installing axolotl prior to quartodoc build

.github/workflows/docs.yml

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

.github/workflows/tests-nightly.yml

@@ -136,4 +136,4 @@ jobs:
           echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
-          modal run cicd.e2e_tests
+          modal run cicd.tests

.github/workflows/tests.yml

@@ -63,7 +63,7 @@ jobs:
           path: |
             /home/runner/.cache/huggingface/hub/datasets--*
             /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
+          key: ${{ runner.os }}-hf-hub-cache-${{ hashFiles('**/conftest.py') }}
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -98,9 +98,8 @@ jobs:
 
       - name: Run tests
         run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
+          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
           pytest -v tests/patched/
-          pytest -v tests/cli/
 
       - name: cleanup pip cache
         run: |
@@ -137,7 +136,7 @@ jobs:
           path: |
             /home/runner/.cache/huggingface/hub/datasets--*
             /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
+          key: ${{ runner.os }}-hf-hub-cache-${{ hashFiles('**/conftest.py') }}
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -171,14 +170,10 @@ jobs:
         run: |
           axolotl --help
 
-      - name: Show HF cache
-        run: huggingface-cli scan-cache
-
       - name: Run tests
         run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
+          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
           pytest -v tests/patched/
-          pytest -v tests/cli/
 
       - name: cleanup pip cache
         run: |
@@ -232,7 +227,7 @@ jobs:
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
-          modal run cicd.e2e_tests
+          modal run cicd.tests
 
   docker-e2e-tests:
     if: github.repository_owner == 'axolotl-ai-cloud'
@@ -279,4 +274,4 @@ jobs:
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
-          modal run cicd.e2e_tests
+          modal run cicd.tests

.isort.cfg

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

_quarto.yml

@@ -133,7 +133,6 @@ quartodoc:
         - utils.schemas.datasets
         - utils.schemas.peft
         - utils.schemas.trl
-        - utils.schemas.multimodal
         - utils.schemas.integrations
         - utils.schemas.enums
         - utils.schemas.utils

cicd/Dockerfile.jinja

@@ -33,9 +33,9 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
 
 RUN pip install packaging==23.2 setuptools==75.8.0
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

cicd/cicd.sh

@@ -3,10 +3,9 @@ set -e
 
 python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
 
-pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli /workspace/axolotl/tests/
+pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ /workspace/axolotl/tests/
 pytest -v --durations=10 /workspace/axolotl/tests/e2e/patched/lora_kernels  # running these with the other patches causes a failure
 pytest -v --durations=10 --ignore=tests/e2e/patched/lora_kernels /workspace/axolotl/tests/e2e/patched
 pytest -v --durations=10 -n1 /workspace/axolotl/tests/e2e/solo/
 pytest -v --durations=10 /workspace/axolotl/tests/e2e/integrations/
-pytest -v --durations=10 /workspace/axolotl/tests/cli
+pytest -v --durations=10 --ignore=tests/e2e/solo/ --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/
-pytest -v --durations=10 --ignore=tests/e2e/solo/ --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ --ignore=tests/cli /workspace/axolotl/tests/e2e/

docs/config.qmd

@@ -32,9 +32,6 @@ tokenizer_legacy:
 resize_token_embeddings_to_32x:
 # Optional[bool] Whether to shrink the embeddings to len(tokenizer). By default, we won't shrink.
 shrink_embeddings:
-# Whether to load the model with randomly initialized weights. Useful for
-# pre-training a model from scratch or debugging purposes.
-random_init_weights:
 
 # (Internal use only)
 # Used to identify which the model is based on
@@ -466,7 +463,6 @@ auto_find_batch_size: # Optional[bool]
 
 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
-do_causal_lm_eval: # Whether to run causal language model evaluation for metrics in `eval_causal_lm_metrics`.
 eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", "chrf", "perplexity"]
 
 profiler_steps: # enable the pytorch profiler to capture the first N steps of training to the output_dir.
@@ -507,58 +503,36 @@ lr_div_factor: # Learning rate div factor
 
 # Specify optimizer
 # Valid values are driven by the Transformers OptimizerNames class, see:
-# https://github.com/huggingface/transformers/blob/cbf924b76c03828101a34069a96d209314114fd5/src/transformers/training_args.py#L144-L189
+# https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134
 #
 # Note that not all optimizers may be available in your environment, ex: 'adamw_anyprecision' is part of
 # torchdistx, 'adamw_bnb_8bit' is part of bnb.optim.Adam8bit, etc. When in doubt, it is recommended to start with the optimizer used
 # in the examples/ for your model and fine-tuning use case.
 #
 # Valid values for 'optimizer' include:
+# - adamw_hf
 # - adamw_torch
 # - adamw_torch_fused
 # - adamw_torch_xla
-# - adamw_torch_npu_fused
 # - adamw_apex_fused
-# - adopt_adamw  (an EXPERIMENTAL optimizer, only for torch version >= 2.5.1)
+# - adopt_adamw (an EXPERIMENTAL optimizer, only for torch version >= 2.5.1)
 # - adafactor
 # - adamw_anyprecision
-# - adamw_torch_4bit
-# - ademamix
 # - sgd
 # - adagrad
 # - adamw_bnb_8bit
-# - adamw_8bit   # alias for adamw_bnb_8bit
-# - ademamix_8bit
 # - lion_8bit
 # - lion_32bit
 # - paged_adamw_32bit
 # - paged_adamw_8bit
-# - paged_ademamix_32bit
-# - paged_ademamix_8bit
 # - paged_lion_32bit
 # - paged_lion_8bit
-# - rmsprop
-# - rmsprop_bnb
-# - rmsprop_bnb_8bit
-# - rmsprop_bnb_32bit
 # - galore_adamw
 # - galore_adamw_8bit
 # - galore_adafactor
 # - galore_adamw_layerwise
 # - galore_adamw_8bit_layerwise
 # - galore_adafactor_layerwise
-# - lomo
-# - adalomo
-# - grokadamw
-# - schedule_free_adamw
-# - schedule_free_sgd
-# - apollo_adamw
-# - apollo_adamw_layerwise
-#
-# Additional custom optimizers include:
-# - optimi_adamw
-# - ao_adamw_8bit
-# - ao_adamw_fp8
 optimizer:
 # Dictionary of arguments to pass to the optimizer
 optim_args:
@@ -610,14 +584,6 @@ resume_from_checkpoint:
 # Be careful with this being turned on between different models.
 auto_resume_from_checkpoints: false
 
-## Multimodal section
-# int | tuple[int, int] | None . Size to resize images to, width x height.
-# Will read from model/processor config if not set.
-image_size:
-# str. Algorithm to use for image resizing. "bilinear", "bicubic", "lanczos". Default is "bilinear".
-image_resize_algorithm: 'bilinear'
-## End of multimodal section
-
 # Don't mess with this, it's here for accelerate and torchrun
 local_rank:
 
@@ -651,14 +617,6 @@ ddp_timeout:
 ddp_bucket_cap_mb:
 ddp_broadcast_buffers:
 
-# Sequence parallelism
-# Set to a divisor of the number of GPUs available to split sequences into chunks of equal size.
-# Use in long context training to prevent OOM when sequences cannot fit into a single GPU's VRAM.
-# E.g., if 4 GPUs are available, set this value to 2 to split each sequence into two equal-sized
-# subsequences, or set to 4 to split into four equal-sized subsequences.
-# See https://axolotl-ai-cloud.github.io/axolotl/docs/sequence_parallelism.html for more details.
-sequence_parallel_degree:
-
 # Path to torch distx for optim 'adamw_anyprecision'
 torchdistx_path:
 

docs/docker.qmd

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

docs/faq.qmd

@@ -37,10 +37,6 @@ description: Frequently asked questions
 
 > A: Yes, since Axolotl is just Python, please see `src/axolotl/cli/main.py` on how each command is called.
 
-**Q: How to know the value to use for `fsdp_transformer_layer_cls_to_wrap`?**
-
-> A: This is the class name of the transformer layer to wrap with FSDP. For example, for `LlamaForCausalLM`, the value is `LlamaDecoderLayer`. To find this for a specific model, check the model's `PreTrainedModel` definition and look for `_no_split_modules` variable in the `modeling_<model_name>.py` file within `transformers` library.
-
 ### Chat templates
 
 **Q: `jinja2.exceptions.UndefinedError: 'dict object' has no attribute 'content' / 'role' / ____`**

docs/multimodal.qmd

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

docs/sequence_parallelism.qmd

@@ -1,90 +0,0 @@
----
-title: Sequence Parallelism
-description: Train with long sequences split across multiple GPUs.
----
-
-# Sequence Parallelism
-
-Sequence parallelism is a technique that splits sequences across multiple GPUs,
-allowing you to train with very long sequences that wouldn't fit on a single GPU. Each
-GPU processes a different portion of the sequence, and the results are aggregated
-through a ring communication pattern.
-
-## When to Use Sequence Parallelism
-
-Use sequence parallelism when:
-
-- You need to train with sequence lengths that don't fit into a single GPU's memory
-- You have multiple GPUs available
-- You're experiencing OOM (Out Of Memory) errors with long sequences
-
-## Configuration
-
-To enable sequence parallelism, add the following to your configuration file:
-
-```yaml
-# Set to a divisor (> 1) of the number of GPUs available
-sequence_parallel_degree: 4  # Split sequences across 4 GPUs
-```
-
-The `sequence_parallel_degree` should be a divisor of the total number of GPUs. For example:
-
-- With 8 GPUs, valid values would be 2, 4, or 8
-- With 4 GPUs, valid values would be 2 or 4
-
-## Implementation Details
-
-When sequence parallelism is enabled:
-
-1. Each sequence is divided into equal chunks across the GPUs in a sequence parallel group
-2. The data collator handles the chunking of input_ids, attention_mask, labels, and position_ids
-3. Position IDs are adjusted to maintain proper relative positions, especially for packed sequences
-4. The trainer uses special ring communication patterns for attention operations
-
-## Requirements
-
-To use sequence parallelism, you need:
-
-- Multiple GPUs (at least 2)
-- The `ring-flash-attn` package. Install with:
-  - `pip install axolotl[ring-flash-attn]` (preferred)
-  - `pip install ring-flash-attn>=0.1.4`
-
-## Limitations
-
-- Flash attention must be enabled for this to work (`flash_attention: true` in config YAML)
-- May have a small performance overhead due to communication between GPUs
-
-## Example
-
-```yaml
-# Example config with sequence parallelism
-base_model: meta-llama/Llama-3-8B-Instruct
-sequence_len: 8192
-sequence_parallel_degree: 2  # Split each sequence into 4 parts
-flash_attention: true  # Required with sequence parallelism
-...
-```
-
-This will train the Llama 3 8B model with 8K context length, with each sequence split
-into 2 subsequences of length 4096 across 2 GPUs.
-
-## Sample Packing with Sequence Parallelism
-
-Sequence parallelism is compatible with Axolotl's sample packing functionality. When using both features together:
-
-1. Samples are first packed together
-2. The packed sequences are then divided across GPUs in the sequence parallel group
-3. Position IDs are automatically adjusted to maintain proper relative positions
-
-## Effect on Batch Size
-
-When using sequence parallelism, your effective global batch size is **divided** by the `sequence_parallel_degree`. This happens because:
-
-- Each group of `sequence_parallel_degree` GPUs works on the same batch (just different parts of each sequence)
-- The number of batches processed per step decreases
-
-For example:
-- With 8 GPUs and no sequence parallelism: 8 different batches processed per step
-- With 8 GPUs and `sequence_parallel_degree=4`: Only 2 different batches processed per step (each split across 4 GPUs)
-- If your per-GPU `micro_batch_size` is 2, the global batch size decreases from 16 to 4

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

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

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

@@ -1,74 +0,0 @@
-base_model: google/gemma-3-1b-it
-# optionally might have model_type or tokenizer_type
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-# huggingface repo
-chat_template: gemma3
-datasets:
-  - path: cgato/SlimOrcaDedupCleaned
-    type: chat_template
-    field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
-
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-adapter: qlora
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-
-sequence_len: 2048
-sample_packing: true
-eval_sample_packing: false
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch:
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:

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

@@ -1,63 +0,0 @@
-base_model: google/gemma-3-4b-it
-processor_type: AutoProcessor
-strict: false
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-chat_template: gemma3
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-    field_messages: messages
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-output_dir: ./outputs/out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 2048
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-local_rank:
-logging_steps: 1
-flash_attention: true
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:

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

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

examples/llava/lora-7b.yaml

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

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

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

examples/pixtral/lora-12b.yml

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

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

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

requirements.txt

@@ -4,18 +4,19 @@
 bitsandbytes==0.45.3
 triton>=3.0.0
 mamba-ssm==1.2.0.post1
+flash-attn==2.7.4.post1
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
-liger-kernel==0.5.5
+liger-kernel==0.5.3
 # END section
 
 packaging==23.2
 
 peft==0.15.0
-transformers==4.50.0
+transformers==4.49.0
 tokenizers>=0.21.1
 accelerate==1.5.2
-datasets==3.5.0
+datasets==3.4.1
 deepspeed==0.16.4
 trl==0.15.1
 
@@ -35,7 +36,6 @@ einops
 colorama
 numba
 numpy>=1.24.4,<=2.0.1
-
 # qlora things
 evaluate==0.4.1
 scipy

requirements_env.txt

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

setup.py

@@ -16,7 +16,13 @@ def parse_requirements():
     with open("./requirements.txt", encoding="utf-8") as requirements_file:
         lines = [r.strip() for r in requirements_file.readlines()]
         for line in lines:
-            is_extras = "deepspeed" in line or "mamba-ssm" in line
+            is_extras = (
+                "flash-attn" in line
+                or "flash-attention" in line
+                or "deepspeed" in line
+                or "mamba-ssm" in line
+                or "lion-pytorch" in line
+            )
             if line.startswith("--extra-index-url"):
                 # Handle custom index URLs
                 _, url = line.split()
@@ -33,6 +39,7 @@ def parse_requirements():
                 "bitsandbytes",
                 "triton",
                 "mamba-ssm",
+                "flash-attn",
                 "xformers",
                 "autoawq",
                 "liger-kernel",
@@ -117,8 +124,9 @@ setup(
         ],
     },
     extras_require={
-        "flash-attn": ["flash-attn==2.7.4.post1"],
+        "flash-attn": [
-        "ring-flash-attn": ["ring-flash-attn>=0.1.4", "yunchang==0.6.0"],
+            "flash-attn==2.7.4.post1",
+        ],
         "deepspeed": [
             "deepspeed==0.16.4",
             "deepspeed-kernels",
@@ -133,15 +141,15 @@ setup(
         "mlflow": [
             "mlflow",
         ],
+        "lion-pytorch": [
+            "lion-pytorch==0.1.2",
+        ],
         "galore": [
             "galore_torch",
         ],
-        "apollo": [
-            "apollo-torch",
-        ],
         "optimizers": [
             "galore_torch",
-            "apollo-torch",
+            "lion-pytorch==0.1.2",
             "lomo-optim==0.1.1",
             "torch-optimi==0.2.1",
         ],

src/axolotl/cli/inference.py

@@ -56,7 +56,7 @@ def do_inference(
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: Inference-specific CLI arguments.
     """
-    model, tokenizer, _ = load_model_and_tokenizer(cfg=cfg, inference=True)
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg, inference=True)
     prompter = cli_args.prompter
 
     prompter_module = None
@@ -151,7 +151,7 @@ def do_inference_gradio(
     """
     import gradio as gr
 
-    model, tokenizer, _ = load_model_and_tokenizer(cfg=cfg, inference=True)
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg, inference=True)
     prompter = cli_args.prompter
 
     prompter_module = None

src/axolotl/cli/merge_lora.py

@@ -27,7 +27,7 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
     """
     print_axolotl_text_art()
 
-    model, tokenizer, processor = load_model_and_tokenizer(cfg=cfg)
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg)
     safe_serialization = cfg.save_safetensors is True
 
     LOG.info("Running merge of LoRA with base model...")
@@ -44,9 +44,6 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
         )
         tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
 
-        if processor:
-            processor.save_pretrained(str(Path(cfg.output_dir) / "merged"))
-
 
 def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
     """

src/axolotl/cli/train.py

@@ -17,14 +17,13 @@ from axolotl.cli.config import load_cfg
 from axolotl.common.datasets import load_datasets, load_preference_datasets
 from axolotl.integrations.base import PluginManager
 from axolotl.train import train
-from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.config import normalize_config, resolve_dtype
 from axolotl.utils.dict import DictDefault
 
 LOG = logging.getLogger(__name__)
 
 
-def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
+def do_train(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
     """
     Trains a `transformers` model by first loading the dataset(s) specified in the
     `axolotl` config, and then calling `axolotl.train.train`. Also runs the plugin
@@ -34,9 +33,6 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: Training-specific CLI arguments.
     """
-    # Enable expandable segments for cuda allocation to improve VRAM usage
-    set_pytorch_cuda_alloc_conf()
-
     print_axolotl_text_art()
     check_accelerate_default_config()
     if int(os.getenv("LOCAL_RANK", "0")) == 0:
@@ -48,13 +44,16 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
     model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
-    del model, tokenizer, trainer
-
     plugin_manager = PluginManager.get_instance()
+
+    del model
+    del tokenizer
+    del trainer
+
     plugin_manager.post_train_unload(cfg)
 
 
-def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
+def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
     """
     Parses `axolotl` config, CLI args, and calls `do_train`.
 

src/axolotl/cli/utils.py

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

src/axolotl/core/trainer_builder.py

@@ -36,7 +36,7 @@ from transformers import (
 from transformers.training_args import OptimizerNames
 from trl.trainer.utils import RewardDataCollatorWithPadding
 
-from axolotl.core.trainers import (
+from axolotl.core.trainers.base import (
     AxolotlCPOTrainer,
     AxolotlKTOTrainer,
     AxolotlMambaTrainer,
@@ -60,7 +60,6 @@ from axolotl.core.training_args import (
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback
-from axolotl.processing_strategies import get_processing_strategy
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
@@ -663,11 +662,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
                 optimizer_cls = MuonOptimizerFactory
                 optimizer_kwargs.update(adam_kwargs)
-            elif self.cfg.optimizer == "soap":
-                from axolotl.utils.optimizers.soap import SOAP
-
-                optimizer_cls = SOAP
-                optimizer_kwargs.update(adam_kwargs)
             elif self.cfg.optimizer == "optimi_adamw":
                 from optimi import AdamW
 
@@ -753,12 +747,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 self.cfg.accelerator_config
             )
 
-        if self.cfg.image_size:
-            training_arguments_kwargs["image_size"] = self.cfg.image_size
-        if self.cfg.image_resize_algorithm:
-            training_arguments_kwargs["image_resize_algorithm"] = (
-                self.cfg.image_resize_algorithm
-            )
         if self.cfg.kd_ce_alpha is not None:
             training_arguments_kwargs["kd_ce_alpha"] = self.cfg.kd_ce_alpha
         if self.cfg.kd_alpha is not None:
@@ -774,10 +762,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 self.cfg.kd_top_k_before_softmax
             )
 
-        training_arguments_kwargs["sequence_parallel_degree"] = (
-            self.cfg.sequence_parallel_degree
-        )
-
         if self.cfg.reward_model:
             training_args_cls = AxolotlRewardConfig
         elif self.cfg.process_reward_model:
@@ -861,10 +845,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         self, training_args: AxolotlTrainingArguments, is_eval=False, **kwargs
     ):
         if training_args.pretraining:
-            if (
+            if self.cfg.pretraining_sample_concatenation is False:
-                self.cfg.pretraining_sample_concatenation is False
+                return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
-                or self.cfg.micro_batch_size > 1
+            if self.cfg.micro_batch_size > 1:
-            ):
                 return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
             return None
 
@@ -892,7 +875,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             if "max_length" in kwargs:
                 kwargs.pop("max_length")
         elif use_batch_sampler_collator:
-            if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES or (
+            if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES:
+                collator = V2BatchSamplerDataCollatorForSeq2Seq
+            elif (
                 self.cfg.model_config_type in ["llama"]
                 and self.cfg.flash_attention is not True
             ):
@@ -902,13 +887,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         else:
             if self.cfg.processor_type and self.processor:
                 collator = MultiModalChatDataCollator
-                kwargs["processing_strategy"] = get_processing_strategy(
+                kwargs["processor"] = self.processor
-                    self.processor,
+                kwargs["chat_template"] = training_args.chat_template
-                    training_args.chat_template,
-                    self.cfg.chat_template,
-                    image_size=training_args.image_size,
-                    image_resize_algorithm=training_args.image_resize_algorithm,
-                )
             elif self.cfg.batch_flattening:
                 collator = DataCollatorWithFlattening
                 collator_args.pop(0)
@@ -928,8 +908,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 collator = DataCollatorForSeq2Seq
 
         kwargs["return_tensors"] = "pt"
-        if issubclass(collator, DataCollatorForSeq2Seq):
-            kwargs["sequence_parallel_degree"] = training_args.sequence_parallel_degree
 
         return collator(
             *collator_args,

src/axolotl/core/trainers/__init__.py

@@ -1,18 +0,0 @@
-"""Init for axolotl.core.trainers"""
-
-# pylint: disable=unused-import
-# flake8: noqa
-
-from .base import AxolotlTrainer
-from .dpo.trainer import AxolotlDPOTrainer
-from .grpo.trainer import AxolotlGRPOTrainer
-from .mamba import AxolotlMambaTrainer
-from .relora import ReLoRATrainer
-from .trl import (
-    AxolotlCPOTrainer,
-    AxolotlKTOTrainer,
-    AxolotlORPOTrainer,
-    AxolotlPRMTrainer,
-    AxolotlRewardTrainer,
-    TRLPPOTrainer,
-)

src/axolotl/core/trainers/base.py

@@ -1,47 +1,365 @@
-"""Module for customized trainers"""
+"""
-
+module for customized trainers
-# pylint: disable=too-many-lines
+"""
 
 from __future__ import annotations
 
+# pylint: disable=too-many-lines
 import logging
 import os
 from collections import defaultdict
 from functools import wraps
-from typing import Any, Literal
+from typing import Dict, Literal, Optional
 
-import datasets
 import torch
 from datasets import Dataset
+from peft.optimizers import create_loraplus_optimizer
 from torch import nn
-from torch.utils.data import (
+from torch.optim.lr_scheduler import OneCycleLR
-    BatchSampler,
+from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
-    DataLoader,
-    RandomSampler,
-    Sampler,
-    SequentialSampler,
-)
 from transformers import Trainer
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, seed_worker
+from transformers.utils import is_sagemaker_mp_enabled
+from trl import CPOTrainer, KTOTrainer, ORPOTrainer, PRMTrainer, RewardTrainer
 from trl.trainer.utils import pad_to_length
-from typing_extensions import override
 
-from axolotl.core.trainers.mixins import (
+from axolotl.integrations.base import BaseOptimizerFactory
-    OptimizerMixin,
+from axolotl.monkeypatch.relora import ReLoRAScheduler
-    SchedulerMixin,
-    SequenceParallelMixin,
-)
-from axolotl.core.trainers.utils import (
-    sanitize_kwargs_for_ds_tagging,
-    sanitize_kwargs_for_tagging,
-)
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
+from axolotl.utils.schedulers import (
+    RexLR,
+    get_cosine_schedule_with_min_lr,
+    get_cosine_schedule_with_quadratic_warmup,
+    get_cosine_schedule_with_warmup_decay_constant,
+)
 
-LOG = logging.getLogger(__name__)
+if is_sagemaker_mp_enabled():
+    import smdistributed.modelparallel.torch as smp
+
+LOG = logging.getLogger("axolotl.core.trainer_builder")
 
