diff --git a/pyproject.toml b/pyproject.toml index 01c09be7c6..09f389b466 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ description = "ZeroBand is a production ready codebase for decentralized trainin readme = "README.md" requires-python = ">=3.10" dependencies = [ - "torch==2.6.0", + "torch==2.5.1", "numpy", "setuptools", "transformers>=4.44.2", @@ -16,7 +16,7 @@ dependencies = [ "zstandard", "pyarrow", "wandb", - "vllm", + "vllm>=0.7.3", "jaxtyping", "beartype" ] diff --git a/src/zeroband/inference.py b/src/zeroband/inference.py index fcce7b700d..50e359b7b5 100644 --- a/src/zeroband/inference.py +++ b/src/zeroband/inference.py @@ -1,17 +1,31 @@ import os +import time +from pathlib import Path +from typing import Iterable, Union import uuid from pydantic import model_validator -from vllm import LLM, SamplingParams + +os.environ["VLLM_CONFIGURE_LOGGING"] = "0" + +from vllm import LLM, RequestOutput, SamplingParams +from vllm.distributed.parallel_state import ( + destroy_model_parallel, + destroy_distributed_environment, +) + from pydantic_config import BaseConfig, parse_argv -import vllm from zeroband.logger import get_logger from zeroband.models import ModelName, name_to_hf_model -from datasets import load_dataset +from datasets import load_dataset, DatasetDict, Dataset, IterableDatasetDict, IterableDataset import pyarrow as pa import pyarrow.parquet as pq +import torch + +DatasetType = Union[DatasetDict, Dataset, IterableDatasetDict, IterableDataset] + class Config(BaseConfig): name_model: ModelName = "150M" @@ -60,7 +74,7 @@ def fake_chat_template(messages): ) -def get_parquet_table(generated_tokens: list[vllm.RequestOutput], step: int) -> pa.Table: +def get_parquet_table(generated_tokens: list[RequestOutput], step: int) -> pa.Table: # Initialize lists for each column input_tokens_list = [] output_tokens_list = [] @@ -101,21 +115,12 @@ def get_parquet_table(generated_tokens: list[vllm.RequestOutput], step: int) -> return pa.Table.from_arrays(arrays, schema=pa_schema) -def main(config: Config): # -> list[dict[str, Any]]: - prompts = ["Write me a novel" for _ in range(5)] - - llm = LLM(model=name_to_hf_model[config.name_model], tensor_parallel_size=config.tp) - logger = get_logger("INFERENCE") - # tokenizer = llm.get_tokenizer() - - sampling_params = SamplingParams(temperature=0.7, top_p=0.95, max_tokens=100, presence_penalty=0.1, frequency_penalty=0.1) - - # Load dataset - dataset = load_dataset(config.dataset, split="train") +def rollout(llm: LLM, prompts: list[str], sampling_params: SamplingParams, dataset: DatasetType, step: int) -> None: + assert isinstance(dataset, Dataset) max_samples = config.max_samples or len(dataset) - step = 0 # step will change once we have the update model api + logger = get_logger("INFERENCE") # Process batches for i in range(0, min(len(dataset), max_samples), config.batch_size): @@ -123,23 +128,87 @@ def main(config: Config): # -> list[dict[str, Any]]: batch = dataset.select(range(i, min(i + config.batch_size, len(dataset)))) # Prepare messages - messages = [[{"role": "user", "content": item["prompt"]}, {"role": "assistant", "content": "\n"}] for item in batch] + messages = [ + [ + {"role": "user", "content": item["prompt"]}, # type: ignore + {"role": "assistant", "content": "\n"}, + ] + for item in batch + ] # Get tokenized inputs prompts = fake_chat_template(messages) + # Run the model on the inputs generated_tokens = llm.generate(prompts, sampling_params, use_tqdm=False) - logger.info(f"Generated {len(prompts)} prompts") + # logger.info(f"Sample output for batch {i}: {generated_tokens[0].outputs[0].text}") + # Write the resulting tokens to disk table = get_parquet_table(generated_tokens, step) - step_path = f"{config.output_path}/step_{step}" os.makedirs(step_path, exist_ok=True) - pq.write_table(table, f"{step_path}/{uuid.uuid4()}.parquet") +def main(config: Config): + prompts = ["Write me a novel" for _ in range(5)] + + sampling_params = SamplingParams(temperature=0.7, top_p=0.95, max_tokens=100, presence_penalty=0.1, frequency_penalty=0.1) + + # Load dataset + dataset = load_dataset(config.dataset, split="train") + + logger = get_logger("INFERENCE") + + MODEL_DIR = "model_dir" + model_locator = name_to_hf_model[config.name_model] + if os.path.exists(MODEL_DIR): + model_locator = MODEL_DIR + + for step in range(50): + + # Wait for model weights to become ready. + if model_locator == MODEL_DIR: + ready_file = Path(model_locator) / "ready" + while not os.path.exists(ready_file): + logger.info(f"Waiting for model weights to become ready at {model_locator}") + time.sleep(3) + logger.info("Model weights ready!") + + # Start vLLM (or in the future swap the weights) + llm = LLM( + model=model_locator, + disable_custom_all_reduce=True, + enforce_eager=True, + tensor_parallel_size=config.tp, + disable_log_stats=True, + ) + + # Signal that the it's loaded and the next weights can be written. + if model_locator == MODEL_DIR: + ready_file.unlink() + + # Run the model to produce batches for training + rollout(llm, prompts, sampling_params, dataset, step) + + # Kill vLLM + # NOTE: vLLM seems to have a bug. There is a zombie thread that never gets joined. + # But it should be hard to notice, and fine for testing. + # NOTE: We should move to swapping the weights. Or doing whatever verl is doing. + destroy_model_parallel() + destroy_distributed_environment() + del llm.llm_engine.model_executor + del llm + + # Once we need to free the vram for other things, do this. + # vLLM does this at startup to claim all the vram it can for kvcache. + # So just do it once at the end, or if swapping inference->training in the same process. + import gc + gc.collect() + torch.cuda.empty_cache() + + if __name__ == "__main__": config = Config(**parse_argv()) # type: ignore diff --git a/src/zeroband/train.py b/src/zeroband/train.py index 32b80b4ba8..ceae925990 100644 --- a/src/zeroband/train.py +++ b/src/zeroband/train.py @@ -1,13 +1,18 @@ import os import time +from pathlib import Path from typing import TYPE_CHECKING, Literal from pydantic import model_validator import torch import torch.distributed as dist -from torch.distributed.fsdp import fully_shard, MixedPrecisionPolicy # type: ignore import wandb +try: + from torch.distributed.fsdp import fully_shard, MixedPrecisionPolicy # type: ignore +except ImportError: + from torch.distributed._composable.fsdp import fully_shard, MixedPrecisionPolicy # type: ignore + from zeroband.models import ModelName, get_model_and_tokenizer from zeroband.training.checkpoint import TrainingProgress, load_checkpoint_fsdp_state, save_checkpoint_fsdp_state @@ -116,6 +121,41 @@ def get_device_placement(gpus_ids: list[int] | None, world_info: WorldInfo) -> i return gpus_ids[world_info.local_rank] +def save_model(model: torch.nn.Module, tokenizer, path: str, world_info: WorldInfo, first_time: bool = False): + from torch.distributed.checkpoint.state_dict import get_model_state_dict, StateDictOptions + + # Check on rank 0 if the model is already ready. Wait until it isn't (it's been loaded by inference) + path_p = Path(path) + if world_info.rank == 0 and first_time: + ready_file = path_p / "ready" + while os.path.exists(ready_file): + get_logger().info(f"Waiting for model weights to be consumed by inference at {path}") + time.sleep(3) + get_logger().info("Previous weights loaded by inference, saving new weights.") + + dist.barrier() + + # Save the model (all ranks gather onto rank 0) + state_dict = get_model_state_dict(model, options=StateDictOptions(full_state_dict=True, cpu_offload=True)) + model.save_pretrained(path, is_main_process=(world_info.rank == 0), state_dict=state_dict) + tokenizer.save_pretrained(path) + + # Replace FSDPLlamaForCausalLM in the config.json with LlamaForCausalLM. It loads the same. + if world_info.rank == 0: + config_path = path_p / "config.json" + bak = config_path.with_suffix(".json.bak") + os.rename(config_path, bak) + with open(bak, "r") as f: + config = f.read() + config = config.replace("FSDPLlamaForCausalLM", "LlamaForCausalLM") + with open(config_path, "w") as f: + f.write(config) + + # Mark the folder as ready again by touching a file + if world_info.rank == 0: + (path_p / "ready").touch() + get_logger().info(f"Model saved to {path}") + def train(config: Config): logger = get_logger() world_info = get_world_info() @@ -225,6 +265,10 @@ def train(config: Config): save_checkpoint_fsdp_state(model, [optimizer], training_progress, train_dataloader, scheduler, config.ckpt.path) if training_progress.step > config.optim.total_steps: + first_time = True + while True: + save_model(model, tokenizer, "model_dir", world_info, first_time=True) + first_time = False break logger.info("Training finished, exiting ...") diff --git a/uv.lock b/uv.lock 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