Update .pre-commit-config.yaml

Co-authored-by: Wing Lian <wing.lian@gmail.com>

.github/workflows/docs.yml

@@ -20,11 +20,9 @@ jobs:
           uses: actions/setup-python@v5
           with:
             python-version: '3.11'
-        - name: Install dependencies
+        - name: install dependencies
           run: |
-            python3 -m pip install jupyter quartodoc
-        - name: Build autodoc
-          run: quartodoc build
+            python3 -m pip install jupyter
         - name: Publish to GitHub Pages (and render)
           uses: quarto-dev/quarto-actions/publish@v2
           with:

.github/workflows/tests.yml

@@ -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: |
@@ -173,9 +172,8 @@ 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: |

.gitignore

@@ -181,10 +181,6 @@ prepared-datasets/
 submit.sh
 *.out*
 
-# Quartodoc generated files
-objects.json
-site_libs/
-
 typings/
 out/
 

README.md

@@ -97,7 +97,6 @@ That's it! Check out our [Getting Started Guide](https://axolotl-ai-cloud.github
 - [Multi-GPU Training](https://axolotl-ai-cloud.github.io/axolotl/docs/multi-gpu.html)
 - [Multi-Node Training](https://axolotl-ai-cloud.github.io/axolotl/docs/multi-node.html)
 - [Multipacking](https://axolotl-ai-cloud.github.io/axolotl/docs/multipack.html)
-- [API Reference](https://axolotl-ai-cloud.github.io/axolotl/docs/api/) - Auto-generated code documentation
 - [FAQ](https://axolotl-ai-cloud.github.io/axolotl/docs/faq.html) - Frequently asked questions
 
 ## 🤝 Getting Help

_quarto.yml

@@ -1,178 +1,6 @@
 project:
   type: website
 
-quartodoc:
-  dir: docs/api
-  package: axolotl
-  title: API Reference
-  parser: google
-
-  sections:
-    - title: Core
-      desc: Core functionality for training
-      contents:
-        - train
-        - evaluate
-        - datasets
-        - convert
-        - prompt_tokenizers
-        - logging_config
-        - core.trainer_builder
-        - core.training_args
-        - core.chat.messages
-        - core.chat.format.chatml
-        - core.chat.format.llama3x
-        - core.chat.format.shared
-        - core.datasets.chat
-        - core.datasets.transforms.chat_builder
-    - title: CLI
-      desc: Command-line interface
-      contents:
-        - cli.main
-        - cli.train
-        - cli.evaluate
-        - cli.args
-        - cli.checks
-        - cli.config
-        - cli.inference
-        - cli.merge_lora
-        - cli.merge_sharded_fsdp_weights
-        - cli.preprocess
-        - cli.sweeps
-        - cli.utils
-        - cli.cloud.base
-        - cli.cloud.modal_
-    - title: Trainers
-      desc: Training implementations
-      contents:
-        - core.trainers.base
-        - core.trainers.trl
-        - core.trainers.dpo.trainer
-        - core.trainers.grpo.trainer
-    - title: Prompt Strategies
-      desc: Prompt formatting strategies
-      contents:
-        - prompt_strategies.base
-        - prompt_strategies.chat_template
-        - prompt_strategies.alpaca_chat
-        - prompt_strategies.alpaca_instruct
-        - prompt_strategies.alpaca_w_system
-        - prompt_strategies.user_defined
-        - prompt_strategies.llama2_chat
-        - prompt_strategies.completion
-        - prompt_strategies.input_output
-        - prompt_strategies.stepwise_supervised
-        - prompt_strategies.metharme
-        - prompt_strategies.orcamini
-        - prompt_strategies.pygmalion
-        - prompt_strategies.messages.chat
-        - prompt_strategies.dpo.chat_template
-        - prompt_strategies.dpo.llama3
-        - prompt_strategies.dpo.chatml
-        - prompt_strategies.dpo.zephyr
-        - prompt_strategies.dpo.user_defined
-        - prompt_strategies.dpo.passthrough
-        - prompt_strategies.kto.llama3
-        - prompt_strategies.kto.chatml
-        - prompt_strategies.kto.user_defined
-        - prompt_strategies.orpo.chat_template
-        - prompt_strategies.bradley_terry.llama3
-    - title: Kernels
-      desc: Low-level performance optimizations
-      contents:
-        - kernels.lora
-        - kernels.geglu
-        - kernels.swiglu
-        - kernels.quantize
-        - kernels.utils
-    - title: MonkeyPatches
-      desc: Runtime patches for model optimizations
-      contents:
-        - monkeypatch.llama_attn_hijack_flash
-        - monkeypatch.llama_attn_hijack_xformers
-        - monkeypatch.mistral_attn_hijack_flash
-        - monkeypatch.multipack
-        - monkeypatch.relora
-        - monkeypatch.llama_expand_mask
-        - monkeypatch.lora_kernels
-        - monkeypatch.utils
-        - monkeypatch.btlm_attn_hijack_flash
-        - monkeypatch.llama_patch_multipack
-        - monkeypatch.stablelm_attn_hijack_flash
-        - monkeypatch.trainer_fsdp_optim
-        - monkeypatch.transformers_fa_utils
-        - monkeypatch.unsloth_
-        - monkeypatch.attention.mllama
-        - monkeypatch.data.batch_dataset_fetcher
-        - monkeypatch.mixtral
-    - title: Utils
-      desc: Utility functions
-      contents:
-        - utils.models
-        - utils.tokenization
-        - utils.chat_templates
-        - utils.lora
-        - utils.lora_embeddings
-        - utils.model_shard_quant
-        - utils.bench
-        - utils.freeze
-        - utils.trainer
-        - utils.schedulers
-        - utils.distributed
-        - utils.dict
-        - utils.optimizers.adopt
-        - utils.data.pretraining
-        - utils.data.sft
-        - utils.gradient_checkpointing.unsloth
-    - title: Schemas
-      desc: Pydantic data models for Axolotl config
-      contents:
-        - utils.schemas.config
-        - utils.schemas.model
-        - utils.schemas.training
-        - utils.schemas.datasets
-        - utils.schemas.peft
-        - utils.schemas.trl
-        - utils.schemas.integrations
-        - utils.schemas.enums
-        - utils.schemas.utils
-    - title: Integrations
-      desc: Third-party integrations and extensions
-      contents:
-        - integrations.base
-        - integrations.cut_cross_entropy.args
-        - integrations.grokfast.optimizer
-        - integrations.kd.trainer
-        - integrations.liger.args
-        - integrations.lm_eval.args
-        - integrations.spectrum.args
-    - title: Common
-      desc: Common utilities and shared functionality
-      contents:
-        - common.architectures
-        - common.const
-        - common.datasets
-    - title: Models
-      desc: Custom model implementations
-      contents:
-        - models.mamba.modeling_mamba
-    - title: Data Processing
-      desc: Data processing utilities
-      contents:
-        - utils.collators.core
-        - utils.collators.batching
-        - utils.collators.mamba
-        - utils.collators.mm_chat
-        - utils.samplers.multipack
-    - title: Callbacks
-      desc: Training callbacks
-      contents:
-        - utils.callbacks.perplexity
-        - utils.callbacks.profiler
-        - utils.callbacks.lisa
-        - utils.callbacks.mlflow_
-        - utils.callbacks.comet_
-
 website:
   title: "Axolotl"
   description: "We make fine-tuning accessible, scalable, and fun"
@@ -207,8 +35,6 @@ website:
             - docs/inference.qmd
             - docs/cli.qmd
             - docs/config.qmd
-            - text: "API Reference"
-              href: docs/api
 
         - section: "Dataset Formats"
           contents: docs/dataset-formats/*
@@ -254,22 +80,3 @@ format:
     theme: darkly
     css: styles.css
     toc: true
-    # Enable better handling of line breaks in markdown
-    preserve-tabs: true
-    html-math-method: mathjax
-    # Improved markdown processing options
-    md-extensions:
-      - markdown_it
-      - def_list
-      - attr_list
-      - fenced_divs
-      - tables
-      - html_admonition
-      - lineblocks
-      - fancy_lists
-    # Control whitespace handling
-    whitespace: preserve
-    # Process newlines in paragraphs
-    wrap: preserve
-    # Better line break handling
-    preserve-linebreaks: true