 
-class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trainer):
+def _sanitize_kwargs_for_tagging(tag_names, kwargs=None):
-    """Extend the base Trainer for axolotl helpers"""
+    if isinstance(tag_names, str):
+        tag_names = [tag_names]
+
+    if kwargs is not None:
+        if "tags" not in kwargs:
+            kwargs["tags"] = tag_names
+        elif "tags" in kwargs and isinstance(kwargs["tags"], list):
+            kwargs["tags"].extend(tag_names)
+        elif "tags" in kwargs and isinstance(kwargs["tags"], str):
+            tag_names.append(kwargs["tags"])
+            kwargs["tags"] = tag_names
+
+    return kwargs
+
+
+def _sanitize_kwargs_for_ds_tagging(dataset_tags, kwargs=None):
+    if isinstance(dataset_tags, str):
+        dataset_tags = [dataset_tags]
+
+    if (dataset_tags is not None) and (kwargs is not None):
+        if "dataset_tags" not in kwargs:
+            kwargs["dataset_tags"] = dataset_tags
+        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], list):
+            kwargs["dataset_tags"].extend(dataset_tags)
+        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], str):
+            dataset_tags.append(kwargs["dataset_tags"])
+            kwargs["dataset_tags"] = dataset_tags
+
+    return kwargs
+
+
+class SchedulerMixin(Trainer):
+    """
+    Mixin class for scheduler setup in CausalTrainer.
+    """
+
+    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
+
+    def create_scheduler(
+        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
+    ):
+        """
+        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
+        passed as an argument.
+
+        Args:
+            num_training_steps (int): The number of training steps to do.
+            optimizer (torch.optim.Optimizer): The training optimizer
+        """
+        use_cosine_quadratic = (
+            self.args.lr_scheduler_type == "cosine"
+            and self.args.lr_quadratic_warmup is True
+        )
+
+        use_cosine_min_lr = (
+            self.args.lr_scheduler_type == "cosine"
+            and self.args.cosine_min_lr_ratio is not None
+        )
+
+        # fmt: off
+        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
+            # fmt: on
+            if self.args.alternate_lr_scheduler_type == "one_cycle":
+                num_warmup_steps = self.args.get_warmup_steps(num_training_steps)
+                pct_start = num_warmup_steps / num_training_steps
+                extra_lr_kwargs = {}
+                if "pct_start" not in self.args.lr_scheduler_kwargs:
+                    extra_lr_kwargs["pct_start"] = pct_start
+                if "anneal_strategy" not in self.args.lr_scheduler_kwargs:
+                    extra_lr_kwargs["anneal_strategy"] = "cos"
+
+                self.lr_scheduler = OneCycleLR(
+                    optimizer,
+                    max_lr=self.args.learning_rate,
+                    total_steps=num_training_steps,
+                    **extra_lr_kwargs,
+                    **self.args.lr_scheduler_kwargs,
+                )
+            elif self.args.alternate_lr_scheduler_type == "rex":
+                if use_cosine_min_lr:
+                    assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+
+                self.lr_scheduler = RexLR(
+                    optimizer=optimizer,
+                    max_lr=self.args.learning_rate,
+                    min_lr=0 if not use_cosine_min_lr else (self.args.learning_rate * self.args.cosine_min_lr_ratio),
+                    total_steps=num_training_steps,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                )
+            elif use_cosine_quadratic:
+                if use_cosine_min_lr:
+                    LOG.warning("Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
+
+                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
+                    optimizer,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                    num_training_steps=num_training_steps,
+                )
+            elif self.args.cosine_min_lr_ratio and self.args.cosine_constant_lr_ratio and use_cosine_min_lr:
+                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+                assert 0 <= self.args.cosine_constant_lr_ratio <= 1.0, "cosine_constant_lr_ratio must be between 0.0 and 1.0"
+                self.lr_scheduler = get_cosine_schedule_with_warmup_decay_constant(  # pylint: disable=attribute-defined-outside-init
+                    optimizer,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                    num_training_steps=num_training_steps,
+                    min_lr_ratio=self.args.cosine_min_lr_ratio,
+                    constant_lr_ratio=self.args.cosine_constant_lr_ratio,
+                )
+            elif self.args.cosine_min_lr_ratio and use_cosine_min_lr:
+                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+                self.lr_scheduler = get_cosine_schedule_with_min_lr(  # pylint: disable=attribute-defined-outside-init
+                    optimizer,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                    num_training_steps=num_training_steps,
+                    min_lr_ratio=self.args.cosine_min_lr_ratio,
+                )
+            else:
+                return super().create_scheduler(num_training_steps, optimizer=optimizer)
+        else:
+            if use_cosine_quadratic:
+                LOG.warning("axolotl's cosine scheduler with quadratic warmup not used (e.g., because of deepspeed).")
+
+            if use_cosine_min_lr:
+                LOG.warning("axolotl's cosine scheduler with min lr not used (e.g., because of deepspeed).")
+
+        return self.lr_scheduler
+
+
+class OptimizerMixin(Trainer):
+    """
+    Mixin class for shared handling of building custom optimizers
+    """
+
+    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
+
+    def create_optimizer_grouped_parameters(
+        self, opt_model, optimizer_kwargs
+    ) -> list[dict]:
+        decay_parameters = self.get_decay_parameter_names(opt_model)
+        params: dict = {
+            "to_weight_decay": {},  # LayerNorm and bias
+            "embeddings": {},  # lm_head, embed_tokens,
+            "no_weight_decay": {},
+        }
+        lr_groups_lookup = {}
+        lr_groups_learning_rates = {}
+        if self.args.lr_groups:
+            for lr_group in self.args.lr_groups:
+                group_name = lr_group["name"]
+                group_modules = lr_group["modules"]
+                for module in group_modules:
+                    lr_groups_lookup[module] = group_name
+                lr_groups_learning_rates[group_name] = lr_group["lr"]
+                params[f"to_weight_decay_{group_name}"] = {}
+
+        for name, param in opt_model.named_parameters():
+            if not param.requires_grad:
+                continue
+            if name.endswith("modules_to_save.default.weight") or any(
+                embed_name in name for embed_name in ["embed_tokens", "lm_head"]
+            ):
+                params["embeddings"][name] = param
+            elif name in decay_parameters:
+                lr_group_modules = [
+                    group_modules
+                    for group_modules in lr_groups_lookup
+                    if group_modules in name
+                ]
+                if lr_groups_lookup and any(lr_group_modules):
+                    lr_group_module = lr_group_modules[0]
+                    group_name = lr_groups_lookup[lr_group_module]
+                    params[f"to_weight_decay_{group_name}"][name] = param
+                else:
+                    params["to_weight_decay"][name] = param
+            else:
+                params["no_weight_decay"][name] = param
+        optimizer_grouped_parameters = []
+        if params["to_weight_decay"]:
+            optimizer_grouped_parameters.append(
+                {
+                    "params": list(params["to_weight_decay"].values()),
+                    "weight_decay": self.args.weight_decay,
+                    "lr": optimizer_kwargs["lr"],
+                }
+            )
+        if params["embeddings"]:
+            lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
+            if self.args.embedding_lr_scale:
+                lr *= self.args.embedding_lr_scale  # pylint: disable=invalid-name
+            elif self.args.embedding_lr:
+                lr = self.args.embedding_lr  # pylint: disable=invalid-name
+            optimizer_grouped_parameters.append(
+                {
+                    "params": list(params["embeddings"].values()),
+                    "weight_decay": 0.0,
+                    "lr": lr,
+                }
+            )
+        if params["no_weight_decay"]:
+            optimizer_grouped_parameters.append(
+                {
+                    "params": list(params["no_weight_decay"].values()),
+                    "weight_decay": 0.0,
+                    "lr": optimizer_kwargs["lr"],
+                }
+            )
+        for group_name, group_lr in lr_groups_learning_rates.items():
+            if params[f"to_weight_decay_{group_name}"]:
+                optimizer_grouped_parameters.append(
+                    {
+                        "params": list(
+                            params[f"to_weight_decay_{group_name}"].values()
+                        ),
+                        "weight_decay": self.args.weight_decay,
+                        "lr": group_lr,
+                    }
+                )
+
+        return optimizer_grouped_parameters
+
+    def create_optimizer(self):
+        if (
+            self.args.loraplus_lr_ratio is None
+            and self.args.embedding_lr_scale is None
+            and self.args.embedding_lr is None
+            and self.args.lr_groups is None
+            and self.optimizer_cls_and_kwargs is None
+        ):
+            return super().create_optimizer()
+
+        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
+
+        if (
+            not self.optimizer
+            and self.optimizer_cls_and_kwargs is not None
+            and issubclass(self.optimizer_cls_and_kwargs[0], BaseOptimizerFactory)
+        ):
+            optimizer_factory_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
+            self.optimizer = optimizer_factory_cls()(
+                opt_model, self.args, **optimizer_kwargs
+            )
+
+        if not self.optimizer:
+            if self.optimizer_cls_and_kwargs is not None:
+                optimizer_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
+            else:
+                optimizer_cls, optimizer_kwargs = self.get_optimizer_cls_and_kwargs(
+                    self.args, opt_model
+                )
+
+            optimizer_grouped_parameters = self.create_optimizer_grouped_parameters(
+                opt_model, optimizer_kwargs
+            )
+
+            if self.args.loraplus_lr_ratio is not None:
+                loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
+                loraplus_lr_embedding = getattr(
+                    self.args, "loraplus_lr_embedding", 1e-6
+                )
+                self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
+                    opt_model,
+                    optimizer_cls,
+                    loraplus_lr_ratio=loraplus_lr_ratio,
+                    loraplus_lr_embedding=loraplus_lr_embedding,
+                    **optimizer_kwargs,
+                )
+            else:
+                # Overwrite `params` in case it's created by `get_optimizer_cls_and_kwargs`
+                # e.g. for GaLore optimizer.
+                if "params" in optimizer_kwargs:
+                    optimizer_grouped_parameters = optimizer_kwargs.pop("params")
+
+                # Overwrite `model` in case it's created by `get_optimizer_cls_and_kwargs`
+                # e.g. for LOMO optimizer.
+                if "model" in optimizer_kwargs:
+                    optimizer_grouped_parameters = optimizer_kwargs.pop("model")
+
+                # For layer-wise dummy optimizers we overwrite optimizer_grouped_parameters with `optimizer_dict`
+                # to avoid arguments conflicts.
+                if "optimizer_dict" in optimizer_kwargs:
+                    optimizer_grouped_parameters = optimizer_kwargs.pop(
+                        "optimizer_dict"
+                    )
+
+                self.optimizer = optimizer_cls(
+                    optimizer_grouped_parameters, **optimizer_kwargs
+                )
+
+            if optimizer_cls.__name__ == "Adam8bit":
+                import bitsandbytes
+
+                manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
+
+                skipped = 0
+                for module in opt_model.modules():
+                    if isinstance(module, nn.Embedding):
+                        skipped += sum(
+                            {
+                                p.data_ptr(): p.numel() for p in module.parameters()
+                            }.values()
+                        )
+                        LOG.info(f"skipped {module}: {skipped/2**20}M params")
+                        manager.register_module_override(
+                            module, "weight", {"optim_bits": 32}
+                        )
+                        LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
+                LOG.info(f"skipped: {skipped/2**20}M params")
+
+        if is_sagemaker_mp_enabled():
+            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
+                self.optimizer
+            )
+
+        return self.optimizer
+
+
+class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
+    """
+    Extend the base Trainer for axolotl helpers
+    """
 
     args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
     tag_names = ["axolotl"]
@@ -58,18 +376,12 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
         self.eval_data_collator = eval_data_collator
         self.dataset_tags = dataset_tags
         self._signature_columns = None  # workaround for pylint
-
         super().__init__(*_args, **kwargs)
-
         self.train_data_collator = self.data_collator
         self._stored_metrics = defaultdict(lambda: defaultdict(list))
         if self.args.orpo_alpha:
             self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
 
-        # Initialize sequence parallelism if enabled
-        if self.args.sequence_parallel_degree > 1:
-            self._setup_sequence_parallel()
-
     def _wrap_model(self, model, training=True, dataloader=None):
         if self.args.torch_compile:
             torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
@@ -82,247 +394,142 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
             )
         return super()._wrap_model(model, training=training, dataloader=dataloader)
 
-    def _create_multipack_sampler(
+    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
-        self, base_sampler: Sampler, dataset: Dataset
+        if self.args.sample_packing and not self.args.pretraining:
-    ) -> MultipackBatchSampler:
+            if self.args.multipack_real_batches:
-        """
+                batch_size = self.args.per_device_train_batch_size
-        Helper method to create a `MultipackBatchSampler` for multipacking sequences
+                batch_max_len = self.args.max_seq_length
-        for training.
+            else:
+                batch_size = 1
+                train_batch_size = (
+                    self.state.train_batch_size or self.args.per_device_train_batch_size
+                )
+                batch_max_len = train_batch_size * self.args.max_seq_length
 
-        Args:
+            if self.args.curriculum_sampling:
-            base_sampler: Sampler to wrap with `MultipackBatchSampler`.
+                sampler = SequentialSampler(self.train_dataset)
-            dataset: Dataset to sample from.
+            else:
+                sampler = RandomSampler(self.train_dataset)
 
-        Returns:
+            return MultipackBatchSampler(
-            Multipack (sample packing) batch sampler.
+                sampler,
-        """
+                lengths=get_dataset_lengths(self.train_dataset),
-        if self.args.multipack_real_batches:
+                packing_efficiency_estimate=self.args.sample_packing_efficiency,
-            batch_size = self.args.per_device_train_batch_size
+                batch_max_len=batch_max_len,
-            batch_max_len = self.args.max_seq_length
+                batch_size=batch_size,
-        else:
+                group_size=self.args.sample_packing_group_size,
-            batch_size = 1
+                bin_size=self.args.sample_packing_bin_size,
-            train_batch_size = (
+                drop_last=True,
-                self.state.train_batch_size or self.args.per_device_train_batch_size
             )
-            batch_max_len = train_batch_size * self.args.max_seq_length
+        if self.args.curriculum_sampling:
+            return SequentialSampler(self.train_dataset)
+        return super()._get_train_sampler()
 
-        return MultipackBatchSampler(
+    def _get_eval_sampler(
-            base_sampler,
+        self, eval_dataset: Dataset
-            lengths=get_dataset_lengths(dataset),
+    ) -> Optional[torch.utils.data.Sampler]:
-            packing_efficiency_estimate=self.args.sample_packing_efficiency,
+        if self.args.sample_packing and self.args.eval_sample_packing is not False:
-            batch_max_len=batch_max_len,
+            if self.args.multipack_real_batches:
-            batch_size=batch_size,
+                batch_size = self.args.per_device_eval_batch_size
-            drop_last=True,
+                batch_max_len = self.args.max_seq_length
-        )
+            else:
-
+                batch_size = 1
-    def _get_train_sampler(self) -> Sampler | None:
+                batch_max_len = (
-        """
+                    self.args.per_device_eval_batch_size * self.args.max_seq_length
-        Helper method to get the sampler for training. Handles cases for sequence
+                )
-        parallelism, sample packing, and curriculum sampling (sequential).
+            return MultipackBatchSampler(
-
+                SequentialSampler(eval_dataset),
-        Returns:
+                lengths=get_dataset_lengths(self.eval_dataset),
-            If the dataset is non-empty, a sampler is returned, the type of which
+                packing_efficiency_estimate=self.args.sample_packing_efficiency,
-                depends on the passed training args.
+                batch_max_len=batch_max_len,
-        """
+                batch_size=batch_size,
-        use_sample_packing = self.args.sample_packing and not self.args.pretraining
+                group_size=self.args.sample_packing_group_size,
-
+                bin_size=self.args.sample_packing_bin_size,
-        # Determine the base sampler first
+                drop_last=True,
-        if self.args.sequence_parallel_degree > 1:
-            base_sampler = self._sp_get_train_sampler(self.train_dataset)
-        elif self.args.curriculum_sampling:
-            base_sampler = SequentialSampler(self.train_dataset)
-        elif use_sample_packing:
-            base_sampler = RandomSampler(self.train_dataset)
-        else:
-            # Default to parent class implementation for standard random sampling
-            return super()._get_train_sampler()
-
-        # Apply multipack wrapper if needed
-        if use_sample_packing:
-            return self._create_multipack_sampler(
-                base_sampler=base_sampler,
-                dataset=self.train_dataset,
             )
+        return super()._get_eval_sampler(eval_dataset)
 
-        return base_sampler
+    def get_train_dataloader(self) -> DataLoader:
+        if self.args.sample_packing and not self.args.pretraining:
+            train_dataset = self.train_dataset
+            if "length" in train_dataset.features.keys():
+                train_dataset = train_dataset.remove_columns(["length"])
+            data_collator = self.data_collator
+            dataloader_params = {
+                "batch_size": self._train_batch_size,
+                "collate_fn": data_collator,
+                "num_workers": self.args.dataloader_num_workers,
+                "pin_memory": self.args.dataloader_pin_memory,
+            }
+            if self.args.dataloader_prefetch_factor:
+                dataloader_params["prefetch_factor"] = (
+                    self.args.dataloader_prefetch_factor
+                )
 
-    def _get_eval_sampler(self, eval_dataset: Dataset | None = None) -> Sampler | None:
+            sampler = self._get_train_sampler()
-        """
-        Helper method to get the sampler for evaluation. Handles sequence parallelism
-        and sample packing cases.
-
-        Returns:
-            If the dataset is non-empty, a sampler is returned, the type of which
-                depends on the passed training args.
-        """
-        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
-
-        # 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
-        )
-
-        # Determine the base sampler
-        if self.args.sequence_parallel_degree > 1:
-            base_sampler = self._sp_get_eval_sampler(eval_dataset)
-        elif use_multipack:
-            base_sampler = SequentialSampler(eval_dataset)
-        else:
-            return super()._get_eval_sampler(eval_dataset)
-
-        # Apply multipack wrapper if needed
-        if use_multipack:
-            return self._create_multipack_sampler(
-                base_sampler=base_sampler,
-                dataset=eval_dataset,
-            )
-
-        return base_sampler
-
-    def _create_dataloader_params(self, is_eval=False, custom_batch_size=None):
-        """Create common dataloader parameters for train or eval."""
-        batch_size = custom_batch_size or (
-            self.args.eval_batch_size if is_eval else self._train_batch_size
-        )
-
-        params = {
-            "batch_size": batch_size,
-            "collate_fn": self.data_collator,
-            "num_workers": self.args.dataloader_num_workers,
-            "pin_memory": self.args.dataloader_pin_memory,
-        }
-
-        # Add persistent workers only for training
-        if not is_eval and hasattr(self.args, "dataloader_persistent_workers"):
-            params["persistent_workers"] = self.args.dataloader_persistent_workers
-
-        # Add prefetch factor if specified
-        if self.args.dataloader_prefetch_factor:
-            params["prefetch_factor"] = self.args.dataloader_prefetch_factor
-
-        return params
-
-    def _prepare_dataloader(
-        self, dataset, sampler, is_eval=False, custom_batch_size=None
-    ):
-        """Prepare a dataloader with the given dataset and sampler."""
-        # Get base parameters
-        dataloader_params = self._create_dataloader_params(is_eval, custom_batch_size)
-
-        # Add sampler configuration
-        if not isinstance(dataset, torch.utils.data.IterableDataset):
             if isinstance(sampler, BatchSampler):
-                # batch_size and batch_sampler are mutually exclusive
                 dataloader_params["batch_sampler"] = sampler
                 del dataloader_params["batch_size"]
             else:
                 dataloader_params["sampler"] = sampler
                 dataloader_params["drop_last"] = self.args.dataloader_drop_last
+            dataloader_params["worker_init_fn"] = seed_worker
 
-            if not is_eval:
-                dataloader_params["worker_init_fn"] = seed_worker
-
-        # Create the dataloader
-        dataloader = DataLoader(dataset, **dataloader_params)
-
-        if self.args.sample_packing and (
-            (not is_eval and not self.args.pretraining)
-            or (is_eval and self.args.eval_sample_packing is not False)
-        ):
             self.accelerator.even_batches = False
-
+            return self.accelerator.prepare_data_loader(
-        # Return unprepared dataloader if using sequence parallelism
+                DataLoader(train_dataset, **dataloader_params)
-        if self.args.sequence_parallel_degree > 1:
-            return dataloader
-
-        # Otherwise prepare with accelerator
-        return self.accelerator.prepare_data_loader(dataloader)
-
-    def get_train_dataloader(self) -> DataLoader:
-        """Get dataloader for training"""
-        train_dataset = self.train_dataset
-        data_collator = self.data_collator  # type: ignore
-
-        # Handle dataset preprocessing
-        if isinstance(train_dataset, datasets.Dataset):
-            if self.args.sample_packing and not self.args.pretraining:
-                train_dataset = train_dataset.remove_columns(["length"])
-            if not self.args.sample_packing or self.args.pretraining:
-                train_dataset = self._remove_unused_columns(
-                    train_dataset, description="training"
-                )
-        else:
-            self.data_collator = self._get_collator_with_removed_columns(  # pylint: disable=attribute-defined-outside-init
-                data_collator,
-                description="training",
             )
+        return super().get_train_dataloader()
 
-        # Get sampler and create dataloader
+    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
-        sampler = self._get_train_sampler()
-        return self._prepare_dataloader(train_dataset, sampler, is_eval=False)
-
-    def get_eval_dataloader(self, eval_dataset: Dataset | None = None) -> DataLoader:
-        """Get dataloader for evaluation"""
-        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
-
-        # Handle special case: sample packing is enabled but eval_sample_packing is False
         if self.args.sample_packing and self.args.eval_sample_packing is False:
             self.data_collator = (  # pylint: disable=attribute-defined-outside-init
                 self.eval_data_collator
             )
-            if "length" in eval_dataset.column_names:
+            if eval_dataset:
                 eval_dataset = eval_dataset.remove_columns(["length"])
             dataloader = super().get_eval_dataloader(eval_dataset)
             self.data_collator = (  # pylint: disable=attribute-defined-outside-init
                 self.train_data_collator
             )
-
             return dataloader
 
-        # Handle sample packing or sequence parallelism
+        if self.args.sample_packing and self.args.eval_sample_packing is not False:
-        if (
+            eval_dataset = (
-            self.args.sample_packing
+                eval_dataset if eval_dataset is not None else self.eval_dataset
-            and self.args.eval_sample_packing is not False
-            or self.args.sequence_parallel_degree > 1
-        ):
-            # Get appropriate data collator
-            self.data_collator = (  # pylint: disable=attribute-defined-outside-init
-                self.eval_data_collator
-                if hasattr(self, "eval_data_collator") and self.eval_data_collator
-                else self.data_collator
-            )
-            if "length" in eval_dataset.column_names:
-                eval_dataset = eval_dataset.remove_columns(["length"])
-
-            # Handle dataset preprocessing for SP
-            if self.args.sequence_parallel_degree > 1:
-                if isinstance(eval_dataset, datasets.Dataset):
-                    eval_dataset = self._remove_unused_columns(
-                        eval_dataset, description="evaluation"
-                    )
-                else:
-                    self.data_collator = self._get_collator_with_removed_columns(  # pylint: disable=attribute-defined-outside-init
-                        self.data_collator, description="evaluation"
-                    )
-
-            # Use eval_batch_size for sample packing, per_device_eval_batch_size otherwise
-            batch_size = (
-                self.args.eval_batch_size
-                if self.args.sample_packing
-                else self.args.per_device_eval_batch_size
-            )
-            sampler = self._get_eval_sampler(eval_dataset)
-            dataloader = self._prepare_dataloader(
-                eval_dataset, sampler, is_eval=True, custom_batch_size=batch_size
             )
 
-            return dataloader
+            eval_sampler = self._get_eval_sampler(eval_dataset)
+            eval_dataset = eval_dataset.remove_columns(["length"])
+            data_collator = self.data_collator
+            dataloader_params = {
+                "batch_size": self.args.eval_batch_size,
+                "collate_fn": data_collator,
+                "num_workers": self.args.dataloader_num_workers,
+                "pin_memory": self.args.dataloader_pin_memory,
+            }
+            if self.args.dataloader_prefetch_factor:
+                dataloader_params["prefetch_factor"] = (
+                    self.args.dataloader_prefetch_factor
+                )
+
+            if isinstance(eval_sampler, BatchSampler):
+                dataloader_params["batch_sampler"] = eval_sampler
+                del dataloader_params["batch_size"]
+            else:
+                dataloader_params["sampler"] = eval_sampler
+                dataloader_params["drop_last"] = self.args.dataloader_drop_last
+
+            self.accelerator.even_batches = False
+            return self.accelerator.prepare_data_loader(
+                DataLoader(eval_dataset, **dataloader_params)
+            )
 
         return super().get_eval_dataloader(eval_dataset)
 
     def _get_bench_sampler(
         self, bench_dataset: Dataset
-    ) -> torch.utils.data.Sampler | None:
+    ) -> Optional[torch.utils.data.Sampler]:
         if self.args.world_size <= 1:
             return SequentialSampler(bench_dataset)
         return None
@@ -347,7 +554,6 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
         return DataLoader(bench_dataset, **dataloader_params)
         # return self.accelerator.prepare(DataLoader(bench_dataset, **dataloader_params))
 
-    @override
     def compute_loss(
         self, model, inputs, return_outputs=False, num_items_in_batch=None
     ):
@@ -364,7 +570,6 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
                 return_outputs=return_outputs,
                 num_items_in_batch=num_items_in_batch,
             )
-
         return super().compute_loss(
             model,
             inputs,
@@ -539,10 +744,10 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
         Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
         model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
         """
-        kwargs = sanitize_kwargs_for_ds_tagging(
+        kwargs = _sanitize_kwargs_for_ds_tagging(
             dataset_tags=self.dataset_tags, kwargs=kwargs
         )
-        kwargs = sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
+        kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
 
         return super().push_to_hub(*args, **kwargs)
 
@@ -559,13 +764,15 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
 
         return res
 
-    def log(self, logs: dict[str, float], start_time: float | None = None) -> None:
+    def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
         """
         Log `logs` on the various objects watching training, including stored metrics.
 
