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111 lines
3.3 KiB
111 lines
3.3 KiB
import argparse
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import torch
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import torch.distributed as dist
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from transformers import AutoTokenizer
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from transformers.models.mixtral import MixtralConfig, MixtralForCausalLM
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import colossalai
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from colossalai.booster import Booster
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from colossalai.booster.plugin.moe_hybrid_parallel_plugin import MoeHybridParallelPlugin
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from colossalai.cluster import DistCoordinator
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def parse_args():
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# basic settings
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_name",
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type=str,
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default="mistralai/Mixtral-8x7B-v0.1",
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--plugin",
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type=str,
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default="ep",
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choices=["ep"],
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help="Parallel methos.",
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)
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parser.add_argument(
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"--precision",
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type=str,
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default="bf16",
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choices=["fp32", "bf16", "fp16"],
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help="The mixed precision training.",
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)
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parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.")
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# kernel
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parser.add_argument(
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"--use_kernel",
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action="store_true",
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help="Use kernel optim. Need to install flash attention and triton to enable all kernel optimizations. Skip if not installed.",
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)
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parser.add_argument(
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"--use_layernorm_kernel",
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action="store_true",
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help="Use layernorm kernel. Need to install apex. Raise error if not installed.",
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)
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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# Launch ColossalAI
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colossalai.launch_from_torch(seed=args.seed)
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coordinator = DistCoordinator()
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config = MixtralConfig.from_pretrained(args.model_name)
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ep_size = min(dist.get_world_size(), config.num_local_experts)
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# Set plugin
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if args.plugin == "ep":
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plugin = MoeHybridParallelPlugin(
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tp_size=1,
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pp_size=1,
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ep_size=ep_size,
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zero_stage=1,
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precision=args.precision,
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enable_fused_normalization=args.use_layernorm_kernel,
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enable_jit_fused=args.use_kernel,
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)
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else:
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raise ValueError(f"Invalid plugin {args.plugin}")
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coordinator.print_on_master(f"Set plugin as {plugin.__class__.__name__}")
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# Build mixtral model
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model = MixtralForCausalLM.from_pretrained(args.model_name)
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coordinator.print_on_master(f"Finish load model")
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# Prepare tokenizer and dataloader
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tokenizer = AutoTokenizer.from_pretrained(args.model_name)
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# Set booster
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booster = Booster(plugin=plugin)
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model, _, _, _, _ = booster.boost(model=model)
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coordinator.print_on_master(f"Finish init booster")
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model.eval()
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if coordinator.rank == 0:
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text = ["Hello my name is"]
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else:
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text = [
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"What's the largest country in the world?",
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"How many people live in China?",
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"帮我续写这首诗：离离原上草",
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]
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tokenizer.pad_token = tokenizer.unk_token
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inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch.cuda.current_device())
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with torch.no_grad():
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outputs = model.module.generate(**inputs, max_new_tokens=20)
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outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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print(f"[{coordinator.rank}] {outputs}")
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if __name__ == "__main__":
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main()
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