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/ --ignore=tests/cli /workspace/axolotl/tests/e2e/
+pytest -v --durations=10 --ignore=tests/e2e/solo/ --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

docs/.gitignore

@@ -1,4 +1,2 @@
 /.quarto/
 _site/
-/api/*.qmd
-/api/*.html

docs/cli.qmd

@@ -1,5 +1,5 @@
 ---
-title: "Command Line Interface (CLI)"
+title: "CLI Reference"
 format:
   html:
     toc: true

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
@@ -620,14 +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/dataset_preprocessing.qmd

@@ -6,7 +6,7 @@ description: How datasets are processed
 ## Overview
 
 Dataset pre-processing is the step where Axolotl takes each dataset you've configured alongside
-the [dataset format](dataset-formats) and prompt strategies to:
+the [dataset format](docs/dataset-formats) and prompt strategies to:
 
  - parse the dataset based on the *dataset format*
  - transform the dataset to how you would interact with the model based on the *prompt strategy*

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/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

requirements-dev.txt

@@ -2,5 +2,3 @@ pre-commit
 black
 mypy
 types-requests
-quartodoc
-jupyter

requirements.txt

@@ -4,6 +4,7 @@
 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==0.5.3
@@ -35,7 +36,6 @@ einops
 colorama
 numba
 numpy>=1.24.4,<=2.0.1
-
 # qlora things
 evaluate==0.4.1
 scipy

setup.py

@@ -17,7 +17,11 @@ def parse_requirements():
         lines = [r.strip() for r in requirements_file.readlines()]
         for line in lines:
             is_extras = (
-                "deepspeed" in line or "mamba-ssm" in line or "lion-pytorch" in line
+                "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
@@ -35,6 +39,7 @@ def parse_requirements():
                 "bitsandbytes",
                 "triton",
                 "mamba-ssm",
+                "flash-attn",
                 "xformers",
                 "autoawq",
                 "liger-kernel",
@@ -119,8 +124,9 @@ setup(
         ],
     },
     extras_require={
-        "flash-attn": ["flash-attn==2.7.4.post1"],
-        "ring-flash-attn": ["ring-flash-attn>=0.1.4", "yunchang==0.6.0"],
+        "flash-attn": [
+            "flash-attn==2.7.4.post1",
+        ],
         "deepspeed": [
             "deepspeed==0.16.4",
             "deepspeed-kernels",

src/axolotl/cli/main.py

@@ -25,7 +25,7 @@ from axolotl.cli.utils import (
 )
 from axolotl.integrations.lm_eval.cli import lm_eval
 from axolotl.utils import set_pytorch_cuda_alloc_conf
-from axolotl.utils.schemas.config import AxolotlInputConfig
+from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
 
 
 @click.group()

src/axolotl/cli/train.py

@@ -23,7 +23,7 @@ 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
@@ -44,13 +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/core/trainer_builder.py

@@ -13,7 +13,9 @@
 # limitations under the License.
 
 # pylint: disable=too-many-lines
-"""Builder for the training args and trainer"""
+"""
+Builder for the training args and trainer
+"""
 
 import abc
 import importlib
@@ -36,7 +38,7 @@ 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,
@@ -83,8 +85,8 @@ from axolotl.utils.collators import (
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
+from axolotl.utils.config.models.input.v0_4_1 import CustomSupportedOptimizers
 from axolotl.utils.models import ensure_dtype
-from axolotl.utils.schemas.enums import CustomSupportedOptimizers
 
 try:
     import torch._dynamo  # pylint: disable=ungrouped-imports
@@ -762,10 +764,6 @@ 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:
@@ -849,10 +847,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         self, training_args: AxolotlTrainingArguments, is_eval=False, **kwargs
     ):
         if training_args.pretraining:
-            if (
-                self.cfg.pretraining_sample_concatenation is False
-                or self.cfg.micro_batch_size > 1
-            ):
+            if self.cfg.pretraining_sample_concatenation is False:
+                return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
+            if self.cfg.micro_batch_size > 1:
                 return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
             return None
 
@@ -880,7 +877,9 @@ 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
             ):
@@ -911,8 +910,6 @@ 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"""
-
-# pylint: disable=too-many-lines
+"""
+module for customized trainers
+"""
 
 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 (
-    BatchSampler,
-    DataLoader,
-    RandomSampler,
-    Sampler,
-    SequentialSampler,
-)
+from torch.optim.lr_scheduler import OneCycleLR
+from torch.utils.data import BatchSampler, DataLoader, RandomSampler, 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 (
-    OptimizerMixin,
-    SchedulerMixin,
-    SequenceParallelMixin,
-)
-from axolotl.core.trainers.utils import (
-    sanitize_kwargs_for_ds_tagging,
-    sanitize_kwargs_for_tagging,
-)
+from axolotl.integrations.base import BaseOptimizerFactory
+from axolotl.monkeypatch.relora import ReLoRAScheduler
 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):
-    """Extend the base Trainer for axolotl helpers"""
+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
+
+
+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(
-        self, base_sampler: Sampler, dataset: Dataset
-    ) -> MultipackBatchSampler:
-        """
-        Helper method to create a `MultipackBatchSampler` for multipacking sequences
-        for training.
+    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
+        if self.args.sample_packing and not self.args.pretraining:
+            if self.args.multipack_real_batches:
+                batch_size = self.args.per_device_train_batch_size
+                batch_max_len = self.args.max_seq_length
+            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:
-            base_sampler: Sampler to wrap with `MultipackBatchSampler`.
-            dataset: Dataset to sample from.
+            if self.args.curriculum_sampling:
+                sampler = SequentialSampler(self.train_dataset)
+            else:
+                sampler = RandomSampler(self.train_dataset)
 
-        Returns:
-            Multipack (sample packing) batch sampler.
-        """
-        if self.args.multipack_real_batches:
-            batch_size = self.args.per_device_train_batch_size
-            batch_max_len = self.args.max_seq_length
-        else:
-            batch_size = 1
-            train_batch_size = (
-                self.state.train_batch_size or self.args.per_device_train_batch_size
+            return MultipackBatchSampler(
+                sampler,
+                lengths=get_dataset_lengths(self.train_dataset),
+                packing_efficiency_estimate=self.args.sample_packing_efficiency,
+                batch_max_len=batch_max_len,
+                batch_size=batch_size,
+                group_size=self.args.sample_packing_group_size,
+                bin_size=self.args.sample_packing_bin_size,
+                drop_last=True,
             )
-            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(
-            base_sampler,
-            lengths=get_dataset_lengths(dataset),
-            packing_efficiency_estimate=self.args.sample_packing_efficiency,
-            batch_max_len=batch_max_len,
-            batch_size=batch_size,
-            drop_last=True,
-        )
-
-    def _get_train_sampler(self) -> Sampler | None:
-        """
-        Helper method to get the sampler for training. Handles cases for sequence
-        parallelism, sample packing, and curriculum sampling (sequential).
-
-        Returns:
-            If the dataset is non-empty, a sampler is returned, the type of which
-                depends on the passed training args.
-        """
-        use_sample_packing = self.args.sample_packing and not self.args.pretraining
-
-        # Determine the base sampler first
-        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,
+    def _get_eval_sampler(
+        self, eval_dataset: Dataset
+    ) -> Optional[torch.utils.data.Sampler]:
+        if self.args.sample_packing and self.args.eval_sample_packing is not False:
+            if self.args.multipack_real_batches:
+                batch_size = self.args.per_device_eval_batch_size
+                batch_max_len = self.args.max_seq_length
+            else:
+                batch_size = 1
+                batch_max_len = (
+                    self.args.per_device_eval_batch_size * self.args.max_seq_length
+                )
+            return MultipackBatchSampler(
+                SequentialSampler(eval_dataset),
+                lengths=get_dataset_lengths(self.eval_dataset),
+                packing_efficiency_estimate=self.args.sample_packing_efficiency,
+                batch_max_len=batch_max_len,
+                batch_size=batch_size,
+                group_size=self.args.sample_packing_group_size,
+                bin_size=self.args.sample_packing_bin_size,
+                drop_last=True,
             )
+        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:
-        """
-        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):
+            sampler = self._get_train_sampler()
             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 unprepared dataloader if using sequence parallelism
-        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 self.accelerator.prepare_data_loader(
+                DataLoader(train_dataset, **dataloader_params)
             )
+        return super().get_train_dataloader()
 