         Args:
-            logs: The values to log.
+            logs (`Dict[str, float]`):
-            start_time: The start of training.
+                The values to log.
+            start_time (`Optional[float]`):
+                The start of training.
         """
         # logs either has 'loss' or 'eval_loss'
         train_eval = "train" if "loss" in logs else "eval"
@@ -577,7 +784,7 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
         return super().log(logs, start_time)
 
     def store_metrics(
-        self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train"
+        self, metrics: Dict[str, float], train_eval: Literal["train", "eval"] = "train"
     ) -> None:
         for key, value in metrics.items():
             self._stored_metrics[train_eval][key].append(value)
@@ -590,26 +797,110 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
         os.makedirs(output_dir, exist_ok=True)
         return super()._save_checkpoint(model, trial, **kwargs)
 
-    def training_step(
+
+class AxolotlMambaTrainer(AxolotlTrainer):
+    """
+    Mamba specific trainer to handle loss calculation
+    """
+
+    tag_names = ["axolotl", "mamba"]
+
+    def compute_loss(
         self,
-        model: nn.Module,
+        model,
-        inputs: dict[str, torch.Tensor | Any],
+        inputs,
-        num_items_in_batch: int | None = None,
+        return_outputs=False,  # pylint: disable=unused-argument
-    ) -> torch.Tensor:
+        num_items_in_batch=None,  # pylint: disable=unused-argument
-        """
+    ):
-        Perform a training step on a batch of inputs. Overrides the
+        input_ids = inputs.pop("input_ids")
-        `transformers.trainer.Trainer` method to handle sequence parallelism if
+        lm_logits = model(input_ids).logits
-        enabled.
 
-        Args:
+        labels = input_ids.to(lm_logits.device)
-            model: Model to perform training step for.
+        shift_logits = lm_logits[:, :-1, :].contiguous()
-            inputs: Dictionary mapping.
+        labels = labels[:, 1:].contiguous()
-        """
-        # Set up sequence parallelism for this step if enabled
-        if self.args.sequence_parallel_degree > 1:
-            self._update_ring_flash_attn_params(inputs)
 
-        # Proceed with normal training step
+        loss_fct = torch.nn.CrossEntropyLoss()
-        loss = super().training_step(model, inputs, num_items_in_batch)
+        lm_loss = loss_fct(
+            shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)
+        )
 
-        return loss
+        return lm_loss
+
+
+class ReLoRATrainer(AxolotlTrainer):
+    """
+    Trainer subclass that uses the OneCycleLR scheduler
+    """
+
+    tag_names = ["axolotl", "relora"]
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.lr_scheduler = None
+
+    def create_scheduler(
+        self,
+        num_training_steps: int,
+        optimizer: Optional[torch.optim.Optimizer] = None,
+    ):
+        optimizer = self.optimizer if optimizer is None else optimizer
+        lr_scheduler = super().create_scheduler(num_training_steps, optimizer)
+
+        if self.args.relora_steps:
+            warmup_steps = (
+                self.args.relora_warmup_steps if self.args.relora_warmup_steps else 10
+            )
+            anneal_steps = (
+                self.args.relora_anneal_steps if self.args.relora_anneal_steps else 1
+            )
+            self.lr_scheduler = ReLoRAScheduler(
+                optimizer,
+                lr_scheduler,
+                self.args.relora_steps,
+                anneal_steps,
+                warmup_steps,
+            )
+        else:
+            self.lr_scheduler = lr_scheduler
+
+        return self.lr_scheduler
+
+
+class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
+    """
+    Extend the base ORPOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "orpo"]
+
+
+class AxolotlKTOTrainer(SchedulerMixin, KTOTrainer):
+    """
+    Extend the base KTOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "kto"]
+
+
+class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
+    """
+    Extend the base CPOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "cpo"]
+
+
+class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
+    """
+    Extend the base RewardTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "reward"]
+
+
+class AxolotlPRMTrainer(SchedulerMixin, PRMTrainer):
+    """
+    Extend the base trl.PRMTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "prm"]

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

@@ -13,10 +13,10 @@ from transformers import Trainer
 from transformers.utils import is_sagemaker_mp_enabled
 from trl import DPOTrainer
 
-from axolotl.core.trainers.mixins import SchedulerMixin
+from axolotl.core.trainers.base import (
-from axolotl.core.trainers.utils import (
+    SchedulerMixin,
-    sanitize_kwargs_for_ds_tagging,
+    _sanitize_kwargs_for_ds_tagging,
-    sanitize_kwargs_for_tagging,
+    _sanitize_kwargs_for_tagging,
 )
 
 if is_sagemaker_mp_enabled():
@@ -74,10 +74,10 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
         Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
         model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
         """
-        kwargs = sanitize_kwargs_for_ds_tagging(
+        kwargs = _sanitize_kwargs_for_ds_tagging(
             dataset_tags=self.dataset_tags, kwargs=kwargs
         )
-        kwargs = sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
+        kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
 
         return super().push_to_hub(*args, **kwargs)
 

src/axolotl/core/trainers/mamba.py

@@ -1,32 +0,0 @@
-"""Module for mamba trainer"""
-
-import torch
-
-from axolotl.core.trainers.base import AxolotlTrainer
-
-
-class AxolotlMambaTrainer(AxolotlTrainer):
-    """Mamba specific trainer to handle loss calculation"""
-
-    tag_names = ["axolotl", "mamba"]
-
-    def compute_loss(
-        self,
-        model,
-        inputs,
-        return_outputs=False,  # pylint: disable=unused-argument
-        num_items_in_batch=None,  # pylint: disable=unused-argument
-    ):
-        input_ids = inputs.pop("input_ids")
-        lm_logits = model(input_ids).logits
-
-        labels = input_ids.to(lm_logits.device)
-        shift_logits = lm_logits[:, :-1, :].contiguous()
-        labels = labels[:, 1:].contiguous()
-
-        loss_fct = torch.nn.CrossEntropyLoss()
-        lm_loss = loss_fct(
-            shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)
-        )
-
-        return lm_loss

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

@@ -1,8 +0,0 @@
-"""Init for axolotl.core.trainers.mixins"""
-
-# pylint: disable=unused-import
-# flake8: noqa
-
-from .optimizer import OptimizerMixin
-from .scheduler import SchedulerMixin
-from .sequence_parallel import SequenceParallelMixin

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

@@ -1,201 +0,0 @@
-"""Module for Axolotl trainer optimizer mixin"""
-
-import logging
-
-from peft.optimizers import create_loraplus_optimizer
-from torch import nn
-from transformers.trainer import Trainer
-from transformers.utils import is_sagemaker_mp_enabled
-
-from axolotl.integrations.base import BaseOptimizerFactory
-
-if is_sagemaker_mp_enabled():
-    import smdistributed.modelparallel.torch as smp
-
-LOG = logging.getLogger(__name__)
-
-
-class OptimizerMixin(Trainer):
-    """Mixin class for shared handling of building custom optimizers"""
-
-    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
-
-    def create_optimizer_grouped_parameters(
-        self, opt_model, optimizer_kwargs
-    ) -> list[dict]:
-        decay_parameters = self.get_decay_parameter_names(opt_model)
-        params: dict = {
-            "to_weight_decay": {},  # LayerNorm and bias
-            "embeddings": {},  # lm_head, embed_tokens,
-            "no_weight_decay": {},
-        }
-        lr_groups_lookup = {}
-        lr_groups_learning_rates = {}
-        if self.args.lr_groups:
-            for lr_group in self.args.lr_groups:
-                group_name = lr_group["name"]
-                group_modules = lr_group["modules"]
-                for module in group_modules:
-                    lr_groups_lookup[module] = group_name
-                lr_groups_learning_rates[group_name] = lr_group["lr"]
-                params[f"to_weight_decay_{group_name}"] = {}
-
-        for name, param in opt_model.named_parameters():
-            if not param.requires_grad:
-                continue
-            if name.endswith("modules_to_save.default.weight") or any(
-                embed_name in name for embed_name in ["embed_tokens", "lm_head"]
-            ):
-                params["embeddings"][name] = param
-            elif name in decay_parameters:
-                lr_group_modules = [
-                    group_modules
-                    for group_modules in lr_groups_lookup
-                    if group_modules in name
-                ]
-                if lr_groups_lookup and any(lr_group_modules):
-                    lr_group_module = lr_group_modules[0]
-                    group_name = lr_groups_lookup[lr_group_module]
-                    params[f"to_weight_decay_{group_name}"][name] = param
-                else:
-                    params["to_weight_decay"][name] = param
-            else:
-                params["no_weight_decay"][name] = param
-        optimizer_grouped_parameters = []
-        if params["to_weight_decay"]:
-            optimizer_grouped_parameters.append(
-                {
-                    "params": list(params["to_weight_decay"].values()),
-                    "weight_decay": self.args.weight_decay,
-                    "lr": optimizer_kwargs["lr"],
-                }
-            )
-        if params["embeddings"]:
-            lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
-            if self.args.embedding_lr_scale:
-                lr *= self.args.embedding_lr_scale  # pylint: disable=invalid-name
-            elif self.args.embedding_lr:
-                lr = self.args.embedding_lr  # pylint: disable=invalid-name
-            optimizer_grouped_parameters.append(
-                {
-                    "params": list(params["embeddings"].values()),
-                    "weight_decay": 0.0,
-                    "lr": lr,
-                }
-            )
-        if params["no_weight_decay"]:
-            optimizer_grouped_parameters.append(
-                {
-                    "params": list(params["no_weight_decay"].values()),
-                    "weight_decay": 0.0,
-                    "lr": optimizer_kwargs["lr"],
-                }
-            )
-        for group_name, group_lr in lr_groups_learning_rates.items():
-            if params[f"to_weight_decay_{group_name}"]:
-                optimizer_grouped_parameters.append(
-                    {
-                        "params": list(
-                            params[f"to_weight_decay_{group_name}"].values()
-                        ),
-                        "weight_decay": self.args.weight_decay,
-                        "lr": group_lr,
-                    }
-                )
-
-        return optimizer_grouped_parameters
-
-    def create_optimizer(self):
-        if (
-            self.args.loraplus_lr_ratio is None
-            and self.args.embedding_lr_scale is None
-            and self.args.embedding_lr is None
-            and self.args.lr_groups is None
-            and self.optimizer_cls_and_kwargs is None
-        ):
-            return super().create_optimizer()
-
-        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
-
-        if (
-            not self.optimizer
-            and self.optimizer_cls_and_kwargs is not None
-            and issubclass(self.optimizer_cls_and_kwargs[0], BaseOptimizerFactory)
-        ):
-            optimizer_factory_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
-            self.optimizer = optimizer_factory_cls()(
-                opt_model, self.args, **optimizer_kwargs
-            )
-
-        if not self.optimizer:
-            if self.optimizer_cls_and_kwargs is not None:
-                optimizer_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
-            else:
-                optimizer_cls, optimizer_kwargs = self.get_optimizer_cls_and_kwargs(
-                    self.args, opt_model
-                )
-
-            optimizer_grouped_parameters = self.create_optimizer_grouped_parameters(
-                opt_model, optimizer_kwargs
-            )
-
-            if self.args.loraplus_lr_ratio is not None:
-                loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
-                loraplus_lr_embedding = getattr(
-                    self.args, "loraplus_lr_embedding", 1e-6
-                )
-                self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
-                    opt_model,
-                    optimizer_cls,
-                    loraplus_lr_ratio=loraplus_lr_ratio,
-                    loraplus_lr_embedding=loraplus_lr_embedding,
-                    **optimizer_kwargs,
-                )
-            else:
-                # Overwrite `params` in case it's created by `get_optimizer_cls_and_kwargs`
-                # e.g. for GaLore optimizer.
-                if "params" in optimizer_kwargs:
-                    optimizer_grouped_parameters = optimizer_kwargs.pop("params")
-
-                # Overwrite `model` in case it's created by `get_optimizer_cls_and_kwargs`
-                # e.g. for LOMO optimizer.
-                if "model" in optimizer_kwargs:
-                    optimizer_grouped_parameters = optimizer_kwargs.pop("model")
-
-                # For layer-wise dummy optimizers we overwrite optimizer_grouped_parameters with `optimizer_dict`
-                # to avoid arguments conflicts.
-                if "optimizer_dict" in optimizer_kwargs:
-                    optimizer_grouped_parameters = optimizer_kwargs.pop(
-                        "optimizer_dict"
-                    )
-
-                self.optimizer = optimizer_cls(
-                    optimizer_grouped_parameters, **optimizer_kwargs
-                )
-
-            if optimizer_cls.__name__ == "Adam8bit":
-                import bitsandbytes
-
-                manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
-
-                skipped = 0
-                for module in opt_model.modules():
-                    if isinstance(module, nn.Embedding):
-                        skipped += sum(
-                            {
-                                p.data_ptr(): p.numel() for p in module.parameters()
-                            }.values()
-                        )
-                        LOG.info(f"skipped {module}: {skipped/2**20}M params")
-                        manager.register_module_override(
-                            module, "weight", {"optim_bits": 32}
-                        )
-                        LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
-                LOG.info(f"skipped: {skipped/2**20}M params")
-
-        if is_sagemaker_mp_enabled():
-            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
-                self.optimizer
-            )
-
-        return self.optimizer

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

@@ -1,113 +0,0 @@
-"""Module for Axolotl trainer scheduler mixin"""
-
-import logging
-
-import torch
-from torch.optim.lr_scheduler import OneCycleLR
-from transformers.trainer import Trainer
-
-from axolotl.utils.schedulers import (
-    RexLR,
-    get_cosine_schedule_with_min_lr,
-    get_cosine_schedule_with_quadratic_warmup,
-    get_cosine_schedule_with_warmup_decay_constant,
-)
-
-LOG = logging.getLogger(__name__)
-
-
-class SchedulerMixin(Trainer):
-    """
-    Mixin class for scheduler setup in CausalTrainer.
-    """
-
-    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
-
-    def create_scheduler(
-        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
-    ):
-        """
-        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
-        passed as an argument.
-
-        Args:
-            num_training_steps (int): The number of training steps to do.
-            optimizer (torch.optim.Optimizer): The training optimizer
-        """
-        use_cosine_quadratic = (
-            self.args.lr_scheduler_type == "cosine"
-            and self.args.lr_quadratic_warmup is True
-        )
-
-        use_cosine_min_lr = (
-            self.args.lr_scheduler_type == "cosine"
-            and self.args.cosine_min_lr_ratio is not None
-        )
-
-        # fmt: off
-        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
-            # fmt: on
-            if self.args.alternate_lr_scheduler_type == "one_cycle":
-                num_warmup_steps = self.args.get_warmup_steps(num_training_steps)
-                pct_start = num_warmup_steps / num_training_steps
-                extra_lr_kwargs = {}
-                if "pct_start" not in self.args.lr_scheduler_kwargs:
-                    extra_lr_kwargs["pct_start"] = pct_start
-                if "anneal_strategy" not in self.args.lr_scheduler_kwargs:
-                    extra_lr_kwargs["anneal_strategy"] = "cos"
-
-                self.lr_scheduler = OneCycleLR(
-                    optimizer,
-                    max_lr=self.args.learning_rate,
-                    total_steps=num_training_steps,
-                    **extra_lr_kwargs,
-                    **self.args.lr_scheduler_kwargs,
-                )
-            elif self.args.alternate_lr_scheduler_type == "rex":
-                if use_cosine_min_lr:
-                    assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
-
-                self.lr_scheduler = RexLR(
-                    optimizer=optimizer,
-                    max_lr=self.args.learning_rate,
-                    min_lr=0 if not use_cosine_min_lr else (self.args.learning_rate * self.args.cosine_min_lr_ratio),
-                    total_steps=num_training_steps,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                )
-            elif use_cosine_quadratic:
-                if use_cosine_min_lr:
-                    LOG.warning("Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
-
-                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                )
-            elif self.args.cosine_min_lr_ratio and self.args.cosine_constant_lr_ratio and use_cosine_min_lr:
-                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
-                assert 0 <= self.args.cosine_constant_lr_ratio <= 1.0, "cosine_constant_lr_ratio must be between 0.0 and 1.0"
-                self.lr_scheduler = get_cosine_schedule_with_warmup_decay_constant(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                    min_lr_ratio=self.args.cosine_min_lr_ratio,
-                    constant_lr_ratio=self.args.cosine_constant_lr_ratio,
-                )
-            elif self.args.cosine_min_lr_ratio and use_cosine_min_lr:
-                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
-                self.lr_scheduler = get_cosine_schedule_with_min_lr(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                    min_lr_ratio=self.args.cosine_min_lr_ratio,
-                )
-            else:
-                return super().create_scheduler(num_training_steps, optimizer=optimizer)
-        else:
-            if use_cosine_quadratic:
-                LOG.warning("axolotl's cosine scheduler with quadratic warmup not used (e.g., because of deepspeed).")
-
-            if use_cosine_min_lr:
-                LOG.warning("axolotl's cosine scheduler with min lr not used (e.g., because of deepspeed).")
-
-        return self.lr_scheduler

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

@@ -1,131 +0,0 @@
-"""Module for Axolotl trainer sequence parallelism mixin"""
-
-import logging
-from typing import Any
-
-import torch
-import torch.distributed as dist
-import torch.nn.functional as F
-from datasets import Dataset
-from torch.utils.data import DistributedSampler, Sampler
-
-from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group
-
-LOG = logging.getLogger(__name__)
-
-try:
-    from ring_flash_attn import update_ring_flash_attn_params
-except ImportError:
-    # We pass silently here, but raise an ImportError in our Axolotl config validation
-    # if cfg.sequence_parallel_degree > 1 and `ring-flash-attn` is not installed.
-    pass
-
-
-class SequenceParallelMixin:
-    """
-    Mixin class for sequence parallelism support in trainers.
-
-    This mixin provides functionality for handling sequence parallelism,
-    including creating appropriate samplers, managing data partitioning,
-    and updating ring flash attention parameters during training.
-    """
-
-    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
-
-    def _setup_sequence_parallel(self):
-        """Set up sequence parallelism environment."""
-        self.ring_attn_group = get_ring_attn_group()
-
-    def _create_sequence_parallel_sampler(
-        self,
-        dataset: Dataset,
-        shuffle: bool = True,
-        is_eval: bool = False,
-    ) -> DistributedSampler:
-        """
-        Helper method to create sampler for sequence parallelism (SP).
-
-        We create a distributed sampler with rank equal to the SP group ID, which
-        means that all ranks in the SP group receive the same sample / set of samples
-        per training step. We also set the number of replicas equal to the number of
-        SP groups, which is a bit of a hack / unintended use, but works!
-
-        Args:
-            dataset: Dataset to sample from.
-            shuffle: Whether to shuffle the dataset.
-            is_eval: Whether we are creating a sampler for evaluation or training.
-
-        Returns:
-            Distributed sampler.
-        """
-        num_sp_groups = self.args.world_size // self.args.sequence_parallel_degree
-        sp_group_id = dist.get_rank() // self.args.sequence_parallel_degree
-
-        return DistributedSampler(
-            dataset,
-            num_replicas=num_sp_groups,
-            rank=sp_group_id,
-            seed=self.args.seed if shuffle else None,
-            shuffle=shuffle,
-            drop_last=not is_eval,
-        )
-
-    def _sp_get_train_sampler(self, dataset) -> Sampler | None:
-        """
-        Get a training sampler configured for sequence parallelism.
-
-        Args:
-            dataset: The training dataset
-
-        Returns:
-            Configured sequence parallel sampler.
-        """
-        return self._create_sequence_parallel_sampler(
-            dataset,
-            shuffle=not self.args.curriculum_sampling,
-        )
-
-    def _sp_get_eval_sampler(self, eval_dataset) -> Sampler | None:
-        """
-        Get an evaluation sampler configured for sequence parallelism.
-
-        Args:
-            eval_dataset: The evaluation dataset.
-
-        Returns:
-            Configured sequence parallel sampler.
-        """
-        return self._create_sequence_parallel_sampler(
-            eval_dataset, shuffle=False, is_eval=True
-        )
-
-    def _update_ring_flash_attn_params(self, inputs: dict[str, torch.Tensor | Any]):
-        """
-        Calculate the cu_seqlens for the current forward pass and pass the value to
-        the substituted ring_flash_attn. This is accomplished by using the passed
-        `input_ids`.
-
-        Args:
-            inputs: Current batch of inputs.
-        """
-        # At this point, inputs should already be partitioned by the sequence
-        # parallel data collator
-        batch_size = inputs["input_ids"].shape[0]
-        seq_len = inputs["input_ids"].shape[1]
-        packed_seq_lens = [seq_len] * batch_size
-
-        # Calculate the full sequence length across all GPUs in this SP group
-        total_seq_len = seq_len * self.args.sequence_parallel_degree
-
-        cu_seqlens = torch.cumsum(
-            torch.tensor(
-                packed_seq_lens, device=torch.cuda.current_device(), dtype=torch.int32
-            ),
-            dim=-1,
-            dtype=torch.int32,
-        )
-        cu_seqlens = F.pad(
-            F.pad(cu_seqlens, (1, 0), value=0), (0, 1), value=total_seq_len
-        )
-
-        update_ring_flash_attn_params(cu_seqlens, self.ring_attn_group)

src/axolotl/core/trainers/relora.py

@@ -1,43 +0,0 @@
-"""Module for ReLoRA trainer"""
-
-import torch
-
-from axolotl.core.trainers.base import AxolotlTrainer
-from axolotl.monkeypatch.relora import ReLoRAScheduler
-
-
-class ReLoRATrainer(AxolotlTrainer):
-    """Trainer subclass that uses the `OneCycleLR` scheduler"""
-
-    tag_names = ["axolotl", "relora"]
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.lr_scheduler = None
-
-    def create_scheduler(
-        self,
-        num_training_steps: int,
-        optimizer: torch.optim.Optimizer | None = None,
-    ):
-        optimizer = self.optimizer if optimizer is None else optimizer
-        lr_scheduler = super().create_scheduler(num_training_steps, optimizer)
-
-        if self.args.relora_steps:
-            warmup_steps = (
-                self.args.relora_warmup_steps if self.args.relora_warmup_steps else 10
-            )
-            anneal_steps = (
-                self.args.relora_anneal_steps if self.args.relora_anneal_steps else 1
-            )
-            self.lr_scheduler = ReLoRAScheduler(
-                optimizer,
-                lr_scheduler,
-                self.args.relora_steps,
-                anneal_steps,
-                warmup_steps,
-            )
-        else:
-            self.lr_scheduler = lr_scheduler
-
-        return self.lr_scheduler

src/axolotl/core/trainers/trl.py

@@ -1,25 +1,16 @@
-"""Module for TRL PPO trainer"""
+"""
-
+module for TRL PPO training
-from typing import Literal, Union
+"""
 
 import torch
 from tqdm import tqdm
-from trl import (
+from trl import PPOTrainer
-    CPOTrainer,
-    KTOTrainer,
-    ORPOTrainer,
-    PPOTrainer,
-    PRMTrainer,
-    RewardTrainer,
-)
-
-from axolotl.core.trainers.mixins.scheduler import SchedulerMixin
 
 
 class TRLPPOTrainer(PPOTrainer):
-    """Wrapper for TRL PPO trainer to handle customizations"""
+    """
-
+    wrapper for ppo trainer to handle customizations
-    tag_names = ["axolotl", "ppo"]
+    """
 
     def train(
         self,
@@ -40,7 +31,9 @@ class TRLPPOTrainer(PPOTrainer):
             "batch_size": 16,
         }
 
-        for _, batch in tqdm(enumerate(self.dataloader)):
+        for epoch, batch in tqdm(  # pylint: disable=unused-variable
+            enumerate(self.dataloader)
+        ):
             query_tensors = batch["input_ids"]
 
             # generate model response
@@ -72,189 +65,3 @@ class TRLPPOTrainer(PPOTrainer):
                 rewards,
                 columns_to_log=["query", "response", "ref_response", "ref_rewards"],
             )
-
-
-class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
-    """
-    Extend the base ORPOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "orpo"]
-
-    def get_batch_loss_metrics(
-        self,
-        model,
-        batch: dict[str, Union[list, torch.LongTensor]],
-        train_eval: Literal["train", "eval"] = "train",
-    ):
-        """Compute the ORPO loss and other metrics for the given batch of inputs for train or test."""
-
-        # TODO remove once https://github.com/huggingface/trl/pull/3069 is included in a trl release
-
-        metrics = {}
-
-        forward_output = self.concatenated_forward(model, batch)
-        (
-            policy_chosen_logps,
-            policy_rejected_logps,
-            policy_chosen_logits,
-            policy_rejected_logits,
-            policy_nll_loss,
-        ) = forward_output[:5]
-        if self.aux_loss_enabled:
-            aux_loss = forward_output[5]
-
-        losses, chosen_rewards, rejected_rewards, log_odds_ratio, log_odds_chosen = (
-            self.odds_ratio_loss(policy_chosen_logps, policy_rejected_logps)
-        )
-        # full ORPO loss
-        loss = policy_nll_loss - losses.mean()
-
-        reward_accuracies = (chosen_rewards > rejected_rewards).float()
-
-        prefix = "eval_" if train_eval == "eval" else ""
-        metrics[f"{prefix}rewards/chosen"] = self.accelerator.gather_for_metrics(
-            chosen_rewards
-        ).mean()
-        metrics[f"{prefix}rewards/rejected"] = self.accelerator.gather_for_metrics(
-            rejected_rewards
-        ).mean()
-        metrics[f"{prefix}rewards/accuracies"] = self.accelerator.gather_for_metrics(
-            reward_accuracies
-        ).mean()
-        metrics[f"{prefix}rewards/margins"] = self.accelerator.gather_for_metrics(
-            chosen_rewards - rejected_rewards
-        ).mean()
-        metrics[f"{prefix}logps/rejected"] = (
-            self.accelerator.gather_for_metrics(policy_rejected_logps).detach().mean()
-        )
-        metrics[f"{prefix}logps/chosen"] = (
-            self.accelerator.gather_for_metrics(policy_chosen_logps).detach().mean()
-        )
-        metrics[f"{prefix}logits/rejected"] = self.accelerator.gather_for_metrics(
-            policy_rejected_logits.detach().mean()
-        ).mean()
-        metrics[f"{prefix}logits/chosen"] = self.accelerator.gather_for_metrics(
-            policy_chosen_logits.detach().mean()
-        ).mean()
-        metrics[f"{prefix}nll_loss"] = (
-            self.accelerator.gather_for_metrics(policy_nll_loss).detach().mean()
-        )
-        metrics[f"{prefix}log_odds_ratio"] = (
-            self.accelerator.gather_for_metrics(log_odds_ratio).detach().mean()
-        )
-        metrics[f"{prefix}log_odds_chosen"] = (
-            self.accelerator.gather_for_metrics(log_odds_chosen).detach().mean()
-        )
-        for k, v in metrics.items():
-            metrics[k] = v.item()
-        if self.aux_loss_enabled:
-            loss += self.aux_loss_coef * aux_loss
-
-        return loss, metrics
-
-
-class AxolotlKTOTrainer(SchedulerMixin, KTOTrainer):
-    """
-    Extend the base KTOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "kto"]
-
-
-class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
-    """
-    Extend the base CPOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "cpo"]
-
-    def get_batch_loss_metrics(
-        self,
-        model,
-        batch: dict[str, Union[list, torch.LongTensor]],
-        train_eval: Literal["train", "eval"] = "train",
-    ):
-        """Compute the CPO loss and other metrics for the given batch of inputs for train or test."""
-        metrics = {}
-
-        forward_output = self.concatenated_forward(model, batch)
-        (
-            policy_chosen_logps,
-            policy_rejected_logps,
-            policy_chosen_logits,
-            policy_rejected_logits,
-            policy_nll_loss,
-        ) = forward_output[:5]
-        if self.aux_loss_enabled:
-            aux_loss = forward_output[5]
-
-        losses, chosen_rewards, rejected_rewards = self.cpo_loss(
-            policy_chosen_logps,
-            policy_rejected_logps,
-        )
-
-        loss = losses.mean() + self.cpo_alpha * policy_nll_loss
-        reward_accuracies = (chosen_rewards > rejected_rewards).float()
-
-        prefix = "eval_" if train_eval == "eval" else ""
-        metrics[f"{prefix}rewards/chosen"] = (
-            self.accelerator.gather_for_metrics(chosen_rewards).mean().item()
-        )
-        metrics[f"{prefix}rewards/rejected"] = (
-            self.accelerator.gather_for_metrics(rejected_rewards).mean().item()
-        )
-        metrics[f"{prefix}rewards/accuracies"] = (
-            self.accelerator.gather_for_metrics(reward_accuracies).mean().item()
-        )
-        metrics[f"{prefix}rewards/margins"] = (
-            self.accelerator.gather_for_metrics(chosen_rewards - rejected_rewards)
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}logps/rejected"] = (
-            self.accelerator.gather_for_metrics(policy_rejected_logps)
-            .detach()
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}logps/chosen"] = (
-            self.accelerator.gather_for_metrics(policy_chosen_logps)
-            .detach()
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}logits/rejected"] = (
-            self.accelerator.gather_for_metrics(policy_rejected_logits.detach().mean())
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}logits/chosen"] = (
-            self.accelerator.gather_for_metrics(policy_chosen_logits.detach().mean())
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}nll_loss"] = (
-            self.accelerator.gather_for_metrics(policy_nll_loss).detach().mean().item()
-        )
-
-        if self.aux_loss_enabled:
-            loss += self.aux_loss_coef * aux_loss
-
-        return loss, metrics
-
-
-class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
-    """
-    Extend the base RewardTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "reward"]
-
-
-class AxolotlPRMTrainer(SchedulerMixin, PRMTrainer):
-    """
-    Extend the base trl.PRMTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "prm"]

src/axolotl/core/trainers/utils.py

@@ -1,33 +0,0 @@
-"""Utils for Axolotl trainers"""
-
-
-def sanitize_kwargs_for_tagging(tag_names, kwargs=None):
-    if isinstance(tag_names, str):
-        tag_names = [tag_names]
-
-    if kwargs is not None:
-        if "tags" not in kwargs:
-            kwargs["tags"] = tag_names
-        elif "tags" in kwargs and isinstance(kwargs["tags"], list):
-            kwargs["tags"].extend(tag_names)
-        elif "tags" in kwargs and isinstance(kwargs["tags"], str):
-            tag_names.append(kwargs["tags"])
-            kwargs["tags"] = tag_names
-
-    return kwargs
-
-
-def sanitize_kwargs_for_ds_tagging(dataset_tags, kwargs=None):
-    if isinstance(dataset_tags, str):
-        dataset_tags = [dataset_tags]
-
-    if (dataset_tags is not None) and (kwargs is not None):
-        if "dataset_tags" not in kwargs:
-            kwargs["dataset_tags"] = dataset_tags
-        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], list):
-            kwargs["dataset_tags"].extend(dataset_tags)
-        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], str):
-            dataset_tags.append(kwargs["dataset_tags"])
-            kwargs["dataset_tags"] = dataset_tags
-
-    return kwargs

src/axolotl/core/training_args.py

@@ -5,7 +5,6 @@ extra axolotl specific training args
 from dataclasses import dataclass, field
 from typing import Optional
 
-from PIL.Image import Resampling
 from transformers import TrainingArguments
 from trl import CPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig
 
@@ -208,33 +207,14 @@ class AxolotlTrainingMixins:
         },
     )
 
-    sequence_parallel_degree: Optional[int] = field(
-        default=1,
-        metadata={"help": "The number of workers to use in sequence parallelism"},
-    )
-
-    # multi-modal section
-
-    image_size: int | tuple[int, int] | None = field(
-        default=None,
-        metadata={"help": "The size of the image to resize to"},
-    )
-
-    image_resize_algorithm: Resampling | None = field(
-        default=None,
-        metadata={"help": "The algorithm to use for image resizing"},
-    )
-
-    # end of multi-modal section
-
 
 @dataclass
 class AxolotlTrainingArguments(AxolotlTrainingMixins, TrainingArguments):
     """
     Training arguments for Causal trainer
 
-    This code is duplicated due to HF TrainingArguments not setting output_dir with a
+    This code is duplicated due to HF TrainingArguments not setting output_dir with a defaujlt value
-    default value so it can't be used as a mixin.
+    so it can't be used as a mixin.
     """
 
 

src/axolotl/integrations/cut_cross_entropy/README.md

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

src/axolotl/integrations/cut_cross_entropy/__init__.py

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

src/axolotl/integrations/liger/README.md

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

src/axolotl/integrations/liger/__init__.py

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

src/axolotl/monkeypatch/attention/ring_attn.py

@@ -1,89 +0,0 @@
-"""
-Ring attention group registration and flash attention patching.
-
-Make use of the `ring-flash-attn` (https://github.com/zhuzilin/ring-flash-attention)
-package, specifically the `hf_adapter.substitute_hf_flash_attn` function to patch in
-their sequence parallel version of Flash Attention 2.
-"""
-
-import torch.distributed as dist
-from accelerate.logging import get_logger
-
-from axolotl.logging_config import configure_logging
-
-configure_logging()
-LOG = get_logger(__name__)
-
-RING_ATTN_GROUP = None
-
-
-def get_ring_attn_group() -> dist.ProcessGroup:
-    """
-    Getter for ring attention group on this rank.
-
-    Returns:
-        The process group for ring attention for this rank.
-    """
-    return RING_ATTN_GROUP
-
-
-def set_ring_attn_group(ring_attn_group: dist.ProcessGroup | None):
-    """
-    Setter for ring attention group on this rank.
-
-    Args:
-        Process group for ring attention.
-    """
-    global RING_ATTN_GROUP  # pylint: disable=global-statement
-    RING_ATTN_GROUP = ring_attn_group
-
-
-def register_ring_attn(sequence_parallel_degree: int):
-    """
-    Create ring attention group and substitute flash attn with ring flash attn.
-
-    Args:
-        sequence_parallel_degree: Sequence parallelism factor.
-    """
-    LOG.info(
-        "Enabling ring attention sequence parallelism: "
-        f"each sequence will be processed across {sequence_parallel_degree} GPUs"
-    )
-
-    world_size = dist.get_world_size()
-    assert sequence_parallel_degree <= world_size, (
-        f"sequence_parallel_degree ({sequence_parallel_degree}) "
-        f"must be less than or equal to world_size ({world_size})"
-    )
-    assert world_size % sequence_parallel_degree == 0, (
-        f"sequence_parallel_degree ({sequence_parallel_degree}) "
-        f"must evenly divide world_size ({world_size})"
-    )
-
-    # Detailed logging of group formation
-    rank = dist.get_rank()
-    group_assignments = {}
-
-    for i in range(world_size // sequence_parallel_degree):
-        ring_attn_ranks = list(
-            range(
-                i * sequence_parallel_degree,
-                (i + 1) * sequence_parallel_degree,
-            )
-        )
-        group = dist.new_group(ranks=ring_attn_ranks, backend="nccl")
-
-        # Track which GPUs are in which groups
-        for r in ring_attn_ranks:
-            group_assignments[r] = i
-
-        if rank in ring_attn_ranks:
-            set_ring_attn_group(group)
-
-    # Log the GPU group assignments
-    if rank == 0:
-        LOG.info(f"Sequence parallel group assignments: {group_assignments}")
-
-    from ring_flash_attn import substitute_hf_flash_attn
-
-    substitute_hf_flash_attn(get_ring_attn_group(), sequence_parallel_degree)

src/axolotl/monkeypatch/multipack.py

@@ -22,9 +22,6 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
     "phi3",
     "gemma",
     "gemma2",
-    "gemma3_text",
-    "cohere",
-    "cohere2",
     "gemmoe",
     "starcoder2",
     "deepseek_v2",

src/axolotl/processing_strategies.py

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

src/axolotl/prompt_strategies/chat_template.py

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

src/axolotl/train.py

@@ -14,7 +14,6 @@ import transformers.modelcard
 from accelerate.logging import get_logger
 from accelerate.utils import save_fsdp_model
 from datasets import Dataset
-from huggingface_hub.errors import OfflineModeIsEnabled
 from peft import PeftConfig, PeftModel
 from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
 from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
@@ -170,7 +169,7 @@ def execute_training(
     cfg: DictDefault, trainer: Any, resume_from_checkpoint: str | None
 ):
     """
-    Execute the training process with appropriate SDP kernel configurations.
+    Execute the training process with appropriate backend configurations.
 
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
@@ -178,6 +177,9 @@ def execute_training(
         resume_from_checkpoint: Path to checkpoint to resume from, if applicable.
     """
     LOG.info("Starting trainer...")
+    if cfg.group_by_length:
+        LOG.info("hang tight... sorting dataset for group_by_length")
+
     if cfg.flash_optimum:
         with torch.backends.cuda.sdp_kernel(
             # TODO configure these from the YAML w/ sdp_kernel_kwargs: ...
@@ -303,7 +305,7 @@ def create_model_card(cfg: DictDefault, trainer: Trainer):
                     model_card_kwarg["dataset_tags"] = dataset_tags
 
             trainer.create_model_card(**model_card_kwarg)
-        except (AttributeError, UnicodeDecodeError, OfflineModeIsEnabled):
+        except (AttributeError, UnicodeDecodeError):
             pass
     elif cfg.hub_model_id:
         # Defensively push to the hub to ensure the model card is updated
@@ -315,7 +317,6 @@ def save_initial_configs(
     tokenizer: PreTrainedTokenizer,
     model: PreTrainedModel,
     peft_config: PeftConfig | None,
-    processor: ProcessorMixin | None,
 ):
     """
     Save initial configurations before training.
@@ -343,10 +344,6 @@ def save_initial_configs(
         LOG.info(f"Pre-saving model config to {cfg.output_dir}...")
         model.config.save_pretrained(str(output_dir))
 
-    if processor:
-        LOG.info(f"Pre-saving processor to {cfg.output_dir}...")
-        processor.save_pretrained(str(output_dir))
-
 
 def setup_model_card(cfg: DictDefault):
     """
@@ -414,7 +411,6 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
     PeftModel | PreTrainedModel,
     PreTrainedTokenizer,
     PeftConfig | None,
-    ProcessorMixin | None,
 ]:
     """
     Load model, tokenizer, trainer, etc. Helper function to encapsulate the full
@@ -430,7 +426,6 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
             - Model
             - Tokenizer
             - PEFT config
-            - Processor
     """
     # Load tokenizer, processor and model
     model, tokenizer, peft_config, processor = setup_model_and_tokenizer(cfg)
@@ -461,7 +456,6 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
         model,
         tokenizer,
         peft_config,
-        processor,
     )
 
 
@@ -484,7 +478,6 @@ def train(
         model,
         tokenizer,
         peft_config,
-        processor,
     ) = setup_model_and_trainer(cfg, dataset_meta)
 
     # Determine if we need to resume from a checkpoint
@@ -500,7 +493,7 @@ def train(
     )
 
     # Save initial configs
-    save_initial_configs(cfg, tokenizer, model, peft_config, processor)
+    save_initial_configs(cfg, tokenizer, model, peft_config)
 
     # Set up signal handler for graceful termination
     setup_signal_handler(cfg, model, safe_serialization)

src/axolotl/utils/collators/batching.py

@@ -1,59 +1,14 @@
 """
-Data collators for axolotl to pad labels and position_ids for packed sequences. Also
+DataCollator for axolotl to pad labels and position_ids for packed sequences
-includes logic for handling sequence parallelism collation.
 """
 
-import logging
 from dataclasses import dataclass
 from typing import Any, Optional, Union
 
 import numpy as np
-import torch
-import torch.distributed as dist
 from transformers import PreTrainedTokenizerBase
 from transformers.utils import PaddingStrategy
 
-logger = logging.getLogger(__name__)
-
-
-def adjust_position_ids_for_slice(
-    position_ids: torch.Tensor, start_idx: int
-) -> torch.Tensor:
-    """
-    Adjust position IDs for a sliced sequence to maintain proper relative positions.
-    This handles the case where position IDs might not be contiguous due to sample
-    packing.
-    """
-    # Convert to tensor if not already
-    # Find the boundaries between samples (where position_ids reset)
-    adjusted_pos_ids = position_ids.clone()
-
-    # Process each sequence in the batch
-    for i in range(position_ids.shape[0]):
-        seq = position_ids[i]
-
-        # Find sample boundaries
-        boundaries = []
-        for j in range(1, len(seq)):
-            if seq[j] < seq[j - 1]:
-                boundaries.append(j)
-
-        # No need to adjust if there are no boundaries or this is a single sample
-        if not boundaries:
-            adjusted_pos_ids[i] = seq - start_idx
-            continue
-
-        # Adjust each segment separately
-        prev_boundary = 0
-        for boundary in boundaries:
-            adjusted_pos_ids[i, prev_boundary:boundary] -= start_idx
-            prev_boundary = boundary
-
-        # Last segment
-        adjusted_pos_ids[i, prev_boundary:] -= start_idx
-
-    return adjusted_pos_ids
-
 
 @dataclass
 class DataCollatorForSeq2Seq:
@@ -88,8 +43,6 @@ class DataCollatorForSeq2Seq:
             The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
         return_tensors (`str`):
             The type of Tensor to return. Allowable values are "np", "pt" and "tf".
-        sequence_parallel_degree (`int`):
-            The degree of sequence parallelism. Default to 1 for no sequence parallelism.
     """
 
     tokenizer: PreTrainedTokenizerBase
@@ -100,16 +53,6 @@ class DataCollatorForSeq2Seq:
     label_pad_token_id: int = -100
     position_pad_token_id: int = 0
     return_tensors: str = "pt"
-    sequence_parallel_degree: int = 1
-
-    def __post_init__(self):
-        if self.sequence_parallel_degree > 1:
-            from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group
-
-            # Get information about our position in the SP group
-            sp_group = get_ring_attn_group()
-            self.local_rank = dist.get_rank(group=sp_group)
-            self.local_world_size = dist.get_world_size(group=sp_group)
 
     def __call__(self, features, return_tensors=None):
         labels = None
@@ -176,43 +119,8 @@ class DataCollatorForSeq2Seq:
             )
             features["decoder_input_ids"] = decoder_input_ids
 
-        if self.sequence_parallel_degree > 1:
-            features = self.apply_sequence_parallelism(features)
-
         return features
 
-    def apply_sequence_parallelism(
-        self, batch: dict[str, torch.Tensor]
-    ) -> torch.Tensor:
-        """
-        Apply sequence parallelism slicing to a batch.
-
-        Args:
-            batch: Batch dictionary from parent collator.
-
-        Returns:
-            Sliced batch dictionary.
-        """
-        keys_to_slice = ["input_ids", "attention_mask", "labels", "position_ids"]
-
-        for key in keys_to_slice:
-            if key in batch:
-                seq_len = batch[key].shape[1]
-                slice_size = seq_len // self.local_world_size
-                start_idx = self.local_rank * slice_size
-                end_idx = (
-                    start_idx + slice_size
-                    if self.local_rank < self.local_world_size - 1
-                    else seq_len
-                )
-                batch[key] = batch[key][:, start_idx:end_idx]
-
-                # Special handling for position_ids
-                if key == "position_ids" and self.local_rank > 0:
-                    batch[key] = adjust_position_ids_for_slice(batch[key], start_idx)
-
-        return batch
-
 
 @dataclass
 class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
@@ -240,7 +148,6 @@ class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
                         np.array(item[feature]) for item in features_ if feature in item
                     ]
                     out_features[i][feature] = np.concatenate(arrays)
-
         return super().__call__(out_features, return_tensors=return_tensors)
 
 
@@ -270,7 +177,6 @@ class V2BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
                         np.array(item[feature]) for item in features_ if feature in item
                     ]
                     out_features[i][feature] = np.concatenate(arrays)
-
         return super().__call__(out_features, return_tensors=return_tensors)
 
 

src/axolotl/utils/collators/mm_chat.py

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

src/axolotl/utils/config/__init__.py

@@ -13,7 +13,7 @@ from axolotl.integrations.base import PluginManager
 from axolotl.integrations.config import merge_input_args
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import MULTIMODAL_AUTO_MODEL_MAPPING, load_model_config
+from axolotl.utils.models import load_model_config
 from axolotl.utils.schemas.config import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
 )
@@ -125,9 +125,6 @@ def normalize_config(cfg):
             with open(ds_config_path, encoding="utf-8") as f:
                 cfg.deepspeed = json.load(f)
 
-    if cfg.sequence_parallel_degree is None:
-        cfg.sequence_parallel_degree = 1
-
     if cfg.saves_per_epoch:
         save_steps = 1.0 / (cfg.saves_per_epoch * cfg.num_epochs)
         if save_steps < 1.0:  # prevent saves on every step
@@ -158,7 +155,7 @@ def normalize_config(cfg):
 
     cfg.is_multimodal = (
         hasattr(model_config, "model_type")
-        and model_config.model_type in MULTIMODAL_AUTO_MODEL_MAPPING
+        and model_config.model_type in ["llava", "mllama"]
         or any(
             multimodal_name in cfg.base_model.lower()
             for multimodal_name in [
@@ -171,6 +168,7 @@ def normalize_config(cfg):
         cfg.processor_config = (
             cfg.processor_config or cfg.base_model_config or cfg.base_model
         )
+        model_config = model_config.text_config
 
     cfg.model_config_type = model_config.model_type
 

src/axolotl/utils/data/shared.py

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

src/axolotl/utils/models.py

@@ -8,7 +8,7 @@ import math
 import os
 import types
 from functools import cached_property
-from typing import Any, Dict, Optional, Tuple
+from typing import Any, Dict, Optional, Tuple, Union  # noqa: F401
 
 import addict
 import bitsandbytes as bnb
@@ -25,7 +25,7 @@ from peft import (
     prepare_model_for_kbit_training,
 )
 from torch import nn
-from transformers import (
+from transformers import (  # noqa: F401
     AddedToken,
     AutoConfig,
     AutoModelForCausalLM,
@@ -34,17 +34,12 @@ from transformers import (
     AutoTokenizer,
     AwqConfig,
     BitsAndBytesConfig,
-    Gemma3ForConditionalGeneration,
     GPTQConfig,
     LlavaForConditionalGeneration,
-    Mistral3ForConditionalGeneration,
     MllamaForConditionalGeneration,
-    PretrainedConfig,
     PreTrainedModel,
     PreTrainedTokenizerBase,
     ProcessorMixin,
-    Qwen2_5_VLForConditionalGeneration,
-    Qwen2VLForConditionalGeneration,
 )
 from transformers.integrations.deepspeed import (
     HfTrainerDeepSpeedConfig,
@@ -72,16 +67,7 @@ from axolotl.utils.gradient_checkpointing import hf_grad_checkpoint_offload_wrap
 from axolotl.utils.lora_embeddings import get_linear_embedding_layers
 from axolotl.utils.model_shard_quant import load_sharded_model, load_sharded_model_quant
 
-LOG = logging.getLogger(__name__)
+LOG = logging.getLogger("axolotl")
-
-MULTIMODAL_AUTO_MODEL_MAPPING = {
-    "mllama": MllamaForConditionalGeneration,
-    "llava": LlavaForConditionalGeneration,
-    "qwen2_vl": Qwen2VLForConditionalGeneration,
-    "qwen2_5_vl": Qwen2_5_VLForConditionalGeneration,
-    "mistral3": Mistral3ForConditionalGeneration,
-    "gemma3": Gemma3ForConditionalGeneration,
-}
 
 
 # copied from accelerator.FullyShardedDataParallelPlugin
@@ -108,30 +94,9 @@ def get_module_class_from_name(module, name):
     return None
 
 
-def check_model_config(cfg: DictDefault, model_config: PretrainedConfig):
+def check_model_config(cfg: DictDefault, model_config: Union[AutoConfig, DictDefault]):
-    # Set use_cache to False
-    if hasattr(model_config, "use_cache"):
-        model_config.use_cache = False
-
     if cfg.is_multimodal:
-        # For multimodal configs, use_cache is set in the text_config
+        model_config = model_config.text_config
-        if hasattr(model_config, "get_text_config"):
-            text_config = model_config.get_text_config()
-            if hasattr(text_config, "use_cache"):
-                text_config.use_cache = False
-        else:
-            raise ValueError(
-                "No text config found for multimodal model. Please raise an Issue with model details."
-            )
-
-        # check if image_size is not set and load image size from model config if available
-        if (
-            cfg.image_size is None
-            and hasattr(model_config, "vision_config")
-            and hasattr(model_config.vision_config, "image_size")
-        ):
-            cfg.image_size = model_config.vision_config.image_size
-            LOG.debug(f"Loaded image size: {cfg.image_size} from model config")
 
     quant_config_exists = (
         hasattr(model_config, "quantization_config")
@@ -470,31 +435,6 @@ def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
         **processor_kwargs,
     )
 
