Ray Train Axolotl Integration (#2251)

* current

not clean working version
move torch trainer to do_cli
update code with config changes and clean up
edit config
cleanup
add run name to trainer

* address comments

* use axolotl train in multigpu tests and add ray tests for multi-gpu

* accelerate uses underscores for main_process_port arg

* chore: lint

* fix order of accelerate args

* include ray train in docker images

* current

not clean working version
move torch trainer to do_cli
update code with config changes and clean up
edit config
cleanup
add run name to trainer

* address comments

* use axolotl train in multigpu tests and add ray tests for multi-gpu

* accelerate uses underscores for main_process_port arg

* chore: lint

* fix order of accelerate args

* include ray train in docker images

* fix bf16 resolution behavior

* move dtype logic

* x

Signed-off-by: SumanthRH <[email protected]>

* rename

Signed-off-by: SumanthRH <[email protected]>

* add to sidebar

Signed-off-by: SumanthRH <[email protected]>

* Apply suggestions from code review

Co-authored-by: Eric Tang <[email protected]>

* Update docs/ray-integration.qmd

Co-authored-by: Eric Tang <[email protected]>

* pre-commit fixes

Signed-off-by: SumanthRH <[email protected]>

* use output_dir instead of hardcoded saves path

Co-authored-by: NanoCode012 <[email protected]>

* bugfix storage dir

* change type\ for resources_per_worker

---------

Signed-off-by: SumanthRH <[email protected]>
Co-authored-by: Wing Lian <[email protected]>
Co-authored-by: SumanthRH <[email protected]>
Co-authored-by: Sumanth R Hegde <[email protected]>
Co-authored-by: Wing Lian <[email protected]>
Co-authored-by: NanoCode012 <[email protected]>

_quarto.yml

@@ -38,6 +38,7 @@ website:
             - docs/multi-node.qmd
             - docs/unsloth.qmd
             - docs/amd_hpc.qmd
+            - docs/ray-integration.qmd
         - section: "Dataset Formats"
           contents: docs/dataset-formats/*
         - section: "Reference"

cicd/Dockerfile.jinja

@@ -32,9 +32,9 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
     fi
 
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

docker/Dockerfile

@@ -20,9 +20,9 @@ WORKDIR /workspace/axolotl
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

