api_key"}
+
 
 def check_remote_config(config: Union[str, Path]) -> Union[str, Path]:
     """
@@ -233,4 +235,15 @@ def load_cfg(
     setup_comet_env_vars(cfg)
     plugin_set_cfg(cfg)
 
+    cfg_to_log = {
+        k: "[REDACTED]" if k in API_KEY_FIELDS else v
+        for k, v in cfg.items()
+        if v is not None
+    }
+
+    LOG.info(
+        "config:\n%s",
+        json.dumps(cfg_to_log, indent=2, default=str, sort_keys=True),
+    )
+
     return cfg

src/axolotl/cli/evaluate.py

@@ -9,7 +9,6 @@ from dotenv import load_dotenv
 from transformers.hf_argparser import HfArgumentParser
 
 from axolotl.cli.args import TrainerCliArgs
-from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.checks import check_accelerate_default_config, check_user_token
 from axolotl.cli.config import load_cfg
 from axolotl.common.datasets import load_datasets, load_preference_datasets
@@ -35,7 +34,6 @@ def do_evaluate(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
     patch_optimized_env()
 
     # pylint: disable=duplicate-code
-    print_axolotl_text_art()
     check_accelerate_default_config()
     if int(os.getenv("LOCAL_RANK", "0")) == 0:
         check_user_token()

src/axolotl/cli/inference.py

@@ -13,7 +13,6 @@ from dotenv import load_dotenv
 from transformers import GenerationConfig, TextIteratorStreamer, TextStreamer
 
 from axolotl.cli.args import InferenceCliArgs
-from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
 from axolotl.cli.utils import load_model_and_tokenizer
 from axolotl.utils.chat_templates import (
@@ -255,7 +254,6 @@ def do_cli(
         kwargs: Additional keyword arguments to override config file values.
     """
     # pylint: disable=duplicate-code
-    print_axolotl_text_art()
     parsed_cfg = load_cfg(config, inference=True, rl=None, **kwargs)
     parsed_cfg.sample_packing = False
     parser = transformers.HfArgumentParser(InferenceCliArgs)

src/axolotl/cli/main.py

@@ -20,6 +20,7 @@ from axolotl.cli.args import (
     TrainerCliArgs,
     VllmServeCliArgs,
 )
+from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.sweeps import generate_sweep_configs
 from axolotl.cli.utils import (
     add_options_from_config,
@@ -40,6 +41,7 @@ LOG = get_logger(__name__)
 @click.version_option(version=axolotl.__version__, prog_name="axolotl")
 def cli():
     """Axolotl CLI - Train and fine-tune large language models"""
+    print_axolotl_text_art()
 
 
 @cli.command()

src/axolotl/cli/merge_lora.py

@@ -6,7 +6,6 @@ from typing import Union
 import fire
 from dotenv import load_dotenv
 
-from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
 from axolotl.cli.utils import load_model_and_tokenizer
 from axolotl.utils.dict import DictDefault
@@ -23,8 +22,6 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
     """
-    print_axolotl_text_art()
-
     model, tokenizer, processor = load_model_and_tokenizer(cfg=cfg)
     safe_serialization = cfg.save_safetensors is True
 

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -22,7 +22,6 @@ from huggingface_hub import split_torch_state_dict_into_shards
 from safetensors.torch import save_file as safe_save_file
 from torch.distributed.checkpoint.format_utils import _EmptyStateDictLoadPlanner
 
-from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
 from axolotl.utils.logging import get_logger
 
@@ -194,7 +193,6 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         kwargs: Additional keyword arguments to override config file values.
     """
     # pylint: disable=duplicate-code
-    print_axolotl_text_art()
     parsed_cfg = load_cfg(config, **kwargs)
 
     fsdp_dir = Path(parsed_cfg.output_dir) / "pytorch_model_fsdp_0"

src/axolotl/cli/preprocess.py

@@ -12,7 +12,6 @@ from dotenv import load_dotenv
 from transformers import AutoModelForCausalLM
 
 from axolotl.cli.args import PreprocessCliArgs
-from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.checks import check_accelerate_default_config, check_user_token
 from axolotl.cli.config import load_cfg
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
@@ -33,7 +32,6 @@ def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: Preprocessing-specific CLI arguments.
     """
-    print_axolotl_text_art()
     check_accelerate_default_config()
     check_user_token()
 

src/axolotl/cli/quantize.py

@@ -7,7 +7,6 @@ from typing import Union
 
 from transformers import AutoModelForCausalLM
 
-from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
 from axolotl.loaders import load_tokenizer
 from axolotl.utils.logging import get_logger
@@ -27,7 +26,6 @@ def do_quantize(
         config (Union[Path, str]): The path to the config file
         cli_args (dict): Additional command-line arguments
     """
-    print_axolotl_text_art()
 
     cfg = load_cfg(config)
 

src/axolotl/cli/train.py

@@ -11,7 +11,6 @@ from dotenv import load_dotenv
 from transformers.hf_argparser import HfArgumentParser
 
 from axolotl.cli.args import TrainerCliArgs
-from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.checks import check_accelerate_default_config, check_user_token
 from axolotl.cli.config import load_cfg
 from axolotl.common.datasets import load_datasets, load_preference_datasets
@@ -35,7 +34,6 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
     # Enable expandable segments for cuda allocation to improve VRAM usage
     patch_optimized_env()
 
-    print_axolotl_text_art()
     check_accelerate_default_config()
     if int(os.getenv("LOCAL_RANK", "0")) == 0:
         check_user_token()

src/axolotl/common/datasets.py

@@ -75,13 +75,17 @@ def load_datasets(
 
         num_examples = cli_args.debug_num_examples if cli_args else 1
         text_only = cli_args.debug_text_only if cli_args else False
-        train_samples = sample_dataset(train_dataset, num_examples)
-        check_dataset_labels(
-            train_samples,
-            tokenizer,
-            num_examples=num_examples,
-            text_only=text_only,
-        )
+        try:
+            train_samples = sample_dataset(train_dataset, num_examples)
+            check_dataset_labels(
+                train_samples,
+                tokenizer,
+                num_examples=num_examples,
+                text_only=text_only,
+            )
+        except AttributeError:
+            # can't sample iterable datasets
+            pass
 
         LOG.info("printing prompters...")
         for prompter in prompters:

src/axolotl/core/builders/causal.py

@@ -253,6 +253,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs["eval_sample_packing"] = bool(
             self.cfg.eval_sample_packing
         )
+        if self.cfg.sample_packing_sequentially is not None:
+            training_arguments_kwargs["sample_packing_sequentially"] = (
+                self.cfg.sample_packing_sequentially
+            )
         if self.cfg.sample_packing_bin_size is not None:
             training_arguments_kwargs["sample_packing_bin_size"] = (
                 self.cfg.sample_packing_bin_size
@@ -413,7 +417,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 or self.cfg.micro_batch_size > 1
             ):
                 return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
-            return None
+            if not (self.cfg.sample_packing and self.cfg.pretrain_multipack_attn):
+                return None
 
         if self.cfg.model_config_type == "mamba":
             return MambaDataCollator(tokenizer=self.tokenizer)

src/axolotl/core/trainers/base.py

@@ -20,7 +20,7 @@ from torch.utils.data import (
     SequentialSampler,
 )
 from transformers import Trainer
-from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, seed_worker
+from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, has_length, seed_worker
 from trl.trainer.utils import pad_to_length
 from typing_extensions import override
 
@@ -116,14 +116,15 @@ class AxolotlTrainer(
             sequential=self.args.sample_packing_sequentially,
             drop_last=True,
             num_processes=self.args.dataset_num_proc,
+            mp_start_method=self.args.sample_packing_mp_start_method or "fork",
         )
 
         len(sampler)
         return sampler
 
     def _get_train_sampler(
-        self, train_dataset: Optional[Dataset] = None
-    ) -> Optional[Sampler]:
+        self, train_dataset: Dataset | None = None
+    ) -> Sampler | None:
         """
         Helper method to get the sampler for training. Handles cases for sample packing
         and curriculum sampling (sequential).
@@ -132,16 +133,22 @@ class AxolotlTrainer(
             If the dataset is non-empty, a sampler is returned, the type of which
                 depends on the passed training args.
         """
+        # from https://github.com/huggingface/transformers/blob/2166b6b4ff09f6dd3867ab982f262f66482aa968/src/transformers/trainer.py#L969C1-L972C24
+        if train_dataset is None:
+            train_dataset = self.train_dataset
+        if train_dataset is None or not has_length(train_dataset):
+            return None
+
         use_sample_packing = self.args.sample_packing and not self.args.pretraining
 
         # Determine the base sampler first
         if self.args.curriculum_sampling:
-            base_sampler = SequentialSampler(self.train_dataset)
+            base_sampler = SequentialSampler(train_dataset)
         elif use_sample_packing:
-            base_sampler = RandomSampler(self.train_dataset)
+            base_sampler = RandomSampler(train_dataset)
         else:
             # Default to parent class implementation for standard random sampling
-            return super()._get_train_sampler()
+            return super()._get_train_sampler(train_dataset)
 
         # Apply multipack wrapper if needed
         if use_sample_packing:
@@ -160,6 +167,10 @@ class AxolotlTrainer(
             If the dataset is non-empty, a sampler is returned, the type of which
                 depends on the passed training args.
         """
+        # from https://github.com/huggingface/transformers/blob/2166b6b4ff09f6dd3867ab982f262f66482aa968/src/transformers/trainer.py#L1065C9-L1066C24
+        if eval_dataset is None or not has_length(eval_dataset):
+            return None
+
         # Multipacking enabled if training is enabled and eval is not explicitly disabled
         use_multipack = (
             self.args.sample_packing and self.args.eval_sample_packing is not False