-    # Attempt to load image size from processor if available
-    if (
-        cfg.image_size is None
-        and hasattr(processor, "size")
-        and any(dim in processor.size for dim in ["width", "height"])
-    ):
-        im_width = None
-        im_height = None
-        if "width" in processor.size:
-            im_width = processor.size["width"]
-        if "height" in processor.size:
-            im_height = processor.size["height"]
-
-        # If both width and height are set, use a tuple
-        if im_width is not None and im_height is not None:
-            cfg.image_size = (im_width, im_height)
-        # If only width is set, use as integer
-        elif im_width is not None:
-            cfg.image_size = im_width
-        # If only height is set, use as integer
-        elif im_height is not None:
-            cfg.image_size = im_height
-
-        LOG.debug(f"Loaded image size: {cfg.image_size} from processor")
-
     return processor
 
 
@@ -531,8 +471,12 @@ class ModelLoader:
 
         # init model config
         self.model_config = load_model_config(cfg)
+        if cfg.is_multimodal:
+            self.text_model_config = self.model_config.text_config
+        else:
+            self.text_model_config = self.model_config
 
-        self.auto_model_loader = AutoModelForCausalLM  # pylint: disable=invalid-name
+        self.AutoModelLoader = AutoModelForCausalLM  # pylint: disable=invalid-name
 
     def apply_patches(self) -> None:
         # load any patches from plugins
@@ -603,14 +547,6 @@ class ModelLoader:
 
             patch_self_attn_lora(self.cfg)
 
-        if self.cfg.sequence_parallel_degree and self.cfg.sequence_parallel_degree > 1:
-            from axolotl.monkeypatch.attention.ring_attn import register_ring_attn
-
-            # Initialize ring attn for sequence parallelism. This must be done after
-            # model init but before the first forward pass, since it modifies flash
-            # attn to use ring comm for SP training across multiple GPUs.
-            register_ring_attn(self.cfg.sequence_parallel_degree)
-
     def patch_attention(self) -> None:
         if hasattr(self.model_config, "model_type"):
             if self.model_config.model_type == "mllama" and self.cfg.flash_attention:
@@ -667,7 +603,7 @@ class ModelLoader:
 
             patch_self_attn_lora()
 
-    def patch_llama_derived_model(self):
+    def patch_llama_derived_model(self) -> None:
         """Modify all llama derived models in one block"""
         self.patch_loss_llama()
 
@@ -717,16 +653,25 @@ class ModelLoader:
                 "Shifted-sparse attention not currently implemented without flash attention."
             )
 
-    def set_auto_model_loader(self):
+    def set_auto_model_loader(self) -> None:
-        """
+        """set self.AutoModelLoader
-        Set self.auto_model_loader. Defaults to `transformers.AutoModelForCausalLM`
+        - default value: AutoModelForCausalLM (set at __init__)
-        (set at `__init__`). When using a multimodal model, `self.auto_model_loader`
+        - when using a multi modality model, self.AutoModelLoader should
-        should be set according to the type of the model.
+          be set according to model type of the model
         """
         if self.cfg.is_multimodal:
-            self.auto_model_loader = MULTIMODAL_AUTO_MODEL_MAPPING.get(
+            if self.model_config.model_type == "llava":
-                self.model_config.model_type, AutoModelForVision2Seq
+                self.AutoModelLoader = (  # pylint: disable=invalid-name
-            )
+                    LlavaForConditionalGeneration
+                )
+            elif self.model_config.model_type == "mllama":
+                self.AutoModelLoader = (  # pylint: disable=invalid-name
+                    MllamaForConditionalGeneration
+                )
+            else:
+                self.AutoModelLoader = (
+                    AutoModelForVision2Seq  # pylint: disable=invalid-name
+                )
 
     def set_device_map_config(self) -> None:
         device_map = self.cfg.device_map
@@ -750,7 +695,7 @@ class ModelLoader:
             from accelerate import infer_auto_device_map
 
             with init_empty_weights():
-                model_canvas = self.auto_model_loader.from_config(
+                model_canvas = self.AutoModelLoader.from_config(
                     self.model_config,
                     trust_remote_code=self.cfg.trust_remote_code or False,
                 )
@@ -947,6 +892,8 @@ class ModelLoader:
             quantization_config = (
                 quantization_config or self.model_kwargs["quantization_config"]
             )
+            if self.cfg.is_multimodal:
+                self.model_config.text_config = self.text_model_config
             self.model = load_sharded_model_quant(
                 self.base_model,
                 self.model_config,
@@ -967,26 +914,13 @@ class ModelLoader:
 
             _ = _configure_zero3_memory_efficient_loading()
 
-            # Load model with random initialization if specified
+            if self.cfg.is_multimodal:
-            if self.cfg.random_init_weights:
+                self.model_config.text_config = self.text_model_config
-                # AutoModel classes support the from_config method
+            self.model = self.AutoModelLoader.from_pretrained(
-                if self.auto_model_loader in [
+                self.base_model,
-                    AutoModelForCausalLM,
+                config=self.model_config,
-                    AutoModelForVision2Seq,
+                **self.model_kwargs,
-                ]:
+            )
-                    self.model = self.auto_model_loader.from_config(
-                        config=self.model_config,
-                    )
-                else:
-                    self.model = self.auto_model_loader(
-                        config=self.model_config,
-                    )
-            else:
-                self.model = self.auto_model_loader.from_pretrained(
-                    self.base_model,
-                    config=self.model_config,
-                    **self.model_kwargs,
-                )
 
             #  TODO (MengqingCao) split these patches seperately
             if self.cfg.flash_attention and not self.inference:
@@ -1021,8 +955,10 @@ class ModelLoader:
             and self.model_type != "AutoModelForCausalLM"
             and not self.cfg.trust_remote_code
         ):
+            if self.cfg.is_multimodal:
+                self.model_config.text_config = self.text_model_config
             if self.cfg.gptq:
-                self.model = self.auto_model_loader.from_pretrained(
+                self.model = self.AutoModelLoader.from_pretrained(
                     self.base_model,
                     config=self.model_config,
                     trust_remote_code=self.cfg.trust_remote_code or False,
@@ -1036,8 +972,26 @@ class ModelLoader:
                     **self.model_kwargs,
                 )
         else:
+            # Shouldn't be a problem most of the time. will obviously error if the model doesn't support this
+            # when training starts
+            if (
+                hasattr(self.text_model_config, "max_seq_len")
+                and self.text_model_config.max_seq_len
+                and self.cfg.sequence_len > self.text_model_config.max_seq_len
+            ):
+                self.text_model_config.max_seq_len = self.cfg.sequence_len
+                LOG.warning(f"increasing context length to {self.cfg.sequence_len}")
+            elif (
+                hasattr(self.text_model_config, "max_sequence_length")
+                and self.text_model_config.max_sequence_length
+                and self.cfg.sequence_len > self.text_model_config.max_sequence_length
+            ):
+                self.text_model_config.max_sequence_length = self.cfg.sequence_len
+                LOG.warning(f"increasing context length to {self.cfg.sequence_len}")
             if self.cfg.gptq:
-                self.model = self.auto_model_loader.from_pretrained(
+                if self.cfg.is_multimodal:
+                    self.model_config.text_config = self.text_model_config
+                self.model = self.AutoModelLoader.from_pretrained(
                     self.base_model,
                     config=self.model_config,
                     trust_remote_code=self.cfg.trust_remote_code or False,
@@ -1055,7 +1009,9 @@ class ModelLoader:
 
                 _ = _configure_zero3_memory_efficient_loading()
 
-                self.model = self.auto_model_loader.from_pretrained(
+                if self.cfg.is_multimodal:
+                    self.model_config.text_config = self.text_model_config
+                self.model = self.AutoModelLoader.from_pretrained(
                     self.base_model,
                     config=self.model_config,
                     trust_remote_code=self.cfg.trust_remote_code or False,
@@ -1218,9 +1174,7 @@ class ModelLoader:
             )
         ):
             resize_kwargs = {}
-            if self.cfg.mean_resizing_embeddings is not None and not (
+            if self.cfg.mean_resizing_embeddings is not None:
-                self.model_config.model_type == "llava"
-            ):
                 resize_kwargs["mean_resizing"] = self.cfg.mean_resizing_embeddings
             self.model.resize_token_embeddings(embeddings_len, **resize_kwargs)
         else:
@@ -1319,6 +1273,8 @@ class ModelLoader:
                 requires_grad.append(f"{name}: {param.requires_grad}")
         if len(requires_grad) == 0:
             LOG.warning("there are no parameters that require gradient updates")
+        if hasattr(self.model, "config"):
+            self.model.config.use_cache = False
 
         if self.cfg.flash_optimum:
             from optimum.bettertransformer import BetterTransformer
@@ -1351,7 +1307,7 @@ def load_model(
     """
     Load a model for a given configuration and tokenizer.
     """
-    model_loader = ModelLoader(
+    loader = ModelLoader(
         cfg,
         tokenizer,
         processor=processor,
@@ -1359,7 +1315,7 @@ def load_model(
         reference_model=reference_model,
         **kwargs,
     )
-    return model_loader.load_model()
+    return loader.load_model()
 
 
 def load_adapter(model, cfg, adapter, inference=False):

src/axolotl/utils/optimizers/soap/LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2024 Nikhil Vyas
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

src/axolotl/utils/optimizers/soap/__init__.py

@@ -1,495 +0,0 @@
-# pylint: skip-file
-# Copied from https://github.com/nikhilvyas/SOAP
-from itertools import chain
-
-import torch
-import torch.optim as optim
-
-# Parts of the code are modifications of Pytorch's AdamW optimizer
-# Parts of the code are modifications of code from https://github.com/jiaweizzhao/GaLore/blob/master/galore_torch/galore_projector.py
-
-
-class SOAP(optim.Optimizer):
-    """
-    Implements SOAP algorithm (https://arxiv.org/abs/2409.11321).
-
-    Parameters:
-        params (`Iterable[nn.parameter.Parameter]`):
-            Iterable of parameters to optimize or dictionaries defining parameter groups.
-        lr (`float`, *optional*, defaults to 0.003):
-            The learning rate to use.
-        betas (`Tuple[float,float]`, *optional*, defaults to `(0.95, 0.95)`):
-            Adam's betas parameters (b1, b2).
-        shampoo_beta (`float`, *optional*, defaults to -1):
-            If >= 0, use this beta for the preconditioner (L and R in paper, state["GG"] below) moving average instead of betas[1].
-        eps (`float`, *optional*, defaults to 1e-08):
-            Adam's epsilon for numerical stability.
-        weight_decay (`float`, *optional*, defaults to 0.01): weight decay coefficient.
-        precondition_frequency (`int`, *optional*, defaults to 10):
-            How often to update the preconditioner.
-        max_precond_dim (`int`, *optional*, defaults to 10000):
-            Maximum dimension of the preconditioner.
-            Set to 10000, so that we exclude most common vocab sizes while including layers.
-        merge_dims (`bool`, *optional*, defaults to `False`):
-            Whether or not to merge dimensions of the preconditioner.
-        precondition_1d (`bool`, *optional*, defaults to `False`):
-            Whether or not to precondition 1D gradients.
-        normalize_grads (`bool`, *optional*, defaults to `False`):
-            Whether or not to normalize gradients per layer.
-            Helps at large precondition_frequency (~100 in our experiments),
-            but hurts performance at small precondition_frequency (~10 in our experiments).
-        data_format (`str`, *optional*, defaults to `channels_first`):
-            Data format of the input for convolutional layers.
-            Should be "channels_last" for data_format of NHWC and "channels_first" for NCHW.
-        correct_bias (`bool`, *optional*, defaults to `True`):
-            Whether or not to use bias correction in Adam.
-    """
-
-    def __init__(
-        self,
-        params,
-        lr: float = 3e-3,
-        betas=(0.95, 0.95),
-        shampoo_beta: float = -1,
-        eps: float = 1e-8,
-        weight_decay: float = 0.01,
-        precondition_frequency: int = 10,
-        max_precond_dim: int = 10000,  #
-        merge_dims: bool = False,  # Merge dimensions till the product of the dimensions is less than or equal to max_precond_dim.
-        precondition_1d: bool = False,
-        normalize_grads: bool = False,
-        data_format: str = "channels_first",
-        correct_bias: bool = True,
-    ):
-        defaults = {
-            "lr": lr,
-            "betas": betas,
-            "shampoo_beta": shampoo_beta,
-            "eps": eps,
-            "weight_decay": weight_decay,
-            "precondition_frequency": precondition_frequency,
-            "max_precond_dim": max_precond_dim,
-            "merge_dims": merge_dims,
-            "precondition_1d": precondition_1d,
-            "normalize_grads": normalize_grads,
-            "correct_bias": correct_bias,
-        }
-        super().__init__(params, defaults)
-        self._data_format = data_format
-
-    def merge_dims(self, grad, max_precond_dim):
-        """
-        Merges dimensions of the gradient tensor till the product of the dimensions is less than or equal to max_precond_dim.
-        """
-        assert self._data_format in ["channels_first", "channels_last"]
-        if self._data_format == "channels_last" and grad.dim() == 4:
-            grad = grad.permute(0, 3, 1, 2)
-        shape = grad.shape
-        new_shape = []
-
-        curr_shape = 1
-        for sh in shape:
-            temp_shape = curr_shape * sh
-            if temp_shape > max_precond_dim:
-                if curr_shape > 1:
-                    new_shape.append(curr_shape)
-                    curr_shape = sh
-                else:
-                    new_shape.append(sh)
-                    curr_shape = 1
-            else:
-                curr_shape = temp_shape
-
-        if curr_shape > 1 or len(new_shape) == 0:
-            new_shape.append(curr_shape)
-
-        new_grad = grad.reshape(new_shape)
-        return new_grad
-
-    @torch.no_grad()
-    def step(self, closure=None):
-        """
-        Performs a single optimization step.
-
-        Arguments:
-            closure (`Callable`, *optional*): A closure that reevaluates the model and returns the loss.
-        """
-        if closure is None:
-            loss = None
-        else:
-            loss = closure()
-
-        for group in self.param_groups:
-            for p in group["params"]:
-                if p.grad is None:
-                    continue
-                grad = p.grad
-
-                state = self.state[p]
-
-                if "step" not in state:
-                    state["step"] = 0
-
-                    # State initialization
-                if "exp_avg" not in state:
-                    # Exponential moving average of gradient values
-                    state["exp_avg"] = torch.zeros_like(grad)
-                    # Exponential moving average of squared gradient values
-                    state["exp_avg_sq"] = torch.zeros_like(grad)
-
-                if "Q" not in state:
-                    self.init_preconditioner(
-                        grad,
-                        state,
-                        precondition_frequency=group["precondition_frequency"],
-                        precondition_1d=group["precondition_1d"],
-                        shampoo_beta=(
-                            group["shampoo_beta"]
-                            if group["shampoo_beta"] >= 0
-                            else group["betas"][1]
-                        ),
-                        max_precond_dim=group["max_precond_dim"],
-                        merge_dims=group["merge_dims"],
-                    )
-                    self.update_preconditioner(
-                        grad,
-                        state,
-                        max_precond_dim=group["max_precond_dim"],
-                        merge_dims=group["merge_dims"],
-                        precondition_1d=group["precondition_1d"],
-                    )
-                    continue  # first step is skipped so that we never use the current gradients in the projection.
-
-                # Projecting gradients to the eigenbases of Shampoo's preconditioner
-                # i.e. projecting to the eigenbases of matrices in state["GG"]
-                grad_projected = self.project(
-                    grad,
-                    state,
-                    merge_dims=group["merge_dims"],
-                    max_precond_dim=group["max_precond_dim"],
-                )
-
-                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
-                beta1, beta2 = group["betas"]
-
-                state["step"] += 1
-
-                # Decay the first and second moment running average coefficient
-                # In-place operations to update the averages at the same time
-                exp_avg.mul_(beta1).add_(grad_projected, alpha=(1.0 - beta1))
-                exp_avg_sq.mul_(beta2).add_(
-                    grad_projected.square(), alpha=(1.0 - beta2)
-                )
-
-                denom = exp_avg_sq.sqrt().add_(group["eps"])
-
-                # Projecting the exponential moving average of gradients to the eigenbases of Shampoo's preconditioner
-                # i.e. projecting to the eigenbases of matrices in state["GG"]
-                # exp_avg_projected = self.project(
-                #     exp_avg,
-                #     state,
-                #     merge_dims=group["merge_dims"],
-                #     max_precond_dim=group["max_precond_dim"],
-                # )
-                exp_avg_projected = exp_avg
-
-                step_size = group["lr"]
-                if group["correct_bias"]:
-                    bias_correction1 = 1.0 - beta1 ** (state["step"])
-                    bias_correction2 = 1.0 - beta2 ** (state["step"])
-                    step_size = step_size * (bias_correction2**0.5) / bias_correction1
-
-                # Projecting back the preconditioned (by Adam) exponential moving average of gradients
-                # to the original space
-                norm_grad = self.project_back(
-                    exp_avg_projected / denom,
-                    state,
-                    merge_dims=group["merge_dims"],
-                    max_precond_dim=group["max_precond_dim"],
-                )
-
-                if group["normalize_grads"]:
-                    norm_grad = norm_grad / (1e-30 + torch.mean(norm_grad**2) ** 0.5)
-
-                p.add_(norm_grad, alpha=-step_size)
-
-                # From AdamW code: Just adding the square of the weights to the loss function is *not*
-                # the correct way of using L2 regularization/weight decay with Adam,
-                # since that will interact with the m and v parameters in strange ways.
-                #
-                # Instead we want to decay the weights in a manner that doesn't interact
-                # with the m/v parameters. This is equivalent to adding the square
-                # of the weights to the loss with plain (non-momentum) SGD.
-                # Add weight decay at the end (fixed version)
-                if group["weight_decay"] > 0.0:
-                    p.add_(p, alpha=(-group["lr"] * group["weight_decay"]))
-
-                # Update is done after the gradient step to avoid using current gradients in the projection.
-                self.update_preconditioner(
-                    grad,
-                    state,
-                    max_precond_dim=group["max_precond_dim"],
-                    merge_dims=group["merge_dims"],
-                    precondition_1d=group["precondition_1d"],
-                )
-
-        return loss
-
-    def init_preconditioner(
-        self,
-        grad,
-        state,
-        precondition_frequency=10,
-        shampoo_beta=0.95,
-        max_precond_dim=10000,
-        precondition_1d=False,
-        merge_dims=False,
-    ):
-        """
-        Initializes the preconditioner matrices (L and R in the paper).
-        """
-        state["GG"] = (
-            []
-        )  # Will hold all the preconditioner matrices (L and R in the paper).
-        if grad.dim() == 1:
-            if not precondition_1d or grad.shape[0] > max_precond_dim:
-                state["GG"].append([])
-            else:
-                state["GG"].append(
-                    torch.zeros(grad.shape[0], grad.shape[0], device=grad.device)
-                )
-        else:
-            if merge_dims:
-                grad = self.merge_dims(grad, max_precond_dim)
-
-            for sh in grad.shape:
-                if sh > max_precond_dim:
-                    state["GG"].append([])
-                else:
-                    state["GG"].append(torch.zeros(sh, sh, device=grad.device))
-
-        state["Q"] = None  # Will hold all the eigenbases of the preconditioner.
-        state["precondition_frequency"] = precondition_frequency
-        state["shampoo_beta"] = shampoo_beta
-
-    def project(self, grad, state, merge_dims=False, max_precond_dim=10000):
-        """
-        Projects the gradient to the eigenbases of the preconditioner.
-        """
-        original_shape = grad.shape
-        if merge_dims:
-            if grad.dim() == 4 and self._data_format == "channels_last":
-                permuted_shape = grad.permute(0, 3, 1, 2).shape
-            grad = self.merge_dims(grad, max_precond_dim)
-
-        for mat in state["Q"]:
-            if len(mat) > 0:
-                grad = torch.tensordot(
-                    grad,
-                    mat,
-                    dims=[[0], [0]],
-                )
-            else:
-                permute_order = list(range(1, len(grad.shape))) + [0]
-                grad = grad.permute(permute_order)
-
-        if merge_dims:
-            if self._data_format == "channels_last" and len(original_shape) == 4:
-                grad = grad.reshape(permuted_shape).permute(0, 2, 3, 1)
-            else:
-                grad = grad.reshape(original_shape)
-        return grad
-
-    def update_preconditioner(
-        self,
-        grad,
-        state,
-        max_precond_dim=10000,
-        merge_dims=False,
-        precondition_1d=False,
-    ):
-        """
-        Updates the preconditioner matrices and the eigenbases (L, R, Q_L, Q_R in the paper).
-        """
-        if state["Q"] is not None:
-            state["exp_avg"] = self.project_back(
-                state["exp_avg"],
-                state,
-                merge_dims=merge_dims,
-                max_precond_dim=max_precond_dim,
-            )
-        if grad.dim() == 1:
-            if precondition_1d and grad.shape[0] <= max_precond_dim:
-                state["GG"][0].lerp_(
-                    grad.unsqueeze(1) @ grad.unsqueeze(0), 1 - state["shampoo_beta"]
-                )
-        else:
-            if merge_dims:
-                new_grad = self.merge_dims(grad, max_precond_dim)
-                for idx, sh in enumerate(new_grad.shape):
-                    if sh <= max_precond_dim:
-                        outer_product = torch.tensordot(
-                            new_grad,
-                            new_grad,
-                            dims=[
-                                [
-                                    *chain(
-                                        range(idx), range(idx + 1, len(new_grad.shape))
-                                    )
-                                ]
-                            ]
-                            * 2,
-                        )
-                        state["GG"][idx].lerp_(outer_product, 1 - state["shampoo_beta"])
-            else:
-                for idx, sh in enumerate(grad.shape):
-                    if sh <= max_precond_dim:
-                        outer_product = torch.tensordot(
-                            grad,
-                            grad,
-                            # Contracts across all dimensions except for k.
-                            dims=[[*chain(range(idx), range(idx + 1, len(grad.shape)))]]
-                            * 2,
-                        )
-                        state["GG"][idx].lerp_(outer_product, 1 - state["shampoo_beta"])
-
-        if state["Q"] is None:
-            state["Q"] = self.get_orthogonal_matrix(state["GG"])
-        if state["step"] > 0 and state["step"] % state["precondition_frequency"] == 0:
-            state["Q"] = self.get_orthogonal_matrix_QR(
-                state, max_precond_dim, merge_dims
-            )
-            # state["Q"] = self.get_fast_QR(state, max_precond_dim, merge_dims)
-
-        if state["step"] > 0:
-            state["exp_avg"] = self.project(
-                state["exp_avg"],
-                state,
-                merge_dims=merge_dims,
-                max_precond_dim=max_precond_dim,
-            )
-
-    def project_back(self, grad, state, merge_dims=False, max_precond_dim=10000):
-        """
-        Projects the gradient back to the original space.
-        """
-        original_shape = grad.shape
-        if merge_dims:
-            if self._data_format == "channels_last" and grad.dim() == 4:
-                permuted_shape = grad.permute(0, 3, 1, 2).shape
-            grad = self.merge_dims(grad, max_precond_dim)
-        for mat in state["Q"]:
-            if len(mat) > 0:
-                grad = torch.tensordot(
-                    grad,
-                    mat,
-                    dims=[[0], [1]],
-                )
-            else:
-                permute_order = list(range(1, len(grad.shape))) + [0]
-                grad = grad.permute(permute_order)
-
-        if merge_dims:
-            if self._data_format == "channels_last" and len(original_shape) == 4:
-                grad = grad.reshape(permuted_shape).permute(0, 2, 3, 1)
-            else:
-                grad = grad.reshape(original_shape)
-        return grad
-
-    def get_orthogonal_matrix(self, mat):
-        """
-        Computes the eigenbases of the preconditioner using torch.linalg.eigh decomposition.
-        """
-        matrix = []
-        for m in mat:
-            if len(m) == 0:
-                matrix.append([])
-                continue
-            if m.data.dtype != torch.float:
-                float_data = False
-                original_type = m.data.dtype
-                original_device = m.data.device
-                matrix.append(m.data.float())
-            else:
-                float_data = True
-                matrix.append(m.data)
-
-        final = []
-        for m in matrix:
-            if len(m) == 0:
-                final.append([])
-                continue
-            try:
-                _, Q = torch.linalg.eigh(
-                    m + 1e-30 * torch.eye(m.shape[0], device=m.device)
-                )
-            except:  # pylint: disable=bare-except # noqa: E722
-                _, Q = torch.linalg.eigh(
-                    m.to(torch.float64) + 1e-30 * torch.eye(m.shape[0], device=m.device)
-                )
-                Q = Q.to(m.dtype)
-            Q = torch.flip(Q, [1])
-
-            if not float_data:
-                Q = Q.to(original_device).type(original_type)
-            final.append(Q)
-        return final
-
-    def get_orthogonal_matrix_QR(self, state, max_precond_dim=10000, merge_dims=False):
-        """
-        Computes the eigenbases of the preconditioner using one round of power iteration
-        followed by torch.linalg.qr decomposition.
-        """
-        precond_list = state["GG"]
-        orth_list = state["Q"]
-
-        matrix = []
-        orth_matrix = []
-        for m, o in zip(precond_list, orth_list):
-            if len(m) == 0:
-                matrix.append([])
-                orth_matrix.append([])
-                continue
-            if m.data.dtype != torch.float:
-                float_data = False
-                original_type = m.data.dtype
-                original_device = m.data.device
-                matrix.append(m.data.float())
-                orth_matrix.append(o.data.float())
-            else:
-                float_data = True
-                matrix.append(m.data.float())
-                orth_matrix.append(o.data.float())
-
-        orig_shape = state["exp_avg_sq"].shape
-        if self._data_format == "channels_last" and len(orig_shape) == 4:
-            permuted_shape = state["exp_avg_sq"].permute(0, 3, 1, 2).shape
-        if merge_dims:
-            exp_avg_sq = self.merge_dims(state["exp_avg_sq"], max_precond_dim)
-        else:
-            exp_avg_sq = state["exp_avg_sq"]
-
-        final = []
-        for ind, (m, o) in enumerate(zip(matrix, orth_matrix)):
-            if len(m) == 0:
-                final.append([])
-                continue
-            est_eig = torch.diag(o.T @ m @ o)
-            sort_idx = torch.argsort(est_eig, descending=True)
-            exp_avg_sq = exp_avg_sq.index_select(ind, sort_idx)
-            o = o[:, sort_idx]
-            power_iter = m @ o
-            Q, _ = torch.linalg.qr(power_iter)
-
-            if not float_data:
-                Q = Q.to(original_device).type(original_type)
-            final.append(Q)
-
-        if merge_dims:
-            if self._data_format == "channels_last" and len(orig_shape) == 4:
-                exp_avg_sq = exp_avg_sq.reshape(permuted_shape).permute(0, 2, 3, 1)
-            else:
-                exp_avg_sq = exp_avg_sq.reshape(orig_shape)
-
-        state["exp_avg_sq"] = exp_avg_sq
-        return final

src/axolotl/utils/samplers/multipack.py

@@ -104,7 +104,9 @@ def allocate(
 
 
 class MultipackBatchSampler(BatchSampler):
-    """Batch sampler class for multipack"""
+    """
+    Batch Sampler class for multipack
+    """
 
     def __init__(
         self,

src/axolotl/utils/schemas/config.py

@@ -1,4 +1,4 @@
-"""Module with Pydantic models for configuration."""
+"""Main Axolotl input configuration Pydantic models"""
 
