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import os
import gradio as gr
import torch
import torch.distributed as dist
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import warnings
# disable some warnings
transformers.logging.set_verbosity_error()
transformers.logging.disable_progress_bar()
warnings.filterwarnings('ignore')
def setup(rank, world_size):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '12355'
dist.init_process_group("nccl", rank=rank, world_size=world_size)
def cleanup():
dist.destroy_process_group()
def load_model_on_gpus(model_name, num_gpus):
# Calculate number of layers to assign to each GPU
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, trust_remote_code=True)
num_layers = len(model.model.layers)
layers_per_gpu = num_layers // num_gpus
# Assign layers to GPUs
device_map = {}
for i in range(num_layers):
device_map[f'model.layers.{i}'] = i // layers_per_gpu
# Assign other components
device_map['model.embed_tokens'] = 0
device_map['model.norm'] = num_gpus - 1
device_map['lm_head'] = num_gpus - 1
return AutoModelForCausalLM.from_pretrained(
model_name,
device_map=device_map,
torch_dtype=torch.float16,
trust_remote_code=True
)
def run_distributed(rank, world_size, model_name):
setup(rank, world_size)
if rank == 0:
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = load_model_on_gpus(model_name, world_size)
def inference(prompt, image, temperature, beam_size):
if rank == 0:
messages = [{"role": "user", "content": f'<image>\n{prompt}'}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0).to(rank)
image_tensor = model.process_images([image], model.config).to(rank)
else:
input_ids = torch.zeros(1, 1, dtype=torch.long).to(rank)
image_tensor = torch.zeros(1, 3, 224, 224).to(rank)
dist.broadcast(input_ids, src=0)
dist.broadcast(image_tensor, src=0)
with torch.cuda.amp.autocast():
output_ids = model.generate(
input_ids,
images=image_tensor,
max_new_tokens=1024,
temperature=temperature,
num_beams=beam_size,
use_cache=True
)[0]
if rank == 0:
return tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()
else:
return ""
if rank == 0:
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
prompt_input = gr.Textbox(label="Prompt", placeholder="Describe this image in detail")
image_input = gr.Image(label="Image", type="pil")
temperature_input = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature")
beam_size_input = gr.Slider(minimum=1, maximum=10, value=4, step=1, label="Beam Size")
submit_button = gr.Button("Submit")
with gr.Column():
output_text = gr.Textbox(label="Output")
submit_button.click(
fn=inference,
inputs=[prompt_input, image_input, temperature_input, beam_size_input],
outputs=output_text
)
demo.launch(share=True)
cleanup()
if __name__ == "__main__":
model_name = 'cognitivecomputations/dolphin-vision-72b'
world_size = torch.cuda.device_count()
print(f"Running on {world_size} GPUs")
torch.multiprocessing.spawn(run_distributed, args=(world_size, model_name), nprocs=world_size, join=True) |