-        # Get sampler and create 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
+    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
         if self.args.sample_packing and self.args.eval_sample_packing is False:
             self.data_collator = (  # pylint: disable=attribute-defined-outside-init
                 self.eval_data_collator
             )
-            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
-            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
+        if self.args.sample_packing and self.args.eval_sample_packing is not False:
+            eval_dataset = (
+                eval_dataset if eval_dataset is not None else self.eval_dataset
             )
 
-            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.
-            start_time: The start of training.
+            logs (`Dict[str, float]`):
+                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,
-        inputs: dict[str, torch.Tensor | Any],
-        num_items_in_batch: int | None = None,
-    ) -> torch.Tensor:
-        """
-        Perform a training step on a batch of inputs. Overrides the
-        `transformers.trainer.Trainer` method to handle sequence parallelism if
-        enabled.
+        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
 
-        Args:
-            model: Model to perform training step for.
-            inputs: Dictionary mapping.
-        """
-        # Set up sequence parallelism for this step if enabled
-        if self.args.sequence_parallel_degree > 1:
-            self._update_ring_flash_attn_params(inputs)
+        labels = input_ids.to(lm_logits.device)
+        shift_logits = lm_logits[:, :-1, :].contiguous()
+        labels = labels[:, 1:].contiguous()
 
-        # Proceed with normal training step
-        loss = super().training_step(model, inputs, num_items_in_batch)
+        loss_fct = torch.nn.CrossEntropyLoss()
+        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.utils import (
-    sanitize_kwargs_for_ds_tagging,
-    sanitize_kwargs_for_tagging,
+from axolotl.core.trainers.base import (
+    SchedulerMixin,
+    _sanitize_kwargs_for_ds_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/grpo/__init__.py

@@ -9,7 +9,7 @@ import logging
 from trl.trainer.grpo_trainer import RewardFunc
 
 from axolotl.core.trainers.grpo.trainer import AxolotlGRPOTrainer
-from axolotl.utils.schemas.trl import TRLConfig
+from axolotl.utils.config.models.input.v0_4_1.trl import TRLConfig
 
 LOG = logging.getLogger("axolotl")
 

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

@@ -1,23 +1,16 @@
-"""Module for TRL PPO trainer"""
+"""
+module for TRL PPO training
+"""
 
 import torch
 from tqdm import tqdm
-from trl import (
-    CPOTrainer,
-    KTOTrainer,
-    ORPOTrainer,
-    PPOTrainer,
-    PRMTrainer,
-    RewardTrainer,
-)
-
-from axolotl.core.trainers.mixins.scheduler import SchedulerMixin
+from trl import PPOTrainer
 
 
 class TRLPPOTrainer(PPOTrainer):
-    """Wrapper for TRL PPO trainer to handle customizations"""
-
-    tag_names = ["axolotl", "ppo"]
+    """
+    wrapper for ppo trainer to handle customizations
+    """
 
     def train(
         self,
@@ -38,7 +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
@@ -70,43 +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"]
-
-
-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/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

@@ -207,19 +207,14 @@ class AxolotlTrainingMixins:
         },
     )
 
-    sequence_parallel_degree: Optional[int] = field(
-        default=1,
-        metadata={"help": "The number of workers to use in sequence parallelism"},
-    )
-
 
 @dataclass
 class AxolotlTrainingArguments(AxolotlTrainingMixins, TrainingArguments):
     """
     Training arguments for Causal trainer
 
-    This code is duplicated due to HF TrainingArguments not setting output_dir with a
-    default value so it can't be used as a mixin.
+    This code is duplicated due to HF TrainingArguments not setting output_dir with a defaujlt value
+    so it can't be used as a mixin.
     """
 
 

src/axolotl/evaluate.py

@@ -8,8 +8,6 @@ from typing import Dict, Optional
 
 import torch
 from accelerate.logging import get_logger
-from datasets import Dataset
-from transformers.trainer import Trainer
 
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
@@ -27,18 +25,18 @@ LOG = get_logger("axolotl.evaluate")
 
 
 def evaluate_dataset(
-    trainer: Trainer, dataset: Dataset, dataset_type: str, flash_optimum: bool = False
+    trainer, dataset, dataset_type: str, flash_optimum: bool = False
 ) -> Optional[Dict[str, float]]:
-    """Helper function to evaluate a single dataset.
+    """Helper function to evaluate a single dataset safely.
 
     Args:
-        trainer: The trainer instance.
-        dataset: Dataset to evaluate.
-        dataset_type: Type of dataset ('train' or 'eval').
-        flash_optimum: Whether to use flash optimum.
+        trainer: The trainer instance
+        dataset: Dataset to evaluate
+        dataset_type: Type of dataset ('train' or 'eval')
+        flash_optimum: Whether to use flash optimum
 
     Returns:
-        Dictionary of metrics or None if dataset is None.
+        Dictionary of metrics or None if dataset is None
     """
     if dataset is None:
         return None
@@ -65,14 +63,17 @@ def evaluate_dataset(
 
 def evaluate(*, cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> Dict[str, float]:
     """
-    Evaluate a model on training and validation datasets.
+    Evaluate a model on training and validation datasets
 
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
         dataset_meta: Dataset metadata containing training and evaluation datasets.
 
     Returns:
-        Dictionary mapping metric names to their values.
+        Tuple containing:
+        - The model (either PeftModel or PreTrainedModel)
+        - The tokenizer
+        - Dictionary of evaluation metrics
     """
     # pylint: disable=duplicate-code
     # Enable expandable segments for cuda allocation to improve VRAM usage

src/axolotl/integrations/config.py

@@ -11,17 +11,19 @@
 # the License.
 
 """
-Module to handle merging the plugins' input arguments with the base configurations.
+module to handle merging the plugins' input arguments with the base configurations.
 
-This was moved here to prevent circular imports.
+this was moved here to prevent circular imports
 """
 
 from typing import Any, Dict, List
 
-from axolotl.utils.schemas.config import (
+from axolotl.utils.config.models.input.v0_4_1 import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
 )
-from axolotl.utils.schemas.config import AxolotlInputConfig as AxolotlInputConfigBase
+from axolotl.utils.config.models.input.v0_4_1 import (
+    AxolotlInputConfig as AxolotlInputConfigBase,
+)
 
 
 def merge_input_args():

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):
-    """
-    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/prompt_strategies/chat_template.py

@@ -13,7 +13,7 @@ from axolotl.prompt_strategies.jinja_template_analyzer import JinjaTemplateAnaly
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
 from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
 from axolotl.utils.chat_templates import get_chat_template_from_config
-from axolotl.utils.schemas.datasets import DatasetConfig
+from axolotl.utils.config.models.input.v0_4_1 import DatasetConfig
 
 # Configure the logger
 LOG = logging.getLogger("axolotl")

src/axolotl/prompt_strategies/dpo/chat_template.py

@@ -3,7 +3,7 @@ DPO prompt strategies for using tokenizer chat templates.
 """
 
 from axolotl.utils.chat_templates import extract_chat_template_args, get_chat_template
-from axolotl.utils.schemas.utils import handle_legacy_message_fields_logic
+from axolotl.utils.config.models.input.v0_4_1 import handle_legacy_message_fields_logic
 
 
 def default(

src/axolotl/train.py

@@ -169,7 +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.
@@ -177,6 +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: ...

