broken

* black, isort, flake8 -> ruff

* remove unused

* add back needed import

* fix

.bandit

@@ -1,3 +1,3 @@
 [bandit]
 exclude = tests
-skips = B101,B615
+skips = B101,B615,B102,B110

.coderabbit.yaml

@@ -12,5 +12,6 @@ reviews:
   auto_review:
     enabled: true
     drafts: false
+    auto_incremental_review: true
 chat:
   auto_reply: true

.flake8

@@ -1,5 +0,0 @@
-[flake8]
-max-line-length = 88
-
-select = C,E,F,W,B,B950
-extend-ignore = E203, E501, W503

.isort.cfg

@@ -1,4 +0,0 @@
-[settings]
-profile=black
-known_third_party=wandb,comet_ml
-known_local_folder=src,tests

.pre-commit-config.yaml

@@ -10,22 +10,12 @@ repos:
     -   id: trailing-whitespace
     -   id: no-commit-to-branch
         args: ['--branch', 'main']
--   repo: https://github.com/psf/black
-    rev: 25.1.0
+-   repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.12.9
     hooks:
-    -   id: black
--   repo: https://github.com/pycqa/isort
-    rev: 6.0.1
-    hooks:
-      - id: isort
--   repo: https://github.com/PyCQA/flake8
-    rev: 7.3.0
-    hooks:
-    - id: flake8
--   repo: https://github.com/pylint-dev/pylint
-    rev: v3.3.8
-    hooks:
-    - id: pylint
+    -   id: ruff
+        args: [--fix]
+    -   id: ruff-format
 -   repo: https://github.com/pre-commit/mirrors-mypy
     rev: v1.17.1
     hooks:

.pylintrc

@@ -1,15 +0,0 @@
-[MASTER]
-init-hook="from pylint.config import find_default_config_files; import sys; sys.path.append(next(find_default_config_files()).parent.as_posix())"
-
-[TYPECHECK]
-
-# List of members which are set dynamically and missed by Pylint inference
-# system, and so shouldn't trigger E1101 when accessed.
-generated-members=numpy.*, torch.*
-
-
-[pylint.messages_control]
-disable=missing-function-docstring, line-too-long, import-error,
-    too-many-arguments, too-many-locals, too-many-statements, too-many-branches, too-few-public-methods,
-    too-many-instance-attributes, fixme, import-outside-toplevel, logging-fstring-interpolation,
-    too-many-positional-arguments, possibly-used-before-assignment

cicd/multigpu.py

@@ -2,8 +2,6 @@
 modal application to run axolotl gpu tests in Modal
 """
 
-# pylint: disable=duplicate-code
-
 import os
 import pathlib
 import tempfile
@@ -63,7 +61,7 @@ def run_cmd(cmd: str, run_folder: str):
 
     # Propagate errors from subprocess.
     if exit_code := subprocess.call(cmd.split(), cwd=run_folder):  # nosec
-        exit(exit_code)  # pylint: disable=consider-using-sys-exit
+        exit(exit_code)
 
 
 @app.function(

cicd/single_gpu.py

@@ -1,7 +1,5 @@
 """Modal app to run axolotl GPU tests"""
 
-# pylint: disable=duplicate-code
-
 import os
 import pathlib
 import tempfile
@@ -70,4 +68,4 @@ def run_cmd(cmd: str, run_folder: str):
 
     # Propagate errors from subprocess.
     if exit_code := subprocess.call(cmd.split(), cwd=run_folder, env=sp_env):  # nosec
-        exit(exit_code)  # pylint: disable=consider-using-sys-exit
+        exit(exit_code)

docs/scripts/generate_config_docs.py

@@ -47,7 +47,6 @@ class QuartoGenerator:
         """Check if a type is a Pydantic BaseModel."""
         return inspect.isclass(type_obj) and issubclass(type_obj, BaseModel)
 
-    # pylint: disable=too-many-return-statements
     def _extract_nested_type(self, field_type) -> Any:
         """Extract the actual type from complex type annotations."""
         # Handle Annotated types (Python 3.9+)
@@ -124,7 +123,6 @@ class QuartoGenerator:
 
         return field_type
 
-    # pylint: disable=too-many-return-statements
     def _extract_all_pydantic_models_from_type(
         self, field_type
     ) -> list[type[BaseModel]]:
@@ -318,7 +316,6 @@ class QuartoGenerator:
 
         return all_groups
 
-    # pylint: disable=too-many-return-statements
     def _extract_field_groups_from_source(
         self, model_class: type[BaseModel]
     ) -> list[dict]:
@@ -503,7 +500,7 @@ class QuartoGenerator:
                     nested_schema = nested_model.model_json_schema()
                     nested_properties = nested_schema.get("properties", {})
                     nested_required = nested_schema.get("required", [])
-                except Exception:  # pylint: disable=broad-exception-caught
+                except Exception:
                     # Fallback: use model fields directly
                     nested_properties = {}
                     nested_required = []
@@ -607,7 +604,7 @@ class QuartoGenerator:
             schema = model_class.model_json_schema()
             properties = schema.get("properties", {})
             required = schema.get("required", [])
-        except Exception as e:  # pylint: disable=broad-exception-caught
+        except Exception as e:
             print(
                 f"Warning: Could not generate JSON schema ({e}). Using model fields instead."
             )

examples/gpt-oss/README.md

@@ -41,6 +41,12 @@ model, and final model output, you may need at least 3TB of free disk space to k
 axolotl train examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml
 ```
 
+To simplify fine-tuning across 2 nodes × 8x H100 (80GB) GPUs, we've partnered with [Baseten](https://baseten.co) to showcase multi-node
+training of the 120B model using Baseten Truss. You can read more about this recipe on
+[Baseten's blog](https://www.baseten.co/blog/how-to-fine-tune-gpt-oss-120b-with-baseten-and-axolotl/). The recipe can
+be found on their
+[GitHub](https://github.com/basetenlabs/ml-cookbook/tree/main/examples/oss-gpt-120b-axolotl/training).
+
 ERRATA: Transformers saves the model Architecture prefixed with `FSDP` which needs to be manually renamed in `config.json`.
 See https://github.com/huggingface/transformers/pull/40207 for the status of this issue.
 
@@ -61,9 +67,23 @@ mv ./outputs/gpt-oss-out/merged/* ./outputs/gpt-oss-out/
 
 ### Inferencing your fine-tuned model
 
+#### vLLM
+
 GPT-OSS support in vLLM does not exist in a stable release yet. See https://x.com/MaziyarPanahi/status/1955741905515323425
 for more information about using a special vllm-openai docker image for inferencing with vLLM.
 
+Optionally, vLLM can be installed from nightly:
+
+```bash
+pip install --no-build-isolation --pre -U vllm --extra-index-url https://wheels.vllm.ai/nightly
+```
+and the vLLM server can be started with the following command (modify `--tensor-parallel-size 8` to match your environment):
+```bash
+vllm serve ./outputs/gpt-oss-out/ --served-model-name axolotl/gpt-oss-20b --host 0.0.0.0 --port 8888  --tensor-parallel-size 8
+```
+
+#### SGLang
+
 SGLang has 0-day support in main, see https://github.com/sgl-project/sglang/issues/8833 for infomation on installing
 SGLang from source. Once you've installed SGLang, run the following command to launch a SGLang server:
 

examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml

@@ -44,7 +44,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-fft-deepspeed-zero3.yaml

@@ -40,7 +40,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-fft-fsdp2-offload.yaml

@@ -15,7 +15,7 @@ datasets:
     field_thinking: thinking
     template_thinking_key: thinking
 
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path: ./outputs/last_run_prepared
 val_set_size: 0
 output_dir: ./outputs/gpt-oss-out/
 
@@ -41,7 +41,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-fft-fsdp2.yaml

@@ -15,7 +15,7 @@ datasets:
     field_thinking: thinking
     template_thinking_key: thinking
 
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path: ./outputs/last_run_prepared
 val_set_size: 0
 output_dir: ./outputs/gpt-oss-out/
 
@@ -40,7 +40,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-sft-lora-singlegpu.yaml

@@ -53,7 +53,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/llama-3/diffusion-3.2-1b-pretrain.yaml

@@ -1,57 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-pretraining_dataset:
-  - path: wikitext
-    name: wikitext-103-raw-v1
-    type: completion
-    field: text
-
-plugins:
-  - diffusion.DiffusionPlugin
-noise_schedule: cosine
-min_mask_ratio: 0.15
-max_mask_ratio: 0.85
-eps: 5e-4
-importance_weighting: true
-mask_token_id: 128002
-generate_samples: true
-generation_interval: 10
-
-output_dir: ./outputs/model-out
-
-sequence_len: 512
-sample_packing: true
-
-gradient_accumulation_steps: 8
-micro_batch_size: 4
-max_steps: 10000
-
-optimizer: adamw_8bit
-lr_scheduler: cosine
-learning_rate: 3e-4
-
-bf16: auto
-tf32: true
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-sdp_attention: true
-
-warmup_steps: 1000
-
-save_strategy: steps
-save_steps: 1000
-
-special_tokens:
-  pad_token: "<|end_of_text|>"
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/llama-3/diffusion-3.2-1b-sft.yaml

@@ -1,58 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-datasets:
-  - path: teknium/GPT4-LLM-Cleaned
-    type: alpaca
-val_set_size: 0.05
-
-plugins:
-  - diffusion.DiffusionPlugin
-noise_schedule: cosine
-min_mask_ratio: 0.1
-max_mask_ratio: 0.9
-num_diffusion_steps: 128
-eps: 1e-3
-importance_weighting: true
-mask_token_id: 128002
-
-output_dir: ./outputs/model-out
-
-sequence_len: 512
-sample_packing: true
-eval_sample_packing: true
-
-gradient_accumulation_steps: 4
-micro_batch_size: 4
-num_epochs: 1
-
-optimizer: adamw_8bit
-lr_scheduler: cosine
-learning_rate: 1e-5
-
-bf16: auto
-tf32: true
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-sdp_attention: true
-
-warmup_steps: 1000
-
-save_strategy: steps
-eval_strategy: steps
-save_steps: 500
-eval_steps: 500
-
-special_tokens:
-  pad_token: "<|end_of_text|>"
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

pyproject.toml

@@ -26,3 +26,34 @@ include-package-data = true
 
 [tool.setuptools.cmdclass]
 build_py = "setuptools_axolotl_dynamic_dependencies.BuildPyCommand"
+
+[tool.ruff]
+line-length = 88
+target-version = "py310"
+
+[tool.ruff.lint]
+select = ["E", "F", "W", "C90", "B"]
+ignore = [
+    "E203",  # Whitespace before ':'
+    "E501",  # Line too long
+    "C901",  # Too complex
+    "B019",  # Use of functools.cache on methods
+    "E722",  # Bare except
+    "F821",  # Undefined name (for dynamic exec)
+]
+
+[tool.ruff.lint.isort]
+known-third-party = ["wandb", "comet_ml"]
+known-local-folder = ["src", "tests"]
+# Black-compatible isort settings
+force-single-line = false
+combine-as-imports = true
+split-on-trailing-comma = true
+
+[tool.ruff.format]
+# Use black's formatting style exactly
+quote-style = "double"
+indent-style = "space"
+skip-magic-trailing-comma = false
+line-ending = "auto"
+docstring-code-format = false

requirements.txt

@@ -13,8 +13,8 @@ liger-kernel==0.6.1
 packaging==23.2
 
 huggingface_hub>=0.33.0
-peft==0.17.0
-transformers==4.55.2
+peft>=0.17.0
+transformers==4.55.3
 tokenizers>=0.21.1
 accelerate==1.10.0
 datasets==4.0.0

scripts/chat_datasets.py

@@ -27,7 +27,7 @@ def parse_dataset(dataset=None, split="train"):
             break
     if not field_messages:
         raise ValueError(
-            f'No conversation field found in dataset: {", ".join(feature_keys)}'
+            f"No conversation field found in dataset: {', '.join(feature_keys)}"
         )
     ds_cfg["field_messages"] = field_messages
 
@@ -40,7 +40,7 @@ def parse_dataset(dataset=None, split="train"):
             break
     if not message_property_mappings["role"]:
         raise ValueError(
-            f'No role field found in messages: {", ".join(message_fields)}'
+            f"No role field found in messages: {', '.join(message_fields)}"
         )
 
     for key in ["content", "text", "value"]:
@@ -49,7 +49,7 @@ def parse_dataset(dataset=None, split="train"):
             break
     if not message_property_mappings["content"]:
         raise ValueError(
-            f'No content field found in messages: {", ".join(message_fields)}'
+            f"No content field found in messages: {', '.join(message_fields)}"
         )
     ds_cfg["message_property_mappings"] = message_property_mappings
 

scripts/unsloth_install.py

@@ -1,11 +1,10 @@
 # noqa
-# pylint: skip-file
 import sys
 
 try:
     import torch
-except ImportError:
-    raise ImportError("Install torch via `pip install torch`")
+except ImportError as error:
+    raise ImportError("Install torch via `pip install torch`") from error
 from packaging.version import Version as V
 
 use_uv = "--uv" in sys.argv[1:]

setup.py

@@ -118,9 +118,9 @@ def get_package_version():
 
 
 extras_require = {
-    "flash-attn": ["flash-attn==2.8.2"],
+    "flash-attn": ["flash-attn==2.8.3"],
     "ring-flash-attn": [
-        "flash-attn==2.8.2",
+        "flash-attn==2.8.3",
         "ring-flash-attn>=0.1.7",
         "yunchang==0.6.0",
     ],

src/axolotl/cli/art.py

@@ -22,7 +22,7 @@ HAS_PRINTED_LOGO = False
 def print_axolotl_text_art():
     """Prints axolotl ASCII art."""
 
-    global HAS_PRINTED_LOGO  # pylint: disable=global-statement
+    global HAS_PRINTED_LOGO
     if HAS_PRINTED_LOGO:
         return
     if is_main_process():

src/axolotl/cli/cloud/modal_.py

@@ -41,7 +41,7 @@ def run_cmd(cmd: str, run_folder: str, volumes=None):
     if exit_code := subprocess.call(  # nosec B603
         cmd.split(), cwd=run_folder, env=new_env
     ):
-        exit(exit_code)  # pylint: disable=consider-using-sys-exit
+        exit(exit_code)
 
     # Commit writes to volume.
     if volumes:
@@ -82,7 +82,7 @@ class ModalCloud(Cloud):
         return res
 
     def get_image(self):
-        docker_tag = "main-py3.11-cu124-2.6.0"
+        docker_tag = "main-py3.11-cu126-2.7.1"
         if self.config.docker_tag:
             docker_tag = self.config.docker_tag
         docker_image = f"axolotlai/axolotl:{docker_tag}"
@@ -130,7 +130,6 @@ class ModalCloud(Cloud):
         res = []
         if self.config.secrets:
             for key in self.config.get("secrets", []):
-                # pylint: disable=duplicate-code
                 if isinstance(key, str):
                     if val := os.environ.get(key, ""):
                         res.append(modal.Secret.from_dict({key: val}))
@@ -177,8 +176,8 @@ class ModalCloud(Cloud):
             with self.app.run(detach=True):
                 modal_fn.remote(
                     config_yaml,
-                    volumes={k: v[0] for k, v in self.volumes.items()},
                     *args,
+                    volumes={k: v[0] for k, v in self.volumes.items()},
                     **kwargs,
                 )
 
@@ -187,7 +186,7 @@ class ModalCloud(Cloud):
             return int(self.config.timeout)
         return 60 * 60 * 24  # 24 hours
 
-    def get_train_gpu(self):  # pylint: disable=too-many-return-statements
+    def get_train_gpu(self):
         count = self.config.gpu_count or 1
         family = self.config.gpu.lower() or "l40s"
 
@@ -200,7 +199,7 @@ class ModalCloud(Cloud):
         if family in ["a10", "a10g"]:
             return modal.gpu.A10G(count=count)
         if family == "h100":
-            return modal.gpu.H100(count=count)
+            return f"H100:{count}"
         if family == "t4":
             return modal.gpu.T4(count=count)
         if family == "l4":
@@ -277,7 +276,7 @@ def _train(
     launcher: Literal["accelerate", "torchrun", "python"] = "accelerate",
     launcher_args: list[str] | None = None,
     volumes=None,
-    **kwargs,  # pylint: disable=unused-argument
+    **kwargs,
 ):
     Path("/workspace/mounts").mkdir(parents=True, exist_ok=True)
     with open("/workspace/mounts/config.yaml", "w", encoding="utf-8") as f_out:

src/axolotl/cli/config.py

@@ -210,7 +210,7 @@ def load_cfg(
     try:
         device_props = torch.cuda.get_device_properties("cuda")
         gpu_version = "sm_" + str(device_props.major) + str(device_props.minor)
-    except:  # pylint: disable=bare-except # noqa: E722
+    except:
         gpu_version = None
 
     prepare_plugins(cfg)

src/axolotl/cli/evaluate.py

@@ -28,7 +28,7 @@ def do_evaluate(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: CLI arguments.
     """
-    # pylint: disable=duplicate-code
+
     check_accelerate_default_config()
     if int(os.getenv("LOCAL_RANK", "0")) == 0:
         check_user_token()
@@ -49,7 +49,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
         config: Path to `axolotl` config YAML file.
         kwargs: Additional keyword arguments to override config file values.
     """
-    # pylint: disable=duplicate-code
+
     parsed_cfg = load_cfg(config, **kwargs)
     parser = HfArgumentParser(TrainerCliArgs)
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(

src/axolotl/cli/inference.py

@@ -35,7 +35,7 @@ def get_multi_line_input() -> str:
 
     instruction = ""
     for line in sys.stdin:
-        instruction += line  # pylint: disable=consider-using-join
+        instruction += line
 
     return instruction
 
@@ -64,7 +64,7 @@ def do_inference(
             importlib.import_module("axolotl.prompters"), prompter
         )
     elif cfg.chat_template:
-        chat_template_str = get_chat_template(cfg.chat_template)
+        chat_template_str = get_chat_template(cfg.chat_template, tokenizer=tokenizer)
     elif cfg.datasets[0].type == "chat_template":
         chat_template_str = get_chat_template_from_config(
             cfg=cfg, ds_cfg=cfg.datasets[0], tokenizer=tokenizer
@@ -167,7 +167,6 @@ def do_inference_gradio(
         if not instruction:
             return
         if prompter_module:
-            # pylint: disable=stop-iteration-return
             prompt: str = next(
                 prompter_module().build_prompt(instruction=instruction.strip("\n"))
             )
@@ -252,7 +251,7 @@ def do_cli(
         config: Path to `axolotl` config YAML file.
         kwargs: Additional keyword arguments to override config file values.
     """
-    # pylint: disable=duplicate-code
+
     parsed_cfg = load_cfg(config, inference=True, rl=None, **kwargs)
     parsed_cfg.sample_packing = False
     parser = transformers.HfArgumentParser(InferenceCliArgs)

src/axolotl/cli/main.py

@@ -1,7 +1,5 @@
 """Click CLI definitions for various axolotl commands."""
 
-# pylint: disable=redefined-outer-name
-
 import os
 import subprocess  # nosec B404
 from typing import Literal, Optional

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -32,7 +32,7 @@ LOG = get_logger(__name__)
 class BFloat16CastPlanner(_EmptyStateDictLoadPlanner):
     """A custom planner to cast tensors to bfloat16 on the fly during loading."""
 
-    def commit_tensor(self, read_item, tensor):  # pylint: disable=unused-argument
+    def commit_tensor(self, read_item, tensor):
         tensor.copy_(tensor.to(torch.bfloat16))
 
 
@@ -59,10 +59,10 @@ def _distributed_checkpoint_to_merged_weights(
     state_dict: Dict = {}
     save_path_ = Path(save_path)
     save_path_.mkdir(exist_ok=True)
-    dist_cp_format_utils._load_state_dict(  # pylint: disable=protected-access
+    dist_cp_format_utils._load_state_dict(
         state_dict,
         storage_reader=dist_cp.FileSystemReader(checkpoint_dir),
-        planner=BFloat16CastPlanner(),  # pylint: disable=protected-access
+        planner=BFloat16CastPlanner(),
         no_dist=True,
     )
 
@@ -191,7 +191,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         config: Path to `axolotl` config YAML file.
         kwargs: Additional keyword arguments to override config file values.
     """
-    # pylint: disable=duplicate-code
+
     parsed_cfg = load_cfg(config, **kwargs)
 
     fsdp_dir = Path(parsed_cfg.output_dir) / "pytorch_model_fsdp_0"

src/axolotl/cli/preprocess.py

@@ -73,7 +73,7 @@ def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
                     AutoModelForCausalLM.from_pretrained(
                         model_name, trust_remote_code=True
                     )
-                except Exception as exc:  # pylint: disable=broad-exception-caught,unused-variable  # nosec B110  # noqa F841
+                except Exception:  # nosec B110
                     pass
                 # fmt: on
 
@@ -95,9 +95,10 @@ def do_cli(
         config: Path to `axolotl` config YAML file.
         kwargs: Additional keyword arguments to override config file values.
     """
-    # pylint: disable=duplicate-code
+
     os.environ["AXOLOTL_IS_PREPROCESS"] = "1"
-    parsed_cfg = load_cfg(config, **kwargs)
+    is_preprocess = kwargs.pop("is_preprocess", True)
+    parsed_cfg = load_cfg(config, is_preprocess=is_preprocess, **kwargs)
     parsed_cfg.is_preprocess = True
     parser = transformers.HfArgumentParser(PreprocessCliArgs)
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(

src/axolotl/cli/train.py

@@ -59,7 +59,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         config: Path to `axolotl` config YAML file.
         kwargs: Additional keyword arguments to override config file values.
     """
-    # pylint: disable=duplicate-code
+
     parsed_cfg = load_cfg(config, **kwargs)
     parser = HfArgumentParser(TrainerCliArgs)
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(

src/axolotl/cli/utils/args.py

@@ -65,7 +65,7 @@ def add_options_from_dataclass(config_class: Type[Any]) -> Callable:
         for field in reversed(dataclasses.fields(config_class)):
             field_type = _strip_optional_type(field.type)
 
-            if field_type == bool:
+            if field_type is bool:
                 field_name = field.name.replace("_", "-")
                 option_name = f"--{field_name}/--no-{field_name}"
                 function = click.option(
@@ -103,7 +103,7 @@ def add_options_from_config(config_class: Type[BaseModel]) -> Callable:
         for name, field in reversed(config_class.model_fields.items()):
             field_type = _strip_optional_type(field.annotation)
 
-            if field_type == bool:
+            if field_type is bool:
                 field_name = name.replace("_", "-")
                 option_name = f"--{field_name}/--no-{field_name}"
                 function = click.option(

src/axolotl/cli/utils/sweeps.py

@@ -3,11 +3,12 @@
 import random
 from copy import deepcopy
 from itertools import product
+from typing import Any
 
 
 def generate_sweep_configs(
     base_config: dict[str, list], sweeps_config: dict[str, list]
-) -> list[dict[str, list]]:
+) -> list[dict[str, Any]]:
     """
     Recursively generates all possible configurations by applying sweeps to the base config.
 
@@ -48,7 +49,10 @@ def generate_sweep_configs(
                 new_config = {}
                 # new_config = deepcopy(base_config)
                 # Combine regular parameters with paired parameters
-                full_combo = {**dict(zip(param_names, reg_combo)), **paired_set}
+                full_combo = {
+                    **dict(zip(param_names, reg_combo, strict=False)),
+                    **paired_set,
+                }
                 for param_name, param_value in full_combo.items():
                     new_config[param_name] = param_value
                 print(new_config)
@@ -57,7 +61,7 @@ def generate_sweep_configs(
             # If no paired values, just use regular combinations
             # new_config = deepcopy(base_config)
             new_config = {}
-            for param_name, param_value in zip(param_names, reg_combo):
+            for param_name, param_value in zip(param_names, reg_combo, strict=False):
                 new_config[param_name] = param_value
             print(new_config)