 # pylint: disable=too-many-lines
 
@@ -42,7 +42,6 @@ from axolotl.utils.schemas.model import (
     ModelOutputConfig,
     SpecialTokensConfig,
 )
-from axolotl.utils.schemas.multimodal import MultiModalConfig
 from axolotl.utils.schemas.peft import LoraConfig, ReLoRAConfig
 from axolotl.utils.schemas.training import HyperparametersConfig
 from axolotl.utils.schemas.trl import TRLConfig
@@ -65,7 +64,6 @@ class AxolotlInputConfig(
     LISAConfig,
     GradioConfig,
     RayConfig,
-    MultiModalConfig,
     RemappedParameters,
     DeprecatedParameters,
     BaseModel,
@@ -247,8 +245,6 @@ class AxolotlInputConfig(
 
     val_set_size: float | None = Field(default=0.0)
 
-    sequence_parallel_degree: int | None = None
-
     special_tokens: SpecialTokensConfig | None = None
     tokens: list[str] | None = None
     added_tokens_overrides: dict[int, str] | None = None
@@ -1106,29 +1102,6 @@ class AxolotlInputConfig(
 
         return data
 
-    @field_validator("sequence_parallel_degree", mode="before")
-    @classmethod
-    def check_sequence_parallel_config(cls, value, info):
-        if not value:
-            value = 1
-
-        if value > 1:
-            if not info.data.get("flash_attention"):
-                raise ValueError(
-                    "flash_attention: true must be set with sequence_parallel_degree > 1"
-                )
-
-            try:
-                import ring_flash_attn  # noqa: F401 # pylint:disable=unused-import
-            except ImportError as exception:
-                raise ImportError(
-                    "sequence_parallel_degree > 1 but ring_flash_attn is not installed. "
-                    "Please install it with `pip install axolotl[ring-flash-attn] "
-                    "or `pip install ring-flash-attn>=0.1.4`."
-                ) from exception
-
-        return value
-
 
 class AxolotlConfigWCapabilities(AxolotlInputConfig):
     """wrapper to valdiate gpu capabilities with the configured options"""

src/axolotl/utils/schemas/enums.py

@@ -22,7 +22,6 @@ class ChatTemplate(str, Enum):
     mistral_v1 = "mistral_v1"  # pylint: disable=invalid-name
     mistral_v2v3 = "mistral_v2v3"  # pylint: disable=invalid-name
     mistral_v3_tekken = "mistral_v3_tekken"  # pylint: disable=invalid-name
-    mistral_v7_tekken = "mistral_v7_tekken"  # pylint: disable=invalid-name
     gemma = "gemma"  # pylint: disable=invalid-name
     cohere = "cohere"  # pylint: disable=invalid-name
     llama3 = "llama3"  # pylint: disable=invalid-name
@@ -37,10 +36,6 @@ class ChatTemplate(str, Enum):
     tokenizer_default = "tokenizer_default"  # pylint: disable=invalid-name
     exaone = "exaone"  # pylint: disable=invalid-name
     metharme = "metharme"  # pylint: disable=invalid-name
-    pixtral = "pixtral"  # pylint: disable=invalid-name
-    llava = "llava"  # pylint: disable=invalid-name
-    qwen2_vl = "qwen2_vl"  # pylint: disable=invalid-name
-    gemma3 = "gemma3"  # pylint: disable=invalid-name
 
 
 class CustomSupportedOptimizers(str, Enum):
@@ -52,4 +47,3 @@ class CustomSupportedOptimizers(str, Enum):
     ao_adamw_fp8 = "ao_adamw_fp8"  # pylint: disable=invalid-name
     adopt_adamw = "adopt_adamw"  # pylint: disable=invalid-name
     muon = "muon"  # pylint: disable=invalid-name
-    soap = "soap"  # pylint: disable=invalid-name

src/axolotl/utils/schemas/multimodal.py

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

src/axolotl/utils/trainer.py

@@ -346,7 +346,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
                     load_from_cache_file=not cfg.is_preprocess,
                     desc="Add position_id column (PoSE)",
                 )
-    elif cfg.sample_packing or cfg.sequence_parallel_degree > 1:
+    elif cfg.sample_packing:
         drop_long_kwargs = {}
         if filter_map_kwargs:
             drop_long_kwargs["desc"] = "Add position_id column (Sample Packing)"
@@ -356,7 +356,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
             **filter_map_kwargs,
             **drop_long_kwargs,
         )
-        if cfg.eval_sample_packing or cfg.sequence_parallel_degree > 1:
+        if cfg.eval_sample_packing is not False:
             if eval_dataset:
                 eval_dataset = eval_dataset.map(
                     add_position_ids,
@@ -443,7 +443,6 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                     - 1
                 )
                 * cfg.num_epochs
-                * cfg.sequence_parallel_degree
             )
             LOG.debug(
                 f"total_num_tokens: {cfg.total_num_tokens:_}, total_num_steps: {total_num_steps:_}",
@@ -474,11 +473,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             LOG.debug(f"data_loader_len: {data_loader_len}", main_process_only=True)
             # FIXME: is there a bug here somewhere? the total num steps depends
             # on the agreed on value for sample_packing_eff_est
-            total_num_steps = int(
+            total_num_steps = int(math.floor(data_loader_len * cfg.num_epochs))
-                math.floor(
-                    data_loader_len * cfg.num_epochs * cfg.sequence_parallel_degree
-                )
-            )
 
             def calc_sample_packing_eff_est(estimates: List[float]):
                 LOG.info(f"sample_packing_eff_est across ranks: {repr(estimates)}")
@@ -499,12 +494,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             )
     else:
         total_num_steps = int(
-            math.ceil(
+            math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
-                len(train_dataset)
-                * cfg.num_epochs
-                * cfg.sequence_parallel_degree
-                / cfg.batch_size
-            )
         )
     LOG.debug(f"total_num_steps: {total_num_steps}", main_process_only=True)
     return total_num_steps

tests/conftest.py

@@ -11,11 +11,7 @@ import time
 
 import pytest
 import requests
-from datasets import load_dataset
 from huggingface_hub import snapshot_download
-from transformers import AutoTokenizer
-
-from tests.hf_offline_utils import disable_hf_offline, enable_hf_offline
 
 
 def retry_on_request_exceptions(max_retries=3, delay=1):
@@ -29,11 +25,9 @@ def retry_on_request_exceptions(max_retries=3, delay=1):
                 except (
                     requests.exceptions.ReadTimeout,
                     requests.exceptions.ConnectionError,
-                    requests.exceptions.HTTPError,
                 ) as exc:
                     if attempt < max_retries - 1:
-                        wait = 2**attempt * delay  # in seconds
+                        time.sleep(delay)
-                        time.sleep(wait)
                     else:
                         raise exc
 
@@ -43,7 +37,6 @@ def retry_on_request_exceptions(max_retries=3, delay=1):
 
 
 @retry_on_request_exceptions(max_retries=3, delay=5)
-@disable_hf_offline
 def snapshot_download_w_retry(*args, **kwargs):
     return snapshot_download(*args, **kwargs)
 
@@ -51,19 +44,19 @@ def snapshot_download_w_retry(*args, **kwargs):
 @pytest.fixture(scope="session", autouse=True)
 def download_smollm2_135m_model():
     # download the model
-    snapshot_download_w_retry("HuggingFaceTB/SmolLM2-135M", repo_type="model")
+    snapshot_download_w_retry("HuggingFaceTB/SmolLM2-135M")
 
 
 @pytest.fixture(scope="session", autouse=True)
 def download_llama_68m_random_model():
     # download the model
-    snapshot_download_w_retry("JackFram/llama-68m", repo_type="model")
+    snapshot_download_w_retry("JackFram/llama-68m")
 
 
 @pytest.fixture(scope="session", autouse=True)
 def download_qwen_2_5_half_billion_model():
     # download the model
-    snapshot_download_w_retry("Qwen/Qwen2.5-0.5B", repo_type="model")
+    snapshot_download_w_retry("Qwen/Qwen2.5-0.5B")
 
 
 @pytest.fixture(scope="session", autouse=True)
@@ -108,37 +101,6 @@ def download_argilla_ultrafeedback_binarized_preferences_cleaned_dataset():
     )
 
 
-@pytest.fixture(scope="session", autouse=True)
-def download_fozzie_alpaca_dpo_dataset():
-    # download the dataset
-    snapshot_download_w_retry(
-        "fozziethebeat/alpaca_messages_2k_dpo_test", repo_type="dataset"
-    )
-    snapshot_download_w_retry(
-        "fozziethebeat/alpaca_messages_2k_dpo_test",
-        repo_type="dataset",
-        revision="ea82cff",
-    )
-
-
-@pytest.fixture(scope="session")
-@disable_hf_offline
-def dataset_fozzie_alpaca_dpo_dataset(
-    download_fozzie_alpaca_dpo_dataset,
-):  # pylint: disable=unused-argument,redefined-outer-name
-    return load_dataset("fozziethebeat/alpaca_messages_2k_dpo_test", split="train")
-
-
-@pytest.fixture(scope="session")
-@disable_hf_offline
-def dataset_fozzie_alpaca_dpo_dataset_rev_ea82cff(
-    download_fozzie_alpaca_dpo_dataset,
-):  # pylint: disable=unused-argument,redefined-outer-name
-    return load_dataset(
-        "fozziethebeat/alpaca_messages_2k_dpo_test", split="train", revision="ea82cff"
-    )
-
-
 @pytest.fixture(scope="session", autouse=True)
 def download_arcee_ai_distilabel_intel_orca_dpo_pairs_dataset():
     # download the dataset
@@ -147,141 +109,10 @@ def download_arcee_ai_distilabel_intel_orca_dpo_pairs_dataset():
     )
 
 
-@pytest.fixture(scope="session", autouse=True)
-def download_argilla_dpo_pairs_dataset():
-    # download the dataset
-    snapshot_download_w_retry(
-        "argilla/distilabel-intel-orca-dpo-pairs", repo_type="dataset"
-    )
-
-
 @pytest.fixture(scope="session", autouse=True)
 def download_tiny_shakespeare_dataset():
     # download the dataset
-    snapshot_download_w_retry("winglian/tiny-shakespeare", repo_type="dataset")
+    snapshot_download_w_retry("Trelis/tiny-shakespeare", repo_type="dataset")
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_deepseek_model_fixture():
-    snapshot_download_w_retry("axolotl-ai-co/DeepSeek-V3-11M", repo_type="model")
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_huggyllama_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "huggyllama/llama-7b",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_llama_1b_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "NousResearch/Llama-3.2-1B",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_llama3_8b_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "NousResearch/Meta-Llama-3-8B", repo_type="model", allow_patterns=["*token*"]
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_llama3_8b_instruct_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "NousResearch/Meta-Llama-3-8B-Instruct",
-        repo_type="model",
-        allow_patterns=["*token*"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_phi_35_mini_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "microsoft/Phi-3.5-mini-instruct", repo_type="model", allow_patterns=["*token*"]
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_phi_3_medium_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "microsoft/Phi-3-medium-128k-instruct",
-        repo_type="model",
-        allow_patterns=["*token*"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_mistral_7b_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "casperhansen/mistral-7b-instruct-v0.1-awq",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_gemma_2b_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "unsloth/gemma-2b-it",
-        revision="703fb4a",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_gemma2_9b_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "mlx-community/gemma-2-9b-it-4bit",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_mlx_mistral_7b_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "mlx-community/Mistral-7B-Instruct-v0.3-4bit",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-def download_llama2_model_fixture():
-    # download the tokenizer only
-    snapshot_download_w_retry(
-        "NousResearch/Llama-2-7b-hf",
-        repo_type="model",
-        allow_patterns=["*token*", "config.json"],
-    )
-
-
-@pytest.fixture(scope="session", autouse=True)
-@enable_hf_offline
-def tokenizer_huggyllama(
-    download_huggyllama_model_fixture,
-):  # pylint: disable=unused-argument,redefined-outer-name
-    tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
-    tokenizer.pad_token = "</s>"
-
-    return tokenizer
 
 
 @pytest.fixture
@@ -347,34 +178,3 @@ def cleanup_monkeypatches():
             module_globals = module_name_tuple[1]
             for module_global in module_globals:
                 globals().pop(module_global, None)
-
-
-# # pylint: disable=redefined-outer-name,unused-argument
-# def test_load_fixtures(
-#     download_smollm2_135m_model,
-#     download_llama_68m_random_model,
-#     download_qwen_2_5_half_billion_model,
-#     download_tatsu_lab_alpaca_dataset,
-#     download_mhenrichsen_alpaca_2k_dataset,
-#     download_mhenrichsen_alpaca_2k_w_revision_dataset,
-#     download_mlabonne_finetome_100k_dataset,
-#     download_argilla_distilabel_capybara_dpo_7k_binarized_dataset,
-#     download_argilla_ultrafeedback_binarized_preferences_cleaned_dataset,
-#     download_fozzie_alpaca_dpo_dataset,
-#     download_arcee_ai_distilabel_intel_orca_dpo_pairs_dataset,
-#     download_argilla_dpo_pairs_dataset,
-#     download_tiny_shakespeare_dataset,
-#     download_deepseek_model_fixture,
-#     download_huggyllama_model_fixture,
-#     download_llama_1b_model_fixture,
-#     download_llama3_8b_model_fixture,
-#     download_llama3_8b_instruct_model_fixture,
-#     download_phi_35_mini_model_fixture,
-#     download_phi_3_medium_model_fixture,
-#     download_mistral_7b_model_fixture,
-#     download_gemma_2b_model_fixture,
-#     download_gemma2_9b_model_fixture,
-#     download_mlx_mistral_7b_model_fixture,
-#     download_llama2_model_fixture,
-# ):
-#     pass

tests/core/chat/test_messages.py

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

tests/e2e/integrations/test_kd.py

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

tests/e2e/integrations/test_liger.py

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

tests/e2e/multigpu/test_grpo.py

@@ -8,12 +8,11 @@ from pathlib import Path
 import pytest
 import yaml
 from accelerate.test_utils import execute_subprocess_async
+from e2e.utils import require_vllm
 from transformers.testing_utils import get_torch_dist_unique_port
 
 from axolotl.utils.dict import DictDefault
 
-from tests.e2e.utils import require_vllm
-
 
 class TestGRPO:
     """

tests/e2e/multigpu/test_llama.py

@@ -9,13 +9,12 @@ from pathlib import Path
 import pytest
 import yaml
 from accelerate.test_utils import execute_subprocess_async
+from e2e.utils import check_tensorboard
 from huggingface_hub import snapshot_download
 from transformers.testing_utils import get_torch_dist_unique_port
 
 from axolotl.utils.dict import DictDefault
 
-from tests.e2e.utils import check_tensorboard
-
 LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
 os.environ["WANDB_DISABLED"] = "true"
 

tests/e2e/multigpu/test_ray.py

@@ -9,11 +9,10 @@ from pathlib import Path
 import pytest
 import yaml
 from accelerate.test_utils import execute_subprocess_async
+from e2e.utils import check_tensorboard, require_torch_lt_2_6_0
 
 from axolotl.utils.dict import DictDefault
 
-from tests.e2e.utils import check_tensorboard, require_torch_lt_2_6_0
-
 LOG = logging.getLogger(__name__)
 os.environ["WANDB_DISABLED"] = "true"
 

tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py

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

tests/e2e/patched/test_sp.py

@@ -1,209 +0,0 @@
-"""Tests for sequence parallelism functionality."""
-
-# pylint: disable=redefined-outer-name,unused-argument
-
-from unittest.mock import MagicMock, patch
-
-import pytest
-import torch
-from accelerate.state import PartialState
-
-from axolotl.monkeypatch.attention.ring_attn import (
-    get_ring_attn_group,
-    set_ring_attn_group,
-)
-from axolotl.utils.collators.batching import adjust_position_ids_for_slice
-from axolotl.utils.dict import DictDefault
-
-
-@pytest.fixture
-def partial_state():
-    """Create a real PartialState instance for testing."""
-    state = PartialState()
-    return state
-
-
-@pytest.fixture(name="cfg")
-def fixture_cfg():
-    cfg = DictDefault(
-        {
-            "base_model": "HuggingFaceTB/SmolLM2-135M",
-            "datasets": [
-                {
-                    "path": "mhenrichsen/alpaca_2k_test",
-                    "type": "alpaca",
-                },
-            ],
-            "micro_batch_size": 1,
-            "gradient_accumulation_steps": 1,
-            "learning_rate": 1e-3,
-            "output_dir": "./model-out",
-            "sequence_len": 512,
-            "special_tokens": {
-                "pad_token": "<|endoftext|>",
-            },
-        }
-    )
-
-    return cfg
-
-
-class TestSequenceParallelHelpers:
-    """Test helper functions used in sequence parallelism."""
-
-    def test_adjust_position_ids_for_slice(self, partial_state):
-        """Test position_ids adjustment for sequence slices."""
-        # Create sample position_ids with multiple sequences
-        position_ids = torch.tensor(
-            [
-                # First sequence with 2 samples
-                [0, 1, 2, 3, 4, 0, 1, 2, 3],
-                # Second sequence with 3 samples
-                [0, 1, 2, 0, 1, 2, 3, 0, 1],
-            ]
-        )
-
-        # Adjust as if this was the second slice (start_idx = 4)
-        adjusted = adjust_position_ids_for_slice(position_ids, start_idx=4)
-
-        # For first sequence: [0,1,2,3,4,0,1,2,3] -> [-4,-3,-2,-1,0,-4,-3,-2,-1]
-        # For second sequence: [0,1,2,0,1,2,3,0,1] -> [-4,-3,-2,-4,-3,-2,-1,-4,-3]
-        expected_first_seq = torch.tensor([0, 1, 2, 3, 4, 0, 1, 2, 3]) - 4
-        expected_second_seq = torch.tensor([0, 1, 2, 0, 1, 2, 3, 0, 1]) - 4
-
-        assert torch.all(adjusted[0] == expected_first_seq)
-        assert torch.all(adjusted[1] == expected_second_seq)
-
-
-class TestRingAttention:
-    """Tests for the ring attention functionality."""
-
-    @patch("torch.distributed.get_rank")
-    @patch("torch.distributed.get_world_size")
-    def test_get_ring_attn_group_no_registration(
-        self, mock_world_size, mock_rank, partial_state
-    ):
-        """Test that get_ring_attn_group returns None when no group has been registered."""
-        # Setup mocks
-        mock_world_size.return_value = 4
-        mock_rank.return_value = 0
-
-        # Get the group without registration
-        group = get_ring_attn_group()
-
-        # Verify that None was returned
-        assert group is None
-
-    @patch("torch.distributed.new_group")
-    @patch("torch.distributed.get_rank")
-    @patch("torch.distributed.get_world_size")
-    def test_register_ring_attn(
-        self, mock_world_size, mock_rank, mock_new_group, partial_state
-    ):
-        """Test that ring attention groups are created correctly."""
-        from axolotl.monkeypatch.attention.ring_attn import register_ring_attn
-
-        # Setup mocks
-        mock_world_size.return_value = 8  # 8 GPUs total
-        mock_rank.return_value = 3  # GPU #3
-        mock_group = MagicMock()
-        mock_new_group.return_value = mock_group
-
-        # Call register_ring_attn with size 4
-        register_ring_attn(sequence_parallel_degree=4)
-
-        # Verify the number of calls without examining the arguments
-        assert mock_new_group.call_count == 2
-
-        # Verify that new_group was called
-        mock_new_group.assert_called()
-
-        # Clean up
-        set_ring_attn_group(None)
-
-
-# Mock a simplified DataCollator test
-@patch("axolotl.monkeypatch.attention.ring_attn.get_ring_attn_group")
-@patch("torch.distributed.get_rank")
-@patch("torch.distributed.get_world_size")
-def test_sequence_parallel_slicing(
-    mock_world_size, mock_rank, mock_get_group, partial_state
-):
-    """Test the basic sequence slicing logic without full collator instantiation."""
-    # Setup mocks
-    mock_get_group.return_value = MagicMock()
-    mock_rank.return_value = 1  # Second GPU
-    mock_world_size.return_value = 4  # 4 GPUs total
-
-    # Create a sample batch
-    batch = {
-        "input_ids": torch.tensor(
-            [
-                [101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112],
-                [201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212],
-            ]
-        ),
-        "attention_mask": torch.ones(2, 12),
-    }
-
-    # Simplified slicing logic from SequenceParallelDataCollator
-    def slice_batch(batch, rank, world_size):
-        result = {}
-        for key in batch:
-            seq_len = batch[key].shape[1]
-            slice_size = seq_len // world_size
-            start_idx = rank * slice_size
-            end_idx = start_idx + slice_size if rank < world_size - 1 else seq_len
-            result[key] = batch[key][:, start_idx:end_idx]
-        return result
-
-    # Slice the batch
-    result = slice_batch(
-        batch, rank=mock_rank.return_value, world_size=mock_world_size.return_value
-    )
-
-    # Check slicing
-    assert result["input_ids"].shape == (2, 3)  # 12 tokens / 4 GPUs = 3 tokens per GPU
-    expected_input_ids = torch.tensor(
-        [
-            [104, 105, 106],  # Second slice of first sequence
-            [204, 205, 206],  # Second slice of second sequence
-        ]
-    )
-    assert torch.all(result["input_ids"] == expected_input_ids)
-
-
-@patch.dict("sys.modules", {"ring_flash_attn": MagicMock()})
-def test_config_validation_with_valid_inputs(cfg):
-    """Test that valid sequence parallelism configurations pass validation."""
-    # Import the actual model class with appropriate mocks
-    from axolotl.utils.schemas.config import AxolotlInputConfig
-
-    # Valid configuration: sequence_parallel_degree > 1 and flash_attention is True
-    cfg = cfg | {
-        "sequence_parallel_degree": 2,
-        "flash_attention": True,
-    }
-
-    # Should validate without errors
-    config = AxolotlInputConfig(**cfg)
-    assert config.sequence_parallel_degree == 2
-    assert config.flash_attention is True
-
-
-def test_config_validation_with_invalid_inputs(cfg):
-    """Test that invalid sequence parallelism configurations fail validation."""
-    from axolotl.utils.schemas.config import AxolotlInputConfig
-
-    # Invalid configuration: sequence_parallel_degree > 1 but flash_attention is False
-    cfg = cfg | {
-        "sequence_parallel_degree": 2,
-        "flash_attention": False,
-    }
-
-    # Should raise ValidationError
-    with pytest.raises(ValueError) as excinfo:
-        AxolotlInputConfig(**cfg)
-
-    # Verify error message
-    assert "flash_attention: true must be set" in str(excinfo.value)

tests/e2e/test_deepseekv3.py

@@ -1,5 +1,5 @@
 """
-E2E tests for deepseekv3
+E2E tests for lora llama
 """
 
 import logging
@@ -14,8 +14,6 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config, validate_config
 from axolotl.utils.dict import DictDefault
 