src/axolotl/utils/callbacks/__init__.py

@@ -33,6 +33,7 @@ from trl.models import unwrap_model_for_generation
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.callbacks.perplexity import Perplexity
+from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
 from axolotl.utils.distributed import (
     barrier,
     broadcast_dict,
@@ -42,7 +43,6 @@ from axolotl.utils.distributed import (
     is_main_process,
     zero_first,
 )
-from axolotl.utils.schemas.config import AxolotlInputConfig
 
 if TYPE_CHECKING:
     from axolotl.core.trainer_builder import AxolotlTrainingArguments

src/axolotl/utils/collators/batching.py

@@ -1,59 +1,14 @@
 """
-Data collators for axolotl to pad labels and position_ids for packed sequences. Also
-includes logic for handling sequence parallelism collation.
+DataCollator for axolotl to pad labels and position_ids for packed sequences
 """
 
-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/config/__init__.py

@@ -12,13 +12,19 @@ from transformers.utils.import_utils import is_torch_npu_available
 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 load_model_config
-from axolotl.utils.schemas.config import (
+from axolotl.utils.config.models.input.v0_4_1 import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
 )
-from axolotl.utils.schemas.config import AxolotlInputConfig as AxolotlInputConfigBase
-from axolotl.utils.schemas.datasets import DPODataset, KTODataset, SFTDataset
+from axolotl.utils.config.models.input.v0_4_1 import (
+    AxolotlInputConfig as AxolotlInputConfigBase,
+)
+from axolotl.utils.config.models.input.v0_4_1 import (
+    DPODataset,
+    KTODataset,
+    SFTDataset,
+)
+from axolotl.utils.dict import DictDefault
+from axolotl.utils.models import load_model_config
 
 LOG = logging.getLogger("axolotl")
 
@@ -125,9 +131,6 @@ 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

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

@@ -1,4 +1,8 @@
-"""Pydantic models for TRL trainer configuration"""
+"""
+GRPO specific configuration args
+"""
+
+from typing import Optional
 
 from pydantic import BaseModel, Field
 
@@ -8,11 +12,11 @@ class TRLConfig(BaseModel):
     Input args for TRL.
     """
 
-    beta: float | None = Field(
+    beta: Optional[float] = Field(
         default=None,
         json_schema_extra={"description": "Beta for RL training"},
     )
-    max_completion_length: int | None = Field(
+    max_completion_length: Optional[int] = Field(
         default=None,
         json_schema_extra={
             "description": "Maximum length of the completion for RL training"
@@ -21,50 +25,50 @@ class TRLConfig(BaseModel):
 
     # GRPO specific args
     # Ref: https://github.com/huggingface/trl/blob/e3244d2d096ff1e2e248c931d06d39e165e20623/trl/trainer/grpo_config.py#L22
-    use_vllm: bool | None = Field(
+    use_vllm: Optional[bool] = Field(
         default=False,
         json_schema_extra={"description": "Whether to use VLLM for RL training"},
     )
-    vllm_device: str | None = Field(
+    vllm_device: Optional[str] = Field(
         default="auto",
         json_schema_extra={"description": "Device to use for VLLM"},
     )
-    vllm_gpu_memory_utilization: float | None = Field(
+    vllm_gpu_memory_utilization: Optional[float] = Field(
         default=0.9,
         json_schema_extra={"description": "GPU memory utilization for VLLM"},
     )
-    vllm_dtype: str | None = Field(
+    vllm_dtype: Optional[str] = Field(
         default="auto",
         json_schema_extra={"description": "Data type for VLLM"},
     )
-    vllm_max_model_len: int | None = Field(
+    vllm_max_model_len: Optional[int] = Field(
         default=None,
         json_schema_extra={
             "description": "Maximum length of the model context for VLLM"
         },
     )
 
-    reward_funcs: list[str] | None = Field(
+    reward_funcs: Optional[list[str]] = Field(
         default=None,
         json_schema_extra={"description": "List of reward functions to load"},
     )
-    reward_weights: list[float] | None = Field(
+    reward_weights: Optional[list[float]] = Field(
         default=None,
         json_schema_extra={
             "description": "Weights for each reward function. Must match the number of reward functions."
         },
     )
-    num_generations: int | None = Field(
+    num_generations: Optional[int] = Field(
         default=None,
         json_schema_extra={
             "description": "Number of generations to sample. The global batch size (num_processes * per_device_batch_size) must be divisible by this value."
         },
     )
-    log_completions: bool | None = Field(
+    log_completions: Optional[bool] = Field(
         default=False,
         json_schema_extra={"description": "Whether to log completions"},
     )
-    sync_ref_model: bool | None = Field(
+    sync_ref_model: Optional[bool] = Field(
         default=False,
         json_schema_extra={
             "description": (
@@ -73,13 +77,13 @@ class TRLConfig(BaseModel):
             )
         },
     )
-    ref_model_mixup_alpha: float | None = Field(
+    ref_model_mixup_alpha: Optional[float] = Field(
         default=0.9,
         json_schema_extra={
             "description": "Mixup alpha for the reference model. Requires `sync_ref_model=True`."
         },
     )
-    ref_model_sync_steps: int | None = Field(
+    ref_model_sync_steps: Optional[int] = Field(
         default=64,
         json_schema_extra={
             "description": "Sync steps for the reference model. Requires `sync_ref_model=True`."

src/axolotl/utils/models.py

@@ -67,12 +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__)
-
-MULTIMODEL_AUTO_MODEL_MAPPING = {
-    "llava": LlavaForConditionalGeneration,
-    "mllama": MllamaForConditionalGeneration,
-}
+LOG = logging.getLogger("axolotl")
 
 
 # copied from accelerator.FullyShardedDataParallelPlugin
@@ -481,7 +476,7 @@ class ModelLoader:
         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
@@ -552,14 +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:
@@ -616,7 +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()
 
@@ -666,16 +653,25 @@ class ModelLoader:
                 "Shifted-sparse attention not currently implemented without flash attention."
             )
 
-    def set_auto_model_loader(self):
-        """
-        Set self.auto_model_loader. Defaults to `transformers.AutoModelForCausalLM`
-        (set at `__init__`). When using a multimodal model, `self.auto_model_loader`
-        should be set according to the type of the model.
+    def set_auto_model_loader(self) -> None:
+        """set self.AutoModelLoader
+        - default value: AutoModelForCausalLM (set at __init__)
+        - when using a multi modality model, self.AutoModelLoader should
+          be set according to model type of the model
         """
         if self.cfg.is_multimodal:
-            self.auto_model_loader = MULTIMODEL_AUTO_MODEL_MAPPING.get(
-                self.model_config.model_type, AutoModelForVision2Seq
-            )
+            if self.model_config.model_type == "llava":
+                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
@@ -699,7 +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,
                 )
@@ -920,27 +916,11 @@ class ModelLoader:
 
             if self.cfg.is_multimodal:
                 self.model_config.text_config = self.text_model_config
-
-            # Load model with random initialization if specified
-            if self.cfg.random_init_weights:
-                # AutoModel classes support the from_config method
-                if self.auto_model_loader in [
-                    AutoModelForCausalLM,
-                    AutoModelForVision2Seq,
-                ]:
-                    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,
-                )
+            self.model = self.AutoModelLoader.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:
@@ -978,7 +958,7 @@ class ModelLoader:
             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,
@@ -1011,7 +991,7 @@ class ModelLoader:
             if self.cfg.gptq:
                 if self.cfg.is_multimodal:
                     self.model_config.text_config = self.text_model_config
-                self.model = self.auto_model_loader.from_pretrained(
+                self.model = self.AutoModelLoader.from_pretrained(
                     self.base_model,
                     config=self.model_config,
                     trust_remote_code=self.cfg.trust_remote_code or False,
@@ -1031,7 +1011,7 @@ class ModelLoader:
 
                 if self.cfg.is_multimodal:
                     self.model_config.text_config = self.text_model_config
-                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,
@@ -1327,7 +1307,7 @@ def load_model(
     """
     Load a model for a given configuration and tokenizer.
     """
-    model_loader = ModelLoader(
+    loader = ModelLoader(
         cfg,
         tokenizer,
         processor=processor,
@@ -1335,7 +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/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/datasets.py