             all_combinations.append(new_config)

src/axolotl/cli/utils/train.py

@@ -4,6 +4,7 @@ import os
 import subprocess  # nosec
 import sys
 import tempfile
+from pathlib import Path
 from typing import Any, Iterator, Literal
 
 import yaml
@@ -88,8 +89,12 @@ def generate_config_files(config: str, sweep: str | None) -> Iterator[tuple[str,
     # Generate all possible configurations
     permutations = generate_sweep_configs(base_config, sweep_config)
     is_group = len(permutations) > 1
-    for permutation in permutations:
-        # pylint: disable=consider-using-with
+    base_output_dir = base_config.get("output_dir", "./model-out")
+    for idx, permutation in enumerate(permutations, start=1):
+        permutation_dir = Path(permutation.get("output_dir", base_output_dir))
+        permutation_id = f"sweep{idx:04d}"
+        permutation["output_dir"] = str(permutation_dir / permutation_id)
+
         temp_file = tempfile.NamedTemporaryFile(
             mode="w",
             suffix=".yaml",

src/axolotl/cli/vllm_serve.py

@@ -39,7 +39,7 @@ def do_vllm_serve(
     model = cfg.base_model
 
     serve_module = cli_args.get("serve_module", "trl.scripts.vllm_serve")
-    vllm_serve_main = getattr(__import__(serve_module, fromlist=["main"]), "main")
+    vllm_serve_main = __import__(serve_module, fromlist=["main"]).main
     tensor_parallel_size = 1
     data_parallel_size = 1
 
@@ -68,7 +68,6 @@ def do_vllm_serve(
         cli_args.get("enable_reasoning") or cfg.vllm.enable_reasoning or False
     )
 
-    # pylint: disable=unexpected-keyword-arg
     vllm_script_args = AxolotlScriptArguments(
         model=model,
         tensor_parallel_size=tensor_parallel_size,

src/axolotl/common/datasets.py

@@ -6,7 +6,7 @@ from dataclasses import dataclass
 
 from datasets import Dataset
 
-import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
+import axolotl.monkeypatch.data.batch_dataset_fetcher  # noqa: F401
 from axolotl.cli.args import PreprocessCliArgs, TrainerCliArgs
 from axolotl.loaders import load_processor, load_tokenizer
 from axolotl.utils.data import prepare_datasets, prepare_preference_datasets

src/axolotl/convert.py

@@ -67,9 +67,7 @@ class JsonToJsonlConverter:
         self.json_parser = json_parser
         self.jsonl_serializer = jsonl_serializer
 
-    def convert(
-        self, input_file_path, output_file_path
-    ):  # pylint: disable=unused-argument
+    def convert(self, input_file_path, output_file_path):
         content = self.file_reader.read(input_file_path)
         data = self.json_parser.parse(content)
         # data = [r for r in data if r["conversations"]]  # vicuna cleaned has rows with empty conversations

src/axolotl/core/attention/flex_block_mask.py

@@ -84,9 +84,7 @@ def create_causal_mask(
     batch_size, dtype = input_embeds.shape[0], input_embeds.dtype
     if attention_mask is not None:
 
-        def causal_doc_mask_mod(
-            batch_idx, head_idx, q_idx, kv_idx
-        ):  # pylint: disable=unused-argument
+        def causal_doc_mask_mod(batch_idx, head_idx, q_idx, kv_idx):
             """
             Defines the logic of a block causal mask by combining both a standard causal mask
             and a block diagonal document mask.
@@ -103,9 +101,7 @@ def create_causal_mask(
         mask_factory_function = causal_doc_mask_mod
     else:
         mask_factory_function = causal_mask_function
-    mask_interface = ALL_MASK_ATTENTION_FUNCTIONS[
-        config._attn_implementation  # pylint: disable=protected-access
-    ]
+    mask_interface = ALL_MASK_ATTENTION_FUNCTIONS[config._attn_implementation]
 
     # Do not allow skip if we are compiling (this is to match BC)
     allow_is_causal_skip = (

src/axolotl/core/builders/base.py

@@ -44,7 +44,7 @@ from axolotl.utils.schemas.enums import CustomSupportedOptimizers
 LOG = logging.getLogger(__name__)
 
 with suppress(ImportError):
-    import torch._dynamo  # pylint: disable=ungrouped-imports
+    import torch._dynamo
 
 
 class TrainerBuilderBase(abc.ABC):
@@ -260,14 +260,14 @@ class TrainerBuilderBase(abc.ABC):
                 adam_kwargs["eps"] = training_args_kwargs.get("adam_epsilon")
 
             if self.cfg.optimizer == "muon":
-                from axolotl.contribs.mit.muon import (  # pylint: disable=no-name-in-module
+                from axolotl.contribs.mit.muon import (
                     MuonOptimizerFactory,
                 )
 
                 optimizer_cls = MuonOptimizerFactory
                 optimizer_kwargs.update(adam_kwargs)
             elif self.cfg.optimizer == "dion":
-                from axolotl.contribs.mit.dion import (  # pylint: disable=no-name-in-module
+                from axolotl.contribs.mit.dion import (
                     DionOptimizerFactory,
                 )
 
@@ -414,12 +414,8 @@ class TrainerBuilderBase(abc.ABC):
 
     def _configure_torch_compile(self, training_args_kwargs: dict):
         if self.cfg.torch_compile and getattr(torch, "_dynamo", None):
-            torch._dynamo.config.suppress_errors = (  # pylint: disable=protected-access
-                True
-            )
-            torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
-                256
-            )
+            torch._dynamo.config.suppress_errors = True
+            torch._dynamo.config.accumulated_cache_size_limit = 256
             training_args_kwargs["torch_compile"] = self.cfg.torch_compile
             if self.cfg.torch_compile_backend:
                 training_args_kwargs["torch_compile_backend"] = (

src/axolotl/core/builders/causal.py

@@ -10,7 +10,6 @@ import transformers
 from transformers import (
     DataCollatorWithFlattening,
     EarlyStoppingCallback,
-    Trainer,
 )
 from trl.trainer.utils import RewardDataCollatorWithPadding
 
@@ -345,16 +344,14 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_args_cls = AxolotlPRMConfig
         else:
             training_args_cls = AxolotlTrainingArguments
-        training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
+        training_args = training_args_cls(
             **training_arguments_kwargs,
         )
         training_args = self.hook_post_create_training_args(training_args)
 
         # unset run_name so wandb sets up experiment names
         if self.cfg.use_wandb and training_args.run_name == training_args.output_dir:
-            training_args.run_name = (  # pylint: disable=attribute-defined-outside-init
-                None
-            )
+            training_args.run_name = None
 
         data_collator_kwargs = {
             "padding": True,  # True/"longest" is the default
@@ -386,11 +383,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 **data_collator_kwargs,
             )
         sig = inspect.signature(trainer_cls)
-        if "processing_class" in sig.parameters or issubclass(trainer_cls, Trainer):
+        if "processing_class" in sig.parameters:
             trainer_kwargs["processing_class"] = self.tokenizer
         elif "tokenizer" in sig.parameters:
             trainer_kwargs["tokenizer"] = self.tokenizer
-
         if (
             trainer_cls not in [AxolotlRewardTrainer, AxolotlPRMTrainer]
             and self.cfg.datasets is not None

src/axolotl/core/builders/rl.py

@@ -168,16 +168,14 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if plugin_training_args:
                 training_args_kwargs.update(plugin_training_args)
 
-        training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
+        training_args = training_args_cls(
             logging_first_step=True,
             **training_args_kwargs,
         )
 
         # unset run_name so wandb sets up experiment names
         if self.cfg.use_wandb and training_args.run_name == training_args.output_dir:
-            training_args.run_name = (  # pylint: disable=attribute-defined-outside-init
-                None
-            )
+            training_args.run_name = None
 
         return training_args, trainer_kwargs
 

src/axolotl/core/chat/format/chatml.py

@@ -10,7 +10,7 @@ from .shared import wrap_tools
 
 def format_message(
     message: Messages,
-    message_index: Optional[int] = None,  # pylint: disable=unused-argument
+    message_index: Optional[int] = None,
 ) -> Messages:
     if message.is_chat_formatted:
         return message

src/axolotl/core/chat/messages.py

@@ -15,11 +15,11 @@ class MessageRoles(str, Enum):
     Message roles for the system, user, assistant, and tools
     """
 
-    system = "system"  # pylint: disable=invalid-name
-    user = "user"  # pylint: disable=invalid-name
-    assistant = "assistant"  # pylint: disable=invalid-name
-    tool = "tool"  # pylint: disable=invalid-name
-    ipython = (  # pylint: disable=invalid-name
+    system = "system"
+    user = "user"
+    assistant = "assistant"
+    tool = "tool"
+    ipython = (
         # for responses from builtin tools
         "ipython"
     )
@@ -30,12 +30,12 @@ class MessageContentTypes(str, Enum):
     Message content types for text, image, audio, tool calls, and tool responses
     """
 
-    special_token = "special_token"  # pylint: disable=invalid-name  # nosec B105
-    text = "text"  # pylint: disable=invalid-name
-    image = "image"  # pylint: disable=invalid-name
-    audio = "audio"  # pylint: disable=invalid-name
-    tool_call = "tool_call"  # pylint: disable=invalid-name  # to differentiate regular responses from tool calls from the assistant
-    tool_response = "tool_response"  # pylint: disable=invalid-name
+    special_token = "special_token"  # nosec B105
+    text = "text"
+    image = "image"
+    audio = "audio"
+    tool_call = "tool_call"
+    tool_response = "tool_response"
 
 
 class SpecialToken(str, Enum):
@@ -43,8 +43,8 @@ class SpecialToken(str, Enum):
     Special tokens for beginning of string and end of string
     """
 
-    bos_token = "bos_token"  # pylint: disable=invalid-name  # nosec B105
-    eos_token = "eos_token"  # pylint: disable=invalid-name  # nosec B105
+    bos_token = "bos_token"  # nosec B105
+    eos_token = "eos_token"  # nosec B105
 
 
 class ToolCallFunction(BaseModel):
@@ -73,7 +73,7 @@ class ToolCallContents(BaseModel):
 
     name: str
     arguments: dict[str, Union[str, int]]
-    id: Optional[str] = None  # pylint: disable=invalid-name
+    id: Optional[str] = None
 
     def __str__(self) -> str:
         data = {"name": self.name, "arguments": self.arguments}
@@ -89,7 +89,7 @@ class ToolResponseContents(BaseModel):
 
     name: str
     content: Union[str, dict[str, Union[str, int, float]]]
-    id: Optional[str] = None  # pylint: disable=invalid-name
+    id: Optional[str] = None
 
     def __str__(self) -> str:
         data = {"name": self.name, "content": self.content}

src/axolotl/core/datasets/transforms/chat_builder.py

@@ -1,23 +1,17 @@
 """
-This module contains a function that builds a transform that takes a row from the dataset and converts it to a Chat.
+This module contains a function that builds a transform that takes a row from the
+dataset and converts it to a Chat.
 """
 
-from typing import Any, Mapping, Union
+from typing import Any, Mapping
 
 
-def chat_message_transform_builder(  # pylint: disable=dangerous-default-value
+def chat_message_transform_builder(
     train_on_inputs=False,
     conversations_field: str = "conversations",
-    message_field_role: Union[str, list[str]] = ["role", "from"],  # commonly "role"
-    message_field_content: Union[str, list[str]] = [
-        "value",
-        "text",
-        "content",
-    ],  # commonly "content"
-    message_field_training: Union[str, list[str]] = [
-        "train",
-        "weight",
-    ],  # commonly "weight"
+    message_field_role: str | list[str] | None = None,  # commonly "role"
+    message_field_content: str | list[str] | None = None,  # commonly "content"
+    message_field_training: str | list[str] | None = None,  # commonly "weight"
 ):
     """Builds a transform that takes a row from the dataset and converts it to a Chat
 
@@ -39,6 +33,12 @@ def chat_message_transform_builder(  # pylint: disable=dangerous-default-value
             A function that takes a list of conversations and returns a list of messages.
     """
 
+    if message_field_training is None:
+        message_field_training = ["train", "weight"]
+    if message_field_content is None:
+        message_field_content = ["value", "text", "content"]
+    if message_field_role is None:
+        message_field_role = ["role", "from"]
     message_field_role = (
         [message_field_role]
         if isinstance(message_field_role, str)

src/axolotl/core/trainers/__init__.py

@@ -1,6 +1,5 @@
 """Init for axolotl.core.trainers"""
 
-# pylint: disable=unused-import
 # flake8: noqa
 
 from .base import AxolotlTrainer

src/axolotl/core/trainers/base.py

@@ -1,7 +1,5 @@
 """Module for customized trainers"""
 
-# pylint: disable=too-many-lines
-
 from __future__ import annotations
 
 import os
@@ -82,9 +80,7 @@ class AxolotlTrainer(
         super().__init__(*_args, **kwargs)
 
         self.train_data_collator = self.data_collator
-        self._stored_metrics = defaultdict(
-            lambda: defaultdict(lambda: {"values": [], "reduction": "mean"})
-        )
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
         if self.args.orpo_alpha:
             self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
 
@@ -287,9 +283,9 @@ class AxolotlTrainer(
         # fmt: off
         if dataloader_key is not None and self.args.dataloader_persistent_workers:
             if hasattr(self, "_eval_dataloaders"):
-                self._eval_dataloaders[dataloader_key] = dataloader  # type: ignore  # pylint: disable=access-member-before-definition
+                self._eval_dataloaders[dataloader_key] = dataloader  # type: ignore
             else:
-                self._eval_dataloaders = {dataloader_key: dataloader}  # pylint: disable=attribute-defined-outside-init
+                self._eval_dataloaders = {dataloader_key: dataloader}
         # fmt: on
 
         return self.accelerator.prepare(dataloader)
@@ -445,7 +441,7 @@ class AxolotlTrainer(
         model,
         inputs,
         return_outputs=False,
-        num_items_in_batch=None,  # pylint: disable=unused-argument
+        num_items_in_batch=None,
     ):
         concat_inputs = AxolotlTrainer.orpo_concatenate_inputs(
             inputs,
@@ -526,9 +522,7 @@ class AxolotlTrainer(
         accelerator_config = self.args.accelerator_config.to_dict()
         use_configured_state = accelerator_config.get("use_configured_state", False)
         if not use_configured_state:
-            AcceleratorState._reset_state(  # pylint: disable=protected-access
-                reset_partial_state=True
-            )
+            AcceleratorState._reset_state(reset_partial_state=True)
 
         super().create_accelerator_and_postprocess()
 
@@ -542,7 +536,6 @@ class AxolotlTrainer(
             ):
                 self.accelerator.state.fsdp_plugin.limit_all_gathers = True
 
-    # pylint: disable=unused-argument
     def additional_accelerator_args(
         self, fp8: bool = False, enable_fsdp_float8_all_gather: bool = False, **kwargs
     ) -> dict[str, Any]:
@@ -575,26 +568,9 @@ class AxolotlTrainer(
         """
         # logs either has 'loss' or 'eval_loss'
         train_eval = "train" if "loss" in logs else "eval"
-
-        # Add reduced stored metrics to logs
-        for key, metric_data in self._stored_metrics[train_eval].items():
-            values = torch.tensor(metric_data["values"])
-            reduction_type = metric_data["reduction"]
-
-            if reduction_type == "mean":
-                logs[key] = values.mean().item()
-            elif reduction_type == "min":
-                logs[key] = values.min().item()
-            elif reduction_type == "max":
-                logs[key] = values.max().item()
-            elif reduction_type == "sum":
-                logs[key] = values.sum().item()
-            else:
-                raise NotImplementedError(
-                    "Metric reduction must be one of [mean, min, max, sum]"
-                )
-
-            logs[key] = round(logs[key], 4)
+        # Add averaged stored metrics to logs
+        for key, metrics in self._stored_metrics[train_eval].items():
+            logs[key] = torch.tensor(metrics).mean().item()
 
         if is_main_process():
             # Add memory usage
@@ -611,27 +587,10 @@ class AxolotlTrainer(
         return super().log(logs, start_time)
 
     def store_metrics(
-        self,
-        metrics: dict[str, float] | dict[str, tuple[int | float, str]],
-        train_eval: Literal["train", "eval"] = "train",
-        reduction: Literal["mean", "min", "max", "sum"] = "mean",
+        self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train"
     ) -> None:
-        """
-        Store metrics with specified reduction type.
-
-        Args:
-            metrics: Dictionary of metric names to values, or metric names to (value,
-                reduction_type) tuples.
-            train_eval: Whether this is for training or evaluation.
-        """
         for key, value in metrics.items():
-            if isinstance(value, tuple):
-                metric_value, metric_reduction = value
-            else:
-                metric_value, metric_reduction = value, reduction
-
-            self._stored_metrics[train_eval][key]["values"].append(metric_value)
-            self._stored_metrics[train_eval][key]["reduction"] = metric_reduction
+            self._stored_metrics[train_eval][key].append(value)
 
     def _save_checkpoint(self, model, trial, **kwargs):
         # make sure the checkpoint dir exists, since trainer is flakey

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

@@ -101,11 +101,11 @@ class AxolotlDPOTrainer(
     ) -> dict[str, torch.Tensor]:
         if self.args.dpo_norm_loss:
             # fmt: off
-            loss_type: str = self.loss_type  # type: ignore[has-type]  # pylint: disable=access-member-before-definition
+            loss_type: str = self.loss_type  # type: ignore[has-type]
             # fmt: on
             # concatenated_forward handles avg token logprob for ipo case already
-            self.loss_type = "ipo"  # pylint: disable=attribute-defined-outside-init
+            self.loss_type = "ipo"
             res = super().concatenated_forward(model, batch, is_ref_model=is_ref_model)