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

@@ -28,7 +28,7 @@ class DPOStrategy:
         training_args_kwargs["max_completion_length"] = None
         training_args_kwargs["max_length"] = cfg.sequence_len
         training_args_kwargs["max_prompt_length"] = cfg.sequence_len
-        training_args_kwargs["generate_during_eval"] = cfg.use_wandb
+        training_args_kwargs["generate_during_eval"] = cfg.dpo_generate_during_eval
         if cfg.dpo_use_weighting is not None:
             training_args_kwargs["use_weighting"] = cfg.dpo_use_weighting
         if cfg.dpo_padding_free is not None:

src/axolotl/core/training_args_base.py

@@ -38,6 +38,10 @@ class AxolotlTrainingMixins:
             "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."
         },
     )
+    sample_packing_mp_start_method: str | None = field(
+        default=None,
+        metadata={"help": "The multiprocessing start method to use."},
+    )
     multipack_real_batches: bool = field(
         default=False,
         metadata={"help": "Use real batches for efficient training."},

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -19,19 +19,11 @@ python scripts/cutcrossentropy_install.py | sh
 
 - If you are installing from pip
 ```bash
-pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@bad6f7b49c75fdec69471abb71b4cddd0f0c6438"
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@78b2a45713a54c9bedf8b33f5e31cf07a1a57154"
 ```
 
 ## Usage
 
-**NOTE**: If you are training a VLM model, please use older version of Axolotl as upstream has applied a major VLM refactor, and our patches have not been updated yet.
-
-```bash
-git checkout 787880215b3ab32ccaf81c1b2e9588c6f3e6e764
-
-pip3 install --no-build-isolation -e .
-```
-
 ```yaml
 plugins:
   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
@@ -39,27 +31,29 @@ plugins:
 
 ## Supported Models
 
-- llama
-- llama4
-- llama4_text
-- mllama
-- phi3
+- cohere
+- cohere2
 - gemma
 - gemma2
 - gemma3
 - gemma3_text
+- glm
+- glm4
+- llama
+- llama4
+- llama4_text
 - mistral
 - mistral3
+- mllama
+- phi
+- phi3
+- phi4_multimodal
 - qwen2
-- qwen2_moe
 - qwen2_vl
+- qwen2_moe
 - qwen2_5_vl
 - qwen3
 - qwen3_moe
-- cohere
-- cohere2
-- glm
-- glm4
 
 ## Citation
 

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -31,8 +31,8 @@ from .args import CutCrossEntropyArgs  # pylint: disable=unused-import. # noqa:
 LOG = get_logger(__name__)
 
 _CCE_INSTALL_MESSAGE = (
-    "Please install cut_cross_entropy with transformers support using "
-    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@bad6f7b49c75fdec69471abb71b4cddd0f0c6438"`'
+    "Please install Axolotl's fork of cut_cross_entropy with transformers support using "
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@7f6afce"`'
 )
 
 
@@ -64,16 +64,28 @@ class CutCrossEntropyPlugin(BasePlugin):
             "cut_cross_entropy.transformers"
         )
         if cce_spec_transformers is None:
-            raise ImportError(_CCE_INSTALL_MESSAGE)
+            raise ImportError(
+                "Transformers support is not installed. " + _CCE_INSTALL_MESSAGE
+            )
+
+        # Check if Axolotl's cce fork is installed
+        try:
+            from cut_cross_entropy.transformers.patch import AXOLOTL_CCE_FORK
+
+            if not AXOLOTL_CCE_FORK:
+                raise ImportError
+        except ImportError as e:
+            raise ImportError(
+                "Axolotl's fork of cut_cross_entropy is not installed. "
+                + _CCE_INSTALL_MESSAGE
+            ) from e
 
     def pre_model_load(self, cfg):
         """Apply cut cross entropy before model loading if enabled."""
         if cfg.cut_cross_entropy:
             self._check_requirements()
 
-            from axolotl.integrations.cut_cross_entropy.monkeypatch.patch import (
-                cce_patch,
-            )
+            from cut_cross_entropy.transformers.patch import cce_patch
 
             LOG.info(
                 f"Applying Cut Cross Entropy to model type: {cfg.model_config_type}"

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

@@ -1,191 +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 (
-    KwargsForCausalLM,
-)
-from transformers.processing_utils import Unpack
-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")
-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 hidden_states by logit_scale in-place of logits
-        loss = apply_lce(
-            hidden_states[:, slice_indices, :] * self.logit_scale,
-            self.lm_head.weight,
-            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

@@ -1,165 +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 (
-    KwargsForCausalLM,
-)
-from transformers.processing_utils import Unpack
-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")
-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

@@ -1,447 +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 (
-    Gemma3CausalLMOutputWithPast,
-    logger,
-)
-from transformers.utils import (
-    is_torchdynamo_compiling,
-)
-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")
-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")
-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/glm4.py

@@ -1,57 +0,0 @@
-"""GLM 4 patch. GLM family inherits from Llama."""
-
-from types import MethodType
-
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-)
-
-
-def patch_glm(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-
-    # Set the _PATCH_OPTS in the llama patch file
-    import cut_cross_entropy.transformers.llama as llama_patch
-
-    llama_patch._PATCH_OPTS = patch_options  # pylint: disable=protected-access
-
-    from cut_cross_entropy.transformers.llama import cce_forward
-    from transformers.models.glm import modeling_glm
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_glm.GlmForCausalLM
-        ), f"Expected a GlmForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_glm.GlmForCausalLM.forward = cce_forward
-    return None
-
-
-def patch_glm4(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-
-    # Set the _PATCH_OPTS in the llama patch file
-    import cut_cross_entropy.transformers.llama as llama_patch
-
-    llama_patch._PATCH_OPTS = patch_options  # pylint: disable=protected-access
-
-    from cut_cross_entropy.transformers.llama import cce_forward
-    from transformers.models.glm4 import modeling_glm4
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_glm4.Glm4ForCausalLM
-        ), f"Expected a Glm4ForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_glm4.Glm4ForCausalLM.forward = cce_forward
-    return None

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

@@ -1,164 +0,0 @@
-"""Llama CCE patch. Adapted from transformers v4.51.2"""
-
-# pylint: disable=duplicate-code
-
-
-from types import MethodType
-from typing import Optional, 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 (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-)
-from transformers.models.llama.modeling_llama import (
-    KwargsForCausalLM,
-)
-from transformers.processing_utils import Unpack
-from transformers.utils.deprecation import deprecate_kwarg
-from transformers.utils.generic import can_return_tuple
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-@can_return_tuple
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-def cce_forward(
-    self,
-    input_ids: Optional[torch.LongTensor] = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[Cache] = 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,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **kwargs: Unpack[KwargsForCausalLM],
-) -> 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, LlamaForCausalLM
-
-    >>> model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
-    >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-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
-    )
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs: BaseModelOutputWithPast = 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,
-        cache_position=cache_position,
-        **kwargs,
-    )
-
-    hidden_states = outputs.last_hidden_state
-    if hidden_states is None:
-        raise ValueError("hidden_states is None")
-
-    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,
-            )
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-def patch_llama(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    """Patch Llama for CCE."""
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.llama import modeling_llama
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_llama.LlamaForCausalLM
-        ), f"Expected a LlamaForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_llama.LlamaForCausalLM.forward = cce_forward
-    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/llama4.py

@@ -1,401 +0,0 @@
-"""Llama4 CCE patch. Adapted from transformers 4.51.0."""
-
-# 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 torch import nn
-from transformers.cache_utils import Cache
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.models.llama4.modeling_llama4 import (
-    Llama4CausalLMOutputWithPast,
-)
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-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,
-    defer_logits_calculation: bool = False,
-    **kwargs,
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-    Args:
-        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*, defaults to `False`):
-            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, Llama4ForCausalLM
-
-    >>> model = Llama4ForCausalLM.from_pretrained("meta-llama4/Llama4-2-7b-hf")
-    >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama4/Llama4-2-7b-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,  # type: ignore
-    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,
-    vision_feature_select_strategy: Optional[str] = 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, Llama4CausalLMOutputWithPast]:
-    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, LlavaForConditionalGeneration
-
-    >>> model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf")
-    >>> processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
-
-    >>> prompt = "USER: <image>\nWhat's the content of the image? ASSISTANT:"
-    >>> url = "https://www.ilankelman.org/stopsigns/australia.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]
-    "USER:  \nWhat's the content of the image? ASSISTANT: The image features a busy city street with a stop sign prominently displayed"
-    ```"""
-
-    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_config.vision_feature_layer
-    )
-    vision_feature_select_strategy = (
-        vision_feature_select_strategy
-        if vision_feature_select_strategy is not None
-        else self.config.vision_config.vision_feature_select_strategy
-    )
-
-    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)  # type: ignore
-
-    if pixel_values is not None:
-        image_features = self.get_image_features(
-            pixel_values=pixel_values,
-            vision_feature_layer=vision_feature_layer,
-            vision_feature_select_strategy=vision_feature_select_strategy,
-            image_sizes=image_sizes,
-        )
-        original_inputs_embeds_shape = inputs_embeds.shape  # type: ignore
-
-        vision_flat = image_features.view(-1, image_features.size(-1))
-        projected_vision_flat = self.multi_modal_projector(vision_flat)
-
-        special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1)
-        final_mask = special_image_mask.to(inputs_embeds.device)  # type: ignore
-        inputs_embeds = inputs_embeds.view(-1, inputs_embeds.size(-1))  # type: ignore
-
-        final_mask_1d = final_mask[..., 0].reshape(-1)
-        num_tokens_to_fill = final_mask_1d.sum()
-
-        if num_tokens_to_fill != projected_vision_flat.size(0):
-            raise ValueError(
-                f"Mismatch: final_mask wants {num_tokens_to_fill} embeddings, "
-                f"but multi_modal_projector returned {projected_vision_flat.size(0)}"
-            )
-
-        expanded_mask = final_mask_1d.unsqueeze(-1).expand(-1, inputs_embeds.size(-1))
-        inputs_embeds = inputs_embeds.masked_scatter(
-            expanded_mask, projected_vision_flat
-        )  # type: ignore
-        inputs_embeds = inputs_embeds.view(original_inputs_embeds_shape)  # 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
-        # TODO: check if need to handle attention_mask
-        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 Llama4CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,  # type: ignore  # TODO: check if need to create dummy 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_llama4_text(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.llama4 import modeling_llama4
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_llama4.Llama4ForCausalLM
-        ), f"Expected a Llama4ForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-
-        return maybe_model
-
-    setattr(
-        modeling_llama4.Llama4ForCausalLM,
-        "forward",
-        cce_forward,
-    )
-    return None
-
-
-def patch_llama4(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.llama4 import modeling_llama4
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_llama4.Llama4ForConditionalGeneration
-        ), f"Expected a Llama4ForConditionalGeneration 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
-
-    setattr(
-        modeling_llama4.Llama4ForConditionalGeneration,
-        "forward",
-        cce_forward_multimodal,
-    )
-
-    # patch the causal language model
-    setattr(modeling_llama4.Llama4ForCausalLM, "forward", cce_forward)
-    return None