@@ -1,165 +0,0 @@
-"""Pydantic models for datasets-related configuration"""
-
-from pydantic import BaseModel, model_validator
-
-from axolotl.utils.schemas.enums import ChatTemplate
-from axolotl.utils.schemas.utils import handle_legacy_message_fields_logic
-
-
-class UserDefinedPrompterType(BaseModel):
-    """Structure for user defined prompt types"""
-
-    system_prompt: str | None = None
-    system_format: str | None = None
-    field_system: str | None = None
-    field_instruction: str | None = None
-    field_input: str | None = None
-    field_output: str | None = None
-
-    format: str | None = None
-    no_input_format: str | None = None
-    field: str | None = None
-
-
-class SFTDataset(BaseModel):
-    """SFT configuration subset"""
-
-    path: str | None = None
-    split: str | None = None
-    type: str | UserDefinedPrompterType | None = None
-    input_transform: str | None = None
-    shards: int | None = None
-    shards_idx: int | None = None
-    preprocess_shards: int | None = None
-    conversation: str | None = None
-    # Do not make this too strict or it will break the validator to choose different dataset class
-    chat_template: ChatTemplate | str | None = None
-    chat_template_jinja: str | None = None
-    data_files: str | list[str] | None = None
-    input_format: str | None = None
-    name: str | None = None
-    ds_type: str | None = None
-    train_on_split: str | None = None
-    field: str | None = None
-    field_human: str | None = None
-    field_model: str | None = None
-    field_messages: str | None = None
-    # deprecated, use message_property_mappings
-    message_field_role: str | None = None
-    # deprecated, use message_property_mappings
-    message_field_content: str | None = None
-    message_property_mappings: dict[str, str] | None = None
-    message_field_training: str | None = None
-    message_field_training_detail: str | None = None
-    logprobs_field: str | None = None
-    temperature: float | None = None
-    roles_to_train: list[str] | None = None
-    train_on_eos: str | None = None
-    roles: dict[str, list[str]] | None = None
-    drop_system_message: bool | None = None
-    trust_remote_code: bool | None = False
-    revision: str | None = None
-
-    @model_validator(mode="before")
-    @classmethod
-    def handle_legacy_message_fields(cls, data):
-        """Handle backwards compatibility between legacy message field mapping and new property mapping system."""
-        return handle_legacy_message_fields_logic(data)
-
-    @model_validator(mode="before")
-    @classmethod
-    # pylint: disable=duplicate-code
-    def check_chat_template_config(cls, data):
-        if isinstance(data, BaseModel):
-            data = data.model_dump()
-
-        # Set chat_template to tokenizer_default if not set
-        if data.get("type") == "chat_template" and not data.get("chat_template"):
-            data["chat_template"] = ChatTemplate.tokenizer_default
-
-        # if chat_template is set to jinja, chat_template_jinja is required
-        if data.get("chat_template") == ChatTemplate.jinja and not data.get(
-            "chat_template_jinja"
-        ):
-            raise ValueError(
-                "chat_template_jinja is required when chat_template is set to jinja"
-            )
-
-        # If chat_template_jinja is set, set chat_template to jinja
-        if data.get("chat_template_jinja") and not data.get("chat_template"):
-            data["chat_template"] = ChatTemplate.jinja
-
-        return data
-
-
-class PretrainingDataset(BaseModel):
-    """Pretraining dataset configuration subset"""
-
-    name: str | None = None
-    path: str | None = None
-    split: str | None = "train"
-    text_column: str | None = "text"
-    type: str | None = "pretrain"
-    trust_remote_code: bool | None = False
-    data_files: str | None = None
-    skip: int | None = None
-
-
-class UserDefinedDPOType(BaseModel):
-    """User defined typing for DPO"""
-
-    field_system: str | None = None
-    field_prompt: str | None = None
-    field_chosen: str | None = None
-    field_rejected: str | None = None
-    prompt_format: str | None = None
-    chosen_format: str | None = None
-    rejected_format: str | None = None
-
-
-class DPODataset(BaseModel):
-    """DPO configuration subset"""
-
-    path: str | None = None
-    split: str | None = None
-    type: UserDefinedDPOType | str | None = None
-    data_files: list[str] | None = None
-    revision: str | None = None
-    field_messages: str | None = None
-
-
-class StepwiseSupervisedDataset(BaseModel):
-    """Stepwise supervised dataset configuration subset"""
-
-    path: str | None = None
-    split: str | None = None
-    data_files: list[str] | None = None
-    revision: str | None = None
-    step_separator: str | None = None
-    max_completion_length: int | None = None
-    train_on_last_step_only: bool | None = None
-
-
-class UserDefinedKTOType(BaseModel):
-    """User defined typing for KTO"""
-
-    field_system: str | None = None
-    field_prompt: str | None = None
-    field_completion: str | None = None
-    field_label: bool | None = None
-    prompt_format: str | None = None
-    completion_format: str | None = None
-
-
-class KTODataset(BaseModel):
-    """KTO configuration subset"""
-
-    path: str | None = None
-    split: str | None = None
-    type: UserDefinedKTOType | str | None = None
-    data_files: list[str] | None = None
-    trust_remote_code: bool | None = False
-    revision: str | None = None
-
-
-DatasetConfig = SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset

src/axolotl/utils/schemas/deprecated.py

@@ -1,68 +0,0 @@
-"""Pydantic models for deprecated and remapped configuration parameters"""
-
-import logging
-from typing import Any
-
-from pydantic import BaseModel, Field, field_validator
-
-LOG = logging.getLogger(__name__)
-
-
-class DeprecatedParameters(BaseModel):
-    """configurations that are deprecated"""
-
-    max_packed_sequence_len: int | None = None
-    rope_scaling: Any | None = None
-    noisy_embedding_alpha: float | None = None
-    dpo_beta: float | None = None
-    evaluation_strategy: str | None = None
-
-    @field_validator("max_packed_sequence_len")
-    @classmethod
-    def validate_max_packed_sequence_len(cls, max_packed_sequence_len):
-        if max_packed_sequence_len:
-            raise DeprecationWarning("`max_packed_sequence_len` is no longer supported")
-        return max_packed_sequence_len
-
-    @field_validator("rope_scaling")
-    @classmethod
-    def validate_rope_scaling(cls, rope_scaling):
-        if rope_scaling:
-            raise DeprecationWarning(
-                "`rope_scaling` is no longer supported, it should now be be a key under `model_config`"
-            )
-        return rope_scaling
-
-    @field_validator("noisy_embedding_alpha")
-    @classmethod
-    def validate_noisy_embedding_alpha(cls, noisy_embedding_alpha):
-        if noisy_embedding_alpha:
-            LOG.warning("noisy_embedding_alpha is deprecated, use neftune_noise_alpha")
-        return noisy_embedding_alpha
-
-    @field_validator("dpo_beta")
-    @classmethod
-    def validate_dpo_beta(cls, dpo_beta):
-        if dpo_beta is not None:
-            LOG.warning("dpo_beta is deprecated, use rl_beta instead")
-        return dpo_beta
-
-    @field_validator("evaluation_strategy")
-    @classmethod
-    def validate_evaluation_strategy(cls, evaluation_strategy):
-        if evaluation_strategy is not None:
-            LOG.warning("evaluation_strategy is deprecated, use eval_strategy instead")
-        return evaluation_strategy
-
-
-class RemappedParameters(BaseModel):
-    """Parameters that have been remapped to other names"""
-
-    overrides_of_model_config: dict[str, Any] | None = Field(
-        default=None, alias="model_config"
-    )
-    overrides_of_model_kwargs: dict[str, Any] | None = Field(
-        default=None, alias="model_kwargs"
-    )
-    type_of_model: str | None = Field(default=None, alias="model_type")
-    revision_of_model: str | None = Field(default=None, alias="model_revision")