-            self.loss_type = loss_type  # pylint: disable=attribute-defined-outside-init
+            self.loss_type = loss_type
             return res
         return super().concatenated_forward(model, batch, is_ref_model=is_ref_model)

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

@@ -128,9 +128,7 @@ class GRPOStrategy:
         return grpo_args_kwargs
 
     @classmethod
-    def set_trainer_args(
-        cls, cfg: DictDefault
-    ) -> list[Any]:  # pylint: disable=unused-argument
+    def set_trainer_args(cls, cfg: DictDefault) -> list[Any]:
         trainer_args = []
         if cfg.trl and cfg.trl.reward_funcs:
             reward_funcs = []
@@ -151,7 +149,7 @@ class GRPOStrategy:
         return trainer_kwargs
 
     @classmethod
-    def get_collator(cls, *args, **kwargs):  # pylint: disable=unused-argument
+    def get_collator(cls, *args, **kwargs):
         # No data collation is needed in GRPO, handled by trl's trainer __init__
         return None
 

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

@@ -1,7 +1,5 @@
 """Axolotl GRPO trainers (with and without sequence parallelism handling)"""
 
-# pylint: disable=too-many-lines,duplicate-code,protected-access,no-member
-
 import warnings
 from functools import partial
 from typing import Any
@@ -52,7 +50,6 @@ from axolotl.core.trainers.mixins.optimizer import OptimizerInitMixin, Optimizer
 from axolotl.monkeypatch.ring_attn import get_ring_attn_group
 
 if is_peft_available():
-    # pylint: disable=unused-import
     from peft import PeftConfig
 
 
@@ -253,7 +250,7 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
     def get_train_dataloader(self) -> DataLoader:
         """Get dataloader for training"""
         train_dataset = self.train_dataset
-        # pylint: disable=access-member-before-definition
+
         data_collator = self.data_collator  # type: ignore
 
         # Handle dataset preprocessing
@@ -266,7 +263,7 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
                     train_dataset, description="training"
                 )
         else:
-            self.data_collator = self._get_collator_with_removed_columns(  # pylint: disable=attribute-defined-outside-init
+            self.data_collator = self._get_collator_with_removed_columns(
                 data_collator,
                 description="training",
             )
@@ -308,10 +305,10 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
         # Generate completions using either vLLM or regular generation
         if self.args.use_vllm:
             # First, have main process load weights if needed
-            # pylint: disable=access-member-before-definition
+
             if self.state.global_step != self._last_loaded_step:  # type: ignore[has-type]
                 self._move_model_to_vllm()
-                # pylint: disable=attribute-defined-outside-init
+
                 self._last_loaded_step = self.state.global_step
 
             # Generate completions using vLLM: gather all prompts and use them in a single call in the main process
@@ -333,8 +330,9 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
                         # Extract prompts from this SP group, accounting for num_generations duplicates
                         # We only need prompts from one rank in each SP group
                         group_prompts = all_prompts_text[
-                            group_leader_rank
-                            * len(prompts_text) : (group_leader_rank + 1)
+                            group_leader_rank * len(prompts_text) : (
+                                group_leader_rank + 1
+                            )
                             * len(prompts_text) : self.num_generations
                         ]
 
@@ -485,7 +483,7 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
         )
         if is_conversational(inputs[0]):
             completions = []
-            for prompt, completion in zip(prompts, completions_text):
+            for prompt, completion in zip(prompts, completions_text, strict=False):
                 bootstrap = (
                     prompt.pop()["content"] if prompt[-1]["role"] == "assistant" else ""
                 )
@@ -503,6 +501,7 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
                 self.reward_funcs,
                 self.reward_processing_classes,
                 self.reward_func_names,
+                strict=False,
             )
         ):
             with profiling_context(self, reward_func_name):
@@ -511,14 +510,17 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
                 ):  # Module instead of PretrainedModel for compat with compiled models
                     if is_conversational(inputs[0]):
                         messages = [
-                            {"messages": p + c} for p, c in zip(prompts, completions)
+                            {"messages": p + c}
+                            for p, c in zip(prompts, completions, strict=False)
                         ]
                         texts = [
                             apply_chat_template(x, reward_processing_class)["text"]
                             for x in messages
                         ]
                     else:
-                        texts = [p + c for p, c in zip(prompts, completions)]
+                        texts = [
+                            p + c for p, c in zip(prompts, completions, strict=False)
+                        ]
                     reward_inputs = reward_processing_class(
                         text=texts,
                         return_tensors="pt",
@@ -564,7 +566,8 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
             row_reward_kwargs["completion"] = completions[nan_row_idx]
             warnings.warn(
                 f"All reward functions returned None for the following kwargs: {row_reward_kwargs}. "
-                "Please ensure that at least one reward function returns a valid reward."
+                "Please ensure that at least one reward function returns a valid reward.",
+                stacklevel=2,
             )
 
         # Gather the reward per function: this part is crucial, because the rewards are normalized per group and the

src/axolotl/core/trainers/mamba.py

@@ -5,7 +5,6 @@ import torch
 from axolotl.core.trainers.base import AxolotlTrainer
 
 
-# pylint: disable=too-many-ancestors
 class AxolotlMambaTrainer(AxolotlTrainer):
     """Mamba specific trainer to handle loss calculation"""
 
@@ -15,8 +14,8 @@ class AxolotlMambaTrainer(AxolotlTrainer):
         self,
         model,
         inputs,
-        return_outputs=False,  # pylint: disable=unused-argument
-        num_items_in_batch=None,  # pylint: disable=unused-argument
+        return_outputs=False,
+        num_items_in_batch=None,
     ):
         input_ids = inputs.pop("input_ids")
         lm_logits = model(input_ids).logits

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

@@ -1,6 +1,5 @@
 """Init for axolotl.core.trainers.mixins"""
 
-# pylint: disable=unused-import
 # flake8: noqa
 
 from .activation_checkpointing import ActivationOffloadingMixin

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

@@ -92,7 +92,7 @@ def get_lora_act_offloading_ctx_manager(
         `contextlib.ContextDecorator`:
             Activation offloading context manager for the model.
     """
-    # pylint: disable=unnecessary-dunder-call
+
     activations_handling_ctx = OffloadActivations(
         use_pin_memory=use_pin_memory,
         use_streams=use_streams,

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

@@ -26,7 +26,6 @@ class DistributedParallelMixin(Trainer):
             self.accelerator.distributed_type == "FSDP"
             and self.accelerator.state.fsdp_plugin is None
         ):
-            # pylint: disable=protected-access
             # handle Context Parallelism without FSDP
             self.accelerator.state.distributed_type = "MULTI_GPU"
             self.accelerator.state._shared_state["distributed_type"] = "MULTI_GPU"

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

@@ -70,11 +70,11 @@ class OptimizerMixin(Trainer):
                 }
             )
         if params["embeddings"]:
-            lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
+            lr = optimizer_kwargs["lr"]
             if self.args.embedding_lr_scale:
-                lr *= self.args.embedding_lr_scale  # pylint: disable=invalid-name
+                lr *= self.args.embedding_lr_scale
             elif self.args.embedding_lr:
-                lr = self.args.embedding_lr  # pylint: disable=invalid-name
+                lr = self.args.embedding_lr
             optimizer_grouped_parameters.append(
                 {
                     "params": list(params["embeddings"].values()),
@@ -143,7 +143,7 @@ class OptimizerMixin(Trainer):
                 loraplus_lr_embedding = getattr(
                     self.args, "loraplus_lr_embedding", 1e-6
                 )
-                self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
+                self.optimizer = create_loraplus_optimizer(
                     opt_model,
                     optimizer_cls,
                     loraplus_lr_ratio=loraplus_lr_ratio,
@@ -185,17 +185,15 @@ class OptimizerMixin(Trainer):
                                 p.data_ptr(): p.numel() for p in module.parameters()
                             }.values()
                         )
-                        LOG.info(f"skipped {module}: {skipped/2**20}M params")
+                        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")
+                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
-            )
+            self.optimizer = smp.DistributedOptimizer(self.optimizer)
 
         return self.optimizer
 

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

@@ -46,7 +46,7 @@ class SchedulerMixin(Trainer):
         )
 
         # fmt: off
-        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
+        if self.lr_scheduler is None:  # type: ignore
             # fmt: on
             plugin_manager = PluginManager.get_instance()
             lr_scheduler: LRScheduler | None = plugin_manager.create_lr_scheduler(
@@ -90,7 +90,7 @@ class SchedulerMixin(Trainer):
                     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
+                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(
                     optimizer,
                     num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
                     num_training_steps=num_training_steps,
@@ -98,7 +98,7 @@ class SchedulerMixin(Trainer):
             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
+                self.lr_scheduler = get_cosine_schedule_with_warmup_decay_constant(
                     optimizer,
                     num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
                     num_training_steps=num_training_steps,
@@ -107,7 +107,7 @@ class SchedulerMixin(Trainer):
                 )
             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
+                self.lr_scheduler = get_cosine_schedule_with_min_lr(
                     optimizer,
                     num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
                     num_training_steps=num_training_steps,
@@ -133,7 +133,7 @@ class SchedulerMixin(Trainer):
             )
             if not self.lr_scheduler:
                 super().create_scheduler(num_training_steps, optimizer)
-            self.lr_scheduler = JaggedLRRestartScheduler(  # pylint: disable=attribute-defined-outside-init
+            self.lr_scheduler = JaggedLRRestartScheduler(
                 optimizer,
                 self.lr_scheduler,
                 self.args.jagged_restart_steps,

src/axolotl/core/training_args_base.py

@@ -14,7 +14,6 @@ class AxolotlTrainingMixins:
     Mixin class for the Axolotl training args.
     """
 
-    # pylint: disable=duplicate-code
     model_type: Optional[str] = field(
         default=None, metadata={"help": "HF model configuration model_type."}
     )

src/axolotl/datasets.py

@@ -26,7 +26,7 @@ class TokenizedPromptDataset(Dataset):
         keep_in_memory: Whether to keep the tokenized dataset in memory.
     """
 
-    def __init__(  # pylint: disable=super-init-not-called
+    def __init__(
         self,
         prompt_tokenizer: PromptTokenizingStrategy,
         dataset: Dataset,
@@ -99,7 +99,7 @@ class ConstantLengthDataset(IterableDataset):
         seq_length: Length of token sequences to return.
     """
 
-    def __init__(  # pylint: disable=super-init-not-called
+    def __init__(
         self,
         tokenizer,
         datasets,

src/axolotl/evaluate.py

@@ -79,7 +79,7 @@ def evaluate(*, cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> Dict[str, f
     model, tokenizer, _, processor = setup_model_and_tokenizer(cfg)
 
     # Get datasets
-    # pylint: disable=duplicate-code
+
     train_dataset = dataset_meta.train_dataset
     eval_dataset = dataset_meta.eval_dataset
     total_num_steps = dataset_meta.total_num_steps

src/axolotl/integrations/base.py

@@ -76,7 +76,7 @@ class BasePlugin:
     def __init__(self):
         """Initializes the BasePlugin."""
 
-    def register(self, cfg: dict):  # pylint: disable=unused-argument
+    def register(self, cfg: dict):
         """Registers the plugin with the given configuration as an unparsed dict.
 
         Args:
@@ -104,14 +104,13 @@ class BasePlugin:
             dataset_meta: The metadata for the training dataset.
         """
 
-    def pre_model_load(self, cfg: DictDefault):  # pylint: disable=unused-argument
+    def pre_model_load(self, cfg: DictDefault):
         """Performs actions before the model is loaded.
 
         Args:
             cfg: The configuration for the plugin.
         """
 
-    # pylint: disable=unused-argument
     def post_model_build(self, cfg: DictDefault, model: PreTrainedModel):
         """Performs actions after the model is built/loaded, but before any adapters are applied.
 
@@ -119,7 +118,6 @@ class BasePlugin:
             cfg: The configuration for the plugin.
         """
 
-    # pylint: disable=unused-argument
     def pre_lora_load(self, cfg: DictDefault, model: PreTrainedModel):
         """Performs actions before LoRA weights are loaded.
 
@@ -128,7 +126,6 @@ class BasePlugin:
             model: The loaded model.
         """
 
-    # pylint: disable=unused-argument
     def post_lora_load(self, cfg: DictDefault, model: PreTrainedModel | PeftModel):
         """Performs actions after LoRA weights are loaded.
 
@@ -137,7 +134,6 @@ class BasePlugin:
             model: The loaded model.
         """
 
-    # pylint: disable=unused-argument
     def post_model_load(self, cfg: DictDefault, model: PreTrainedModel | PeftModel):
         """Performs actions after the model is loaded.
 
@@ -146,8 +142,7 @@ class BasePlugin:
             model: The loaded model.
         """
 
-    # pylint: disable=unused-argument
-    def get_trainer_cls(self, cfg: DictDefault) -> type[Trainer] | None:
+    def get_trainer_cls(self, cfg: DictDefault) -> Trainer | None:
         """Returns a custom class for the trainer.
 
         Args:
@@ -157,7 +152,6 @@ class BasePlugin:
             The first non-`None` trainer class returned by a plugin.
         """
 
-    # pylint: disable=unused-argument
     def post_trainer_create(self, cfg: DictDefault, trainer: Trainer):
         """Performs actions after the trainer is created.
 
@@ -166,7 +160,7 @@ class BasePlugin:
             trainer: The trainer object for training.
         """
 
-    def get_training_args(self, cfg: DictDefault):  # pylint: disable=unused-argument):
+    def get_training_args(self, cfg: DictDefault):
         """
         Returns custom training arguments to set on TrainingArgs.
 
@@ -177,9 +171,7 @@ class BasePlugin:
             object: dict containing the training arguments.
         """
 
-    def get_collator_cls_and_kwargs(
-        self, cfg: DictDefault, is_eval: bool = False
-    ):  # pylint: disable=unused-argument):
+    def get_collator_cls_and_kwargs(self, cfg: DictDefault, is_eval: bool = False):
         """
         Returns a custom class for the collator.
 
@@ -191,7 +183,6 @@ class BasePlugin:
             class: The class for the collator.
         """
 
-    # pylint: disable=unused-argument
     def create_optimizer(self, cfg: DictDefault, trainer: Trainer) -> Optimizer | None:
         """Creates and returns an optimizer for training.
 
@@ -203,7 +194,6 @@ class BasePlugin:
             The created optimizer.
         """
 
-    # pylint: disable=unused-argument
     def create_lr_scheduler(
         self,
         cfg: DictDefault,
@@ -223,7 +213,6 @@ class BasePlugin:
             The created learning rate scheduler.
         """
 
-    # pylint: disable=unused-argument
     def add_callbacks_pre_trainer(
         self, cfg: DictDefault, model: PreTrainedModel
     ) -> list[Callable]:
@@ -238,7 +227,6 @@ class BasePlugin:
         """
         return []
 
-    # pylint: disable=unused-argument
     def add_callbacks_post_trainer(
         self, cfg: DictDefault, trainer: Trainer
     ) -> list[Callable]:
@@ -254,7 +242,6 @@ class BasePlugin:
         """
         return []
 
-    # pylint: disable=unused-argument
     def post_train(self, cfg: DictDefault, model: PreTrainedModel | PeftModel):
         """Performs actions after training is complete.
 
@@ -263,7 +250,7 @@ class BasePlugin:
             model: The loaded model.
         """
 
-    def post_train_unload(self, cfg: DictDefault):  # pylint: disable=unused-argument
+    def post_train_unload(self, cfg: DictDefault):
         """Performs actions after training is complete and the model is unloaded.
 
         Args:
@@ -311,7 +298,7 @@ def load_plugin(plugin_name: str) -> BasePlugin:
     return plugin
 
 
-class PluginManager:  # pylint: disable=too-many-public-methods
+class PluginManager:
     """The `PluginManager` class is responsible for loading and managing plugins. It
     should be a singleton so it can be accessed from anywhere in the codebase.
 

src/axolotl/integrations/config.py

@@ -50,15 +50,9 @@ def merge_input_args():
         dynamic_input += f"class AxolotlInputConfig(AxolotlInputConfigBase, {', '.join(plugin_classes)}):\n    pass\n"
 
         namespace: Dict[Any, Any] = {}
-        exec(  # pylint: disable=exec-used  # nosec B102
-            dynamic_input, globals(), namespace
-        )
-        AxolotlInputConfig = namespace[  # pylint: disable=invalid-name
-            "AxolotlInputConfig"
-        ]
-        AxolotlConfigWCapabilities = namespace[  # pylint: disable=invalid-name
-            "AxolotlConfigWCapabilities"
-        ]
+        exec(dynamic_input, globals(), namespace)  # nosec B102
+        AxolotlInputConfig = namespace["AxolotlInputConfig"]
+        AxolotlConfigWCapabilities = namespace["AxolotlConfigWCapabilities"]
         return AxolotlConfigWCapabilities, AxolotlInputConfig
     return AxolotlConfigWCapabilitiesBase, AxolotlInputConfigBase
 
@@ -74,7 +68,7 @@ def merge_training_args() -> Type:
     Returns:
     tuple: A tuple containing the newly created classes, AxolotlTrainingMixins.
     """
-    # pylint: disable=duplicate-code
+
     from axolotl.core.training_args_base import (
         AxolotlTrainingMixins as AxolotlTrainingMixinsBase,
     )
@@ -93,11 +87,7 @@ def merge_training_args() -> Type:
 
         namespace: Dict[Any, Any] = {}
         local_vars = {"AxolotlTrainingMixinsBase": AxolotlTrainingMixinsBase}
-        exec(  # pylint: disable=exec-used  # nosec B102
-            dynamic_input, {**globals(), **local_vars}, namespace
-        )
-        AxolotlTrainingMixins = namespace[  # pylint: disable=invalid-name
-            "AxolotlTrainingMixins"
-        ]
+        exec(dynamic_input, {**globals(), **local_vars}, namespace)  # nosec B102
+        AxolotlTrainingMixins = namespace["AxolotlTrainingMixins"]
         return AxolotlTrainingMixins
     return AxolotlTrainingMixinsBase

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -18,6 +18,7 @@ Module for the Plugin for Cut Cross Entropy integration with Axolotl.
 Cut Cross Entropy is an optimized implementation of cross entropy loss
 from Apple's ML team.
 """
+
 import importlib
 from functools import partial
 
@@ -28,7 +29,7 @@ from axolotl.utils import get_pytorch_version
 from axolotl.utils.callbacks.models import get_causal_lm_model_cls_prefix
 from axolotl.utils.logging import get_logger
 
-from .args import CutCrossEntropyArgs  # pylint: disable=unused-import. # noqa: F401
+from .args import CutCrossEntropyArgs as CutCrossEntropyArgs
 
 LOG = get_logger(__name__)
 
@@ -106,9 +107,7 @@ class CutCrossEntropyPlugin(BasePlugin):
         """
         from cut_cross_entropy.transformers.patch import PATCH_FNS
 
-        def patch_generic(
-            maybe_model, patch_options, model_type: str
-        ):  # pylint: disable=unused-argument
+        def patch_generic(maybe_model, patch_options, model_type: str):
             import cut_cross_entropy.transformers.llama
             from cut_cross_entropy.transformers.llama import cce_forward
 
@@ -121,12 +120,10 @@ class CutCrossEntropyPlugin(BasePlugin):
                 )
                 model_cls = getattr(module, f"{model_cls_prefix}ForCausalLM")
 
-                cut_cross_entropy.transformers.llama._PATCH_OPTS = (  # pylint: disable=protected-access
-                    patch_options
-                )
+                cut_cross_entropy.transformers.llama._PATCH_OPTS = patch_options
 
                 model_cls.forward = cce_forward
-            # pylint: disable=duplicate-code
+
             except (ImportError, AttributeError) as e:
                 raise RuntimeError(
                     f"Could not import ForCausalLM class for model_type: {model_type}. "

src/axolotl/integrations/cut_cross_entropy/args.py

@@ -15,6 +15,7 @@
 """
 Module for handling Cut Cross Entropy input arguments.
 """
+
 from typing import Optional
 
 from pydantic import BaseModel, model_validator

src/axolotl/integrations/diffusion/README.md

@@ -1,164 +0,0 @@
-# Diffusion LM Training Plugin for Axolotl
-
-This plugin enables diffusion language model training using the LLaDA (Large Language
-And Diffusion Assistant) approach within the Axolotl framework.
-
-## Overview
-
-LLaDA is a diffusion-based approach to language model training that uses:
-- **Random token masking** during training instead of next-token prediction
-- **Bidirectional attention** to allow the model to see the full context
-- **Importance weighting** based on masking probabilities for stable training
-
-This approach can lead to more robust language models with better understanding of
-bidirectional context.
-
-## Installation
-
-The plugin is included with Axolotl. To use it, simply add the plugin configuration to
-your training config.
-
-## Quickstart
-
-### Basic Configuration
-
-Add the following to your Axolotl configuration YAML:
-
-```yaml
-# Enable diffusion LM training plugin
-plugins:
-  - axolotl.integrations.diffusion.DiffusionPlugin
-
-# Diffusion-specific configuration
-noise_schedule: linear  # or "cosine"
-min_mask_ratio: 0.1
-max_mask_ratio: 0.9
-num_diffusion_steps: 128
-eps: 1e-3
-importance_weighting: true
-mask_token_id: 128002
-
-# Sample generation (optional)
-generate_samples: true
-generation_interval: 100
-num_generation_samples: 3
-generation_steps: 128
-generation_temperature: 0.0
-generation_max_length: 100
-
-# Model configuration
-base_model: meta-llama/Llama-3.2-1B
-model_type: llama
-
-# Standard Axolotl configuration
-datasets:
-  - path: your_dataset
-    ...
-
-# Other config
-sequence_len: 1024
-micro_batch_size: 8
-gradient_accumulation_steps: 4
-learning_rate: 3e-4
-```
-
-## Supported Models
-
-Currently supported base model types:
-- **Llama** (meta-llama/Llama-*, etc.) - Uses `LlamaForDiffusionLM`
-- **Mistral** (mistralai/Mistral-*, etc.) - Uses `MistralForDiffusionLM`
-
-The plugin automatically creates custom model classes that inherit from the base model
-while adding diffusion training capabilities. This provides full compatibility with
-HuggingFace's ecosystem for saving, loading, and inference.
-
-## How It Works
-
-### Custom Model Architecture
-
-The plugin creates custom model classes (`LlamaForDiffusionLM`, `MistralForDiffusionLM`) that inherit from
-standard HuggingFace models. During training, these models:
-
-1. **Apply forward diffusion process**: Randomly mask tokens based on sampled timesteps
-2. **Use bidirectional attention**: Override causal attention with full bidirectional attention
-3. **Compute diffusion loss**: Calculate loss only on masked tokens with optional importance weighting
-
-### Random Masking
-During training, tokens are randomly masked based on a sampled timestep:
-- Sample timestep `t` uniformly from [0, 1]
-- Calculate masking probability: `p = (1 - eps) * t + eps`
-- Randomly mask tokens with probability `p`
-
-### Bidirectional Attention
-The models override causal attention with bidirectional attention:
-- Creates 4D attention masks allowing all-to-all attention
-- Maintains proper padding and sample packing masks
-- Compatible with standard HuggingFace attention implementations
-
-### Diffusion Loss
-
-Loss is computed only on masked tokens with (optional) importance weighting:
-
-```python
-loss = sum(cross_entropy(pred, target) / p_mask) / total_tokens
-```
-
-### Model Loading and Saving
-
-The custom models work seamlessly with HuggingFace's AutoModel system:
-
-```python
-from transformers import AutoModel, AutoConfig
-
-# Load a diffusion model
-model = AutoModel.from_pretrained("path/to/diffusion/model", trust_remote_code=True)
-
-# Save a diffusion model
-model.save_pretrained("path/to/save/diffusion/model")
-```
-
-During inference, the models behave like standard causal language models.
-
-## Sample Generation
-
-When `generate_samples: true`, the plugin generates samples during training:
-
-```
-Sample 1:
-   Original (45 tokens): The quick brown fox jumps over the lazy dog...
-   Masked (18/45 tokens, 40.0%): The [MASK] [MASK] fox [MASK] over [MASK] lazy [MASK]...
-   Generated: The quick brown fox jumps over the lazy dog...
-```
-
-Samples are logged to console and wandb (if enabled).
-
-## Metrics and Monitoring
-
-The plugin adds several metrics to track diffusion training:
-
-- `train/loss`: Weighted diffusion loss
-- `train/accuracy`: Accuracy on masked tokens
-- `train/mask_ratio`: Average fraction of tokens masked
-- `train/num_masked_tokens`: Number of tokens masked
-- `train/avg_p_mask`: Average masking probability
-- `train/ce_loss`: Unweighted cross-entropy loss
-- `train/importance_weight_avg`: Average importance weight
-
-## Benefits of Custom Model Approach
-
-✅ **Type Safety**: Full IDE support and type checking  
-✅ **HuggingFace Integration**: Works with AutoModel, Hub, pipelines  
-✅ **Maintainability**: Clean architecture, no monkey patching  
-✅ **Ecosystem Compatibility**: Standard save/load, PEFT support  
-✅ **Testing**: Easier to test and debug  
-
-## Limitations
-
-- **Model Support**: Currently limited to Llama and Mistral architectures
-- **Flash Attention**: Not yet optimized for flash attention
-- **Inference Speed**: Bidirectional attention is slower than causal for generation
-
-## References
-
-- [LLaDA Paper](https://arxiv.org/abs/2404.10406)
-- [Axolotl Documentation](https://docs.axolotl.ai/)

src/axolotl/integrations/diffusion/__init__.py

@@ -1,26 +0,0 @@
-"""Diffusion LM training plugin init."""
-
-from transformers import AutoConfig, AutoModel
-
-from .args import DiffusionArgs
-from .configuration import DiffusionConfig, LlamaForDiffusionConfig, MistralForDiffusionConfig
-from .models import LlamaForDiffusionLM, MistralForDiffusionLM
-from .plugin import DiffusionPlugin
-
-# Register custom configurations
-AutoConfig.register("llama_diffusion", LlamaForDiffusionConfig)
-AutoConfig.register("mistral_diffusion", MistralForDiffusionConfig)
-
-# Register custom models
-AutoModel.register(LlamaForDiffusionConfig, LlamaForDiffusionLM)
-AutoModel.register(MistralForDiffusionConfig, MistralForDiffusionLM)
-
-__all__ = [
-    "DiffusionArgs", 
-    "DiffusionPlugin",
-    "DiffusionConfig",
-    "LlamaForDiffusionConfig",
-    "MistralForDiffusionConfig", 
-    "LlamaForDiffusionLM",
-    "MistralForDiffusionLM",
-]

src/axolotl/integrations/diffusion/args.py

@@ -1,70 +0,0 @@
-"""Config args for diffusion LM training."""
-
-from typing import Literal
-
-from pydantic import BaseModel, Field
-
-
-class DiffusionArgs(BaseModel):
-    """Arguments for diffusion LM training plugin."""
-
-    # Noise schedule config
-    noise_schedule: Literal["linear", "cosine"] = Field(
-        default="linear", description="Type of noise schedule for diffusion training"
-    )
-    min_mask_ratio: float = Field(
-        default=0.1,
-        ge=0.0,
-        le=1.0,
-        description="Minimum masking ratio for diffusion noise schedule",
-    )
-    max_mask_ratio: float = Field(
-        default=0.9,
-        ge=0.0,
-        le=1.0,
-        description="Maximum masking ratio for diffusion noise schedule",
-    )
-    num_diffusion_steps: int = Field(
-        default=128, ge=1, description="Number of diffusion timesteps"
-    )
-    eps: float = Field(
-        default=1e-3,
-        ge=0.0,
-        le=1.0,
-        description="Epsilon value for minimum masking probability in forward process",
-    )
-
-    # Training config
-    importance_weighting: bool = Field(
-        default=True,
-        description="Apply importance weighting to loss based on masking probability",
-    )
-    mask_token_id: int = Field(
-        default=128002,
-        description=(
-            "Token ID to use for masking. Default is 128002 "
-            "(<|reserved_special_token_0|> for Llama 3.2)"
-        ),
-    )
-
-    # Sample generation config
-    generate_samples: bool = Field(
-        default=True, description="Enable sample generation during training"
-    )
-    generation_interval: int = Field(
-        default=100, ge=1, description="Generate samples every N steps"
-    )
-    num_generation_samples: int = Field(
-        default=3, ge=1, description="Number of samples to generate each time"
-    )
-    generation_steps: int = Field(
-        default=128, ge=1, description="Number of diffusion steps for generation"
-    )
-    generation_temperature: float = Field(
-        default=0.0,
-        ge=0.0,
-        description="Temperature for generation sampling (0.0 = deterministic)",
-    )
-    generation_max_length: int = Field(
-        default=100, ge=1, description="Maximum sequence length for generation"
-    )

src/axolotl/integrations/diffusion/callbacks.py

@@ -1,116 +0,0 @@
-"""Callbacks for diffusion training."""
-
-import wandb
-from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState
-from transformers.training_args import TrainingArguments
-
-from axolotl.utils.logging import get_logger
-
-from .generation import generate_samples
-
-LOG = get_logger(__name__)
-
-
-class DiffusionGenerationCallback(TrainerCallback):
-    """Callback for generating samples during diffusion training."""
-
-    def __init__(self, trainer):
-        self.trainer = trainer
-
-    # pylint: disable=unused-argument
-    def on_step_end(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        """Generate samples at specified intervals."""
-        config = getattr(self.trainer, 'diffusion_config', self.trainer.args)
-        
-        if (
-            state.global_step > 0
-            and state.global_step % config.get('generation_interval', 100) == 0
-        ):
-            # Use eval dataloader if available, otherwise use train dataloader
-            if (
-                hasattr(self.trainer, "eval_dataset")
-                and self.trainer.eval_dataset is not None
-            ):
-                dataloader = self.trainer.get_eval_dataloader()
-            else:
-                dataloader = self.trainer.get_train_dataloader()
-
-            # Generate samples
-            samples = generate_samples(
-                model=self.trainer.model,
-                tokenizer=self.trainer.tokenizer,
-                dataloader=dataloader,
-                num_generation_samples=config.get('num_generation_samples', 3),
-                max_length=config.get('generation_max_length', 256),
-                num_diffusion_steps=config.get('generation_steps', 10),
-                temperature=config.get('generation_temperature', 1.0),
-                mask_token_id=config.get('mask_token_id', 32000),
-            )
-
-            # Log samples
-            self._log_samples(samples, state.global_step)
-
-    def _log_samples(self, samples: list, step: int):
-        """Log generated samples."""
-        if not samples:
-            return
-
-        LOG.info("=" * 60)
-        LOG.info("GENERATED SAMPLES")
-        LOG.info("=" * 60)
-
-        for i, sample_data in enumerate(samples, 1):
-            original = sample_data["original"]
-            masked = sample_data["masked"]
-            generated = sample_data["generated"]
-            mask_ratio = sample_data["mask_ratio"]
-            masked_tokens = sample_data["masked_tokens"]
-            total_tokens = sample_data["total_tokens"]
-
-            LOG.info(f"\nSample {i}:")
-            LOG.info(f"\tOriginal ({total_tokens} tokens): {original}")
-            LOG.info(
-                f"\tMasked ({masked_tokens}/{total_tokens} tokens, "
-                f"{mask_ratio:.1%}): {masked}"
-            )
-            LOG.info(f"\tGenerated: {generated}")
-
-        LOG.info("=" * 60)
-
-        config = getattr(self.trainer, 'diffusion_config', self.trainer.args)
-        if config.get('use_wandb', False) and self.trainer.state.is_world_process_zero:
-            if wandb.run is not None:
-                wandb.log(
-                    {
-                        "generated_samples": wandb.Table(
-                            columns=[
-                                "step",
-                                "original",
-                                "masked",
-                                "generated",
-                                "mask_ratio",
-                                "masked_tokens",
-                                "total_tokens",
-                            ],
-                            data=[
-                                [
-                                    step,
-                                    sample["original"],
-                                    sample["masked"],
-                                    sample["generated"],
-                                    f"{sample['mask_ratio']:.1%}",
-                                    sample["masked_tokens"],
-                                    sample["total_tokens"],
-                                ]
-                                for sample in samples
-                            ],
-                        )
-                    },
-                    step=step,
-                )

src/axolotl/integrations/diffusion/configuration.py

@@ -1,71 +0,0 @@
-"""Configuration classes for diffusion language models."""
-
-from transformers import LlamaConfig, MistralConfig
-
-
-class LlamaForDiffusionConfig(LlamaConfig):
-    """Configuration class for Llama models with diffusion training."""
-    
-    model_type = "llama_diffusion"
-    
-    def __init__(
-        self,
-        mask_token_id: int = 32000,
-        eps: float = 1e-3,
-        importance_weighting: bool = False,
-        sample_packing: bool = False,
-        min_mask_ratio: float = 0.0,
-        max_mask_ratio: float = 1.0,
-        noise_schedule: str = "linear",
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        
-        # Diffusion-specific parameters
-        self.mask_token_id = mask_token_id
-        self.eps = eps
-        self.importance_weighting = importance_weighting
-        self.sample_packing = sample_packing
-        self.min_mask_ratio = min_mask_ratio
-        self.max_mask_ratio = max_mask_ratio
-        self.noise_schedule = noise_schedule
-
-
-class MistralForDiffusionConfig(MistralConfig):
-    """Configuration class for Mistral models with diffusion training."""
-    
-    model_type = "mistral_diffusion"
-    
-    def __init__(
-        self,
-        mask_token_id: int = 32000,
-        eps: float = 1e-3,
-        importance_weighting: bool = False,
-        sample_packing: bool = False,
-        min_mask_ratio: float = 0.0,
-        max_mask_ratio: float = 1.0,
-        noise_schedule: str = "linear",
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        
-        # Diffusion-specific parameters
-        self.mask_token_id = mask_token_id
-        self.eps = eps
-        self.importance_weighting = importance_weighting
-        self.sample_packing = sample_packing
-        self.min_mask_ratio = min_mask_ratio
-        self.max_mask_ratio = max_mask_ratio
-        self.noise_schedule = noise_schedule
-
-
-# Keep the base class for backward compatibility but mark as deprecated
-class DiffusionConfig(LlamaForDiffusionConfig):
-    """
-    Deprecated: Use LlamaForDiffusionConfig or MistralForDiffusionConfig instead.
-    """
-    
-    model_type = "diffusion"
-    
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)

src/axolotl/integrations/diffusion/generation.py

@@ -1,269 +0,0 @@
-"""Sample generation utilities for diffusion training."""
-
-import logging
-from typing import Any, List, Optional
-
-import torch
-
-logger = logging.getLogger(__name__)
-
-
-def generate_samples(
-    model: torch.nn.Module,
-    tokenizer: Any,
-    dataloader: Optional[Any] = None,
-    num_generation_samples: int = 3,
-    max_length: int = 100,
-    num_diffusion_steps: int = 128,
-    temperature: float = 0.0,
-    mask_token_id: int = 32000,
-) -> List[dict]:
-    """
-    Generate text samples using the diffusion model by randomly masking sequences from
-    the given dataset and running the reverse diffusion process.
-
-    Args:
-        model: The wrapped or unwrapped model
-        tokenizer: Tokenizer for encoding/decoding
-        dataloader: Validation dataloader (for sampling sequences)
-        num_generation_samples: Number of samples to generate
-        max_length: Maximum length of sequences to use
-        num_diffusion_steps: Number of diffusion steps for generation
-        temperature: Temperature for sampling (0.0 = deterministic)
-        mask_token_id: Token ID used for masking
-
-    Returns:
-        List of dictionaries with original text, masked text, and generated text
-    """
-    if dataloader is None:
-        logger.warning("No validation dataloader provided, cannot generate samples")
-        return []
-
-    # Get the actual model (unwrap if needed)
-    unwrapped_model = model.module if hasattr(model, "module") else model
-    unwrapped_model.eval()
-    generations = []
-
-    # Sample sequences from validation dataset
-    sampled_sequences = _sample_sequences_from_dataloader(
-        dataloader, num_generation_samples, max_length, unwrapped_model.device
-    )
-    logger.info(f"Sampled {len(sampled_sequences)} sequences from validation dataset")
-
-    # Generate samples using reverse diffusion process
-    with torch.no_grad():
-        for original_sequence in sampled_sequences:
-            generation_result = _generate(
-                unwrapped_model,
-                tokenizer,
-                original_sequence,
-                num_diffusion_steps,
-                temperature,
-                mask_token_id,
-            )
-            generations.append(generation_result)
-
-    unwrapped_model.train()
-    return generations
-
-
-def _sample_sequences_from_dataloader(
-    dataloader: Any, num_samples: int, max_length: int, device: torch.device
-) -> List[torch.Tensor]:
-    """Sample sequences from validation dataloader."""
-    sampled_sequences = []
-    sample_count = 0
-
-    # Add randomness by skipping a random number of batches
-    skip_batches = torch.randint(0, 6, (1,)).item()
-    batch_count = 0
-
-    for batch in dataloader:
-        # Skip some batches for variety
-        if batch_count < skip_batches:
-            batch_count += 1
-            continue
-
-        if sample_count >= num_samples:
-            break
-
-        batch_count += 1
-        input_ids = batch["input_ids"]
-        attention_mask = batch.get("attention_mask")
-
-        # Randomly sample from sequences in this batch
-        batch_indices = torch.randperm(input_ids.size(0)).tolist()
-
-        for i in batch_indices:
-            if sample_count >= num_samples:
-                break
-
-            # Get actual sequence length (non-padded)
-            if attention_mask is not None:
-                seq_len = attention_mask[i].sum().item()
-            else:
-                seq_len = input_ids.size(1)
-
-            # Limit sequence length to max_length
-            actual_length = min(seq_len, max_length)
-            if actual_length < 10:  # Skip very short sequences
-                continue
-
-            # Extract the sequence
-            sequence = input_ids[i][:actual_length].unsqueeze(0).to(device)
-            sampled_sequences.append(sequence)
-            sample_count += 1
-
-    return sampled_sequences
-
-
-def _generate(
-    model: torch.nn.Module,
-    tokenizer: Any,
-    original_sequence: torch.Tensor,
-    num_diffusion_steps: int,
-    temperature: float,
-    mask_token_id: int,
-) -> dict:
-    """Generate a single sample using reverse diffusion."""
-    # Get original text for comparison
-    original_text = tokenizer.decode(
-        original_sequence[0].cpu(), skip_special_tokens=True
-    )
-
-    # Apply custom masking with random ratio (10% to 70%)
-    total_tokens = original_sequence.size(1)
-    min_ratio, max_ratio = 0.1, 0.7
-    target_mask_ratio = torch.rand(1).item() * (max_ratio - min_ratio) + min_ratio
-    target_masked_tokens = int(total_tokens * target_mask_ratio)
-
-    # Create random mask indices
-    mask_positions = torch.randperm(total_tokens)[:target_masked_tokens]
-    masked_indices = torch.zeros(
-        1, total_tokens, dtype=torch.bool, device=original_sequence.device
-    )
-    masked_indices[0, mask_positions] = True
-
-    # Create masked sequence
-    masked_sequence = original_sequence.clone()
-    masked_sequence[masked_indices] = mask_token_id
-
-    # Calculate actual mask ratio
-    masked_tokens = masked_indices.sum().item()
-    mask_ratio = masked_tokens / total_tokens
-
-    # Get masked text for comparison
-    masked_text = tokenizer.decode(masked_sequence[0].cpu(), skip_special_tokens=False)
-    # Clean up mask token representation
-    masked_text = _clean_masked_text(masked_text, tokenizer, mask_token_id)
-
-    # Run reverse diffusion process
-    sequence = masked_sequence.clone()
-    for step in range(num_diffusion_steps):
-        sequence = _diffusion_step(
-            model, sequence, step, num_diffusion_steps, temperature, mask_token_id
-        )
-
-    # Get final generated text
-    generated_text = tokenizer.decode(sequence[0].cpu(), skip_special_tokens=True)
-
-    return {
-        "original": original_text,
-        "masked": masked_text,
-        "generated": generated_text,
-        "mask_ratio": mask_ratio,
-        "masked_tokens": masked_tokens,
-        "total_tokens": total_tokens,
-        "formatted": (
-            f"Original: '{original_text}' → Masked: '{masked_text}' "
-            f"({mask_ratio:.1%}) → Generated: '{generated_text}'"
-        ),
-    }
-
-
-def _clean_masked_text(masked_text: str, tokenizer: Any, mask_token_id: int) -> str:
-    """Clean up masked text for display."""
-    mask_token_repr = tokenizer.decode([mask_token_id], skip_special_tokens=False)
-    cleaned = masked_text.replace(mask_token_repr, "[MASK]")
-
-    if hasattr(tokenizer, "special_tokens_map"):
-        for token_value in tokenizer.special_tokens_map.values():
-            if token_value and isinstance(token_value, str):
-                cleaned = cleaned.replace(token_value, "")
-
-    cleaned = " ".join(cleaned.split()).strip()
-
-    return cleaned
-
-
-def _diffusion_step(
-    model: torch.nn.Module,
-    sequence: torch.Tensor,
-    step: int,
-    num_diffusion_steps: int,
-    temperature: float,
-    mask_token_id: int,
-) -> torch.Tensor:
-    """Perform a single diffusion step with remasking."""
-    # Only process if there are masked tokens remaining
-    current_mask = sequence == mask_token_id
-    if not current_mask.any():
-        return sequence
-
-    # Create bidirectional attention mask for diffusion
-    batch_size, seq_len = sequence.shape
-    attention_mask = torch.ones(
-        batch_size, 1, seq_len, seq_len, dtype=torch.bool, device=sequence.device
-    )
-
-    # Forward pass
-    outputs = model(input_ids=sequence, attention_mask=attention_mask)
-    logits = outputs.logits
-
-    # Only sample at currently masked positions
-    if current_mask.any():
-        masked_logits = logits[current_mask]
-
-        # Apply temperature scaling
-        if temperature > 0:
-            scaled_logits = masked_logits / temperature
-        else:
-            scaled_logits = masked_logits
-
-        # Suppress mask token in outputs
-        scaled_logits[:, mask_token_id] = -float("inf")
-
-        # Sample predictions
-        if temperature > 0:
-            # Add Gumbel noise for sampling
-            gumbel_noise = -torch.log(
-                -torch.log(torch.rand_like(scaled_logits, dtype=torch.float32))
-            )
-            gumbel_logits = scaled_logits + gumbel_noise
-            predicted_tokens = torch.argmax(gumbel_logits, dim=-1)
-        else:
-            # Deterministic sampling when temperature is 0
-            predicted_tokens = torch.argmax(scaled_logits, dim=-1)
-
-        # Calculate probabilities for confidence scoring
-        probs = torch.softmax(scaled_logits, dim=-1)
-        predicted_token_probs = probs[range(len(predicted_tokens)), predicted_tokens]
-
-        # Determine how many tokens to unmask this step
-        remaining_masked = current_mask.sum().item()
-        if step == num_diffusion_steps - 1:
-            num_to_unmask = remaining_masked
-        else:
-            unmask_ratio = 1.0 / (num_diffusion_steps - step)
-            num_to_unmask = max(1, int(remaining_masked * unmask_ratio))
-
-        # Select highest confidence predictions to unmask
-        if num_to_unmask >= remaining_masked:
-            sequence[current_mask] = predicted_tokens
-        else:
-            _, top_indices = predicted_token_probs.topk(num_to_unmask)
-            mask_positions = torch.where(current_mask)[1]
-            positions_to_unmask = mask_positions[top_indices]
-            sequence[0, positions_to_unmask] = predicted_tokens[top_indices]
-
-    return sequence

src/axolotl/integrations/diffusion/models.py

@@ -1,426 +0,0 @@
-"""Custom model classes for diffusion language models."""
-
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-from transformers import LlamaForCausalLM, MistralForCausalLM
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-from .configuration import LlamaForDiffusionConfig, MistralForDiffusionConfig
-
-
-class DiffusionModelMixin:
-    """Mixin class providing diffusion functionality to language models."""
-    
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._special_token_ids = None
-        
-    def _cache_special_token_ids(self, tokenizer=None):
-        """Cache special token IDs to avoid repeated tokenizer access."""
-        if tokenizer is None:
-            self._special_token_ids = set()
-            return
-            
-        special_tokens = set()
-        
-        if hasattr(tokenizer, "bos_token_id") and tokenizer.bos_token_id is not None:
-            special_tokens.add(tokenizer.bos_token_id)
-        if hasattr(tokenizer, "eos_token_id") and tokenizer.eos_token_id is not None:
-            special_tokens.add(tokenizer.eos_token_id)
-        if hasattr(tokenizer, "pad_token_id") and tokenizer.pad_token_id is not None:
-            special_tokens.add(tokenizer.pad_token_id)
-            
-        self._special_token_ids = special_tokens
-    
-    def _forward_process(
-        self,
-        input_ids: torch.Tensor,
-        attention_mask: torch.Tensor | None = None,
-        labels: torch.Tensor | None = None,
-        eps: float = 1e-3,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Forward noising process. A timestep is sampled along the process, and tokens are
-        masked with probability determined by the configured noise schedule.
-
-        Args:
-            input_ids: Input token ids [batch_size, seq_len].
-            attention_mask: Attention mask [batch_size, seq_len].
-            labels: Labels for SFT training [batch_size, seq_len].
-            eps: Small epsilon value for minimum masking probability.
-
-        Returns:
-            noisy_batch: Input with some tokens masked.
-            masked_indices: Boolean mask indicating which tokens were masked.
-            p_mask: Masking probabilities for each token [batch_size, seq_len].
-        """
-        batch_size, seq_len = input_ids.shape
-        device = input_ids.device
-
-        # Sample random timesteps for each sample in batch
-        t = torch.rand(batch_size, device=device)
-
-        # Calculate masking probability with epsilon
-        p_mask = (1 - eps) * t + eps  # [batch_size]
-        p_mask = p_mask[:, None].repeat(1, seq_len)  # [batch_size, seq_len]
-
-        # Don't mask padding tokens if attention_mask is provided
-        if attention_mask is not None:
-            valid_mask = attention_mask.bool()
-            p_mask = p_mask * valid_mask.float()
-
-        # Create mask to exclude special tokens
-        special_token_mask = torch.zeros_like(input_ids, dtype=torch.bool)
-        if self._special_token_ids:
-            for token_id in self._special_token_ids:
-                special_token_mask |= input_ids == token_id
-
-        # Create random mask based on p_mask
-        masked_indices = torch.rand((batch_size, seq_len), device=device) < p_mask
-        masked_indices = masked_indices & ~special_token_mask
-        if attention_mask is not None:
-            masked_indices = masked_indices & attention_mask.bool()
-
-        # For SFT data, only mask answer tokens
-        if labels is not None:
-            answer_mask = labels != -100
-            masked_indices = masked_indices & answer_mask
-
-        # Create masked input
-        mask_token_id = self.config.mask_token_id
-        noisy_batch = torch.where(masked_indices, mask_token_id, input_ids)
-
-        return noisy_batch, masked_indices, p_mask
-
-    def _create_bidirectional_attention_mask(
-        self, input_ids: torch.Tensor, attention_mask: torch.Tensor | None = None
-    ) -> torch.Tensor:
-        """
-        Create bidirectional attention mask to override default causal masking. Handles
-        sample-packed sequences where different samples are identified by different
-        attention mask values.
-
-        Args:
-            input_ids: Input token ids [batch_size, seq_len].
-            attention_mask: Attention mask [batch_size, seq_len]
-
-        Returns:
-            bidirectional_mask: 4D attention mask [batch_size, 1, seq_len, seq_len].
-        """
-        batch_size, seq_len = input_ids.shape
-        device = input_ids.device
-
-        if attention_mask is None or not self.config.sample_packing:
-            return torch.ones(
-                batch_size, 1, seq_len, seq_len, dtype=torch.bool, device=device
-            )
-
-        # Create attention mask by comparing sample IDs element-wise
-        mask_i = attention_mask.unsqueeze(2)  # [batch_size, seq_len, 1]
-        mask_j = attention_mask.unsqueeze(1)  # [batch_size, 1, seq_len]
-
-        # Tokens can attend to each other if they have the same non-zero sample ID
-        bidirectional_mask = (mask_i == mask_j) & (mask_i > 0)
-
-        # Add head dimension: [batch_size, 1, seq_len, seq_len]
-        bidirectional_mask = bidirectional_mask.unsqueeze(1)
-
-        return bidirectional_mask
-
-    def _compute_diffusion_loss(
-        self,
-        input_ids: torch.Tensor,
-        attention_mask: torch.Tensor | None = None,
-        labels: torch.Tensor | None = None,
-        logits: torch.Tensor | None = None,
-        masked_indices: torch.Tensor | None = None,
-        p_mask: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        """
-        Compute diffusion loss given logits and masking information.
-
-        Args:
-            input_ids: Ground truth token ids [batch_size, seq_len].
-            attention_mask: Attention mask [batch_size, seq_len].
-            labels: Labels for SFT training [batch_size, seq_len].
-            logits: Model logits [batch_size, seq_len, vocab_size].
-            masked_indices: Boolean mask indicating which tokens were masked.
-            p_mask: Masking probabilities for each token [batch_size, seq_len].
-
-        Returns:
-            loss: Cross-entropy loss.
-        """
-        if masked_indices.sum() > 0:
-            valid_indices = torch.where(masked_indices)
-            batch_indices, seq_indices = valid_indices
-
-            masked_logits = logits[batch_indices, seq_indices]
-            masked_targets = input_ids[batch_indices, seq_indices]
-            masked_p_mask = p_mask[batch_indices, seq_indices]
-
-            # Compute cross-entropy loss without reduction
-            token_loss = F.cross_entropy(
-                masked_logits.float(), masked_targets, reduction="none"
-            )
-
-            if self.config.importance_weighting:
-                masked_p_mask = masked_p_mask.float()
-                weighted_loss = token_loss / masked_p_mask
-            else:
-                weighted_loss = token_loss
-
-            # Final loss: sum weighted losses, normalize
-            if labels is not None:
-                # For SFT data: normalize by answer length per sample
-                answer_mask = labels != -100
-                answer_lengths = answer_mask.sum(dim=1).float()  # [batch_size]
-
-                # Get batch indices for masked tokens
-                masked_batch_indices = batch_indices
-
-                # Sum losses per sample and divide by answer length
-                loss_per_sample = torch.zeros(
-                    input_ids.shape[0], device=input_ids.device
-                )
-                for i in range(input_ids.shape[0]):
-                    sample_mask = masked_batch_indices == i
-                    if sample_mask.sum() > 0:
-                        sample_loss = weighted_loss[sample_mask].sum()
-                        loss_per_sample[i] = sample_loss / answer_lengths[i]
-
-                loss = loss_per_sample.mean()
-            else:
-                # Original normalization for non-SFT data
-                loss = weighted_loss.sum() / (input_ids.shape[0] * input_ids.shape[1])
-        else:
-            loss = torch.tensor(0.0, device=input_ids.device, requires_grad=True)
-
-        return loss
-
-
-class LlamaForDiffusionLM(DiffusionModelMixin, LlamaForCausalLM):
-    """