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

@@ -1,384 +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 (
-    KwargsForCausalLM,
-)
-from transformers.processing_utils import Unpack
-from transformers.utils import (
-    is_torchdynamo_compiling,
-)
-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")
-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

@@ -1,366 +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 (
-    _prepare_cross_attention_mask,
-)
-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")
-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")
-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

@@ -1,126 +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.phi3 import patch_phi3
-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.glm4 import (
-    patch_glm,
-    patch_glm4,
-)
-from axolotl.integrations.cut_cross_entropy.monkeypatch.llama import (
-    patch_llama,
-)
-from axolotl.integrations.cut_cross_entropy.monkeypatch.llama4 import (
-    patch_llama4,
-    patch_llama4_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
-from axolotl.integrations.cut_cross_entropy.monkeypatch.qwen2 import (
-    patch_qwen2,
-)
-from axolotl.integrations.cut_cross_entropy.monkeypatch.qwen2_5_vl import (
-    patch_qwen2_5_vl,
-)
-from axolotl.integrations.cut_cross_entropy.monkeypatch.qwen2_moe import (
-    patch_qwen2_moe,
-)
-from axolotl.integrations.cut_cross_entropy.monkeypatch.qwen2_vl import (
-    patch_qwen2_vl,
-)
-from axolotl.integrations.cut_cross_entropy.monkeypatch.qwen3 import patch_qwen3
-from axolotl.integrations.cut_cross_entropy.monkeypatch.qwen3_moe import (
-    patch_qwen3_moe,
-)
-
-CUT_CROSS_ENTROPY_MODEL_MAPPING = {
-    "llama": patch_llama,
-    "llama4": patch_llama4,
-    "llama4_text": patch_llama4_text,
-    "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,
-    "qwen2_moe": patch_qwen2_moe,
-    "qwen2_vl": patch_qwen2_vl,
-    "qwen2_5_vl": patch_qwen2_5_vl,
-    "qwen3": patch_qwen3,
-    "qwen3_moe": patch_qwen3_moe,
-    "cohere": patch_cohere,
-    "cohere2": patch_cohere2,
-    "glm": patch_glm,
-    "glm4": patch_glm4,
-}
-
-
-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):
-        if hasattr(model_type_or_model, "config"):
-            model_type = getattr(
-                getattr(model_type_or_model, "config", None), "model_type", None
-            )
-        else:
-            raise ValueError(
-                "model_type_or_model is a PreTrainedModel but does not have a config attribute"
-            )
-    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/qwen2.py

@@ -1,37 +0,0 @@
-"""Qwen2 CCE patch. The model inherits Llama's modeling code and uses the same forward method."""
-
-# pylint: disable=duplicate-code
-
-from types import MethodType
-
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-)
-
-
-def patch_qwen2(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    from transformers.models.qwen2 import modeling_qwen2
-
-    # Set the _PATCH_OPTS in the llama patch file
-    import axolotl.integrations.cut_cross_entropy.monkeypatch.llama as llama_patch
-
-    llama_patch._PATCH_OPTS = patch_options  # pylint: disable=protected-access
-
-    from axolotl.integrations.cut_cross_entropy.monkeypatch.llama import (
-        cce_forward,
-    )
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_qwen2.Qwen2ForCausalLM
-        ), f"Expected a Qwen2ForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_qwen2.Qwen2ForCausalLM.forward = cce_forward
-    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen2_5_vl.py