src/axolotl/utils/schemas/enums.py

@@ -1,49 +0,0 @@
-"""Enums for Axolotl input config"""
-
-from enum import Enum
-
-
-class RLType(str, Enum):
-    """RL trainer type configuration subset"""
-
-    dpo = "dpo"  # pylint: disable=invalid-name
-    grpo = "grpo"  # pylint: disable=invalid-name
-    ipo = "ipo"  # pylint: disable=invalid-name
-    orpo = "orpo"  # pylint: disable=invalid-name
-    kto = "kto"  # pylint: disable=invalid-name
-    simpo = "simpo"  # pylint: disable=invalid-name
-
-
-class ChatTemplate(str, Enum):
-    """Chat templates configuration subset"""
-
-    alpaca = "alpaca"  # pylint: disable=invalid-name
-    chatml = "chatml"  # pylint: disable=invalid-name
-    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
-    gemma = "gemma"  # pylint: disable=invalid-name
-    cohere = "cohere"  # pylint: disable=invalid-name
-    llama3 = "llama3"  # pylint: disable=invalid-name
-    llama3_2_vision = "llama3_2_vision"  # pylint: disable=invalid-name
-    phi_3 = "phi_3"  # pylint: disable=invalid-name
-    phi_35 = "phi_35"  # pylint: disable=invalid-name
-    deepseek_v2 = "deepseek_v2"  # pylint: disable=invalid-name
-    deepseek_v3 = "deepseek_v3"  # pylint: disable=invalid-name
-    jamba = "jamba"  # pylint: disable=invalid-name
-    jinja = "jinja"  # pylint: disable=invalid-name
-    qwen_25 = "qwen_25"  # pylint: disable=invalid-name
-    tokenizer_default = "tokenizer_default"  # pylint: disable=invalid-name
-    exaone = "exaone"  # pylint: disable=invalid-name
-    metharme = "metharme"  # pylint: disable=invalid-name
-
-
-class CustomSupportedOptimizers(str, Enum):
-    """Custom supported optimizers"""
-
-    optimi_adamw = "optimi_adamw"  # pylint: disable=invalid-name
-    ao_adamw_4bit = "ao_adamw_4bit"  # pylint: disable=invalid-name
-    ao_adamw_8bit = "ao_adamw_8bit"  # pylint: disable=invalid-name
-    ao_adamw_fp8 = "ao_adamw_fp8"  # pylint: disable=invalid-name
-    adopt_adamw = "adopt_adamw"  # pylint: disable=invalid-name
-    muon = "muon"  # pylint: disable=invalid-name

src/axolotl/utils/schemas/integrations.py

@@ -1,108 +0,0 @@
-"""Pydantic models for Axolotl integrations"""
-
-import logging
-from typing import Any
-
-from pydantic import BaseModel, Field, model_validator
-
-LOG = logging.getLogger(__name__)
-
-
-class MLFlowConfig(BaseModel):
-    """MLFlow configuration subset"""
-
-    use_mlflow: bool | None = None
-    mlflow_tracking_uri: str | None = None
-    mlflow_experiment_name: str | None = None
-    mlflow_run_name: str | None = None
-    hf_mlflow_log_artifacts: bool | None = None
-
-
-class LISAConfig(BaseModel):
-    """LISA configuration subset"""
-
-    lisa_n_layers: int | None = Field(
-        default=None,
-        json_schema_extra={"description": "the number of activate layers in LISA"},
-    )
-    lisa_step_interval: int | None = Field(
-        default=None,
-        json_schema_extra={"description": "how often to switch layers in LISA"},
-    )
-    lisa_layers_attribute: str | None = Field(
-        default="model.layers",
-        json_schema_extra={"description": "path under the model to access the layers"},
-    )
-
-
-class WandbConfig(BaseModel):
-    """Wandb configuration subset"""
-
-    use_wandb: bool | None = None
-    wandb_name: str | None = None
-    wandb_run_id: str | None = None
-    wandb_mode: str | None = None
-    wandb_project: str | None = None
-    wandb_entity: str | None = None
-    wandb_watch: str | None = None
-    wandb_log_model: str | None = None
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_wandb_run(cls, data):
-        if data.get("wandb_run_id") and not data.get("wandb_name"):
-            data["wandb_name"] = data.get("wandb_run_id")
-
-            LOG.warning(
-                "wandb_run_id sets the ID of the run. If you would like to set the name, please use wandb_name instead."
-            )
-
-        return data
-
-
-class CometConfig(BaseModel):
-    """Comet configuration subset"""
-
-    use_comet: bool | None = None
-    comet_api_key: str | None = None
-    comet_workspace: str | None = None
-    comet_project_name: str | None = None
-    comet_experiment_key: str | None = None
-    comet_mode: str | None = None
-    comet_online: bool | None = None
-    comet_experiment_config: dict[str, Any] | None = None
-
-
-class GradioConfig(BaseModel):
-    """Gradio configuration subset"""
-
-    gradio_title: str | None = None
-    gradio_share: bool | None = None
-    gradio_server_name: str | None = None
-    gradio_server_port: int | None = None
-    gradio_max_new_tokens: int | None = None
-    gradio_temperature: float | None = None
-
-
-class RayConfig(BaseModel):
-    """Ray launcher configuration subset"""
-
-    use_ray: bool = Field(default=False)
-    ray_run_name: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "help": "The training results will be saved at `saves/ray_run_name`."
-        },
-    )
-    ray_num_workers: int = Field(
-        default=1,
-        json_schema_extra={
-            "help": "The number of workers for Ray training. Default is 1 worker."
-        },
-    )
-    resources_per_worker: dict = Field(
-        default_factory=lambda: {"GPU": 1},
-        json_schema_extra={
-            "help": "The resources per worker for Ray training. Default is to use 1 GPU per worker."
-        },
-    )

src/axolotl/utils/schemas/model.py

@@ -1,55 +0,0 @@
-"""Pydantic models for model input / output, etc. configuration"""
-
-import logging
-
-from pydantic import BaseModel, Field, field_validator
-
-LOG = logging.getLogger(__name__)
-
-
-class ModelInputConfig(BaseModel):
-    """Model configuration subset"""
-
-    model_config = {"protected_namespaces": ()}
-
-    base_model: str
-    base_model_config: str | None = None
-    cls_model_config: str | None = None
-    tokenizer_config: str | None = None
-    tokenizer_use_fast: bool | None = None
-    tokenizer_legacy: bool | None = None
-    tokenizer_type: str | None = Field(
-        default=None, json_schema_extra={"description": "transformers tokenizer class"}
-    )
-    processor_type: str | None = Field(
-        default=None, json_schema_extra={"description": "transformers processor class"}
-    )
-    trust_remote_code: bool | None = None
-
-    @field_validator("trust_remote_code")
-    @classmethod
-    def hint_trust_remote_code(cls, trust_remote_code):
-        if trust_remote_code:
-            LOG.warning(
-                "`trust_remote_code` is set to true. Please make sure that you reviewed the remote code/model."
-            )
-        return trust_remote_code
-
-
-class ModelOutputConfig(BaseModel):
-    """model save configuration subset"""
-
-    output_dir: str = Field(default="./model-out")
-    hub_model_id: str | None = None
-    hub_strategy: str | None = None
-    save_safetensors: bool | None = True
-
-
-class SpecialTokensConfig(BaseModel):
-    """Special tokens configuration subset"""
-
-    bos_token: str | None = None
-    eos_token: str | None = None
-    pad_token: str | None = None
-    unk_token: str | None = None
-    additional_special_tokens: list[str] | None = None

