mirror of
https://github.com/hpcaitech/ColossalAI.git
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bcf0181ecd
* Distrifusion Support source * comp comm overlap optimization * sd3 benchmark * pixart distrifusion bug fix * sd3 bug fix and benchmark * generation bug fix * naming fix * add docstring, fix counter and shape error * add reference * readme and requirement
82 lines
3.4 KiB
Python
82 lines
3.4 KiB
Python
import argparse
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from diffusers import DiffusionPipeline
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from torch import bfloat16
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from torch import distributed as dist
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from torch import float16, float32
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import colossalai
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from colossalai.cluster import DistCoordinator
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from colossalai.inference.config import DiffusionGenerationConfig, InferenceConfig
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from colossalai.inference.core.engine import InferenceEngine
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# For Stable Diffusion 3, we'll use the following configuration
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MODEL_CLS = DiffusionPipeline
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TORCH_DTYPE_MAP = {
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"fp16": float16,
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"fp32": float32,
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"bf16": bfloat16,
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}
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def infer(args):
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# ==============================
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# Launch colossalai, setup distributed environment
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# ==============================
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colossalai.launch_from_torch()
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coordinator = DistCoordinator()
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# ==============================
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# Load model and tokenizer
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# ==============================
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model_path_or_name = args.model
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model = MODEL_CLS.from_pretrained(model_path_or_name, torch_dtype=TORCH_DTYPE_MAP.get(args.dtype, None))
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# ==============================
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# Initialize InferenceEngine
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# ==============================
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coordinator.print_on_master(f"Initializing Inference Engine...")
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inference_config = InferenceConfig(
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dtype=args.dtype,
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max_batch_size=args.max_batch_size,
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tp_size=args.tp_size,
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use_cuda_kernel=args.use_cuda_kernel,
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patched_parallelism_size=dist.get_world_size(),
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)
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engine = InferenceEngine(model, inference_config=inference_config, verbose=True)
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# ==============================
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# Generation
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# ==============================
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coordinator.print_on_master(f"Generating...")
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out = engine.generate(prompts=[args.prompt], generation_config=DiffusionGenerationConfig())[0]
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if dist.get_rank() == 0:
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out.save(f"cat_parallel_size{dist.get_world_size()}.jpg")
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coordinator.print_on_master(out)
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# colossalai run --nproc_per_node 1 examples/inference/stable_diffusion/sd3_generation.py -m MODEL_PATH
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# colossalai run --nproc_per_node 1 examples/inference/stable_diffusion/sd3_generation.py -m "stabilityai/stable-diffusion-3-medium-diffusers" --tp_size 1
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# colossalai run --nproc_per_node 2 examples/inference/stable_diffusion/sd3_generation.py -m "stabilityai/stable-diffusion-3-medium-diffusers" --tp_size 1
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# colossalai run --nproc_per_node 1 examples/inference/stable_diffusion/sd3_generation.py -m "PixArt-alpha/PixArt-XL-2-1024-MS" --tp_size 1
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# colossalai run --nproc_per_node 2 examples/inference/stable_diffusion/sd3_generation.py -m "PixArt-alpha/PixArt-XL-2-1024-MS" --tp_size 1
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if __name__ == "__main__":
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# ==============================
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# Parse Arguments
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# ==============================
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parser = argparse.ArgumentParser()
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parser.add_argument("-m", "--model", type=str, help="Path to the model or model name")
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parser.add_argument("-t", "--tp_size", type=int, default=1, help="Tensor Parallelism size")
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parser.add_argument("-p", "--prompt", type=str, default="A cat holding a sign that says hello world", help="Prompt")
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parser.add_argument("-b", "--max_batch_size", type=int, default=1, help="Max batch size")
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parser.add_argument("-d", "--dtype", type=str, default="fp16", help="Data type", choices=["fp16", "fp32", "bf16"])
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parser.add_argument("--use_cuda_kernel", action="store_true", help="Use CUDA kernel, use Triton by default")
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args = parser.parse_args()
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infer(args)
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