docs/ray-integration.qmd

@@ -0,0 +1,93 @@
+---
+title: Ray Train integration
+description: How to use Axolotl with Ray Train
+---
+
+Axolotl supports using Ray as an alternative to `accelerate` for orchestrating training. This is especially useful for multi-node training since you only have to setup code and dependencies in a single node and launch training as if you were using a single node.
+
+With the `--use-ray` CLI flag, Axolotl will use Ray Train's [`TorchTrainer`](https://docs.ray.io/en/latest/train/api/doc/ray.train.torch.TorchTrainer.html#ray.train.torch.TorchTrainer) to run training.
+
+## Ray cluster setup
+
+A prerequisite using the Ray Train integration is to setup a Ray cluster on your desired node(s). For a detailed guide on how you can get started with ray clusters, check the official Ray docs here: https://docs.ray.io/en/latest/cluster/getting-started.html
+
+Every Ray cluster has one _head_ node and a set of worker nodes. The head node is just like any other worker node, but it also runs certain special processes related to scheduling and orchestration. Ray-enabled scripts are run on the head node and depending on the resources (number of CPUs, GPUs, etc) they request, will be scheduled to run certain tasks on the worker nodes. For more on key concepts behind a Ray cluster, you can refer this [doc](https://docs.ray.io/en/latest/cluster/key-concepts.html#cluster-key-concepts).
+
+## Sanity check
+
+To run a sanity check on whether your ray cluster is setup properly, execute the following on the head node:
+
+```bash
+ray status
+```
+
+The output should have a summary of your Ray cluster - list of all the nodes in your cluster, the number of CPUs and GPUs in your cluster, etc. For example, if you have a cluster with 1 CPU-only head node and 2 4xL40S worker nodes, the output can look like this:
+
+
+```
+Node status
+---------------------------------------------------------------
+Active:
+ 1 head
+Idle:
+ 2 4xL40S:48CPU-384GB
+Pending:
+ (no pending nodes)
+Recent failures:
+ (no failures)
+
+Resources
+---------------------------------------------------------------
+Usage:
+ 0.0/96.0 CPU
+ 0.0/8.0 GPU
+ 0B/800.00GiB memory
+ 0B/229.57GiB object_store_memory
+
+Demands:
+ (no resource demands)
+```
+
+You should also be able to see the same on the [Ray dashboard](https://docs.ray.io/en/latest/ray-observability/getting-started.html).
+
+
+## Configuring training with Ray Train
+
+You can find an example configuration at `configs/llama-3/lora-1b-ray.yaml`.
+
+The key parameters to note here are:
+
+```yaml
+...
+use_ray: true
+ray_num_workers: 4
+# optional
+resources_per_worker:
+    GPU: 1
+...
+```
+
+- `use_ray`: This is the flag that enables the Ray Train integration. You can either use the corresponding `--use-ray` flag in the CLI or set `use_ray` in the config file.
+- `ray_num_workers`: This is the number of workers/GPUs to use for training.
+- `resources_per_worker`: This is the Ray [resource request](https://docs.ray.io/en/latest/ray-core/scheduling/resources.html) for each worker. This can be used to request a specific GPU type or a custom resource for each worker. For example, if your ray cluster has GPUs of different types, and you only want to use NVIDIA L40S GPUs, you can do
+
+```yaml
+resources_per_worker:
+    accelerator_type:L40S: 0.001
+```
+
+## Launching training
+
+You can simply run the following command on the head node:
+
+```bash
+axolotl train examples/llama-3/lora-1b-ray.yml --use-ray
+```
+
+This will launch training on the head node and workers will be scheduled automatically by Ray Train to run on the appropriate head or worker nodes.
+
+You can also monitor training progress on the Ray dashboard.
+
+Coming back to the example on a Ray cluster with 1 head node and 2 4xL40S worker nodes, let's say you want to make use of all 8 GPUs. You would be able to just set `ray_num_workers: 8` and run the previous command. The Cluster tab will show the following:
+
+![Ray dashboard](./images/ray-cluster-dashboard.png)

examples/llama-3/lora-1b-ray.yml

@@ -0,0 +1,79 @@
+base_model: NousResearch/Llama-3.2-1B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: teknium/GPT4-LLM-Cleaned
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/lora-out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0.05
+lora_fan_in_fan_out:
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+loss_watchdog_threshold: 5.0
+loss_watchdog_patience: 3
+
+warmup_steps: 10
+evals_per_epoch: 4
+saves_per_epoch: 1
+debug:
+deepspeed: deepspeed_configs/zero3.json
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: "<|end_of_text|>"
+
+use_ray: true
+ray_num_workers: 4

setup.py

@@ -150,5 +150,8 @@ def get_package_version():
             "lomo-optim==0.1.1",
             "torch-optimi==0.2.1",
         ],
+        "ray": [
+            "ray[train]",
+        ],
     },
 )

src/axolotl/cli/args.py

@@ -25,6 +25,8 @@ class TrainerCliArgs:
     merge_lora: bool = field(default=False)
     prompter: Optional[str] = field(default=None)
     shard: bool = field(default=False)
+    main_process_port: Optional[int] = field(default=None)
+    num_processes: Optional[int] = field(default=None)
 
 
 @dataclass

src/axolotl/cli/main.py

@@ -67,8 +67,23 @@ def train(config: str, accelerate: bool, **kwargs) -> None:
     # Enable expandable segments for cuda allocation to improve VRAM usage
     set_pytorch_cuda_alloc_conf()
 