src/axolotl/utils/schemas/peft.py

@@ -1,132 +0,0 @@
-"""Pydantic models for PEFT-related configuration"""
-
-from typing import Any
-
-from pydantic import BaseModel, Field, field_validator, model_validator
-
-
-class LoftQConfig(BaseModel):
-    """LoftQ configuration subset"""
-
-    loftq_bits: int = Field(
-        default=4, json_schema_extra={"description": "Quantization bits for LoftQ"}
-    )
-    # loftq_iter: int = Field(default=1, json_schema_extra={"description": "Alternating iterations for LoftQ"})
-
-
-class PeftConfig(BaseModel):
-    """peftq configuration subset"""
-
-    loftq_config: LoftQConfig | None = None
-
-
-class LoraConfig(BaseModel):
-    """Peft / LoRA configuration subset"""
-
-    load_in_8bit: bool | None = Field(default=False)
-    load_in_4bit: bool | None = Field(default=False)
-
-    adapter: str | None = None
-    lora_model_dir: str | None = None
-    lora_r: int | None = None
-    lora_alpha: int | None = None
-    lora_fan_in_fan_out: bool | None = None
-    lora_target_modules: str | list[str] | None = None
-    lora_target_linear: bool | None = None
-    lora_modules_to_save: list[str] | None = None
-    lora_dropout: float | None = 0.0
-    peft_layers_to_transform: list[int] | None = None
-    peft_layers_pattern: list[str] | None = None
-    peft: PeftConfig | None = None
-    peft_use_dora: bool | None = None
-    peft_use_rslora: bool | None = None
-    peft_layer_replication: list[tuple[int, int]] | None = None
-    peft_init_lora_weights: bool | str | None = None
-
-    qlora_sharded_model_loading: bool | None = Field(
-        default=False,
-        json_schema_extra={
-            "description": "load qlora model in sharded format for FSDP using answer.ai technique."
-        },
-    )
-    lora_on_cpu: bool | None = None
-    gptq: bool | None = None
-    bnb_config_kwargs: dict[str, Any] | None = None
-
-    loraplus_lr_ratio: float | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4."
-        },
-    )
-    loraplus_lr_embedding: float | None = Field(
-        default=1e-6,
-        json_schema_extra={
-            "description": "loraplus learning rate for lora embedding layers."
-        },
-    )
-
-    merge_lora: bool | None = None
-
-    @model_validator(mode="before")
-    @classmethod
-    def validate_adapter(cls, data):
-        if (
-            not data.get("adapter")
-            and not data.get("inference")
-            and (data.get("load_in_8bit") or data.get("load_in_4bit"))
-        ):
-            raise ValueError(
-                "load_in_8bit and load_in_4bit are not supported without setting an adapter for training."
-                "If you want to full finetune, please turn off load_in_8bit and load_in_4bit."
-            )
-        return data
-
-    @model_validator(mode="after")
-    def validate_qlora(self):
-        if self.adapter == "qlora":
-            if self.merge_lora:
-                # can't merge qlora if loaded in 8bit or 4bit
-                if self.load_in_8bit:
-                    raise ValueError("Can't merge qlora if loaded in 8bit")
-
-                if self.gptq:
-                    raise ValueError("Can't merge qlora if gptq")
-
-                if self.load_in_4bit:
-                    raise ValueError("Can't merge qlora if loaded in 4bit")
-
-            else:
-                if self.load_in_8bit:
-                    raise ValueError("Can't load qlora in 8bit")
-
-                if self.gptq:
-                    raise ValueError("Can't load qlora if gptq")
-
-                if not self.load_in_4bit:
-                    raise ValueError("Require cfg.load_in_4bit to be True for qlora")
-        return self
-
-    @field_validator("loraplus_lr_embedding")
-    @classmethod
-    def convert_loraplus_lr_embedding(cls, loraplus_lr_embedding):
-        if loraplus_lr_embedding and isinstance(loraplus_lr_embedding, str):
-            loraplus_lr_embedding = float(loraplus_lr_embedding)
-        return loraplus_lr_embedding
-
-    @model_validator(mode="before")
-    @classmethod
-    def validate_lora_dropout(cls, data):
-        if data.get("adapter") is not None and data.get("lora_dropout") is None:
-            data["lora_dropout"] = 0.0
-        return data
-
-
-class ReLoRAConfig(BaseModel):
-    """ReLoRA configuration subset"""
-
-    relora_steps: int | None = None
-    relora_warmup_steps: int | None = None
-    relora_anneal_steps: int | None = None
-    relora_prune_ratio: float | None = None
-    relora_cpu_offload: bool | None = None

src/axolotl/utils/schemas/training.py

@@ -1,99 +0,0 @@
-"""Pydantic models for training hyperparameters"""
-
-import logging
-from typing import Any, Literal
-
-from pydantic import BaseModel, Field, field_validator
-from transformers import SchedulerType
-from transformers.training_args import OptimizerNames
-
-from axolotl.utils.schemas.enums import CustomSupportedOptimizers
-
-LOG = logging.getLogger(__name__)
-
-
-class LrGroup(BaseModel):
-    """Custom learning rate group configuration"""
-
-    name: str
-    modules: list[str]
-    lr: float
-
-
-class HyperparametersConfig(BaseModel):
-    """Training hyperparams configuration subset"""
-
-    gradient_accumulation_steps: int | None = Field(default=1)
-    micro_batch_size: int | None = Field(
-        default=1,
-        json_schema_extra={"description": "per gpu micro batch size for training"},
-    )
-    batch_size: int | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Total batch size, we do not recommended setting this manually"
-        },
-    )
-    eval_batch_size: int | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "per gpu micro batch size for evals, defaults to value of micro_batch_size"
-        },
-    )
-
-    auto_find_batch_size: bool | None = None
-
-    train_on_inputs: bool | None = False
-    group_by_length: bool | None = None
-
-    learning_rate: str | float
-    embedding_lr: float | None = None
-    embedding_lr_scale: float | None = None
-    weight_decay: float | None = 0.0
-    optimizer: (OptimizerNames | CustomSupportedOptimizers) | None = (
-        OptimizerNames.ADAMW_TORCH_FUSED
-    )
-    optim_args: (str | dict[str, Any]) | None = Field(
-        default=None,
-        json_schema_extra={"description": "Optional arguments to supply to optimizer."},
-    )
-    optim_target_modules: (list[str] | Literal["all_linear"]) | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "The target modules to optimize, i.e. the module names that you would like to train."
-        },
-    )
-    torchdistx_path: str | None = None
-    lr_scheduler: (SchedulerType | Literal["one_cycle"] | Literal["rex"]) | None = (
-        SchedulerType.COSINE
-    )
-    lr_scheduler_kwargs: dict[str, Any] | None = None
-    lr_quadratic_warmup: bool | None = None
-    cosine_min_lr_ratio: float | None = None
-    cosine_constant_lr_ratio: float | None = None
-    lr_div_factor: float | None = None
-    lr_groups: list[LrGroup] | None = None
-
-    adam_epsilon: float | None = None
-    adam_beta1: float | None = None
-    adam_beta2: float | None = None
-    max_grad_norm: float | None = None
-    num_epochs: float = Field(default=1.0)
-
-    @field_validator("batch_size")
-    @classmethod
-    def hint_batch_size_set(cls, batch_size):
-        if batch_size:
-            LOG.warning(
-                "%s\n%s",
-                "batch_size is not recommended. Please use gradient_accumulation_steps instead.",
-                "To calculate the equivalent gradient_accumulation_steps, divide batch_size / micro_batch_size / number of gpus.",
-            )
-        return batch_size
-
-    @field_validator("learning_rate")
-    @classmethod
-    def convert_learning_rate(cls, learning_rate):
-        if learning_rate and isinstance(learning_rate, str):
-            learning_rate = float(learning_rate)
-        return learning_rate