-from tests.hf_offline_utils import enable_hf_offline
-
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 
@@ -25,7 +23,6 @@ class TestDeepseekV3:
     Test case for DeepseekV3 models
     """
 
-    @enable_hf_offline
     @pytest.mark.parametrize(
         "sample_packing",
         [True, False],
@@ -83,7 +80,6 @@ class TestDeepseekV3:
         train(cfg=cfg, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "adapter_model.safetensors").exists()
 
-    @enable_hf_offline
     @pytest.mark.parametrize(
         "sample_packing",
         [True, False],

tests/e2e/test_gemma2.py

@@ -1,133 +0,0 @@
-"""
-E2E tests for gemma2
-"""
-
-import logging
-import os
-from pathlib import Path
-
-import pytest
-
-from axolotl.cli.args import TrainerCliArgs
-from axolotl.common.datasets import load_datasets
-from axolotl.train import train
-from axolotl.utils.config import normalize_config, validate_config
-from axolotl.utils.dict import DictDefault
-
-LOG = logging.getLogger("axolotl.tests.e2e")
-os.environ["WANDB_DISABLED"] = "true"
-
-
-class TestGemma2:
-    """
-    Test case for Gemma2 models
-    """
-
-    @pytest.mark.parametrize(
-        "sample_packing",
-        [True, False],
-    )
-    def test_lora_gemma2(self, temp_dir, sample_packing):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "axolotl-ai-co/gemma-2-33M",
-                "trust_remote_code": True,
-                "sample_packing": sample_packing,
-                "flash_attention": True,
-                "sequence_len": 2048,
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0,
-                "datasets": [
-                    {
-                        "path": "mlabonne/FineTome-100k",
-                        "type": "chat_template",
-                        "field_messages": "conversations",
-                        "message_property_mappings": {
-                            "role": "from",
-                            "content": "value",
-                        },
-                        "drop_system_message": True,
-                        "split": "train[:1%]",
-                    },
-                ],
-                "special_tokens": {
-                    "bos_token": "<bos>",
-                    "eos_token": "<eos>",
-                },
-                "chat_template": "gemma",  # gemma2's template is same as gemma
-                "num_epochs": 1,
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 4,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_bnb_8bit",
-                "lr_scheduler": "cosine",
-                "max_steps": 5,
-                "save_safetensors": True,
-                "bf16": True,
-            }
-        )
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "adapter_model.safetensors").exists()
-
-    @pytest.mark.parametrize(
-        "sample_packing",
-        [True, False],
-    )
-    def test_fft_gemma2(self, temp_dir, sample_packing):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "axolotl-ai-co/gemma-2-33M",
-                "trust_remote_code": True,
-                "sample_packing": sample_packing,
-                "flash_attention": True,
-                "sequence_len": 2048,
-                "val_set_size": 0,
-                "datasets": [
-                    {
-                        "path": "mlabonne/FineTome-100k",
-                        "type": "chat_template",
-                        "field_messages": "conversations",
-                        "message_property_mappings": {
-                            "role": "from",
-                            "content": "value",
-                        },
-                        "split": "train[:1%]",
-                        "drop_system_message": True,
-                    },
-                ],
-                "chat_template": "gemma",  # gemma2's template is same as gemma
-                "special_tokens": {
-                    "bos_token": "<bos>",
-                    "eos_token": "<eos>",
-                },
-                "num_epochs": 1,
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 4,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_bnb_8bit",
-                "lr_scheduler": "cosine",
-                "max_steps": 5,
-                "save_safetensors": True,
-                "bf16": True,
-            }
-        )
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "model.safetensors").exists()

tests/e2e/test_gemma3_text.py

@@ -1,131 +0,0 @@
-"""
-E2E tests for gemma3_text
-"""
-
-import logging
-import os
-from pathlib import Path
-
-import pytest
-
-from axolotl.cli.args import TrainerCliArgs
-from axolotl.common.datasets import load_datasets
-from axolotl.train import train
-from axolotl.utils.config import normalize_config, validate_config
-from axolotl.utils.dict import DictDefault
-
-LOG = logging.getLogger("axolotl.tests.e2e")
-os.environ["WANDB_DISABLED"] = "true"
-
-
-class TestGemma3Text:
-    """
-    Test case for Gemma3Text models
-    """
-
-    @pytest.mark.parametrize(
-        "sample_packing",
-        [True, False],
-    )
-    def test_lora_gemma3_text(self, temp_dir, sample_packing):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "axolotl-ai-co/gemma-3-34M",
-                "trust_remote_code": True,
-                "sample_packing": sample_packing,
-                "flash_attention": True,
-                "sequence_len": 2048,
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0,
-                "datasets": [
-                    {
-                        "path": "mlabonne/FineTome-100k",
-                        "type": "chat_template",
-                        "field_messages": "conversations",
-                        "message_property_mappings": {
-                            "role": "from",
-                            "content": "value",
-                        },
-                        "split": "train[:1%]",
-                    },
-                ],
-                "special_tokens": {
-                    "bos_token": "<bos>",
-                    "eos_token": "<eos>",
-                },
-                "chat_template": "gemma3",
-                "num_epochs": 1,
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 4,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_bnb_8bit",
-                "lr_scheduler": "cosine",
-                "max_steps": 5,
-                "save_safetensors": True,
-                "bf16": True,
-            }
-        )
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "adapter_model.safetensors").exists()
-
-    @pytest.mark.parametrize(
-        "sample_packing",
-        [True, False],
-    )
-    def test_fft_gemma3_text(self, temp_dir, sample_packing):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "axolotl-ai-co/gemma-3-34M",
-                "trust_remote_code": True,
-                "sample_packing": sample_packing,
-                "flash_attention": True,
-                "sequence_len": 2048,
-                "val_set_size": 0,
-                "datasets": [
-                    {
-                        "path": "mlabonne/FineTome-100k",
-                        "type": "chat_template",
-                        "field_messages": "conversations",
-                        "message_property_mappings": {
-                            "role": "from",
-                            "content": "value",
-                        },
-                        "split": "train[:1%]",
-                    },
-                ],
-                "chat_template": "gemma3",
-                "special_tokens": {
-                    "bos_token": "<bos>",
-                    "eos_token": "<eos>",
-                },
-                "num_epochs": 1,
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 4,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_bnb_8bit",
-                "lr_scheduler": "cosine",
-                "max_steps": 5,
-                "save_safetensors": True,
-                "bf16": True,
-            }
-        )
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "model.safetensors").exists()

tests/e2e/test_llama.py

@@ -5,14 +5,14 @@ E2E tests for llama
 import logging
 import os
 
+from e2e.utils import check_model_output_exists
+
 from axolotl.cli.args import TrainerCliArgs
 from axolotl.common.datasets import load_datasets
 from axolotl.train import train
 from axolotl.utils.config import normalize_config, validate_config
 from axolotl.utils.dict import DictDefault
 
-from tests.e2e.utils import check_model_output_exists
-
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 

tests/e2e/test_optimizers.py

@@ -201,46 +201,3 @@ class TestCustomOptimizers(unittest.TestCase):
 
         train(cfg=cfg, dataset_meta=dataset_meta)
         check_model_output_exists(temp_dir, cfg)
-
-    @with_temp_dir
-    def test_soap(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM-135M",
-                "sequence_len": 1024,
-                "load_in_8bit": True,
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0.1,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                },
-                "datasets": [
-                    {
-                        "path": "vicgalle/alpaca-gpt4",
-                        "type": "alpaca",
-                    },
-                ],
-                "num_epochs": 1,
-                "micro_batch_size": 8,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "soap",
-                "adam_beta1": 0.9,
-                "adam_beta2": 0.95,
-                "lr_scheduler": "cosine",
-            }
-        )
-
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, dataset_meta=dataset_meta)
-        check_model_output_exists(temp_dir, cfg)

tests/e2e/test_schedulers.py

@@ -54,7 +54,7 @@ class TestCustomSchedulers(unittest.TestCase):
                 "gradient_accumulation_steps": 1,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
-                "optimizer": "adamw_torch_fused",
+                "optimizer": "adamw_hf",
                 "max_steps": 20,
                 "lr_scheduler": "rex",
                 "warmup_steps": 5,

tests/hf_offline_utils.py

@@ -1,85 +0,0 @@
-"""
-test utils for helpers and decorators
-"""
-
-import os
-from functools import wraps
-
-from huggingface_hub.utils import reset_sessions
-
-
-def reload_modules(hf_hub_offline):
-    # Force reload of the modules that check this variable
-    import importlib
-
-    import datasets
-    import huggingface_hub.constants
-
-    # Reload the constants module first, as others depend on it
-    importlib.reload(huggingface_hub.constants)
-    huggingface_hub.constants.HF_HUB_OFFLINE = hf_hub_offline
-    importlib.reload(datasets.config)
-    setattr(datasets.config, "HF_HUB_OFFLINE", hf_hub_offline)
-    reset_sessions()
-
-
-def enable_hf_offline(test_func):
-    """
-    test decorator that sets HF_HUB_OFFLINE environment variable to True and restores it after the test even if the test fails.
-    :param test_func:
-    :return:
-    """
-
-    @wraps(test_func)
-    def wrapper(*args, **kwargs):
-        # Save the original value of HF_HUB_OFFLINE environment variable
-        original_hf_offline = os.getenv("HF_HUB_OFFLINE")
-
-        # Set HF_OFFLINE environment variable to True
-        os.environ["HF_HUB_OFFLINE"] = "1"
-
-        reload_modules(True)
-        try:
-            # Run the test function
-            return test_func(*args, **kwargs)
-        finally:
-            # Restore the original value of HF_HUB_OFFLINE environment variable
-            if original_hf_offline is not None:
-                os.environ["HF_HUB_OFFLINE"] = original_hf_offline
-                reload_modules(bool(original_hf_offline))
-            else:
-                del os.environ["HF_HUB_OFFLINE"]
-                reload_modules(False)
-
-    return wrapper
-
-
-def disable_hf_offline(test_func):
-    """
-    test decorator that sets HF_HUB_OFFLINE environment variable to False and restores it after the wrapped func
-    :param test_func:
-    :return:
-    """
-
-    @wraps(test_func)
-    def wrapper(*args, **kwargs):
-        # Save the original value of HF_HUB_OFFLINE environment variable
-        original_hf_offline = os.getenv("HF_HUB_OFFLINE")
-
-        # Set HF_OFFLINE environment variable to True
-        os.environ["HF_HUB_OFFLINE"] = "0"
-
-        reload_modules(False)
-        try:
-            # Run the test function
-            return test_func(*args, **kwargs)
-        finally:
-            # Restore the original value of HF_HUB_OFFLINE environment variable
-            if original_hf_offline is not None:
-                os.environ["HF_HUB_OFFLINE"] = original_hf_offline
-                reload_modules(bool(original_hf_offline))
-            else:
-                del os.environ["HF_HUB_OFFLINE"]
-                reload_modules(False)
-
-    return wrapper

tests/prompt_strategies/conftest.py

@@ -4,13 +4,12 @@ shared fixtures for prompt strategies tests
 
 import pytest
 from datasets import Dataset
+from huggingface_hub import hf_hub_download
 from transformers import AutoTokenizer
 
 from axolotl.prompt_strategies.jinja_template_analyzer import JinjaTemplateAnalyzer
 from axolotl.utils.chat_templates import _CHAT_TEMPLATES
 
-from tests.hf_offline_utils import enable_hf_offline
-
 
 @pytest.fixture(name="assistant_dataset")
 def fixture_assistant_dataset():
@@ -109,27 +108,31 @@ def fixture_toolcalling_dataset():
 
 
 @pytest.fixture(name="llama3_tokenizer", scope="session", autouse=True)
-@enable_hf_offline
+def fixture_llama3_tokenizer():
-def fixture_llama3_tokenizer(
+    hf_hub_download(
-    download_llama3_8b_instruct_model_fixture,
+        repo_id="NousResearch/Meta-Llama-3-8B-Instruct",
-):  # pylint: disable=unused-argument,redefined-outer-name
+        filename="special_tokens_map.json",
+    )
+    hf_hub_download(
+        repo_id="NousResearch/Meta-Llama-3-8B-Instruct",
+        filename="tokenizer_config.json",
+    )
+    hf_hub_download(
+        repo_id="NousResearch/Meta-Llama-3-8B-Instruct", filename="tokenizer.json"
+    )
     tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B-Instruct")
 
     return tokenizer
 
 
 @pytest.fixture(name="smollm2_tokenizer", scope="session", autouse=True)
-@enable_hf_offline
 def fixture_smollm2_tokenizer():
     tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M")
     return tokenizer
 
 
 @pytest.fixture(name="mistralv03_tokenizer", scope="session", autouse=True)
-@enable_hf_offline
+def fixture_mistralv03_tokenizer():
-def fixture_mistralv03_tokenizer(
-    download_mlx_mistral_7b_model_fixture,
-):  # pylint: disable=unused-argument,redefined-outer-name
     tokenizer = AutoTokenizer.from_pretrained(
         "mlx-community/Mistral-7B-Instruct-v0.3-4bit"
     )
@@ -137,7 +140,6 @@ def fixture_mistralv03_tokenizer(
 
 
 @pytest.fixture(name="phi35_tokenizer", scope="session", autouse=True)
-@enable_hf_offline
 def fixture_phi35_tokenizer():
     tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-mini-instruct")
     return tokenizer

tests/prompt_strategies/test_alpaca.py

@@ -11,8 +11,6 @@ from axolotl.datasets import TokenizedPromptDataset
 from axolotl.prompt_tokenizers import AlpacaPromptTokenizingStrategy
 from axolotl.prompters import AlpacaPrompter, PromptStyle
 
-from tests.hf_offline_utils import enable_hf_offline
-
 
 @pytest.fixture(name="alpaca_dataset")
 def fixture_alpaca_dataset():
@@ -28,7 +26,6 @@ def fixture_alpaca_dataset():
 
 
 @pytest.fixture(name="tokenizer")
-@enable_hf_offline
 def fixture_tokenizer():
     # pylint: disable=all
     tokenizer = AutoTokenizer.from_pretrained(

tests/prompt_strategies/test_chat_template_utils.py

@@ -13,11 +13,8 @@ from axolotl.utils.chat_templates import (
     get_chat_template,
 )
 
-from tests.hf_offline_utils import enable_hf_offline
-
 
 @pytest.fixture(name="llama3_tokenizer")
-@enable_hf_offline
 def fixture_llama3_tokenizer():
     tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B")
 

tests/prompt_strategies/test_chat_templates_advanced.py

@@ -17,8 +17,6 @@ from axolotl.prompt_strategies.chat_template import (
 from axolotl.prompters import IGNORE_TOKEN_ID
 from axolotl.utils.chat_templates import get_chat_template
 
-from tests.hf_offline_utils import enable_hf_offline
-
 logging.basicConfig(level=logging.DEBUG)
 LOG = logging.getLogger("axolotl")
 
@@ -32,14 +30,12 @@ PARAMETRIZE_PARAMS = [
         "mistralv03_tokenizer_chat_template_jinja",
         "[/INST]",
     ),
-    # TODO: temporarily skip gemma due to gemma3 template
+    (
-    # Re-enable on new chat_template implementation for perf
+        "gemma2_tokenizer",
-    # (
+        "jinja",
-    #     "gemma2_tokenizer",
+        "gemma2_tokenizer_chat_template_jinja",
-    #     "jinja",
+        "<end_of_turn>",
-    #     "gemma2_tokenizer_chat_template_jinja",
+    ),
-    #     "<end_of_turn>",
-    # ),
     ("phi35_tokenizer", "phi_35", None, "<|end|>"),
 ]
 
@@ -97,11 +93,7 @@ class TestChatTemplateConfigurations:
         if (
             turn_idx == 0
             and turn.get("from") in ["system", "context"]
-            and (
+            and "mistral" in tokenizer.name_or_path.lower()
-                "mistral" in tokenizer.name_or_path.lower()
-                or "gemma"
-                in tokenizer.name_or_path.lower()  # temporarily skip gemma due to gemma3 template
-            )
         ):
             assert (
                 start_idx == -1 and end_idx == -1
@@ -109,7 +101,6 @@ class TestChatTemplateConfigurations:
             return True
         return False
 
-    @enable_hf_offline
     def test_train_on_inputs_true(
         self,
         tokenizer,

tests/prompt_strategies/test_dpo_chat_templates.py

@@ -11,8 +11,6 @@ from transformers import AutoTokenizer
 from axolotl.prompt_strategies.dpo.chat_template import default
 from axolotl.utils.dict import DictDefault
 
-from tests.hf_offline_utils import enable_hf_offline
-
 
 @pytest.fixture(name="assistant_dataset")
 def fixture_assistant_dataset():
@@ -80,8 +78,15 @@ def fixture_custom_assistant_dataset():
     )
 
 
+@pytest.fixture(name="llama3_tokenizer")
+def fixture_llama3_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B")
+    tokenizer.eos_token = "<|eot_id|>"
+
+    return tokenizer
+
+
 @pytest.fixture(name="phi3_tokenizer")
-@enable_hf_offline
 def fixture_phi3_tokenizer():
     tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-medium-128k-instruct")
 
@@ -89,7 +94,6 @@ def fixture_phi3_tokenizer():
 
 
 @pytest.fixture(name="gemma_tokenizer")
-@enable_hf_offline
 def fixture_gemma_tokenizer():
     tokenizer = AutoTokenizer.from_pretrained("unsloth/gemma-2b-it", revision="703fb4a")
 

tests/prompt_strategies/test_dpo_chatml.py

@@ -10,8 +10,6 @@ from axolotl.prompt_strategies.dpo import load as load_dpo
 from axolotl.utils.data.rl import load_prepare_preference_datasets
 from axolotl.utils.dict import DictDefault
 
-from tests.hf_offline_utils import enable_hf_offline
-
 
 @pytest.fixture(name="minimal_dpo_cfg")
 def fixture_cfg():
@@ -36,8 +34,6 @@ class TestDPOChatml:
     Test loading DPO preference datasets with chatml formatting
     """
 
-    @pytest.mark.skip(reason="TODO: fix hf hub offline to work with HF rate limits")
-    @enable_hf_offline
     def test_default(self, minimal_dpo_cfg):
         cfg = DictDefault(
             {

tests/test_data.py

@@ -8,15 +8,12 @@ from transformers import LlamaTokenizer
 
 from axolotl.utils.data import encode_pretraining, md5
 
-from tests.hf_offline_utils import enable_hf_offline
-
 
 class TestEncodePretraining(unittest.TestCase):
     """
     test class for encode pretraining and md5 helper
     """
 
-    @enable_hf_offline
     def setUp(self):
         self.tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b")
         self.tokenizer.add_special_tokens(

tests/test_datasets.py

@@ -4,37 +4,31 @@ Test dataset loading under various conditions.
 
 import shutil
 import tempfile
+import unittest
 from pathlib import Path
-from unittest.mock import patch
 
-import pytest
+from conftest import snapshot_download_w_retry
+from constants import (
+    ALPACA_MESSAGES_CONFIG_OG,
+    ALPACA_MESSAGES_CONFIG_REVISION,
+    SPECIAL_TOKENS,
+)
 from datasets import Dataset
-from huggingface_hub import snapshot_download
+from transformers import AutoTokenizer
-from transformers import PreTrainedTokenizer
 
 from axolotl.utils.data import load_tokenized_prepared_datasets
 from axolotl.utils.data.rl import load_prepare_preference_datasets
 from axolotl.utils.dict import DictDefault
 
-from tests.constants import (
-    ALPACA_MESSAGES_CONFIG_OG,
-    ALPACA_MESSAGES_CONFIG_REVISION,
-    SPECIAL_TOKENS,
-)
-from tests.hf_offline_utils import enable_hf_offline
 
-
+class TestDatasetPreparation(unittest.TestCase):
-class TestDatasetPreparation:
     """Test a configured dataloader."""
 