-    Llama model for diffusion language modeling.
-    
-    This model extends LlamaForCausalLM with diffusion training capabilities,
-    including bidirectional attention and forward diffusion process.
-    """
-    
-    config_class = LlamaForDiffusionConfig
-    
-    def __init__(self, config):
-        super().__init__(config)
-        
-        # Initialize diffusion-specific attributes
-        self._special_token_ids = None
-        
-        # Initialize weights and apply final processing
-        self.post_init()
-        
-    def set_tokenizer(self, tokenizer):
-        """Set tokenizer for special token handling."""
-        self._cache_special_token_ids(tokenizer)
-    
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[list[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        """
-        Forward pass with diffusion training logic.
-        
-        During training, applies forward diffusion process and bidirectional attention.
-        During inference, behaves like standard causal language model.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        
-        if self.training and input_ids is not None:
-            # Apply diffusion process during training
-            original_input_ids = input_ids.clone()
-            
-            # Apply forward process to get noisy input
-            noisy_input_ids, masked_indices, p_mask = self._forward_process(
-                input_ids, attention_mask, labels, self.config.eps
-            )
-            
-            # Create bidirectional attention mask
-            bidirectional_attention_mask = self._create_bidirectional_attention_mask(
-                input_ids, attention_mask
-            )
-            
-            # Forward pass with noisy input and bidirectional attention
-            outputs = super().forward(
-                input_ids=noisy_input_ids,
-                attention_mask=bidirectional_attention_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                inputs_embeds=inputs_embeds,
-                labels=None,  # Don't use standard loss computation
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-                cache_position=cache_position,
-                **kwargs,
-            )
-            
-            # Compute diffusion loss
-            loss = self._compute_diffusion_loss(
-                original_input_ids,
-                attention_mask,
-                labels,
-                outputs.logits,
-                masked_indices,
-                p_mask,
-            )
-            
-            if return_dict:
-                outputs.loss = loss
-                return outputs
-            else:
-                return (loss,) + outputs[1:]
-        else:
-            # Standard forward pass for inference
-            return super().forward(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                inputs_embeds=inputs_embeds,
-                labels=labels,
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-                cache_position=cache_position,
-                **kwargs,
-            )
-
-
-class MistralForDiffusionLM(DiffusionModelMixin, MistralForCausalLM):
-    """
-    Mistral model for diffusion language modeling.
-    
-    This model extends MistralForCausalLM with diffusion training capabilities,
-    including bidirectional attention and forward diffusion process.
-    """
-    
-    config_class = MistralForDiffusionConfig
-    
-    def __init__(self, config):
-        super().__init__(config)
-        
-        # Initialize diffusion-specific attributes
-        self._special_token_ids = None
-        
-        # Initialize weights and apply final processing
-        self.post_init()
-        
-    def set_tokenizer(self, tokenizer):
-        """Set tokenizer for special token handling."""
-        self._cache_special_token_ids(tokenizer)
-    
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[list[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        """
-        Forward pass with diffusion training logic.
-        
-        During training, applies forward diffusion process and bidirectional attention.
-        During inference, behaves like standard causal language model.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        
-        if self.training and input_ids is not None:
-            # Apply diffusion process during training
-            original_input_ids = input_ids.clone()
-            
-            # Apply forward process to get noisy input
-            noisy_input_ids, masked_indices, p_mask = self._forward_process(
-                input_ids, attention_mask, labels, self.config.eps
-            )
-            
-            # Create bidirectional attention mask
-            bidirectional_attention_mask = self._create_bidirectional_attention_mask(
-                input_ids, attention_mask
-            )
-            
-            # Forward pass with noisy input and bidirectional attention
-            outputs = super().forward(
-                input_ids=noisy_input_ids,
-                attention_mask=bidirectional_attention_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                inputs_embeds=inputs_embeds,
-                labels=None,  # Don't use standard loss computation
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-                cache_position=cache_position,
-                **kwargs,
-            )
-            
-            # Compute diffusion loss
-            loss = self._compute_diffusion_loss(
-                original_input_ids,
-                attention_mask,
-                labels,
-                outputs.logits,
-                masked_indices,
-                p_mask,
-            )
-            
-            if return_dict:
-                outputs.loss = loss
-                return outputs
-            else:
-                return (loss,) + outputs[1:]
-        else:
-            # Standard forward pass for inference
-            return super().forward(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                inputs_embeds=inputs_embeds,
-                labels=labels,
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-                cache_position=cache_position,
-                **kwargs,
-            )

src/axolotl/integrations/diffusion/plugin.py

@@ -1,98 +0,0 @@
-"""Diffusion LM training plugin for Axolotl."""
-
-from typing import TYPE_CHECKING
-
-from peft import PeftModel
-from transformers import AutoConfig, AutoModel, PreTrainedModel
-
-from axolotl.integrations.base import BasePlugin
-from axolotl.utils.dict import DictDefault
-from axolotl.utils.logging import get_logger
-
-from .callbacks import DiffusionGenerationCallback
-from .configuration import LlamaForDiffusionConfig, MistralForDiffusionConfig
-from .models import LlamaForDiffusionLM, MistralForDiffusionLM
-
-if TYPE_CHECKING:
-    from transformers import Trainer
-
-LOG = get_logger(__name__)
-
-
-class DiffusionPlugin(BasePlugin):
-    """
-    Plugin for diffusion language model training.
-
-    This plugin enables diffusion-based training using the LLaDA approach, which uses
-    random masking and bidirectional attention to train language models.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.cfg = None
-
-    def get_input_args(self) -> str:
-        """Returns the pydantic model for LLaDA plugin arguments."""
-        return "axolotl.integrations.diffusion.DiffusionArgs"
-
-    def pre_model_load(self, cfg: DictDefault):
-        """Configure model loading to use diffusion model classes."""
-        # Map base model types to diffusion equivalents
-        base_model_type = cfg.get("model_type")
-        
-        if base_model_type == "llama":
-            # Create diffusion config from base config
-            diffusion_config = LlamaForDiffusionConfig(
-                mask_token_id=getattr(cfg, "mask_token_id", 32000),
-                eps=getattr(cfg, "eps", 1e-3),
-                importance_weighting=getattr(cfg, "importance_weighting", False),
-                sample_packing=getattr(cfg, "sample_packing", False),
-                min_mask_ratio=getattr(cfg, "min_mask_ratio", 0.0),
-                max_mask_ratio=getattr(cfg, "max_mask_ratio", 1.0),
-                noise_schedule=getattr(cfg, "noise_schedule", "linear"),
-            )
-            
-            # Override model type for loading
-            cfg.model_type = "llama_diffusion"
-            
-        elif base_model_type == "mistral":
-            # Create diffusion config from base config
-            diffusion_config = MistralForDiffusionConfig(
-                mask_token_id=getattr(cfg, "mask_token_id", 32000),
-                eps=getattr(cfg, "eps", 1e-3),
-                importance_weighting=getattr(cfg, "importance_weighting", False),
-                sample_packing=getattr(cfg, "sample_packing", False),
-                min_mask_ratio=getattr(cfg, "min_mask_ratio", 0.0),
-                max_mask_ratio=getattr(cfg, "max_mask_ratio", 1.0),
-                noise_schedule=getattr(cfg, "noise_schedule", "linear"),
-            )
-            
-            # Override model type for loading
-            cfg.model_type = "mistral_diffusion"
-        else:
-            LOG.warning(f"Diffusion plugin not implemented for model type: {base_model_type}")
-
-    def post_model_load(self, cfg: DictDefault, model: PreTrainedModel | PeftModel):
-        """Configure model after loading."""
-        self.cfg = cfg
-        
-        # Set tokenizer on diffusion models for special token handling
-        if hasattr(model, "set_tokenizer"):
-            # Get tokenizer from cfg if available
-            tokenizer = getattr(cfg, "tokenizer", None)
-            if tokenizer is not None:
-                model.set_tokenizer(tokenizer)
-
-    def add_callbacks_post_trainer(self, cfg: DictDefault, trainer: "Trainer"):
-        """Add diffusion-specific callbacks after trainer creation."""
-        callbacks = []
-        
-        # Store diffusion config on trainer for callbacks
-        trainer.diffusion_config = cfg
-        
-        # Add generation callback if enabled
-        if cfg.get("generate_samples", False):
-            generation_callback = DiffusionGenerationCallback(trainer)
-            callbacks.append(generation_callback)
-            
-        return callbacks

src/axolotl/integrations/grokfast/__init__.py

@@ -7,7 +7,7 @@ from transformers.trainer_callback import TrainerCallback
 from axolotl.utils.logging import get_logger
 
 from ..base import BasePlugin
-from .args import GrokfastArgs  # pylint: disable=unused-import. # noqa: F401
+from .args import GrokfastArgs as GrokfastArgs
 from .optimizer import gradfilter_ema
 
 LOG = get_logger(__name__)
@@ -24,12 +24,10 @@ class GrokfastCallbackHandler(TrainerCallback):
         self.alpha = alpha
         self.lamb = lamb
 
-    def on_train_begin(self, *args_, **kwargs):  # pylint: disable=unused-argument
+    def on_train_begin(self, *args_, **kwargs):
         self.grads = None
 
-    def on_pre_optimizer_step(
-        self, args_, state, control, **kwargs
-    ):  # pylint: disable=unused-argument
+    def on_pre_optimizer_step(self, args_, state, control, **kwargs):
         model = kwargs.pop("model")
         self.grads = gradfilter_ema(model, self.grads, alpha=self.alpha, lamb=self.lamb)
         return control

src/axolotl/integrations/grokfast/optimizer.py

@@ -1,7 +1,6 @@
 # Copyright: MIT License (c) 2024 Jaerin Lee, Bong Gyun Kang, Kihoon Kim, Kyoung Mu Lee
 # Reference: https://github.com/ironjr/grokfast
 
-# pylint: skip-file
 from collections import deque
 from typing import Dict, Literal, Optional
 

src/axolotl/integrations/kd/__init__.py

@@ -15,6 +15,7 @@
 """
 Plugin init to add KD support to Axolotl.
 """
+
 from typing import Any
 
 from transformers import Trainer
@@ -22,7 +23,7 @@ from transformers import Trainer
 from axolotl.integrations.base import BasePlugin
 from axolotl.integrations.kd.callbacks import KDTemperatureSchedulerCallback
 
-from .args import KDArgs  # pylint: disable=unused-import. # noqa: F401
+from .args import KDArgs as KDArgs
 
 
 class KDPlugin(BasePlugin):

src/axolotl/integrations/kd/args.py

@@ -15,6 +15,7 @@
 """
 Plugin args for KD support.
 """
+
 from dataclasses import dataclass
 from enum import Enum
 
@@ -26,8 +27,8 @@ class InferenceServerType(str, Enum):
     Online inferences server types to handle different request args
     """
 
-    vllm = "vllm"  # pylint: disable=invalid-name
-    sglang = "sglang"  # pylint: disable=invalid-name
+    vllm = "vllm"
+    sglang = "sglang"
 
 
 class KDArgs(BaseModel):

src/axolotl/integrations/kd/callbacks.py

@@ -19,9 +19,7 @@ class KDTemperatureSchedulerCallback(TrainerCallback):
 
         self.trainer = trainer
 
-    def on_step_end(
-        self, args, state, control, **kwargs
-    ):  # pylint: disable=unused-argument
+    def on_step_end(self, args, state, control, **kwargs):
         # cosine decay temperature over the max steps
 
         progress = state.global_step / state.max_steps

src/axolotl/integrations/kd/chat_template.py

@@ -15,6 +15,7 @@
 """
 Chat template prompt strategy loader with KD support
 """
+
 import logging
 from typing import Any, Dict
 
@@ -192,7 +193,6 @@ class ChatTemplateStrategyWithKDv2(ChatTemplateStrategyWithKD):
         """
         Transform logprobs to target format for KD training
         """
-        # pylint: disable=duplicate-code
 
         logprobs = sample.pop(self.logprobs_field)
         target_seq_len = len(logprobs)
@@ -240,7 +240,7 @@ class ChatTemplateStrategyWithKDv2(ChatTemplateStrategyWithKD):
                 target_mask.append([1] * top_k)
 
         for token_pos_logprobs, pos_target_token_ids in zip(
-            logprobs, sample["target_token_ids"]
+            logprobs, sample["target_token_ids"], strict=False
         ):
             # Convert to a tensor for easier manipulation
             position_logprobs_tensor = torch.tensor(
@@ -299,7 +299,7 @@ class KDStrategyLoader(StrategyLoader):
     Load ChatTemplateStrategy with KD support using StrategyLoader.
     """
 
-    def _get_strategy_cls(self, cfg):  # pylint: disable=unused-argument
+    def _get_strategy_cls(self, cfg):
         return ChatTemplateStrategyWithKD
 
     def _get_strategy_params(self, cfg, ds_cfg: Dict[str, Any]):
@@ -319,7 +319,7 @@ class KDStrategyLoaderV2(KDStrategyLoader):
     Load KD chat template datasets with pre-tokenized logprob data
     """
 
-    def _get_strategy_cls(self, cfg):  # pylint: disable=unused-argument
+    def _get_strategy_cls(self, cfg):
         return ChatTemplateStrategyWithKDv2
 
 

src/axolotl/integrations/kd/collator.py

@@ -37,7 +37,6 @@ class DataCollatorForKD(DataCollatorForSeq2Seq):
     target_logprobs. It also creates a teacher_mask to indicate which entries are valid.
     """
 
-    # pylint: disable=duplicate-code
     tokenizer: PreTrainedTokenizerBase
     model: Optional[Any] = None
     padding: Union[bool, str, PaddingStrategy] = True
@@ -72,7 +71,7 @@ class DataCollatorForKD(DataCollatorForSeq2Seq):
                         // self.pad_to_multiple_of
                     ) * self.pad_to_multiple_of
 
-                for f in features:  # pylint: disable=invalid-name
+                for f in features:
                     remainder = [pad_token_id] * (max_len - len(f[feature_name]))
                     if isinstance(f[feature_name], list):
                         f[feature_name] = (
@@ -101,7 +100,7 @@ class DataCollatorForKD(DataCollatorForSeq2Seq):
 
         if has_teacher_data:
             # Extract and remove from features
-            for f in features:  # pylint: disable=invalid-name
+            for f in features:
                 target_logprobs_list.append(f.pop("target_logprobs"))
                 target_token_ids_list.append(f.pop("target_token_ids"))
                 target_mask_list.append(f.pop("target_mask"))
@@ -117,24 +116,25 @@ class DataCollatorForKD(DataCollatorForSeq2Seq):
             padded_teacher_mask_list = []
 
             for t_logprobs, t_ids, t_mask in zip(
-                target_logprobs_list, target_token_ids_list, target_mask_list
+                target_logprobs_list,
+                target_token_ids_list,
+                target_mask_list,
+                strict=False,
             ):
                 t_logprobs_padded = []
                 t_ids_padded = []
                 t_mask_padded = []
 
-                for lp, ids, mask in zip(  # pylint: disable=invalid-name
-                    t_logprobs, t_ids, t_mask
-                ):
+                for lp, ids, mask in zip(t_logprobs, t_ids, t_mask, strict=False):
                     lp_len = len(lp)
                     if lp_len < max_k:
                         # Use -1e9 for padding logprobs and 0 for token_ids
                         pad_len = max_k - lp_len
-                        lp = lp + [-1e9] * pad_len  # pylint: disable=invalid-name
+                        lp = lp + [-1e9] * pad_len
                         ids = ids + [0] * pad_len
                         mask = mask + [0] * pad_len
                     else:
-                        lp = lp[:max_k]  # pylint: disable=invalid-name
+                        lp = lp[:max_k]
                         ids = ids[:max_k]
                         mask = mask[:max_k]
 
@@ -216,9 +216,7 @@ class KDBatchSamplerDataCollatorForSeq2Seq(DataCollatorForKD):
         #    We want to produce a single "merged" feature dict for each sub-batch.
         out_features = [{} for _ in features]
 
-        for i, sub_features in enumerate(  # pylint: disable=too-many-nested-blocks
-            features
-        ):
+        for i, sub_features in enumerate(features):
             # sub_features is a list of dicts, each dict = one sequence’s features
             # We'll merge them into out_features[i].
             #
@@ -255,9 +253,7 @@ class KDBatchSamplerDataCollatorForSeq2Seq(DataCollatorForKD):
                         if field_name in feat and isinstance(
                             feat[field_name], (list, torch.Tensor)
                         ):
-                            if isinstance(
-                                feat[field_name][0], (dict, str)
-                            ):  # pylint: disable=too-many-nested-blocks
+                            if isinstance(feat[field_name][0], (dict, str)):
                                 continue