@@ -1,246 +0,0 @@
-"""Qwen2.5 VL CCE patch. Adapted from transformers v4.51.2"""
-
-# 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.nn import CrossEntropyLoss
-from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
-    Qwen2_5_VLCausalLMOutputWithPast,
-)
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-def cce_forward_multimodal(
-    self,
-    input_ids: Optional[torch.LongTensor] = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[list[torch.FloatTensor]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    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,
-    pixel_values: Optional[torch.Tensor] = None,
-    pixel_values_videos: Optional[torch.FloatTensor] = None,
-    image_grid_thw: Optional[torch.LongTensor] = None,
-    video_grid_thw: Optional[torch.LongTensor] = None,
-    rope_deltas: Optional[torch.LongTensor] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    second_per_grid_ts: Optional[torch.Tensor] = None,
-) -> Union[Tuple, Qwen2_5_VLCausalLMOutputWithPast]:
-    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]`.
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from PIL import Image
-    >>> import requests
-    >>> from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
-
-    >>> model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
-    >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
-
-    >>> messages = [
-        {
-            "role": "user",
-            "content": [
-                {"type": "image"},
-                {"type": "text", "text": "What is shown in this image?"},
-            ],
-        },
-    ]
-    >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
-    >>> image = Image.open(requests.get(url, stream=True).raw)
-
-    >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
-
-    >>> # 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]
-    "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
-    ```"""
-
-    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 inputs_embeds is None:
-        inputs_embeds = self.model.embed_tokens(input_ids)
-        if pixel_values is not None:
-            pixel_values = pixel_values.type(self.visual.dtype)
-            image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
-            n_image_tokens = (input_ids == self.config.image_token_id).sum().item()
-            n_image_features = image_embeds.shape[0]
-            if n_image_tokens != n_image_features:
-                raise ValueError(
-                    f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
-                )
-
-            mask = input_ids == self.config.image_token_id
-            mask_unsqueezed = mask.unsqueeze(-1)
-            mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
-            image_mask = mask_expanded.to(inputs_embeds.device)
-
-            image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
-            inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)  # type: ignore
-
-        if pixel_values_videos is not None:
-            pixel_values_videos = pixel_values_videos.type(self.visual.dtype)
-            video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
-            n_video_tokens = (input_ids == self.config.video_token_id).sum().item()
-            n_video_features = video_embeds.shape[0]
-            if n_video_tokens != n_video_features:
-                raise ValueError(
-                    f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}"
-                )
-
-            mask = input_ids == self.config.video_token_id
-            mask_unsqueezed = mask.unsqueeze(-1)
-            mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
-            video_mask = mask_expanded.to(inputs_embeds.device)
-
-            video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
-            inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)  # type: ignore
-
-        if attention_mask is not None:
-            attention_mask = attention_mask.to(inputs_embeds.device)
-
-    # if we get 4D attention mask we cannot calculate rope deltas anymore. TODO @raushan fixme
-    if position_ids is None and (attention_mask is None or attention_mask.ndim == 2):
-        # calculate RoPE index once per generation in the pre-fill stage only
-        if (
-            (cache_position is not None and cache_position[0] == 0)
-            or self.rope_deltas is None
-            or (past_key_values is None or past_key_values.get_seq_length() == 0)  # type: ignore
-        ):
-            position_ids, rope_deltas = self.get_rope_index(
-                input_ids,
-                image_grid_thw,
-                video_grid_thw,
-                second_per_grid_ts,
-                attention_mask,
-            )
-            self.rope_deltas = rope_deltas
-        # then use the prev pre-calculated rope-deltas to get the correct position ids
-        else:
-            batch_size, seq_length, _ = inputs_embeds.shape
-            delta = (
-                (cache_position[0] + self.rope_deltas).to(inputs_embeds.device)
-                if cache_position is not None
-                else 0
-            )
-            position_ids = torch.arange(seq_length, device=inputs_embeds.device)  # type: ignore
-            position_ids = position_ids.view(1, -1).expand(batch_size, -1)  # type: ignore
-            if cache_position is not None:  # otherwise `deltas` is an int `0`
-                delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0)  # type: ignore
-            position_ids = position_ids.add(delta)  # type: ignore
-            position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)  # type: ignore
-
-    outputs = self.model(
-        input_ids=None,
-        position_ids=position_ids,
-        attention_mask=attention_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]
-    logits = None
-    loss = 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.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-        )
-    else:
-        logits = self.lm_head(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 so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return Qwen2_5_VLCausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-        rope_deltas=self.rope_deltas,
-    )
-
-
-def patch_qwen2_5_vl(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-
-    from transformers.models.qwen2_5_vl import modeling_qwen2_5_vl
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_qwen2_5_vl.Qwen2_5_VLForConditionalGeneration
-        ), f"Expected a Qwen2_5_VLForConditionalGeneration model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward_multimodal, maybe_model)
-
-        return maybe_model
-
-    modeling_qwen2_5_vl.Qwen2_5_VLForConditionalGeneration.forward = (
-        cce_forward_multimodal
-    )
-    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen2_moe.py

@@ -1,178 +0,0 @@
-"""Qwen2 MoE CCE patch. Adapted from transformers v4.51.2"""
-
-# pylint: disable=duplicate-code
-
-from types import MethodType
-from typing import Optional, Union
-
-import torch
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-    apply_lce,
-)
-from transformers.models.qwen2_moe.modeling_qwen2_moe import (
-    MoeCausalLMOutputWithPast,
-    MoeModelOutputWithPast,
-    load_balancing_loss_func,
-)
-from transformers.utils.deprecation import deprecate_kwarg
-from transformers.utils.generic import can_return_tuple
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-@can_return_tuple
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-def forward(
-    self,
-    input_ids: Optional[torch.LongTensor] = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[list[torch.FloatTensor]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    output_router_logits: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **loss_kwargs,
-) -> MoeCausalLMOutputWithPast:
-    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, Qwen2MoeForCausalLM
-
-    >>> model = Qwen2MoeForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
-    >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
-
-    >>> 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_router_logits = (
-        output_router_logits
-        if output_router_logits is not None
-        else self.config.output_router_logits
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs: MoeModelOutputWithPast = 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,
-        output_router_logits=output_router_logits,
-        cache_position=cache_position,
-    )
-
-    hidden_states = outputs.last_hidden_state
-    loss = None
-    logits = None
-
-    if hidden_states is None:
-        raise ValueError("hidden_states is 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,
-            **loss_kwargs,
-        )
-    else:
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
-
-    aux_loss = None
-    if output_router_logits:
-        aux_loss = load_balancing_loss_func(
-            outputs.router_logits,
-            self.num_experts,
-            self.num_experts_per_tok,
-            attention_mask,
-        )
-        if labels is not None:
-            loss += self.router_aux_loss_coef * aux_loss.to(  # type: ignore
-                loss.device  # type: ignore
-            )  # make sure to reside in the same device
-
-    return MoeCausalLMOutputWithPast(
-        loss=loss,
-        aux_loss=aux_loss,  # type: ignore
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-        router_logits=outputs.router_logits,
-    )
-
-
-def patch_qwen2_moe(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-
-    from transformers.models.qwen2_moe import modeling_qwen2_moe
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_qwen2_moe.Qwen2MoeForCausalLM
-        ), f"Expected a Qwen3MoeForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(forward, maybe_model)
-
-        return maybe_model
-
-    modeling_qwen2_moe.Qwen2MoeForCausalLM.forward = forward
-    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen2_vl.py

@@ -1,239 +0,0 @@
-"""Qwen2 VL CCE patch. Adapted from transformers v4.51.2"""
-
-# 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.nn import CrossEntropyLoss
-from transformers.models.qwen2_vl.modeling_qwen2_vl import (
-    Qwen2VLCausalLMOutputWithPast,
-)
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-def cce_forward_multimodal(
-    self,
-    input_ids: Optional[torch.LongTensor] = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[list[torch.FloatTensor]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    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,
-    pixel_values: Optional[torch.Tensor] = None,
-    pixel_values_videos: Optional[torch.FloatTensor] = None,
-    image_grid_thw: Optional[torch.LongTensor] = None,
-    video_grid_thw: Optional[torch.LongTensor] = None,
-    rope_deltas: Optional[torch.LongTensor] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-) -> Union[Tuple, Qwen2VLCausalLMOutputWithPast]:
-    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]`.
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from PIL import Image
-    >>> import requests
-    >>> from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
-
-    >>> model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
-    >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
-
-    >>> messages = [
-        {
-            "role": "user",
-            "content": [
-                {"type": "image"},
-                {"type": "text", "text": "What is shown in this image?"},
-            ],
-        },
-    ]
-    >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
-    >>> image = Image.open(requests.get(url, stream=True).raw)
-
-    >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
-
-    >>> # 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]
-    "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
-    ```"""
-
-    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 inputs_embeds is None:
-        inputs_embeds = self.model.embed_tokens(input_ids)
-        if pixel_values is not None:
-            pixel_values = pixel_values.type(self.visual.get_dtype())
-            image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
-            n_image_tokens = (input_ids == self.config.image_token_id).sum().item()
-            n_image_features = image_embeds.shape[0]
-            if n_image_tokens != n_image_features:
-                raise ValueError(
-                    f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
-                )
-            image_mask = (
-                (input_ids == self.config.image_token_id)
-                .unsqueeze(-1)
-                .expand_as(inputs_embeds)
-                .to(inputs_embeds.device)
-            )
-            image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
-            inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)  # type: ignore
-
-        if pixel_values_videos is not None:
-            pixel_values_videos = pixel_values_videos.type(self.visual.get_dtype())
-            video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
-            n_video_tokens = (input_ids == self.config.video_token_id).sum().item()
-            n_video_features = video_embeds.shape[0]
-            if n_video_tokens != n_video_features:
-                raise ValueError(
-                    f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}"
-                )
-            video_mask = (
-                (input_ids == self.config.video_token_id)
-                .unsqueeze(-1)
-                .expand_as(inputs_embeds)
-                .to(inputs_embeds.device)
-            )
-            video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
-            inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)  # type: ignore
-
-        if attention_mask is not None:
-            attention_mask = attention_mask.to(inputs_embeds.device)
-
-    # if we get 4D attention mask we cannot calculate rope deltas anymore. TODO @raushan fixme
-    if position_ids is None and (attention_mask is None or attention_mask.ndim == 2):
-        # calculate RoPE index once per generation in the pre-fill stage only
-        if (
-            (cache_position is not None and cache_position[0] == 0)
-            or self.rope_deltas is None
-            or (past_key_values is None or past_key_values.get_seq_length() == 0)  # type: ignore
-        ):
-            position_ids, rope_deltas = self.get_rope_index(
-                input_ids, image_grid_thw, video_grid_thw, attention_mask
-            )
-            self.rope_deltas = rope_deltas
-        # then use the prev pre-calculated rope-deltas to get the correct position ids
-        else:
-            batch_size, seq_length, _ = inputs_embeds.shape
-            delta = (
-                cache_position[0] + self.rope_deltas
-                if cache_position is not None
-                else 0
-            )
-            position_ids = torch.arange(seq_length, device=inputs_embeds.device)  # type: ignore
-            position_ids = position_ids.view(1, -1).expand(batch_size, -1)  # type: ignore
-            if cache_position is not None:  # otherwise `deltas` is an int `0`
-                delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0)  # type: ignore
-                delta = delta.to(position_ids.device)  # type: ignore
-            position_ids = position_ids.add(delta)  # type: ignore
-            position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)  # type: ignore
-
-    outputs = self.model(
-        input_ids=None,
-        position_ids=position_ids,
-        attention_mask=attention_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]
-    logits = None
-    loss = 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.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-        )
-    else:
-        logits = self.lm_head(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 so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return Qwen2VLCausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-        rope_deltas=self.rope_deltas,
-    )
-
-
-def patch_qwen2_vl(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-
-    from transformers.models.qwen2_vl import modeling_qwen2_vl
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_qwen2_vl.Qwen2VLForConditionalGeneration
-        ), f"Expected a Qwen2VLForConditionalGeneration model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward_multimodal, maybe_model)
-
-        return maybe_model
-
-    modeling_qwen2_vl.Qwen2VLForConditionalGeneration.forward = cce_forward_multimodal
-    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen3.py

@@ -1,35 +0,0 @@
-"""Qwen3 CCE patch. The model inherits Llama's modeling code and uses the same forward method."""
-
-# pylint: disable=duplicate-code
-
-from types import MethodType
-
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-)
-
-
-def patch_qwen3(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    from transformers.models.qwen3 import modeling_qwen3
-
-    # Set the _PATCH_OPTS in the llama patch file
-    import axolotl.integrations.cut_cross_entropy.monkeypatch.llama as llama_patch
-
-    llama_patch._PATCH_OPTS = patch_options  # pylint: disable=protected-access
-
-    from axolotl.integrations.cut_cross_entropy.monkeypatch.llama import cce_forward
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_qwen3.Qwen3ForCausalLM
-        ), f"Expected a Qwen3ForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_qwen3.Qwen3ForCausalLM.forward = cce_forward
-    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen3_moe.py

@@ -1,183 +0,0 @@
-"""Qwen3 MoE CCE patch. Adapted from transformers v4.51.2"""
-
-# pylint: disable=duplicate-code
-
-from types import MethodType
-from typing import Optional, Union
-
-import torch
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-    apply_lce,
-)
-from transformers.models.qwen3_moe.modeling_qwen3_moe import (
-    KwargsForCausalLM,
-    MoeCausalLMOutputWithPast,
-    MoeModelOutputWithPast,
-    load_balancing_loss_func,
-)
-from transformers.processing_utils import Unpack
-from transformers.utils.deprecation import deprecate_kwarg
-from transformers.utils.generic import can_return_tuple
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-@can_return_tuple
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-def forward(
-    self,
-    input_ids: Optional[torch.LongTensor] = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[list[torch.FloatTensor]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    output_router_logits: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **kwargs: Unpack[KwargsForCausalLM],
-) -> MoeCausalLMOutputWithPast:
-    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, Qwen3MoeForCausalLM
-
-    >>> model = Qwen3MoeForCausalLM.from_pretrained("Qwen/Qwen3-MoE-15B-A2B")
-    >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-MoE-15B-A2B")
-
-    >>> 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_router_logits = (
-        output_router_logits
-        if output_router_logits is not None
-        else self.config.output_router_logits
-    )
-
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs: MoeModelOutputWithPast = 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,
-        output_router_logits=output_router_logits,
-        cache_position=cache_position,
-        **kwargs,
-    )
-
-    hidden_states = outputs.last_hidden_state
-
-    if hidden_states is None:
-        raise ValueError("hidden_states is None")
-
-    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, labels, self.vocab_size, **kwargs)
-
-    aux_loss = None
-    if output_router_logits:
-        aux_loss = load_balancing_loss_func(
-            outputs.router_logits,
-            self.num_experts,
-            self.num_experts_per_tok,
-            attention_mask,
-        )
-        if labels is not None:
-            loss += self.router_aux_loss_coef * aux_loss.to(  # type: ignore
-                loss.device  # type: ignore
-            )  # make sure to reside in the same device
-
-    return MoeCausalLMOutputWithPast(
-        loss=loss,
-        aux_loss=aux_loss,  # type: ignore
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-        router_logits=outputs.router_logits,
-    )
-
-
-def patch_qwen3_moe(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-
-    from transformers.models.qwen3_moe import modeling_qwen3_moe
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_qwen3_moe.Qwen3MoeForCausalLM
-        ), f"Expected a Qwen3MoeForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(forward, maybe_model)
-
-        return maybe_model
-
-    modeling_qwen3_moe.Qwen3MoeForCausalLM.forward = forward
-    return None