-    @pytest.fixture
+    def setUp(self) -> None:
-    def tokenizer(self, tokenizer_huggyllama) -> PreTrainedTokenizer:
+        self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
-        tokenizer_huggyllama.add_special_tokens(SPECIAL_TOKENS)
+        self.tokenizer.add_special_tokens(SPECIAL_TOKENS)
-        yield tokenizer_huggyllama
+        # Alpaca dataset.
-
+        self.dataset = Dataset.from_list(
-    @pytest.fixture
-    def dataset_fixture(self):
-        yield Dataset.from_list(
             [
                 {
                     "instruction": "Evaluate this sentence for spelling and grammar mistakes",
@@ -44,9 +38,7 @@ class TestDatasetPreparation:
             ]
         )
 
-    @pytest.mark.skip(reason="TODO: fix hf hub offline to work with HF rate limits")
+    def test_load_hub(self):
-    @enable_hf_offline
-    def test_load_hub(self, tokenizer):
         """Core use case.  Verify that processing data from the hub works"""
         with tempfile.TemporaryDirectory() as tmp_dir:
             prepared_path = Path(tmp_dir) / "prepared"
@@ -63,28 +55,25 @@ class TestDatasetPreparation:
                 }
             )
 
-            dataset, _ = load_tokenized_prepared_datasets(tokenizer, cfg, prepared_path)
+            dataset, _ = load_tokenized_prepared_datasets(
+                self.tokenizer, cfg, prepared_path
+            )
 
             assert len(dataset) == 2000
             assert "input_ids" in dataset.features
             assert "attention_mask" in dataset.features
             assert "labels" in dataset.features
 
-    @enable_hf_offline
+    def test_load_local_hub(self):
-    @pytest.mark.skip("datasets bug with local datasets when offline")
-    def test_load_local_hub(self, tokenizer):
         """Niche use case.  Verify that a local copy of a hub dataset can be loaded"""
         with tempfile.TemporaryDirectory() as tmp_dir:
             tmp_ds_path = Path(tmp_dir) / "mhenrichsen/alpaca_2k_test"
             tmp_ds_path.mkdir(parents=True, exist_ok=True)
-            snapshot_path = snapshot_download(
+            snapshot_download_w_retry(
                 repo_id="mhenrichsen/alpaca_2k_test",
                 repo_type="dataset",
                 local_dir=tmp_ds_path,
             )
-            # offline mode doesn't actually copy it to local_dir, so we
-            # have to copy all the contents in the dir manually from the returned snapshot_path
-            shutil.copytree(snapshot_path, tmp_ds_path, dirs_exist_ok=True)
 
             prepared_path = Path(tmp_dir) / "prepared"
             # Right now a local copy that doesn't fully conform to a dataset
@@ -107,7 +96,9 @@ class TestDatasetPreparation:
                 }
             )
 
-            dataset, _ = load_tokenized_prepared_datasets(tokenizer, cfg, prepared_path)
+            dataset, _ = load_tokenized_prepared_datasets(
+                self.tokenizer, cfg, prepared_path
+            )
 
             assert len(dataset) == 2000
             assert "input_ids" in dataset.features
@@ -115,12 +106,11 @@ class TestDatasetPreparation:
             assert "labels" in dataset.features
             shutil.rmtree(tmp_ds_path)
 
-    @enable_hf_offline
+    def test_load_from_save_to_disk(self):
-    def test_load_from_save_to_disk(self, tokenizer, dataset_fixture):
         """Usual use case.  Verify datasets saved via `save_to_disk` can be loaded."""
         with tempfile.TemporaryDirectory() as tmp_dir:
             tmp_ds_name = Path(tmp_dir) / "tmp_dataset"
-            dataset_fixture.save_to_disk(str(tmp_ds_name))
+            self.dataset.save_to_disk(str(tmp_ds_name))
 
             prepared_path = Path(tmp_dir) / "prepared"
             cfg = DictDefault(
@@ -136,21 +126,22 @@ class TestDatasetPreparation:
                 }
             )
 
-            dataset, _ = load_tokenized_prepared_datasets(tokenizer, cfg, prepared_path)
+            dataset, _ = load_tokenized_prepared_datasets(
+                self.tokenizer, cfg, prepared_path
+            )
 
             assert len(dataset) == 1
             assert "input_ids" in dataset.features
             assert "attention_mask" in dataset.features
             assert "labels" in dataset.features
 
-    @enable_hf_offline
+    def test_load_from_dir_of_parquet(self):
-    def test_load_from_dir_of_parquet(self, tokenizer, dataset_fixture):
         """Usual use case.  Verify a directory of parquet files can be loaded."""
         with tempfile.TemporaryDirectory() as tmp_dir:
             tmp_ds_dir = Path(tmp_dir) / "tmp_dataset"
             tmp_ds_dir.mkdir()
             tmp_ds_path = tmp_ds_dir / "shard1.parquet"
-            dataset_fixture.to_parquet(tmp_ds_path)
+            self.dataset.to_parquet(tmp_ds_path)
 
             prepared_path: Path = Path(tmp_dir) / "prepared"
             cfg = DictDefault(
@@ -171,21 +162,22 @@ class TestDatasetPreparation:
                 }
             )
 
-            dataset, _ = load_tokenized_prepared_datasets(tokenizer, cfg, prepared_path)
+            dataset, _ = load_tokenized_prepared_datasets(
+                self.tokenizer, cfg, prepared_path
+            )
 
             assert len(dataset) == 1
             assert "input_ids" in dataset.features
             assert "attention_mask" in dataset.features
             assert "labels" in dataset.features
 
-    @enable_hf_offline
+    def test_load_from_dir_of_json(self):
-    def test_load_from_dir_of_json(self, tokenizer, dataset_fixture):
         """Standard use case.  Verify a directory of json files can be loaded."""
         with tempfile.TemporaryDirectory() as tmp_dir:
             tmp_ds_dir = Path(tmp_dir) / "tmp_dataset"
             tmp_ds_dir.mkdir()
             tmp_ds_path = tmp_ds_dir / "shard1.json"
-            dataset_fixture.to_json(tmp_ds_path)
+            self.dataset.to_json(tmp_ds_path)
 
             prepared_path: Path = Path(tmp_dir) / "prepared"
             cfg = DictDefault(
@@ -206,19 +198,20 @@ class TestDatasetPreparation:
                 }
             )
 
-            dataset, _ = load_tokenized_prepared_datasets(tokenizer, cfg, prepared_path)
+            dataset, _ = load_tokenized_prepared_datasets(
+                self.tokenizer, cfg, prepared_path
+            )
 
             assert len(dataset) == 1
             assert "input_ids" in dataset.features
             assert "attention_mask" in dataset.features
             assert "labels" in dataset.features
 
-    @enable_hf_offline
+    def test_load_from_single_parquet(self):
-    def test_load_from_single_parquet(self, tokenizer, dataset_fixture):
         """Standard use case.  Verify a single parquet file can be loaded."""
         with tempfile.TemporaryDirectory() as tmp_dir:
             tmp_ds_path = Path(tmp_dir) / "tmp_dataset.parquet"
-            dataset_fixture.to_parquet(tmp_ds_path)
+            self.dataset.to_parquet(tmp_ds_path)
 
             prepared_path: Path = Path(tmp_dir) / "prepared"
             cfg = DictDefault(
@@ -235,19 +228,20 @@ class TestDatasetPreparation:
                 }
             )
 
-            dataset, _ = load_tokenized_prepared_datasets(tokenizer, cfg, prepared_path)
+            dataset, _ = load_tokenized_prepared_datasets(
+                self.tokenizer, cfg, prepared_path
+            )
 
             assert len(dataset) == 1
             assert "input_ids" in dataset.features
             assert "attention_mask" in dataset.features
             assert "labels" in dataset.features
 
-    @enable_hf_offline
+    def test_load_from_single_json(self):
-    def test_load_from_single_json(self, tokenizer, dataset_fixture):
         """Standard use case.  Verify a single json file can be loaded."""
         with tempfile.TemporaryDirectory() as tmp_dir:
             tmp_ds_path = Path(tmp_dir) / "tmp_dataset.json"
-            dataset_fixture.to_json(tmp_ds_path)
+            self.dataset.to_json(tmp_ds_path)
 
             prepared_path: Path = Path(tmp_dir) / "prepared"
             cfg = DictDefault(
@@ -264,15 +258,15 @@ class TestDatasetPreparation:
                 }
             )
 
-            dataset, _ = load_tokenized_prepared_datasets(tokenizer, cfg, prepared_path)
+            dataset, _ = load_tokenized_prepared_datasets(
+                self.tokenizer, cfg, prepared_path
+            )
 
             assert len(dataset) == 1
             assert "input_ids" in dataset.features
             assert "attention_mask" in dataset.features
             assert "labels" in dataset.features
 
-    @pytest.mark.skip(reason="TODO: fix hf offline mode for CI rate limits")
-    @enable_hf_offline
     def test_load_hub_with_dpo(self):
         """Verify that processing dpo data from the hub works"""
 
@@ -291,9 +285,7 @@ class TestDatasetPreparation:
         assert len(train_dataset) == 1800
         assert "conversation" in train_dataset.features
 
-    @pytest.mark.skip(reason="TODO: fix hf hub offline to work with HF rate limits")
+    def test_load_hub_with_revision(self):
-    @enable_hf_offline
-    def test_load_hub_with_revision(self, tokenizer):
         """Verify that processing data from the hub works with a specific revision"""
         with tempfile.TemporaryDirectory() as tmp_dir:
             prepared_path = Path(tmp_dir) / "prepared"
@@ -315,17 +307,16 @@ class TestDatasetPreparation:
                 }
             )
 
-            dataset, _ = load_tokenized_prepared_datasets(tokenizer, cfg, prepared_path)
+            dataset, _ = load_tokenized_prepared_datasets(
+                self.tokenizer, cfg, prepared_path
+            )
 
             assert len(dataset) == 2000
             assert "input_ids" in dataset.features
             assert "attention_mask" in dataset.features
             assert "labels" in dataset.features
 
-    @enable_hf_offline
+    def test_load_hub_with_revision_with_dpo(self):
-    def test_load_hub_with_revision_with_dpo(
-        self, dataset_fozzie_alpaca_dpo_dataset_rev_ea82cff
-    ):
         """Verify that processing dpo data from the hub works with a specific revision"""
 
         cfg = DictDefault(
@@ -338,34 +329,22 @@ class TestDatasetPreparation:
             }
         )
 
-        # pylint: disable=duplicate-code
+        train_dataset, _ = load_prepare_preference_datasets(cfg)
-        with patch(
-            "axolotl.utils.data.shared.load_dataset_w_config"
-        ) as mock_load_dataset:
-            # Set up the mock to return different values on successive calls
-            mock_load_dataset.return_value = (
-                dataset_fozzie_alpaca_dpo_dataset_rev_ea82cff
-            )
 
-            train_dataset, _ = load_prepare_preference_datasets(cfg)
+        assert len(train_dataset) == 1800
+        assert "conversation" in train_dataset.features
 
-            assert len(train_dataset) == 1800
+    def test_load_local_hub_with_revision(self):
-            assert "conversation" in train_dataset.features
-
-    @enable_hf_offline
-    @pytest.mark.skip("datasets bug with local datasets when offline")
-    def test_load_local_hub_with_revision(self, tokenizer):
         """Verify that a local copy of a hub dataset can be loaded with a specific revision"""
         with tempfile.TemporaryDirectory() as tmp_dir:
             tmp_ds_path = Path(tmp_dir) / "mhenrichsen/alpaca_2k_test"
             tmp_ds_path.mkdir(parents=True, exist_ok=True)
-            snapshot_path = snapshot_download(
+            snapshot_download_w_retry(
                 repo_id="mhenrichsen/alpaca_2k_test",
                 repo_type="dataset",
                 local_dir=tmp_ds_path,
                 revision="d05c1cb",
             )
-            shutil.copytree(snapshot_path, tmp_ds_path, dirs_exist_ok=True)
 
             prepared_path = Path(tmp_dir) / "prepared"
             cfg = DictDefault(
@@ -386,7 +365,9 @@ class TestDatasetPreparation:
                 }
             )
 
-            dataset, _ = load_tokenized_prepared_datasets(tokenizer, cfg, prepared_path)
+            dataset, _ = load_tokenized_prepared_datasets(
+                self.tokenizer, cfg, prepared_path
+            )
 
             assert len(dataset) == 2000
             assert "input_ids" in dataset.features
@@ -394,19 +375,17 @@ class TestDatasetPreparation:
             assert "labels" in dataset.features
             shutil.rmtree(tmp_ds_path)
 
-    @enable_hf_offline
+    def test_loading_local_dataset_folder(self):
-    def test_loading_local_dataset_folder(self, tokenizer):
         """Verify that a dataset downloaded to a local folder can be loaded"""
 
         with tempfile.TemporaryDirectory() as tmp_dir:
             tmp_ds_path = Path(tmp_dir) / "mhenrichsen/alpaca_2k_test"
             tmp_ds_path.mkdir(parents=True, exist_ok=True)
-            snapshot_path = snapshot_download(
+            snapshot_download_w_retry(
                 repo_id="mhenrichsen/alpaca_2k_test",
                 repo_type="dataset",
                 local_dir=tmp_ds_path,
             )
-            shutil.copytree(snapshot_path, tmp_ds_path, dirs_exist_ok=True)
 
             prepared_path = Path(tmp_dir) / "prepared"
             cfg = DictDefault(
@@ -422,10 +401,16 @@ class TestDatasetPreparation:
                 }
             )
 
-            dataset, _ = load_tokenized_prepared_datasets(tokenizer, cfg, prepared_path)
+            dataset, _ = load_tokenized_prepared_datasets(
+                self.tokenizer, cfg, prepared_path
+            )
 
             assert len(dataset) == 2000
             assert "input_ids" in dataset.features
             assert "attention_mask" in dataset.features
             assert "labels" in dataset.features
             shutil.rmtree(tmp_ds_path)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_exact_deduplication.py

@@ -8,19 +8,16 @@ import hashlib
 import unittest
 from unittest.mock import patch
 
-import pytest
+from constants import ALPACA_MESSAGES_CONFIG_REVISION, SPECIAL_TOKENS
 from datasets import Dataset
+from transformers import AutoTokenizer
 
-from axolotl.utils.config import normalize_config
 from axolotl.utils.data import prepare_dataset
 from axolotl.utils.data.rl import load_prepare_preference_datasets
 from axolotl.utils.data.utils import deduplicate_and_log_datasets
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_processor, load_tokenizer
 
-from tests.constants import ALPACA_MESSAGES_CONFIG_REVISION
-from tests.hf_offline_utils import enable_hf_offline
-
 
 def verify_deduplication(actual_dataset, expected_dataset, dataset_name):
     """
@@ -216,12 +213,13 @@ class TestDeduplicateIndividualFunctions(unittest.TestCase):
         verify_deduplication(eval_dataset, expected_dataset_eval, "eval_dataset")
 
 
-class TestDeduplicateRLDataset:
+class TestDeduplicateRLDataset(unittest.TestCase):
     """Test a configured dataloader with deduplication."""
 
-    @pytest.fixture
+    def setUp(self) -> None:
-    def cfg(self):
+        self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
-        fixture = DictDefault(
+        self.tokenizer.add_special_tokens(SPECIAL_TOKENS)
+        self.cfg = DictDefault(
             {
                 "tokenizer_config": "huggyllama/llama-7b",
                 "sequence_len": 1024,
@@ -234,69 +232,36 @@ class TestDeduplicateRLDataset:
                 ],
             }
         )
-        yield fixture
 
-    @enable_hf_offline
+    def test_load_with_deduplication(self):
-    def test_load_with_deduplication(
-        self, cfg, dataset_fozzie_alpaca_dpo_dataset_rev_ea82cff, tokenizer_huggyllama
-    ):
         """Verify that loading with deduplication removes duplicates."""
 
-        # pylint: disable=duplicate-code
+        # Load the dataset using the deduplication setting
-        with (
+        train_dataset, _ = load_prepare_preference_datasets(self.cfg)
-            patch(
-                "axolotl.utils.data.shared.load_dataset_w_config"
-            ) as mock_load_dataset,
-            patch("axolotl.utils.models.load_tokenizer") as mock_load_tokenizer,
-        ):
-            # Set up the mock to return different values on successive calls
-            mock_load_dataset.side_effect = [
-                dataset_fozzie_alpaca_dpo_dataset_rev_ea82cff,
-                dataset_fozzie_alpaca_dpo_dataset_rev_ea82cff,
-            ]
-            mock_load_tokenizer.return_value = tokenizer_huggyllama
 
-            train_dataset, _ = load_prepare_preference_datasets(cfg)
+        # Verify that the dataset has been deduplicated
+        assert len(train_dataset) == 1800, "Dataset was not properly deduplicated"
 
-            # Verify that the dataset has been deduplicated
+    def test_load_without_deduplication(self):
-            assert len(train_dataset) == 1800, "Dataset was not properly deduplicated"
+        """Verify that loading without deduplication retains duplicates."""
+        self.cfg.dataset_exact_deduplication = False
+        # Load the dataset without deduplication
+        train_dataset, _ = load_prepare_preference_datasets(self.cfg)
 
-    @enable_hf_offline
+        # Verify that the dataset retains duplicates
-    def test_load_without_deduplication(
+        assert (
-        self, cfg, dataset_fozzie_alpaca_dpo_dataset_rev_ea82cff, tokenizer_huggyllama
+            len(train_dataset) == 1800 * 2
-    ):
+        ), "Dataset deduplication occurred when it should not have"
-        # pylint: disable=duplicate-code
-        with (
-            patch(
-                "axolotl.utils.data.shared.load_dataset_w_config"
-            ) as mock_load_dataset,
-            patch("axolotl.utils.models.load_tokenizer") as mock_load_tokenizer,
-        ):
-            # Set up the mock to return different values on successive calls
-            mock_load_dataset.side_effect = [
-                dataset_fozzie_alpaca_dpo_dataset_rev_ea82cff,
-                dataset_fozzie_alpaca_dpo_dataset_rev_ea82cff,
-            ]
-            mock_load_tokenizer.return_value = tokenizer_huggyllama
-
-            cfg.dataset_exact_deduplication = False
-            # Load the dataset without deduplication
-            train_dataset, _ = load_prepare_preference_datasets(cfg)
-
-            # Verify that the dataset retains duplicates
-            assert (
-                len(train_dataset) == 1800 * 2
-            ), "Dataset deduplication occurred when it should not have"
 
 
 class TestDeduplicateNonRL(unittest.TestCase):
     """Test prepare_dataset function with different configurations."""
 
-    @enable_hf_offline
     def setUp(self) -> None:
+        self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
+        self.tokenizer.add_special_tokens(SPECIAL_TOKENS)
         self.cfg_1 = DictDefault(
             {
-                "base_model": "huggyllama/llama-7b",
                 "tokenizer_config": "huggyllama/llama-7b",
                 "sequence_len": 1024,
                 "dataset_exact_deduplication": True,
@@ -317,10 +282,7 @@ class TestDeduplicateNonRL(unittest.TestCase):
                 "num_epochs": 1,
             }
         )
-        normalize_config(self.cfg_1)
 
-    @pytest.mark.skip(reason="TODO: fix hf hub offline to work with HF rate limits")
-    @enable_hf_offline
     def test_prepare_dataset_with_deduplication_train(self):
         """Verify that prepare_dataset function processes the dataset correctly with deduplication."""
         self.cfg_1.dataset_exact_deduplication = True
@@ -346,8 +308,6 @@ class TestDeduplicateNonRL(unittest.TestCase):
             "Train dataset should have 2000 samples after deduplication.",
         )
 
-    @pytest.mark.skip(reason="TODO: fix hf hub offline to work with HF rate limits")
-    @enable_hf_offline
     def test_prepare_dataset_with_deduplication_eval(self):
         """Verify that prepare_dataset function processes the dataset correctly with deduplication."""
         self.cfg_1.dataset_exact_deduplication = True
@@ -373,8 +333,6 @@ class TestDeduplicateNonRL(unittest.TestCase):
             "Eval dataset should have 2000 samples after deduplication.",
         )
 
-    @pytest.mark.skip(reason="TODO: fix hf hub offline to work with HF rate limits")
-    @enable_hf_offline
     def test_prepare_dataset_without_deduplication(self):
         """Verify that prepare_dataset function processes the dataset correctly without deduplication."""
         self.cfg_1.dataset_exact_deduplication = False

tests/test_packed_batch_sampler.py

@@ -12,8 +12,6 @@ from axolotl.utils.data.utils import drop_long_seq_in_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
-from tests.hf_offline_utils import enable_hf_offline
-
 
 @pytest.fixture(name="tokenizer")
 def fixture_tokenizer():
@@ -27,7 +25,6 @@ class TestBatchedSamplerPacking:
     Test class for packing streaming dataset sequences
     """
 
-    @pytest.mark.skip(reason="TODO: fix hf offline mode for CI rate limits")
     @pytest.mark.parametrize(
         "batch_size, num_workers",
         [
@@ -38,12 +35,11 @@ class TestBatchedSamplerPacking:
         ],
     )
     @pytest.mark.parametrize("max_seq_length", [4096, 512])
-    @enable_hf_offline
     def test_packing(self, batch_size, num_workers, tokenizer, max_seq_length):
         import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
 
         dataset = load_dataset(
-            "winglian/tiny-shakespeare",
+            "Trelis/tiny-shakespeare",
             split="train",
         )
 

tests/test_packed_dataset.py

@@ -10,15 +10,12 @@ from axolotl.datasets import ConstantLengthDataset, TokenizedPromptDataset
 from axolotl.prompt_tokenizers import AlpacaPromptTokenizingStrategy
 from axolotl.prompters import AlpacaPrompter
 
-from tests.hf_offline_utils import enable_hf_offline
-
 
 class TestPacking(unittest.TestCase):
     """
     Test class for packing dataset sequences
     """
 
-    @enable_hf_offline
     def setUp(self) -> None:
         # pylint: disable=duplicate-code
         self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")

tests/test_packed_pretraining.py

@@ -1,60 +1,43 @@
 """Module for testing streaming dataset sequence packing"""
 
 import functools
-import random
+import unittest
-import string
 
 import pytest
 import torch
-from datasets import IterableDataset
+from datasets import load_dataset
 from torch.utils.data import DataLoader
+from transformers import AutoTokenizer
 
 from axolotl.utils.data import get_dataset_wrapper, wrap_pretraining_dataset
 from axolotl.utils.dict import DictDefault
 
 
-class TestPretrainingPacking:
+class TestPretrainingPacking(unittest.TestCase):
     """
     Test class for packing streaming dataset sequences
     """
 
-    @pytest.fixture
+    def setUp(self) -> None:
-    def random_text(self):
+        # pylint: disable=duplicate-code
-        # seed with random.seed(0) for reproducibility
+        self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
-        random.seed(0)
+        self.tokenizer.pad_token = "</s>"
 
-        # generate row of random text with "words" of between 2 and 10 characters and
+    @pytest.mark.flaky(retries=3, delay=5)
-        # between 400 to 1200 characters per line
+    def test_packing_stream_dataset(self):
-        def rand_txt():
+        # pylint: disable=duplicate-code
-            return " ".join(
+        dataset = load_dataset(
-                [
+            "allenai/c4",
-                    "".join(
+            "en",
-                        random.choices(string.ascii_lowercase, k=random.randint(2, 10))
+            streaming=True,
-                    )
+        )["train"]
-                    for _ in range(random.randint(50, 200))
-                ]
-            )
-
-        # Create a list of 2000 random texts rather than just using it within the
-        # generator so the test runs faster
-        data = [rand_txt() for _ in range(500)]
-
-        # Create an IterableDataset
-        def generator():
-            for row in data:
-                yield {"text": row}
-
-        return IterableDataset.from_generator(generator)
-
-    @pytest.mark.flaky(retries=1, delay=5)
-    def test_packing_stream_dataset(self, tokenizer_huggyllama, random_text):
-        dataset = random_text
 
         cfg = DictDefault(
             {
                 "pretraining_dataset": [
                     {
-                        "path": "winglian/tiny-shakespeare",
+                        "path": "allenai/c4",
+                        "name": "en",
                         "type": "pretrain",
                     }
                 ],
@@ -71,16 +54,15 @@ class TestPretrainingPacking:
         ds_wrapper_partial = functools.partial(
             get_dataset_wrapper,
             cfg.pretraining_dataset[0],
-            tokenizer_huggyllama,
+            self.tokenizer,
             cfg,
             cfg.pretraining_dataset[0]["type"] or "pretrain",
         )
 
-        # pylint: disable=duplicate-code
         original_bsz = cfg.micro_batch_size
         train_dataset = wrap_pretraining_dataset(
             dataset,
-            tokenizer_huggyllama,
+            self.tokenizer,
             cfg,
             ds_wrapper_partial,
             max_tokens=cfg.sequence_len,
@@ -96,7 +78,7 @@ class TestPretrainingPacking:
         )
         idx = 0
         for data in trainer_loader:
-            if idx > 3:
+            if idx > 10:
                 break
             assert data["input_ids"].shape == torch.Size(
                 [1, original_bsz * cfg.sequence_len]
@@ -113,3 +95,7 @@ class TestPretrainingPacking:
             #     [1, original_bsz * cfg.sequence_len]
             # )
             idx += 1
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_prompt_tokenizers.py

@@ -5,7 +5,6 @@ import logging
 import unittest
 from pathlib import Path
 
-import pytest
 from datasets import load_dataset
 from transformers import AddedToken, AutoTokenizer, LlamaTokenizer
 
@@ -23,8 +22,6 @@ from axolotl.prompt_tokenizers import AlpacaPromptTokenizingStrategy
 from axolotl.prompters import AlpacaPrompter, PromptStyle
 from axolotl.utils.dict import DictDefault
 
-from tests.hf_offline_utils import enable_hf_offline
-
 LOG = logging.getLogger("axolotl")
 
 test_data = {
@@ -66,7 +63,6 @@ class TestPromptTokenizationStrategies(unittest.TestCase):
     Test class for prompt tokenization strategies.
     """
 
-    @enable_hf_offline
     def setUp(self) -> None:
         # pylint: disable=duplicate-code
         self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
@@ -123,7 +119,6 @@ class InstructionWSystemPromptTokenizingStrategyTest(unittest.TestCase):
     Test class for prompt tokenization strategies with sys prompt from the dataset
     """
 
-    @enable_hf_offline
     def setUp(self) -> None:
         # pylint: disable=duplicate-code
         self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
@@ -165,7 +160,6 @@ class Llama2ChatTokenizationTest(unittest.TestCase):
     Test class for prompt tokenization strategies with sys prompt from the dataset
     """
 
-    @enable_hf_offline
     def setUp(self) -> None:
         # pylint: disable=duplicate-code
         self.tokenizer = LlamaTokenizer.from_pretrained("NousResearch/Llama-2-7b-hf")
@@ -244,7 +238,6 @@ If a question does not make any sense, or is not factually coherent, explain why
 class OrpoTokenizationTest(unittest.TestCase):
     """test case for the ORPO tokenization"""
 
-    @enable_hf_offline
     def setUp(self) -> None:
         # pylint: disable=duplicate-code
         tokenizer = LlamaTokenizer.from_pretrained(
@@ -269,7 +262,6 @@ class OrpoTokenizationTest(unittest.TestCase):
             "argilla/ultrafeedback-binarized-preferences-cleaned", split="train"
         ).select([0])
 
-    @pytest.mark.skip(reason="TODO: fix hf hub offline to work with HF rate limits")
     def test_orpo_integration(self):
         strat = load(
             self.tokenizer,