+    if "use_ray" in kwargs and kwargs["use_ray"]:
+        accelerate = False
+
     if accelerate:
-        base_cmd = ["accelerate", "launch", "-m", "axolotl.cli.train"]
+        accelerate_args = []
+        if "main_process_port" in kwargs:
+            main_process_port = kwargs.pop("main_process_port", None)
+            accelerate_args.append("--main_process_port")
+            accelerate_args.append(str(main_process_port))
+        if "num_processes" in kwargs:
+            num_processes = kwargs.pop("num_processes", None)
+            accelerate_args.append("--num-processes")
+            accelerate_args.append(str(num_processes))
+
+        base_cmd = ["accelerate", "launch"]
+        base_cmd.extend(accelerate_args)
+        base_cmd.extend(["-m", "axolotl.cli.train"])
         if config:
             base_cmd.append(config)
         cmd = build_command(base_cmd, kwargs)

src/axolotl/cli/train.py

@@ -5,6 +5,7 @@
 from typing import Union
 
 import fire
+from accelerate import Accelerator
 from dotenv import load_dotenv
 from transformers.hf_argparser import HfArgumentParser
 
@@ -15,6 +16,7 @@
 from axolotl.common.datasets import load_datasets, load_preference_datasets
 from axolotl.integrations.base import PluginManager
 from axolotl.train import train
+from axolotl.utils.config import normalize_config, resolve_dtype
 from axolotl.utils.dict import DictDefault
 
 LOG = logging.getLogger(__name__)
@@ -63,7 +65,47 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
         return_remaining_strings=True
     )
 
-    do_train(parsed_cfg, parsed_cli_args)
+    if parsed_cfg.use_ray:
+        from ray.train import RunConfig, ScalingConfig
+        from ray.train.torch import TorchTrainer
+
+        train_loop_config = {"cfg": parsed_cfg.to_dict(), "cli_args": parsed_cli_args}
+        trainer = TorchTrainer(
+            ray_train_func,
+            train_loop_config=train_loop_config,
+            scaling_config=ScalingConfig(
+                num_workers=parsed_cfg.ray_num_workers,
+                resources_per_worker=parsed_cfg.resources_per_worker.to_dict(),
+                use_gpu=True,
+            ),
+            run_config=RunConfig(
+                name=parsed_cfg.ray_run_name,
+                storage_path=Path(parsed_cfg.output_dir).absolute().as_posix(),
+            ),
+        )
+        return trainer.fit()
+    return do_train(parsed_cfg, parsed_cli_args)
+
+
+def ray_train_func(kwargs: dict):
+    # cast `cfg` back to DictDefault (ray tune deepcopy has issues with DictDefault so needed it to be dict)
+    # also renormalize the config now that TorchTrainer has spawned distributed workers
+    cfg = DictDefault(kwargs["cfg"])
+    normalize_config(cfg)
+
+    # now that we are on the worker node, we can check `is_torch_bf16_gpu_available` to resolve dtype
+    resolve_dtype(cfg)
+
+    # ray serializing objects gets rid of frozen attribute - HF expects dict not DefaultDict
+    if cfg.deepspeed:
+        cfg.deepspeed = cfg.deepspeed.to_dict()
+
+    # initialize accelerator before model instantiation
+    Accelerator(gradient_accumulation_steps=cfg.gradient_accumulation_steps)
+
+    kwargs["cfg"] = cfg
+
+    do_train(**kwargs)
 
 
 if __name__ == "__main__":

src/axolotl/train.py

@@ -141,7 +141,9 @@ def train(
         model.config.save_pretrained(str(Path(cfg.output_dir)))
 
     # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
-    if cfg.local_rank == 0:
+    if (
+        cfg.local_rank == 0 and not cfg.use_ray
+    ):  # ray workers don't have access to this signal
 
         def terminate_handler(_, __, model_weakref):
             if model_weakref() is not None: