Merge branch 'main' into map-dataset-fetcher-fix

* fix(doc): default messages example used wrong key

* feat: add links to SP, multi-gpu, multi-node on readme

.pre-commit-config.yaml

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

README.md

@@ -43,7 +43,7 @@ Features:
 - **Multiple Model Support**: Train various models like LLaMA, Mistral, Mixtral, Pythia, and more. We are compatible with HuggingFace transformers causal language models.
 - **Training Methods**: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), Multimodal, and Reward Modelling (RM) / Process Reward Modelling (PRM).
 - **Easy Configuration**: Re-use a single YAML file between dataset preprocess, training, evaluation, quantization, and inference.
-- **Performance Optimizations**: [Multipacking](https://docs.axolotl.ai/docs/multipack.html), [Flash Attention](https://github.com/Dao-AILab/flash-attention), [Xformers](https://github.com/facebookresearch/xformers), [Flex Attention](https://pytorch.org/blog/flexattention/), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), [Cut Cross Entropy](https://github.com/apple/ml-cross-entropy/tree/main), Sequence Parallelism (SP), LoRA optimizations, Multi-GPU training (FSDP1, FSDP2, DeepSpeed), Multi-node training (Torchrun, Ray), and many more!
+- **Performance Optimizations**: [Multipacking](https://docs.axolotl.ai/docs/multipack.html), [Flash Attention](https://github.com/Dao-AILab/flash-attention), [Xformers](https://github.com/facebookresearch/xformers), [Flex Attention](https://pytorch.org/blog/flexattention/), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), [Cut Cross Entropy](https://github.com/apple/ml-cross-entropy/tree/main), [Sequence Parallelism (SP)](https://docs.axolotl.ai/docs/sequence_parallelism.html), [LoRA optimizations](https://docs.axolotl.ai/docs/lora_optims.html), [Multi-GPU training (FSDP1, FSDP2, DeepSpeed)](https://docs.axolotl.ai/docs/multi-gpu.html), [Multi-node training (Torchrun, Ray)](https://docs.axolotl.ai/docs/multi-node.html), and many more!
 - **Flexible Dataset Handling**: Load from local, HuggingFace, and cloud (S3, Azure, GCP, OCI) datasets.
 - **Cloud Ready**: We ship [Docker images](https://hub.docker.com/u/axolotlai) and also [PyPI packages](https://pypi.org/project/axolotl/) for use on cloud platforms and local hardware.
 

docs/dataset-formats/conversation.qmd

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

src/axolotl/core/trainers/base.py

@@ -116,6 +116,7 @@ class AxolotlTrainer(
             sequential=self.args.sample_packing_sequentially,
             drop_last=True,
             num_processes=self.args.dataset_num_proc,
+            mp_start_method=self.args.sample_packing_mp_start_method or "fork",
         )
 
         len(sampler)

src/axolotl/core/training_args_base.py

@@ -38,6 +38,10 @@ class AxolotlTrainingMixins:
             "help": "Use next-fit sample packing that preserves the order of samples coming from the sampler. Use in combination with curriculum_sampling for fully sequential packing."
         },
     )
+    sample_packing_mp_start_method: str | None = field(
+        default=None,
+        metadata={"help": "The multiprocessing start method to use."},
+    )
     multipack_real_batches: bool = field(
         default=False,
         metadata={"help": "Use real batches for efficient training."},

src/axolotl/monkeypatch/data/batch_dataset_fetcher.py

@@ -9,6 +9,9 @@ from torch.utils.data._utils.worker import _worker_loop
 
 class _MapDatasetFetcher(_BaseDatasetFetcher):
     def fetch(self, possibly_batched_index):
+        if not possibly_batched_index:
+            return self.collate_fn([])
+
         if isinstance(possibly_batched_index[0], list):
             data = [None for i in possibly_batched_index]
             for i, possibly_batched_index_ in enumerate(possibly_batched_index):

src/axolotl/utils/ctx_managers/sequence_parallel.py

@@ -208,9 +208,6 @@ class SequenceParallelContextManager:
         self.original_seq_len = 0
         self.pad_len = 0
 
-        # Store kwargs passed to model forward pass
-        self.original_kwargs: None | dict[str, torch.Tensor] = None
-
         # Create a partially applied version of the apply_sequence_parallelism function
         self.apply_sequence_parallelism = functools.partial(
             apply_sequence_parallelism,
@@ -263,9 +260,6 @@ class SequenceParallelContextManager:
             # Any excess positional arguments are kept as-is
             remaining_args = args[len(forward_params) :]
 
-            # Store original kwargs
-            self.original_kwargs = {key: value.clone() for key, value in updated_kwargs.items()}
-
             # Apply sequence parallelism to updated kwargs
             updated_kwargs, self.original_seq_len, self.pad_len = (
                 self.apply_sequence_parallelism(updated_kwargs)

src/axolotl/utils/samplers/multipack.py

@@ -127,7 +127,7 @@ def pack_parallel(
     bin_size: int,
     num_processes: int | None = None,
     safe_mode: bool = True,
-    mp_start_method: str | None = "spawn",
+    mp_start_method: str | None = "fork",
 ) -> list[list[int]]:
     """Pack sequences into bins using parallel processing.
 
@@ -266,6 +266,7 @@ class MultipackBatchSampler(BatchSampler):
         bin_size: int = 200,  # The max number of samples that can be packed in a single bin
         num_processes: int | None = None,  # Number of processes for parallel packing
         safe_mode: bool = True,  # Conservative packing to prevent training instability
+        mp_start_method: str = "fork",
         **kwargs,  # pylint: disable=unused-argument
     ):
         super().__init__(sampler, batch_size, drop_last)
@@ -278,6 +279,7 @@ class MultipackBatchSampler(BatchSampler):
         self.bin_size = bin_size
         self.num_processes = num_processes
         self.safe_mode = safe_mode
+        self.mp_start_method = mp_start_method
 
         assert isinstance(self.lengths, np.ndarray)
 
@@ -338,8 +340,9 @@ class MultipackBatchSampler(BatchSampler):
                 bin_capacity=self.batch_max_len,
                 group_size=self.group_size,
                 bin_size=self.bin_size,
-                num_processes=self.num_processes,
+                num_processes=max(4, self.num_processes) if self.num_processes else 4,
                 safe_mode=self.safe_mode,
+                mp_start_method=self.mp_start_method,
             )
 
             # Map bin indices back to original indices

src/axolotl/utils/schemas/config.py

@@ -393,6 +393,12 @@ class AxolotlInputConfig(
         default=None,
         json_schema_extra={"description": "Whether to pack samples sequentially"},
     )
+    sample_packing_mp_start_method: str | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "The multiprocessing start method to use for packing. Should be 'fork', 'spawn' or 'forkserver'"
+        },
+    )
     eval_sample_packing: bool | None = Field(
         default=None,
         json_schema_extra={

src/axolotl/utils/trainer.py

@@ -467,6 +467,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                 sequential=cfg.sample_packing_sequentially,
                 drop_last=True,
                 num_processes=cfg.dataset_processes,
+                mp_start_method=cfg.sample_packing_mp_start_method or "fork",
             )
 
             data_loader = DataLoader(