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/loaders/constants.py

@@ -1,6 +1,7 @@
 """Shared constants for axolotl.loaders module"""
 
 from transformers import (
+    AutoModelForCausalLM,
     Gemma3ForConditionalGeneration,
     Llama4ForConditionalGeneration,
     LlavaForConditionalGeneration,
@@ -18,4 +19,6 @@ MULTIMODAL_AUTO_MODEL_MAPPING = {
     "qwen2_5_vl": Qwen2_5_VLForConditionalGeneration,
     "mistral3": Mistral3ForConditionalGeneration,
     "gemma3": Gemma3ForConditionalGeneration,
+    # phi3_v modeling code is not available in transformers yet
+    "phi3_v": AutoModelForCausalLM,
 }

src/axolotl/loaders/model.py

@@ -504,6 +504,9 @@ class ModelLoader:
                 # for some reason, this causes the loss to be off by an order of magnitude
                 # but deepspeed needs this still in bfloat16
                 bnb_config["bnb_4bit_quant_storage"] = torch.float32
+            if self.cfg.model_config_type == "falcon_h1":
+                # output projection cannot be quantized for Falcon-H1 models
+                bnb_config["llm_int8_skip_modules"] = ["out_proj"]
 
             if self.cfg.bnb_config_kwargs:
                 bnb_config.update(self.cfg.bnb_config_kwargs)
@@ -518,6 +521,9 @@ class ModelLoader:
             # Exclude mamba blocks from int8 quantization for jamba
             if self.cfg.model_config_type == "jamba":
                 bnb_config["llm_int8_skip_modules"] = ["mamba"]
+            if self.cfg.model_config_type == "falcon_h1":
+                # output projection cannot be quantized for Falcon-H1 models
+                bnb_config["llm_int8_skip_modules"] = ["out_proj"]
             self.model_kwargs["quantization_config"] = BitsAndBytesConfig(
                 **bnb_config,
             )
@@ -770,6 +776,9 @@ class ModelLoader:
         dist_dtype: torch.dtype,
         before_kbit_train_or_finetune: bool,
     ):
+        dest = {"dtype": dist_dtype}
+        if self.cfg.lora_on_cpu:
+            dest["device"] = "cpu"
         for name, module in self.model.named_modules():
             if "norm" in name:
                 module.to(dist_dtype)
@@ -780,4 +789,4 @@ class ModelLoader:
                     # don't upcast lm_head for btlm
                     continue
             if any(m in name for m in embedding_modules) and hasattr(module, "weight"):
-                module.to(dist_dtype)
+                module.to(**dest)

src/axolotl/loaders/patch_manager.py

@@ -50,6 +50,7 @@ class PatchManager:
     def apply_pre_model_load_patches(self):
         """Apply pre-model load patches based on config."""
         self._apply_flash_attention_patches()
+        self._apply_chunked_cross_entropy_patch()
         self._apply_fsdp_patches()
         self._apply_adapter_patches()
         self._apply_flex_attention_patches()
@@ -63,6 +64,8 @@ class PatchManager:
         self._patch_llama_derived_model()
         self._apply_mistral_cross_entropy_patch()
         self._apply_self_attention_lora_patch()
+        self._apply_gemma3_conditional_generation_forward_patch()
+        self._apply_sequence_parallel_patches()
 
     def apply_post_model_load_patches(self, model: PreTrainedModel):
         """Apply patches that require the model instance."""
@@ -78,6 +81,15 @@ class PatchManager:
             patch_xformers_attn_over_fa2()
             self.cfg.flash_attention = True
 
+    def _apply_chunked_cross_entropy_patch(self):
+        if self.cfg.chunked_cross_entropy:
+            from axolotl.monkeypatch.loss.chunked import patch_chunked_ce_loss_fn
+
+            if self.cfg.chunked_cross_entropy_num_chunks:
+                patch_chunked_ce_loss_fn(self.cfg.chunked_cross_entropy_num_chunks)
+            else:
+                patch_chunked_ce_loss_fn()
+
     def _apply_fsdp_patches(self):
         """Apply patches for FSDP configurations."""
         if self.cfg.fsdp_config and str(self.cfg.fsdp_config.fsdp_version) == "2":
@@ -211,6 +223,26 @@ class PatchManager:
                 has_remote_code=has_remote_code,
             )
 
+    def _apply_gemma3_conditional_generation_forward_patch(self):
+        """Apply gemma3 conditional generation forward patch."""
+        if self.model_config.model_type in ["gemma3", "gemma3_text"]:
+            from axolotl.monkeypatch.models.gemma3.modeling import (
+                patch_gemma3_conditional_generation_forward,
+            )
+
+            patch_gemma3_conditional_generation_forward()
+
+    def _apply_sequence_parallel_patches(self):
+        """Apply sequence parallelism patches."""
+        if self.cfg.sequence_parallel_degree and self.cfg.sequence_parallel_degree > 1:
+            from axolotl.monkeypatch.ring_attn.patch import (
+                patch_prepare_data_loader,
+                patch_prepare_device_mesh,
+            )
+
+            patch_prepare_data_loader()
+            patch_prepare_device_mesh(self.cfg.sequence_parallel_degree, self.cfg.fsdp)
+
     def _patch_attention(self):
         """Apply attention-specific patches based on model type."""
         if not (self.cfg.flash_attention and hasattr(self.model_config, "model_type")):

src/axolotl/monkeypatch/loss/chunked.py

@@ -0,0 +1,134 @@
+"""
+chunked ce loss
+"""
+
+from typing import List, Optional
+
+import torch
+import torch.nn.functional as F
+
+
+# copied and modified from torchtune.modules.loss.CEWithChunkedOutputLoss
+class CEWithChunkedOutputLoss(torch.nn.Module):
+    """
+    Cross-entropy with chunked outputs that saves memory by only upcasting one chunk at a time.
+
+    For more details, please refer to: https://github.com/pytorch/torchtune/pull/1390
+    """
+
+    def __init__(self, num_output_chunks: int = 8, ignore_index: int = -100):
+        super().__init__()
+        self.num_output_chunks = num_output_chunks
+        self.ignore_index = ignore_index
+
+    def compute_cross_entropy(
+        self,
+        logits: torch.Tensor,
+        labels: torch.Tensor,
+        normalize: bool = True,  # pylint: disable=unused-argument
+    ) -> torch.Tensor:
+        """
+        Upcast logits to fp32 and compute cross entropy loss.
+        """
+        return F.cross_entropy(
+            logits.float(), labels, ignore_index=self.ignore_index, reduction="sum"
+        )
+
+    def forward(
+        self, logits: List[torch.Tensor], labels: torch.Tensor, reduction="sum"
+    ) -> torch.Tensor:
+        """
+        Args:
+            logits (List[torch.Tensor]): List of chunked logits of length
+                ``self.num_output_chunks``, where each chunk has shape
+                ``(batch_size, num_tokens / num_output_chunks, vocab_size)``.
+            labels (torch.Tensor): Ground truth labels of shape ``(batch_size, num_tokens)``.
+            reduction (str): The reduction to apply to the output.
+
+        Returns:
+            torch.Tensor: Cross entropy loss of shape (1,).
+        """
+
+        total_elements = (labels != self.ignore_index).sum()
+
+        # chunk and reshape labels (bsz, num_tokens, vocab) -> [(bsz*num_tokens/num_chunks, vocab)]
+        labels = [
+            target_chunk.reshape(-1)
+            for target_chunk in labels.chunk(self.num_output_chunks, dim=1)
+        ]
+        # reshape logits [(bsz, num_tokens/num_chunks, vocab)] -> [(bsz*num_tokens/num_chunks, vocab)]
+        logits = [
+            logit_chunk.reshape(-1, logit_chunk.size(-1)) for logit_chunk in logits
+        ]
+
+        # compute one chunk at a time
+        total_loss = 0.0
+        for logits_chunk, labels_chunk in zip(logits, labels):
+            total_loss += self.compute_cross_entropy(logits_chunk, labels_chunk)
+
+        if reduction == "sum":
+            return total_loss
+        return total_loss / total_elements
+
+
+def _build_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -100):
+    loss_fn_ce = CEWithChunkedOutputLoss(num_output_chunks, ignore_index)
+    loss_fn_ce.compute_cross_entropy = torch.compile(
+        loss_fn_ce.compute_cross_entropy, backend="inductor"
+    )
+    return loss_fn_ce
+
+
+def get_causal_lm_loss(num_output_chunks: int = 8, ignore_index: int = -100):
+    loss_fn_ce = _build_chunked_ce_loss_fn(num_output_chunks, ignore_index)
+
+    def chunked_fix_cross_entropy(
+        source,
+        target,
+        num_items_in_batch: int = None,
+        ignore_index: int = -100,
+        **kwargs,
+    ):  # pylint: disable=unused-argument
+        reduction = "sum" if num_items_in_batch is not None else "mean"
+        logit_chunks = [  # pylint: disable=unnecessary-comprehension
+            chunk for chunk in source.chunk(loss_fn_ce.num_output_chunks, dim=1)
+        ]
+        loss = loss_fn_ce(logit_chunks, target, reduction=reduction)
+        if reduction == "sum":
+            loss = loss / num_items_in_batch
+        return loss
+
+    def for_causal_lm_chunked_loss(
+        logits,
+        labels,
+        vocab_size: int = None,  # pylint: disable=unused-argument
+        num_items_in_batch: Optional[int] = None,
+        ignore_index: int = -100,
+        shift_labels: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        # skip the upcast to float since we handle that in the chunking loss
+        if shift_labels is None:
+            # Shift so that tokens < n predict n
+            labels = F.pad(labels, (0, 1), value=ignore_index)
+            shift_labels = labels[..., 1:].contiguous()
+
+        # Skip Flattening the tokens
+        # Enable model parallelism
+        shift_labels = shift_labels.to(logits.device)
+        loss = chunked_fix_cross_entropy(
+            logits, shift_labels, num_items_in_batch, ignore_index, **kwargs
+        )
+        return loss
+
+    return for_causal_lm_chunked_loss
+
+
+def patch_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -100):
+    import transformers.loss.loss_utils
+
+    for_causal_lm_chunked_loss = get_causal_lm_loss(num_output_chunks, ignore_index)
+    transformers.loss.loss_utils.ForCausalLMLoss = for_causal_lm_chunked_loss
+    transformers.loss.loss_utils.LOSS_MAPPING["ForCausalLM"] = (
+        for_causal_lm_chunked_loss
+    )