src/axolotl/utils/schemas/utils.py

@@ -1,79 +0,0 @@
-"""Utilities for Axolotl Pydantic models"""
-
-import logging
-
-LOG = logging.getLogger(__name__)
-
-
-def handle_legacy_message_fields_logic(data: dict) -> dict:
-    """
-    Handle backwards compatibility between legacy message field mapping and new property mapping system.
-
-    Previously, the config only supported mapping 'role' and 'content' fields via dedicated config options:
-    - message_field_role: Mapped to the role field
-    - message_field_content: Mapped to the content field
-
-    The new system uses message_property_mappings to support arbitrary field mappings:
-    message_property_mappings:
-        role: source_role_field
-        content: source_content_field
-        additional_field: source_field
-
-    Args:
-        data: Dictionary containing configuration data
-
-    Returns:
-        Updated dictionary with message field mappings consolidated
-
-    Raises:
-        ValueError: If there are conflicts between legacy and new mappings
-    """
-    data = data.copy()  # Create a copy to avoid modifying the original
-
-    if data.get("message_property_mappings") is None:
-        data["message_property_mappings"] = {}
-
-    # Check for conflicts and handle role
-    if "message_field_role" in data:
-        LOG.warning(
-            "message_field_role is deprecated, use message_property_mappings instead. "
-            f"Example: message_property_mappings: {{role: {data['message_field_role']}}}"
-        )
-        if (
-            "role" in data["message_property_mappings"]
-            and data["message_property_mappings"]["role"] != data["message_field_role"]
-        ):
-            raise ValueError(
-                f"Conflicting message role fields: message_field_role='{data['message_field_role']}' "
-                f"conflicts with message_property_mappings.role='{data['message_property_mappings']['role']}'"
-            )
-        data["message_property_mappings"]["role"] = data["message_field_role"] or "role"
-
-        del data["message_field_role"]
-    elif "role" not in data["message_property_mappings"]:
-        data["message_property_mappings"]["role"] = "role"
-
-    # Check for conflicts and handle content
-    if "message_field_content" in data:
-        LOG.warning(
-            "message_field_content is deprecated, use message_property_mappings instead. "
-            f"Example: message_property_mappings: {{content: {data['message_field_content']}}}"
-        )
-        if (
-            "content" in data["message_property_mappings"]
-            and data["message_property_mappings"]["content"]
-            != data["message_field_content"]
-        ):
-            raise ValueError(
-                f"Conflicting message content fields: message_field_content='{data['message_field_content']}' "
-                f"conflicts with message_property_mappings.content='{data['message_property_mappings']['content']}'"
-            )
-        data["message_property_mappings"]["content"] = (
-            data["message_field_content"] or "content"
-        )
-
-        del data["message_field_content"]
-    elif "content" not in data["message_property_mappings"]:
-        data["message_property_mappings"]["content"] = "content"
-
-    return data

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(
-                math.floor(
-                    data_loader_len * cfg.num_epochs * cfg.sequence_parallel_degree
-                )
-            )
+            total_num_steps = int(math.floor(data_loader_len * cfg.num_epochs))
 
             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(
-                len(train_dataset)
-                * cfg.num_epochs
-                * cfg.sequence_parallel_degree
-                / cfg.batch_size
-            )
+            math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
         )
     LOG.debug(f"total_num_steps: {total_num_steps}", main_process_only=True)
     return total_num_steps

styles.css

@@ -14,7 +14,7 @@
 h1 {
     font-family: var(--font-title);
     font-weight: 400;
-    font-size: 3rem;
+    font-size: 5rem;
     line-height: 1.1;
     letter-spacing: -0.05em;
     font-feature-settings: "ss01" on;
@@ -24,7 +24,7 @@ h1 {
 h2 {
     font-family: var(--font-title);
     font-weight: 500;
-    font-size: 1.5rem;
+    font-size: 2rem;
     line-height: 1.2;
     letter-spacing: -0.03em;
     font-feature-settings: "ss01" on;
@@ -35,7 +35,7 @@ h3,
 h4 {
     font-family: var(--font-body);
     font-weight: 400;
-    font-size: 1.25rem;
+    font-size: 1.5rem;
     line-height: 1.5;
     letter-spacing: -0.02em;
 }
@@ -191,87 +191,3 @@ code span.er {
     color: #5cb85c !important;
     text-decoration: none !important;
 }
-
-/* API Documentation Styling */
-
-/* Improve docstring section rendering */
-.level3 p {
-    white-space: pre-line !important;
-}
-
-/* Format docstring sections */
-.level3 p strong {
-    display: block;
-    margin-top: 1em;
-    font-weight: bold;
-    color: var(--cyan);
-}
-
-/* Add spacing after sections */
-.level3 p:has(strong) {
-    margin-bottom: 0.5em;
-}
-
-/* Format Args and Returns sections */
-p:has(code) {
-    line-height: 1.6;
-}
-
-/* Function signatures */
-.sourceCode {
-    margin-bottom: 1.5em;
-}
-
-/* Parameter tables */
-.doc-section-parameters table,
-.doc-section-returns table {
-    margin-top: 1em;
-    margin-bottom: 1.5em;
-}
-
-/* Make parameter and returns headers smaller */
-h2.anchored[data-anchor-id="parameters"],
-h2.anchored[data-anchor-id="returns"],
-.doc-section-parameters h4,
-.doc-section-returns h4 {
-    font-size: 1.25rem;
-    margin-top: 2rem;
-    margin-bottom: 1rem;
-    color: var(--lime);
-    border-bottom: 1px solid var(--lime);
-    padding-bottom: 0.3rem;
-    font-family: var(--font-body);
-    font-weight: 500;
-    letter-spacing: normal;
-}
-
-/* Style documentation tables */
-table {
-    width: 100%;
-    margin-bottom: 1.5rem;
-    border-collapse: collapse;
-}
-
-table th {
-    background-color: #1a1a1a;
-    padding: 0.5rem 1rem;
-    border-bottom: 2px solid var(--greige-600);
-    text-align: left;
-}
-
-table td {
-    padding: 0.5rem 1rem;
-    border-bottom: 1px solid var(--greige-600);
-}
-
-/* Code in table cells */
-table td code {
-    background-color: transparent !important;
-    padding: 0;
-}
-
-/* Improve spacing in parameter and return tables */
-.doc-section-parameters,
-.doc-section-returns {
-    margin-top: 1rem;
-}

tests/e2e/patched/test_sp.py

@@ -1,207 +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.utils.dict import DictDefault
-
-# Use a single patch for ring_flash_attn if it's not available
-ring_flash_attn_mock = MagicMock()
-with patch.dict("sys.modules", {"ring_flash_attn": ring_flash_attn_mock}):
-    from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group
-    from axolotl.utils.collators.batching import adjust_position_ids_for_slice
-
-
-@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.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
-
-        # Just verify that new_group was called
-        mock_new_group.assert_called()
-
-    @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
-
-
-# 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/patched/test_validation.py

@@ -11,10 +11,10 @@ from pydantic import ValidationError
 
 from axolotl.utils import is_comet_available
 from axolotl.utils.config import validate_config
+from axolotl.utils.config.models.input.v0_4_1 import AxolotlConfigWCapabilities
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
 from axolotl.utils.models import check_model_config
-from axolotl.utils.schemas.config import AxolotlConfigWCapabilities
 from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 warnings.filterwarnings("error")

tests/test_exact_deduplication.py

@@ -12,7 +12,6 @@ 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
@@ -263,7 +262,6 @@ class TestDeduplicateNonRL(unittest.TestCase):
         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,
@@ -284,7 +282,6 @@ class TestDeduplicateNonRL(unittest.TestCase):
                 "num_epochs": 1,
             }
         )
-        normalize_config(self.cfg_1)
 
     def test_prepare_dataset_with_deduplication_train(self):
         """Verify that prepare_dataset function processes the dataset correctly with deduplication."""

tests/test_validation_dataset.py

@@ -6,8 +6,8 @@ from typing import Optional
 import pytest
 
 from axolotl.utils.config import validate_config
+from axolotl.utils.config.models.input.v0_4_1 import ChatTemplate
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.schemas.datasets import ChatTemplate
 
 warnings.filterwarnings("error")
 

tests/utils/test_models.py

@@ -119,7 +119,7 @@ class TestModelsUtils:
 
     def test_message_property_mapping(self):
         """Test message property mapping configuration validation"""
-        from axolotl.utils.schemas.datasets import SFTDataset
+        from axolotl.utils.config.models.input.v0_4_1 import SFTDataset
 
         # Test legacy fields are mapped orrectly
         dataset = SFTDataset(