                             arr = np.array(feat[field_name])
                             arrays.append(arr)

src/axolotl/integrations/kd/collator_online_teacher.py

@@ -144,7 +144,7 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
                 }
 
             for sequence_data, seq_input_ids, seq_labels in zip(
-                api_data, batch_input_ids, labels
+                api_data, batch_input_ids, labels, strict=False
             ):
                 current_target_logprobs = []
                 current_target_token_ids = []
@@ -165,7 +165,7 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
                 assert len(seq_input_ids) == len(input_top_logprobs)
 
                 for i, _, label in zip(
-                    range(len(seq_input_ids)), seq_input_ids, seq_labels
+                    range(len(seq_input_ids)), seq_input_ids, seq_labels, strict=False
                 ):
                     if i < len(input_top_logprobs) and input_top_logprobs[i] is None:
                         # this is always the case for the first token.
@@ -202,7 +202,8 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
 
                         # pos_top_logprobs: list of logprobs, pos_token_ids: list of token_ids
                         pos_logprobs_raw, pos_token_ids, _ = [
-                            list(row) for row in zip(*pos_top_logprobs_data)
+                            list(row)
+                            for row in zip(*pos_top_logprobs_data, strict=False)
                         ]
 
                         # Ensure correct length (top_k)
@@ -317,7 +318,7 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
                 }
 
             for sequence_data, seq_input_ids, seq_labels in zip(
-                choices, batch_input_ids, labels
+                choices, batch_input_ids, labels, strict=False
             ):
                 # seq_input_ids: List[int]
                 # seq_labels: List[int]
@@ -342,7 +343,9 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
 
                 seq_len = len(seq_input_ids)
 
-                for i, _, label in zip(range(seq_len), seq_input_ids, seq_labels):
+                for i, _, label in zip(
+                    range(seq_len), seq_input_ids, seq_labels, strict=False
+                ):
                     if i < len(input_top_logprobs) and input_top_logprobs[i] is None:
                         # this is always the case for the first token.
                         # there is never logprob data for the first token since that's a true input
@@ -424,7 +427,7 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
                             list(range(self.kd_online_topk))
                         )
                         current_target_mask.append([0] * self.kd_online_topk)
-                for i in range(max(0, seq_len - len(current_target_logprobs))):
+                for _ in range(max(0, seq_len - len(current_target_logprobs))):
                     current_target_logprobs.append(
                         [-float("inf")] * self.kd_online_topk
                     )

src/axolotl/integrations/kd/kernels/liger.py

@@ -197,7 +197,7 @@ class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
         compute_ce_loss: bool = True,
         normalize_topk: bool = True,
     ):
-        CHUNK_SIZE = chunk_size  # pylint: disable=invalid-name
+        CHUNK_SIZE = chunk_size
         grad_weight_acc = torch.zeros_like(student_lm_head_weight)
         grad_inputs_list = []
         grad_bias_acc = (
@@ -298,8 +298,8 @@ class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
             accumulate_chunk_grads_compiled = accumulate_chunk_grads
 
         # Use the same chunking logic as LigerFusedLinearDistillationBase.forward
-        B, N, D = student_input.shape  # pylint: disable=invalid-name
-        K = target_token_ids.shape[-1]  # pylint: disable=invalid-name
+        B, N, D = student_input.shape
+        K = target_token_ids.shape[-1]
 
         student_input_flat = student_input.reshape(-1, student_input.shape[-1])
         target_token_ids_flat = target_token_ids.reshape(-1, target_token_ids.shape[-1])

src/axolotl/integrations/kd/kernels/models.py

@@ -40,10 +40,9 @@ def kldiv_forward_llama_like(
     output_attentions: Optional[bool] = None,
     output_hidden_states: Optional[bool] = None,
     cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,  # pylint: disable=unused-argument
+    logits_to_keep: Union[int, torch.Tensor] = 0,
     **kwargs: Unpack[TransformersKwargs],  # type: ignore[misc]
 ) -> CausalLMOutputWithPast:
-    # pylint: disable=duplicate-code
     output_attentions = (
         output_attentions
         if output_attentions is not None

src/axolotl/integrations/kd/topk_logprob/forward_kl.py

@@ -15,6 +15,7 @@
 """
 loss for top_k KL divergence
 """
+
 import torch
 from torch import nn
 
@@ -117,7 +118,6 @@ class ChunkedTopKKDLoss(nn.Module):
         target_mask: torch.Tensor,  # [B, seq_len, K]
         num_items_in_batch: int = -1,  # optional batch size for normalization
     ) -> torch.Tensor:
-
         # 1. Split along the "token" dimension (dim=1).
         student_logits_chunks = student_logits.chunk(self.num_output_chunks, dim=1)
         token_ids_chunks = target_token_ids.chunk(self.num_output_chunks, dim=1)
@@ -131,7 +131,11 @@ class ChunkedTopKKDLoss(nn.Module):
 
         # 2. Loop over each chunk and compute a chunk-specific loss.
         for st_chunk, tid_chunk, lp_chunk, msk_chunk in zip(
-            student_logits_chunks, token_ids_chunks, logprobs_chunks, mask_chunks
+            student_logits_chunks,
+            token_ids_chunks,
+            logprobs_chunks,
+            mask_chunks,
+            strict=False,
         ):
             # We pass num_items_in_batch=-1 so that the kd_loss
             # will average over *this chunk's* valid tokens only.

src/axolotl/integrations/kd/trainer.py

@@ -21,7 +21,6 @@ from axolotl.core.trainers.base import AxolotlTrainer
 from .kernels.liger import LigerFusedLinearKLTopKLogprobLoss
 
 
-# pylint: disable=too-many-ancestors
 class AxolotlKDTrainer(AxolotlTrainer):
     """
     Custom trainer subclass for Knowledge Distillation (KD)

src/axolotl/integrations/liger/__init__.py

@@ -18,6 +18,7 @@ Module for the Plugin for LIGER integraton with Axolotl.
 Liger Kernel is the collection of Triton-native kernels for LLM Training.
 It is designed to be performant, correct, and light-weight.
 """
+
 from .args import LigerArgs
 from .plugin import LigerPlugin
 

src/axolotl/integrations/liger/models/base.py

@@ -41,7 +41,6 @@ def lce_forward(
             This is useful when using packed tensor format (single dimension for batch and sequence length).
     """
 
-    # pylint: disable=duplicate-code
     output_attentions = (
         output_attentions
         if output_attentions is not None
@@ -181,7 +180,7 @@ def patch_lce_forward(
         model_cls = getattr(module, f"{model_cls_prefix}ForCausalLM")
 
         model_cls.forward = lce_forward
-    # pylint: disable=duplicate-code
+
     except (ImportError, AttributeError) as e:
         raise RuntimeError(
             f"Could not import ForCausalLM class for model_type: {model_type}. "

src/axolotl/integrations/liger/models/deepseekv2.py

@@ -2,8 +2,6 @@
 DeepseekV2 model with LigerFusedLinearCrossEntropyLoss
 """
 
-# pylint: disable=duplicate-code
-
 from typing import List, Optional, Tuple, Union
 
 import torch

src/axolotl/integrations/liger/models/jamba.py

@@ -2,8 +2,6 @@
 Jamba model with LigerFusedLinearCrossEntropyLoss
 """
 
-# pylint: disable=duplicate-code
-
 from typing import Optional, Tuple, Union
 
 import torch

src/axolotl/integrations/liger/models/llama4.py

@@ -46,7 +46,6 @@ def lce_forward(
     Returns:
     """
 
-    # pylint: disable=duplicate-code
     output_attentions = (
         output_attentions
         if output_attentions is not None
@@ -78,9 +77,7 @@ def lce_forward(
     hidden_states = outputs[0]
 
     if hasattr(self.config, "pretraining_tp") and self.config.pretraining_tp > 1:
-        raise Exception(  # pylint: disable=broad-exception-raised
-            "Liger Kernel does not support pretraining_tp!!"
-        )
+        raise Exception("Liger Kernel does not support pretraining_tp!!")
 
     logits = None
     loss = None
@@ -128,7 +125,7 @@ def apply_liger_kernel_to_llama4(
     rms_norm: bool = False,
     glu_activation: bool = False,
     layer_norm: bool = False,
-    **kwargs,  # pylint: disable=unused-argument
+    **kwargs,
 ) -> None:
     """
     Apply Liger kernels to replace original implementation in HuggingFace Llama models (2 and 3)
@@ -144,15 +141,15 @@ def apply_liger_kernel_to_llama4(
         layer_norm (bool): Whether to apply Liger's LayerNorm. Default is False.
     """
 
-    import transformers.models.llama4.modeling_llama4  # noqa: F401  # pylint: disable=unused-import
+    import transformers.models.llama4.modeling_llama4  # noqa: F401
     from liger_kernel.transformers.functional import liger_cross_entropy
     from liger_kernel.transformers.layer_norm import LigerLayerNorm
     from liger_kernel.transformers.rms_norm import LigerRMSNorm
     from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
 
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     modeling_llama4 = sys.modules["transformers.models.llama4.modeling_llama4"]
 
@@ -165,7 +162,7 @@ def apply_liger_kernel_to_llama4(
             # clone config to avoid modifying the original
             config = deepcopy(config)
             if intermediate_size:
-                setattr(config, "intermediate_size", intermediate_size)
+                config.intermediate_size = intermediate_size
             return LigerSwiGLUMLP(config, **kwargs)
 
         modeling_llama4.Llama4TextMLP = _liger_swiglu_mlp_wrapper

src/axolotl/integrations/liger/models/qwen3.py

@@ -43,7 +43,6 @@ def lce_forward(
     Returns:
     """
 
-    # pylint: disable=duplicate-code
     output_attentions = (
         output_attentions
         if output_attentions is not None
@@ -113,9 +112,8 @@ def apply_liger_kernel_to_qwen3(
     rms_norm: bool = False,
     glu_activation: bool = False,
     layer_norm: bool = False,
-    **kwargs,  # pylint: disable=unused-argument
+    **kwargs,
 ) -> None:
-    # pylint: disable=duplicate-code
     """
     Apply Liger kernels to replace original implementation in HuggingFace Llama models (2 and 3)
 
@@ -130,15 +128,15 @@ def apply_liger_kernel_to_qwen3(
         layer_norm (bool): Whether to apply Liger's LayerNorm. Default is False.
     """
 
-    import transformers.models.qwen3.modeling_qwen3  # noqa: F401  # pylint: disable=unused-import
+    import transformers.models.qwen3.modeling_qwen3  # noqa: F401
     from liger_kernel.transformers.functional import liger_cross_entropy
     from liger_kernel.transformers.layer_norm import LigerLayerNorm
     from liger_kernel.transformers.rms_norm import LigerRMSNorm
     from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
 
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     modeling_qwen3 = sys.modules["transformers.models.qwen3.modeling_qwen3"]
 

src/axolotl/integrations/liger/models/qwen3_moe.py

@@ -45,7 +45,6 @@ def lce_forward(
     Returns:
     """
 
-    # pylint: disable=duplicate-code
     output_attentions = (
         output_attentions
         if output_attentions is not None
@@ -135,9 +134,8 @@ def apply_liger_kernel_to_qwen3_moe(
     rms_norm: bool = False,
     glu_activation: bool = False,
     layer_norm: bool = False,
-    **kwargs,  # pylint: disable=unused-argument
+    **kwargs,
 ) -> None:
-    # pylint: disable=duplicate-code
     """
     Apply Liger kernels to replace original implementation in HuggingFace Llama models (2 and 3)
 
@@ -152,15 +150,15 @@ def apply_liger_kernel_to_qwen3_moe(
         layer_norm (bool): Whether to apply Liger's LayerNorm. Default is False.
     """
 
-    import transformers.models.qwen3_moe.modeling_qwen3_moe  # noqa: F401  # pylint: disable=unused-import
+    import transformers.models.qwen3_moe.modeling_qwen3_moe  # noqa: F401
     from liger_kernel.transformers.functional import liger_cross_entropy
     from liger_kernel.transformers.layer_norm import LigerLayerNorm
     from liger_kernel.transformers.rms_norm import LigerRMSNorm
     from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
 
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     modeling_qwen3_moe = sys.modules["transformers.models.qwen3_moe.modeling_qwen3_moe"]
 
@@ -174,7 +172,7 @@ def apply_liger_kernel_to_qwen3_moe(
             # clone config to avoid modifying the original
             config = deepcopy(config)
             if intermediate_size:
-                setattr(config, "intermediate_size", intermediate_size)
+                config.intermediate_size = intermediate_size
             return LigerSwiGLUMLP(config, **kwargs)
 
         modeling_qwen3_moe.Qwen3MoeMLP = _liger_swiglu_mlp_wrapper

src/axolotl/integrations/lm_eval/__init__.py

@@ -7,7 +7,7 @@ import subprocess  # nosec
 from axolotl.integrations.base import BasePlugin
 from axolotl.integrations.lm_eval.cli import build_lm_eval_command
 
-from .args import LMEvalArgs  # pylint: disable=unused-import. # noqa: F401
+from .args import LMEvalArgs as LMEvalArgs
 
 
 class LMEvalPlugin(BasePlugin):
@@ -20,7 +20,6 @@ class LMEvalPlugin(BasePlugin):
 
     def post_train_unload(self, cfg):
         if cfg.lm_eval_post_train:
-            # pylint: disable=duplicate-code
             for lm_eval_args in build_lm_eval_command(
                 cfg.lm_eval_tasks,
                 bfloat16=cfg.bfloat16 or cfg.bf16,

src/axolotl/integrations/lm_eval/cli.py

@@ -99,7 +99,6 @@ def lm_eval(config: str, cloud: Optional[str] = None):
         with open(config, encoding="utf-8") as file:
             cfg: DictDefault = DictDefault(yaml.safe_load(file))
 
-        # pylint: disable=duplicate-code
         for lm_eval_args in build_lm_eval_command(
             cfg.lm_eval_tasks,
             bfloat16=cfg.bfloat16 or cfg.bf16,

src/axolotl/integrations/spectrum/__init__.py

@@ -23,7 +23,7 @@ import requests
 from axolotl.integrations.base import BasePlugin
 from axolotl.utils.logging import get_logger
 
-from .args import SpectrumArgs  # pylint: disable=unused-import. # noqa: F401
+from .args import SpectrumArgs as SpectrumArgs
 
 LOG = get_logger(__name__)
 
@@ -46,7 +46,7 @@ def _generate_unfrozen_params_yaml(snr_data, top_fraction=0.5):
         "^lm_head.weight$",
         "^model.embed_tokens.weight$",
     ]
-    for layer_type, layer_names in top_layers_by_type.items():
+    for _, layer_names in top_layers_by_type.items():
         for layer_name in layer_names:
             unfrozen_parameters.append(layer_name)
     return unfrozen_parameters
@@ -84,7 +84,7 @@ class SpectrumPlugin(BasePlugin):
                 snr_data = json.load(fin)
         except FileNotFoundError:
             pass
-        except Exception as exc:  # pylint: disable=broad-exception-caught
+        except Exception as exc:
             LOG.warning(f"Failed to read SNR data from {snr_path}: {exc}")
 
         if not snr_data:

src/axolotl/integrations/spectrum/args.py

@@ -15,6 +15,7 @@
 """
 Module for handling Spectrum input arguments.
 """
+
 from typing import Optional
 
 from pydantic import BaseModel, model_validator

src/axolotl/kernels/geglu.py

@@ -5,8 +5,6 @@ See "GLU Variants Improve Transformer" (https://arxiv.org/abs/2002.05202).
 Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.
 """
 
-# pylint: disable=invalid-name,unnecessary-lambda-assignment,duplicate-code
-
 import torch
 import triton
 import triton.language as tl

src/axolotl/kernels/lora.py

@@ -7,8 +7,6 @@ See "LoRA: Low-Rank Adaptation of Large Language Models"
 Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.
 """
 
-# pylint: disable=invalid-name
-
 from typing import Callable
 
 import torch

src/axolotl/kernels/quantize.py

@@ -1,7 +1,5 @@
 """Dequantization utilities for `bitsandbytes` integration."""
 
-# pylint: disable=invalid-name,global-statement
-
 import ctypes
 
 import bitsandbytes as bnb

src/axolotl/kernels/swiglu.py

@@ -99,7 +99,6 @@ def _swiglu_bwd_kernel(
     tl.store(up_ptr + offsets, grad_up, mask=mask)  # grad wrt up
 
 
-# pylint: disable=unnecessary-lambda-assignment
 def swiglu_forward(gate: torch.Tensor, up: torch.Tensor) -> torch.Tensor:
     """
     SwiGLU forward pass. Computes SwiGLU activation: `x * sigmoid(x) * up`, where
@@ -128,7 +127,6 @@ def swiglu_forward(gate: torch.Tensor, up: torch.Tensor) -> torch.Tensor:
     return out
 
 
-# pylint: disable=unnecessary-lambda-assignment
 def swiglu_backward(
     grad_output: torch.Tensor, gate: torch.Tensor, up: torch.Tensor
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:

src/axolotl/loaders/__init__.py

@@ -1,6 +1,5 @@
 """Init for axolotl.loaders module"""
 
-# pylint: disable=unused-import
 # flake8: noqa
 
 from .adapter import load_adapter, load_lora

src/axolotl/loaders/adapter.py

@@ -28,14 +28,12 @@ LOG = get_logger(__name__)
 def setup_quantized_meta_for_peft(model: torch.nn.Module):
     """Replaces `quant_state.to` with a dummy function to prevent PEFT from moving `quant_state` to meta device"""
 
-    def temp_to_method(self, *args, **kwargs):  # pylint: disable=unused-argument
+    def temp_to_method(self, *args, **kwargs):
         return self
 
     for param in model.parameters():
         if isinstance(param, Params4bit):
-            param.quant_state._orig_to = (  # pylint: disable=protected-access
-                param.quant_state.to
-            )
+            param.quant_state._orig_to = param.quant_state.to
             param.quant_state.to = types.MethodType(temp_to_method, param.quant_state)
 
 
@@ -43,10 +41,8 @@ def setup_quantized_peft_meta_for_training(model: torch.nn.Module):
     """Replaces dummy `quant_state.to` method with the original function to allow training to continue"""
     for param in model.parameters():
         if isinstance(param, Params4bit) and hasattr(param.quant_state, "_orig_to"):
-            param.quant_state.to = (
-                param.quant_state._orig_to  # pylint: disable=protected-access
-            )
-            param.quant_state._orig_to = None  # pylint: disable=protected-access
+            param.quant_state.to = param.quant_state._orig_to
+            param.quant_state._orig_to = None
 
 
 def find_all_linear_names(model):