src/axolotl/monkeypatch/models/gemma3/modeling.py

@@ -0,0 +1,16 @@
+"""Monkeypatch for gemma3 conditional generation forward to fix high loss"""
+
+
+def patch_gemma3_conditional_generation_forward():
+    # Remove when https://github.com/huggingface/transformers/pull/37208 merged
+
+    from transformers.models.gemma3.modeling_gemma3 import (
+        Gemma3ForConditionalGeneration,
+    )
+
+    setattr(Gemma3ForConditionalGeneration, "accepts_loss_kwargs", False)
+
+    def unpatch():
+        delattr(Gemma3ForConditionalGeneration, "accepts_loss_kwargs")
+
+    return unpatch

src/axolotl/monkeypatch/multipack.py

@@ -42,6 +42,10 @@ def patch_for_multipack(model_type, model_name=None, has_remote_code=False):
     if has_remote_code:
         patch_remote(model_name)
     elif hasattr(transformers, "modeling_flash_attention_utils"):
+        # sanity check in case upstream api changes on this
+        assert hasattr(
+            transformers.modeling_flash_attention_utils, "_get_unpad_data"
+        ), "transformers api changed for _get_unpad_data for flash attention"
         transformers.modeling_flash_attention_utils._get_unpad_data = (  # pylint: disable=protected-access
             get_unpad_data
         )

src/axolotl/monkeypatch/ring_attn/patch.py

@@ -152,7 +152,7 @@ def update_ring_attn_params(position_ids: torch.Tensor | None):
 def patch_prepare_data_loader():
     """Patch `accelerate.data_loader.prepare_data_loader` to respect the SP degree.
 
-    Raies:
+    Raises:
         RuntimeError: If source code to patch does not exist.
     """
     original_fn = accelerate.data_loader.prepare_data_loader
@@ -168,23 +168,34 @@ def patch_prepare_data_loader():
         ORIGINAL_PREPARE_DATALOADER_CODE, NEW_PREPARE_DATALOADER_CODE
     )
 
+    items_to_import = []
+    for item in dir(accelerate.data_loader):
+        if item in patched_source:
+            items_to_import.append(item)
+
     # Create a new function from the patched source
     namespace = {}
     exec(  # pylint: disable=exec-used  # nosec B102
-        patched_source, accelerate.data_loader.__dict__, namespace
+        f"from accelerate.data_loader import ({', '.join(items_to_import)})",
+        globals(),
+    )
+    exec(  # pylint: disable=exec-used  # nosec B102
+        patched_source, globals(), namespace
     )
-    patched_function = namespace["prepare_data_loader"]
 
-    accelerate.data_loader.prepare_data_loader = patched_function
+    patched_function = namespace["prepare_data_loader"]
+    original_fn.__code__ = patched_function.__code__
+
     LOG.info("Patched accelerate.data_loader.prepare_data_loader for SP support")
 
 
-def patch_prepare_device_mesh(sequence_parallel_degree: int):
+def patch_prepare_device_mesh(sequence_parallel_degree: int, fsdp: bool = False):
     """Patches the `Accelerator._prepare_device_mesh` method to create a device mesh
     that includes sequence parallelism with the specified degree.
 
     Args:
-        sequence_parallel_degree (int): The degree of sequence parallelism to use.
+        sequence_parallel_degree: The degree of sequence parallelism to use.
+        fsdp: Whether to use FSDP.
     """
 
     def _prepare_device_mesh(self):
@@ -207,12 +218,14 @@ def patch_prepare_device_mesh(sequence_parallel_degree: int):
         )
         device_ids = list(range(world_size))
 
-        # Note that we use "cp" instead of "sp" to match the PyTorch native "context
-        # parallelism" implementation naming
+        # NOTE: We use "cp" instead of "sp" to match the PyTorch native "context
+        # parallelism" implementation naming.
+        # NOTE: We have a simplified FSDP handling here; i.e., if FSDP is enabled, we
+        # only use "fsdp" and "cp" for the device mesh.
         return dist.DeviceMesh(
             "cuda",
             torch.tensor(device_ids).reshape(mesh_shape),
-            mesh_dim_names=("dp", "cp"),
+            mesh_dim_names=("dp", "cp") if not fsdp else ("fsdp", "cp"),
         )
 
     # Replace the original method with our new method

src/axolotl/processing_strategies.py

@@ -142,7 +142,7 @@ class ProcessingStrategy:
                 # 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
-                if len(processed_example[image_key]) > 0:
+                if len(processed_example[image_key]) > 1:
                     LOG.warning(
                         f"Found {len(processed_example[image_key])} images in a sample. Using the first one."
                         "If you are using a dataset with multiple images per sample, please convert it to use multi-content Messages."
@@ -264,6 +264,23 @@ class Gemma3ProcessingStrategy(ProcessingStrategy):
         return labels
 
 
+class Phi35VLProcessingStrategy(ProcessingStrategy):
+    """Processing Strategy class for Phi-3.5-vision-instruct"""
+
+    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|>"  # nosec
+        self.image_token_id = processor.tokenizer.convert_tokens_to_ids(
+            self.image_token
+        )
+
+
 def get_processing_strategy(
     processor: ProcessorMixin,
     chat_template,
@@ -279,6 +296,10 @@ def get_processing_strategy(
         return Gemma3ProcessingStrategy(
             processor, chat_template, image_size, image_resize_algorithm
         )
+    if chat_template_type == "phi_35_vl":
+        return Phi35VLProcessingStrategy(
+            processor, chat_template, image_size, image_resize_algorithm
+        )
     if chat_template_type in [
         "llama3_2_vision",
         "llama4",

src/axolotl/prompt_strategies/chat_template.py

@@ -103,6 +103,7 @@ class ChatTemplatePrompter(Prompter):
         chat_template_kwargs = {
             "chat_template": self.chat_template,
             "add_generation_prompt": add_generation_prompt,
+            **self.chat_template_kwargs,
         }
 
         if tools:

src/axolotl/train.py

@@ -23,7 +23,6 @@ from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
 from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
 from transformers.trainer import Trainer
 
-from axolotl.cli.art import print_axolotl_text_art
 from axolotl.common.datasets import TrainDatasetMeta
 from axolotl.contribs.lgpl import (  # pylint: disable = no-name-in-module
     fix_untrained_tokens,
@@ -219,10 +218,14 @@ def execute_training(
                     gradient_accumulation_steps=cfg.gradient_accumulation_steps,
                     ring_attn_func=cfg.ring_attn_func,
                     heads_k_stride=cfg.heads_k_stride,
+                    gather_outputs=cfg.rl is RLType.GRPO,
                 )
             )
 
         LOG.info("Starting trainer...")
+        # TODO: disabling for now as not compatible with FSDP2 + torchao low bit optimizers
+        # if cfg.bf16:
+        #     torch.set_default_dtype(torch.bfloat16)
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)
 
 
@@ -545,8 +548,6 @@ def train(
     Returns:
         Tuple of (model, tokenizer) after training
     """
-    print_axolotl_text_art()
-
     # Setup model, tokenizer, (causal or RLHF) trainer, etc.
     (
         trainer,

src/axolotl/utils/ctx_managers/sequence_parallel.py

@@ -12,8 +12,6 @@ from transformers.utils import ModelOutput
 
 from axolotl.monkeypatch.ring_attn import (
     get_ring_attn_group,
-    patch_prepare_data_loader,
-    patch_prepare_device_mesh,
     register_ring_attn,
     update_ring_attn_params,
 )
@@ -174,6 +172,8 @@ class SequenceParallelContextManager:
         ring_attn_func: Which ring attention function to use. Currently unused.
         heads_k_stride: Sequence parallelism K head stride size. Passed through to
             `varlen_llama3` `ring_flash_attn` implementation.
+        gather_outputs: Whether to gather outputs after model forward pass across the
+            sequence parallel group.
     """
 
     def __init__(
@@ -183,12 +183,15 @@ class SequenceParallelContextManager:
         gradient_accumulation_steps: int,
         ring_attn_func: RingAttnFunc,
         heads_k_stride: int | None,
+        gather_outputs: bool,
     ):
         self.models = models
         self.sequence_parallel_degree = sequence_parallel_degree
         self.gradient_accumulation_steps = gradient_accumulation_steps
         self.ring_attn_func = ring_attn_func
         self.heads_k_stride = heads_k_stride
+        self.gather_outputs = gather_outputs
+
         self._register_ring_attn()
 
         # Set distributed info for local rank
@@ -233,12 +236,6 @@ class SequenceParallelContextManager:
             ring_attn_func=self.ring_attn_func,
         )
 
-        # Patches for accelerate functionality
-        patch_prepare_data_loader()
-        patch_prepare_device_mesh(
-            sequence_parallel_degree=self.sequence_parallel_degree
-        )
-
     def _register_model_hooks(self):
         # Forward pre-hook to apply sequence parallelism
         def sequence_parallel_pre_hook(_, args, kwargs):
@@ -277,16 +274,17 @@ class SequenceParallelContextManager:
 
             return output
 
-        # Register both hooks
+        # Register hooks
         for model in self.models:
             self.hook_handles.append(
                 model.register_forward_pre_hook(
                     sequence_parallel_pre_hook, with_kwargs=True
                 )
             )
-            self.hook_handles.append(
-                model.register_forward_hook(sequence_parallel_post_hook)
-            )
+            if self.gather_outputs:
+                self.hook_handles.append(
+                    model.register_forward_hook(sequence_parallel_post_hook)
+                )
 
     def _gather_outputs(self, output: CausalLMOutputWithPast) -> CausalLMOutputWithPast:
         """Gather sharded outputs from all ranks and reconstruct the full tensor."""

src/axolotl/utils/data/pretraining.py

@@ -224,10 +224,10 @@ def wrap_pretraining_dataset(
     remove_columns = []
     if dataset.features is None:
         for first_row in dataset:
-            remove_columns = first_row.keys()
+            remove_columns = list(first_row.keys())
             break
     else:
-        remove_columns = dataset.features.keys()
+        remove_columns = list(dataset.features.keys())
 
     dataset = dataset.map(
         encode,
@@ -267,6 +267,7 @@ def encode_packed_pretraining(
         batch_size=1,
         batch_max_len=batch_size * max_seq_length,
         drop_last=True,
+        num_processes=1,
     )
 
     chunked_data = defaultdict(list)

src/axolotl/utils/data/sft.py

@@ -334,7 +334,10 @@ def _load_raw_datasets(
     dataset = merge_datasets(datasets, cfg)
 
     if not cfg.skip_prepare_dataset:
-        dataset = drop_long_seq_in_dataset(dataset, cfg)
+        if split == "test" and cfg.eval_sequence_len:
+            dataset = drop_long_seq_in_dataset(dataset, cfg.eval_sequence_len, cfg)
+        else:
+            dataset = drop_long_seq_in_dataset(dataset, cfg.sequence_len, cfg)
         if cfg.sample_packing:
             dataset, _ = process_datasets_for_packing(cfg, dataset, None)
 

src/axolotl/utils/data/shared.py

@@ -524,13 +524,24 @@ def merge_datasets(datasets: list[Dataset], cfg: DictDefault) -> Dataset:
         Merged dataset.
     """
     if len(datasets) == 1:
-        return datasets[0]
+        ds = datasets[0]
+
+        # Do not shuffle if curriculum sampling is enabled
+        if cfg.curriculum_sampling:
+            return ds
+
+        return ds.shuffle(seed=cfg.seed)
 
     LOG.info("Merging datasets...")
     merged_dataset = concatenate_datasets(datasets)
 
     if cfg.shuffle_merged_datasets:
         LOG.debug("Shuffling merged datasets...")
+        if cfg.curriculum_sampling:
+            LOG.warning(
+                "Shuffling merged datasets with curriculum sampling is not recommended. "
+                "This will randomize the order of samples."
+            )
         merged_dataset = merged_dataset.shuffle(seed=cfg.seed)
     else:
         LOG.debug("Not shuffling merged datasets.")

src/axolotl/utils/data/utils.py

@@ -148,11 +148,14 @@ def deduplicate_and_log_datasets(
     return dataset, other_dataset
 
 
-def drop_long_seq_in_dataset(dataset: Dataset, cfg: DictDefault) -> Dataset:
+def drop_long_seq_in_dataset(
+    dataset: Dataset, sequence_len: int, cfg: DictDefault
+) -> Dataset:
     """Remove sequences longer than configured maximum from dataset.
 
     Args:
         dataset: Dataset to filter.
+        sequence_len: Maximum length for sequences to keep
         cfg: Dictionary mapping `axolotl` config keys to values.
 
     Returns:
@@ -167,7 +170,7 @@ def drop_long_seq_in_dataset(dataset: Dataset, cfg: DictDefault) -> Dataset:
 
     drop_long = functools.partial(
         drop_long_seq,
-        sequence_len=cfg.sequence_len,
+        sequence_len=sequence_len,
         min_sequence_len=cfg.min_sample_len,
     )
 
@@ -187,7 +190,7 @@ def drop_long_seq_in_dataset(dataset: Dataset, cfg: DictDefault) -> Dataset:
 
     drop_long_kwargs = {}
     if filter_map_kwargs:
-        drop_long_kwargs["desc"] = "Dropping Long Sequences"
+        drop_long_kwargs["desc"] = f"Dropping Long Sequences (>{sequence_len})"
 
     dataset = dataset.filter(
         drop_long,

src/axolotl/utils/mistral_tokenizer.py

@@ -8,7 +8,7 @@ from typing import TYPE_CHECKING, Optional
 import numpy as np
 from huggingface_hub import hf_hub_download
 from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
-from mistral_common.tokens.tokenizers.tekken import Tekkenizer
+from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy, Tekkenizer
 from torch import Tensor
 from transformers.utils import PaddingStrategy
 
@@ -251,10 +251,13 @@ class HFMistralTokenizer:
             token_ids = [token_ids]
 
         if skip_special_tokens:
-            return self._mistral.instruct_tokenizer.tokenizer.decode(token_ids)
+            return self._mistral.instruct_tokenizer.tokenizer.decode(
+                token_ids, special_token_policy=SpecialTokenPolicy.IGNORE
+            )
 
-        # to_string returns a string with special tokens
-        return self._mistral.instruct_tokenizer.tokenizer.to_string(token_ids)
+        return self._mistral.instruct_tokenizer.tokenizer.decode(
+            token_ids, special_token_policy=SpecialTokenPolicy.KEEP
+        )
 
     def _create_mistral_chat_completion_request(
         self, conversation: list[dict], tools: list[dict] | None = None

src/axolotl/utils/samplers/multipack.py

@@ -127,7 +127,7 @@ def pack_parallel(
     bin_size: int,
     num_processes: int | None = None,
     safe_mode: bool = True,
-    mp_start_method: str | None = "spawn",
+    mp_start_method: str | None = "fork",
 ) -> list[list[int]]:
     """Pack sequences into bins using parallel processing.
 
@@ -260,12 +260,13 @@ class MultipackBatchSampler(BatchSampler):
         lengths: np.ndarray,  # Sequence lengths
         packing_efficiency_estimate: float = 1.0,  # Initial efficiency estimate
         drop_last: bool = True,  # Whether to drop final batches (might be incomplete)
-        num_count_samples: int = 8,  # Number of times to estimate batch count
+        num_count_samples: int = 4,  # Number of times to estimate batch count
         sequential: bool = False,  # Whether to use sequential packing
         group_size: int = 100_000,  # Size of groups for parallel packing
         bin_size: int = 200,  # The max number of samples that can be packed in a single bin
         num_processes: int | None = None,  # Number of processes for parallel packing
         safe_mode: bool = True,  # Conservative packing to prevent training instability
+        mp_start_method: str = "fork",
         **kwargs,  # pylint: disable=unused-argument
     ):
         super().__init__(sampler, batch_size, drop_last)
@@ -278,6 +279,7 @@ class MultipackBatchSampler(BatchSampler):
         self.bin_size = bin_size
         self.num_processes = num_processes
         self.safe_mode = safe_mode
+        self.mp_start_method = mp_start_method
 
         assert isinstance(self.lengths, np.ndarray)
 
@@ -333,13 +335,15 @@ class MultipackBatchSampler(BatchSampler):
             bins = [[indices[b_idx] for b_idx in bin_indices] for bin_indices in bins]
         else:
             # Use parallel packing
+            num_processes = self.num_processes or 1
             all_bins = pack_parallel(
                 lengths,
                 bin_capacity=self.batch_max_len,
                 group_size=self.group_size,
                 bin_size=self.bin_size,
-                num_processes=self.num_processes,
+                num_processes=min(4, num_processes) if num_processes else 4,
                 safe_mode=self.safe_mode,
+                mp_start_method=self.mp_start_method,
             )
 
             # Map bin indices back to original indices

src/axolotl/utils/schemas/config.py

@@ -146,6 +146,7 @@ class AxolotlInputConfig(
     dpo_label_smoothing: float | None = None
     dpo_norm_loss: bool | None = None
     dpo_padding_free: bool | None = None
+    dpo_generate_during_eval: bool | None = None
 
     datasets: (
         Annotated[
@@ -366,6 +367,12 @@ class AxolotlInputConfig(
             "description": "The maximum length of an input to train with, this should typically be less than 2048 as most models have a token/context limit of 2048"
         },
     )
+    eval_sequence_len: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "The maximum length of an input for evaluation. If not specified, defaults to sequence_len"
+        },
+    )
     min_sample_len: int | None = None
     max_prompt_len: int = Field(
         default=512,
@@ -393,6 +400,12 @@ class AxolotlInputConfig(
         default=None,
         json_schema_extra={"description": "Whether to pack samples sequentially"},
     )
+    sample_packing_mp_start_method: str | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "The multiprocessing start method to use for packing. Should be 'fork', 'spawn' or 'forkserver'"
+        },
+    )
     eval_sample_packing: bool | None = Field(
         default=None,
         json_schema_extra={
@@ -523,6 +536,19 @@ class AxolotlInputConfig(
         },
     )
 
+    chunked_cross_entropy: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Whether to use chunked cross entropy loss for memory efficiency"
+        },
+    )
+    chunked_cross_entropy_num_chunks: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Number of chunks to use for chunked cross entropy loss"
+        },
+    )
+
     llama4_linearized_experts: bool | None = None
 
     deepspeed: str | dict[str, Any] | None = Field(
@@ -759,6 +785,12 @@ class AxolotlInputConfig(
             "description": "Custom jinja template for chat template. This will be only used if chat_template is set to `jinja` or `null` (in which case chat_template is automatically set to `jinja`). Default is null."
         },
     )
+    chat_template_kwargs: dict[str, Any] | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Additional kwargs to pass to the chat template. This is useful for customizing the chat template. For example, you can pass `thinking=False` to add a generation prompt to the chat template."
+        },
+    )
     eot_tokens: list[str] | None = Field(
         default=None,
         json_schema_extra={

src/axolotl/utils/schemas/enums.py

@@ -48,12 +48,15 @@ class ChatTemplate(str, Enum):
     llama4 = "llama4"
     phi_3 = "phi_3"
     phi_35 = "phi_35"
+    phi_35_vl = "phi_35_vl"
+    phi_4 = "phi_4"
     deepseek_v2 = "deepseek_v2"
     deepseek_v3 = "deepseek_v3"
     jamba = "jamba"
     jinja = "jinja"
     qwen_25 = "qwen_25"
     qwen3 = "qwen3"
+    falcon_h1 = "falcon_h1"
     tokenizer_default = "tokenizer_default"
     exaone = "exaone"
     metharme = "metharme"

src/axolotl/utils/schemas/validation.py

@@ -462,6 +462,20 @@ class TrainingValidationMixin:
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def pretrain_with_tps(cls, data):
+        if data.get("pretraining_dataset") and data.get(
+            "include_tokens_per_second", False
+        ):
+            # combining these would raise `TypeError: cannot pickle 'dict_keys' object`
+            # due to trying to count the number of tokens total in the dataset
+            raise ValueError(
+                "pretraining_dataset and include_tokens_per_second cannot be used together."
+            )
+
+        return data
+
 
 class LoRAValidationMixin:
     """Validation methods related to LoRA/QLoRA configuration."""

src/axolotl/utils/trainer.py

@@ -381,6 +381,7 @@ def process_pretraining_datasets_for_packing(
     if not skip_position_ids:
         train_dataset = train_dataset.map(
             add_position_ids,
+            batched=True,
             desc="Add position_id column (Pretraining Sample Packing)",
         )
     if drop_attention_mask:
@@ -467,6 +468,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                 sequential=cfg.sample_packing_sequentially,
                 drop_last=True,
                 num_processes=cfg.dataset_processes,
+                mp_start_method=cfg.sample_packing_mp_start_method or "fork",
             )
 
             data_loader = DataLoader(

tests/conftest.py

@@ -4,12 +4,14 @@ shared pytest fixtures
 
 import functools
 import importlib
+import logging
 import os
 import shutil
 import sys
 import tempfile
 import time
-from pathlib import Path
+from pathlib import Path, PosixPath
+from typing import Generator
 
 import datasets
 import pytest
@@ -24,6 +26,8 @@ from tests.hf_offline_utils import (
     hf_offline_context,
 )
 
+logging.getLogger("filelock").setLevel(logging.CRITICAL)
+
 
 def retry_on_request_exceptions(max_retries=3, delay=1):
     # pylint: disable=duplicate-code
@@ -411,7 +415,7 @@ def tokenizer_mistral_7b_instruct_chatml(tokenizer_mistral_7b_instruct):
 
 
 @pytest.fixture
-def temp_dir():
+def temp_dir() -> Generator[str, None, None]:
     # Create a temporary directory
     _temp_dir = tempfile.mkdtemp()
     yield _temp_dir
@@ -419,6 +423,11 @@ def temp_dir():
     shutil.rmtree(_temp_dir)
 
 
+@pytest.fixture(scope="function", autouse=True)
+def unique_triton_cache_dir(temp_dir: str | PosixPath) -> None:
+    os.environ["TRITON_CACHE_DIR"] = str(temp_dir) + "/.triton/cache"
+
+
 @pytest.fixture(scope="function", autouse=True)
 def cleanup_monkeypatches():
     from transformers import Trainer

tests/e2e/multigpu/patched/test_sp.py

@@ -54,6 +54,7 @@ class TestSequenceParallelism:
                 "micro_batch_size": micro_batch_size,
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",

tests/e2e/multigpu/solo/test_flex.py

@@ -54,6 +54,7 @@ class TestPackedFlex:
                 "gradient_accumulation_steps": 2,
                 "gradient_checkpointing": True,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",

tests/e2e/multigpu/solo/test_grpo.py

@@ -309,6 +309,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
                 "warmup_steps": 10,
                 "val_set_size": 0.0,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.0001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
@@ -400,6 +401,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
                 "warmup_steps": 10,
                 "val_set_size": 0.0,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.0001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",

tests/e2e/multigpu/test_eval.py

@@ -38,12 +38,13 @@ class TestMultiGPUEval:
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
                 "lora_modules_to_save": ["embed_tokens", "lm_head"],
-                "val_set_size": 0.004,
+                "val_set_size": 0.05,
                 "special_tokens": {"pad_token": "<|endoftext|>"},
                 "datasets": [
                     {
                         "path": "teknium/GPT4-LLM-Cleaned",
                         "type": "alpaca",
+                        "split": "train[:5%]",
                     },
                 ],
                 "num_epochs": 1,
@@ -51,6 +52,7 @@ class TestMultiGPUEval:
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
@@ -107,12 +109,13 @@ class TestMultiGPUEval:
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
                 "lora_modules_to_save": ["embed_tokens", "lm_head"],
-                "val_set_size": 0.0004,
+                "val_set_size": 0.01,
                 "special_tokens": {"pad_token": "<|endoftext|>"},
                 "datasets": [
                     {
                         "path": "teknium/GPT4-LLM-Cleaned",
                         "type": "alpaca",
+                        "split": "train[:5%]",
                     },
                 ],
                 "num_epochs": 1,
@@ -120,6 +123,7 @@ class TestMultiGPUEval:
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",

tests/e2e/multigpu/test_gemma3.py

@@ -64,6 +64,7 @@ class TestMultiGPUGemma3:
                 },
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.0001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",

tests/e2e/multigpu/test_llama.py

@@ -62,6 +62,7 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": 2,
                 # "gradient_checkpointing": True,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
@@ -127,6 +128,7 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 # "gradient_checkpointing": True,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
@@ -200,6 +202,7 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": 2,
                 # "gradient_checkpointing": True,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "warmup_steps": 0,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
@@ -278,6 +281,7 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": 2,
                 # "gradient_checkpointing": True,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "warmup_steps": 0,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
@@ -340,6 +344,7 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 # "gradient_checkpointing": True,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
@@ -412,6 +417,7 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": 2,
                 # "gradient_checkpointing": True,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
@@ -491,6 +497,7 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": 2,
                 "gradient_checkpointing": True,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_8bit",
                 "lr_scheduler": "cosine",
@@ -573,6 +580,7 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": 2,
                 # "gradient_checkpointing": True,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
@@ -669,6 +677,7 @@ class TestMultiGPULlama:
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
@@ -743,6 +752,7 @@ class TestMultiGPULlama:
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
@@ -817,6 +827,7 @@ class TestMultiGPULlama:
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",

tests/e2e/multigpu/test_qwen2.py

@@ -46,6 +46,7 @@ class TestMultiGPUQwen2:
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",

tests/e2e/multigpu/test_ray.py

@@ -48,6 +48,7 @@ class TestMultiGPURay:
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
@@ -107,6 +108,7 @@ class TestMultiGPURay:
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",

tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py

@@ -396,7 +396,7 @@ def test_model_architecture(model_config):
 
 
 # pylint: disable=duplicate-code
-def test_kernel_training_integration():
+def test_kernel_training_integration(temp_dir):
     """Test model loading with kernel patches enabled."""
     from axolotl.cli.utils import load_model_and_tokenizer
 
@@ -426,6 +426,14 @@ def test_kernel_training_integration():
         }
     )
 
+    # Write cfg to yaml file
+    path = Path(temp_dir) / "config.yaml"
+    with open(path, "w", encoding="utf-8") as fout:
+        fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+    # Load config
+    cfg = load_cfg(str(path))
+
     # Load model
     model, _, _ = load_model_and_tokenizer(cfg=cfg)
 
@@ -505,7 +513,7 @@ def test_kernel_training_integration_auto_enable(temp_dir):
     assert found_patched_attn
 
 
-def test_kernel_training_integration_dropout_non_zero():
+def test_kernel_training_integration_dropout_non_zero(temp_dir):
     """Test model loading with dropout non-zero should not patch."""
 
     from axolotl.cli.utils import load_model_and_tokenizer
@@ -533,6 +541,14 @@ def test_kernel_training_integration_dropout_non_zero():
         }
     )
 
+    # Write cfg to yaml file
+    path = Path(temp_dir) / "config.yaml"
+    with open(path, "w", encoding="utf-8") as fout:
+        fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+    # Load config
+    cfg = load_cfg(str(path))
+
     # Get original attention class
     attention_cls = get_attention_cls_from_config(cfg)
 

tests/test_chunked_xentropy.py

@@ -0,0 +1,40 @@
+"""
+test suite for chunked cross entropy
+"""
+
+import pytest
+import torch
+from torch import nn
+
+from axolotl.monkeypatch.loss.chunked import get_causal_lm_loss
+
+
+@pytest.fixture
+def chunked_fixtures():
+    model_dim = 512
+    vocab_size = 1024 * 256
+    seq_len = 2048
+    batch_size = 1
+
+    lm_head = nn.Linear(model_dim, vocab_size)
+    hidden_state = torch.randn(batch_size, seq_len, model_dim)
+    labels = torch.randint(low=0, high=vocab_size, size=(batch_size, seq_len))
+    return lm_head, hidden_state, labels, vocab_size
+
+
+def test_chunked_forward(chunked_fixtures):  # pylint: disable=redefined-outer-name
+    lm_head, hidden_state, labels, vocab_size = chunked_fixtures
+    lm_loss = get_causal_lm_loss()
+
+    logits = lm_head(hidden_state)
+
+    chunked_lm_loss = lm_loss(logits, labels)
+
+    logits_flattened = logits.view(-1, vocab_size)
+    labels_flattened = labels.view(-1)
+
+    loss = nn.functional.cross_entropy(
+        logits_flattened.float(), labels_flattened, reduction="mean"
+    )
+
+    assert torch.allclose(chunked_lm_loss, loss, atol=1e-2, rtol=1e-2)

tests/test_packed_batch_sampler.py

@@ -70,7 +70,7 @@ class TestBatchedSamplerPacking:
         )
         train_dataset = concatenate_datasets([dataset_wrapper])
 
-        train_dataset = drop_long_seq_in_dataset(train_dataset, cfg)
+        train_dataset = drop_long_seq_in_dataset(train_dataset, cfg.sequence_len, cfg)
 
         lengths = get_dataset_lengths(train_dataset)
         batch_sampler = MultipackBatchSampler(