app.py

import os
os.environ["RWKV_JIT_ON"] = '1'
os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)
from modeling_rwkv import RWKV

import gc
import gradio as gr
import base64
from io import BytesIO
import torch
import torch.nn.functional as F
from transformers import CLIPImageProcessor
from huggingface_hub import hf_hub_download
from pynvml import *
nvmlInit()
gpu_h = nvmlDeviceGetHandleByIndex(0)

ctx_limit = 3500
title = 'ViusualRWKV-v5'
rwkv_remote_path = "rwkv1b5-vitl336p14-577token_mix665k_rwkv.pth"
vision_remote_path = "rwkv1b5-vitl336p14-577token_mix665k_visual.pth"
vision_tower_name = 'openai/clip-vit-large-patch14-336'

model_path = hf_hub_download(repo_id="howard-hou/visualrwkv-5", filename=rwkv_remote_path)
model = RWKV(model=model_path, strategy='cuda fp16')
from rwkv.utils import PIPELINE, PIPELINE_ARGS
pipeline = PIPELINE(model, "rwkv_vocab_v20230424")

##########################################################################
from modeling_vision import VisionEncoder, VisionEncoderConfig
config = VisionEncoderConfig(n_embd=model.args.n_embd, 
 vision_tower_name=vision_tower_name, 
 grid_size=-1)
visual_encoder = VisionEncoder(config)
vision_local_path = hf_hub_download(repo_id="howard-hou/visualrwkv-5", filename=vision_remote_path)
vision_state_dict = torch.load(vision_local_path, map_location='cpu')
visual_encoder.load_state_dict(vision_state_dict)
image_processor = CLIPImageProcessor.from_pretrained(vision_tower_name)
if torch.cuda.is_available():
 visual_encoder = visual_encoder.cuda()
##########################################################################
def generate_prompt(instruction):
 instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
 return f"\n{instruction}\n\nAssistant:"

def generate(
 ctx,
 image_state,
 token_count=200,
 temperature=0.2,
 top_p=0.3,
 presencePenalty = 0.0,
 countPenalty = 1.0,
):
 args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
 alpha_frequency = countPenalty,
 alpha_presence = presencePenalty,
 token_ban = [], # ban the generation of some tokens
 token_stop = [0, 261]) # stop generation whenever you see any token here
 ctx = ctx.strip()
 all_tokens = []
 out_last = 0
 out_str = ''
 occurrence = {}
 for i in range(int(token_count)):
 if i == 0:
 input_ids = pipeline.encode(ctx)[-ctx_limit:]
 out, state = model.forward(tokens=input_ids, state=image_state)
 else:
 input_ids = [token]
 out, state = model.forward(tokens=input_ids, state=state)
 for n in occurrence:
 out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)

 token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
 if token in args.token_stop:
 break
 all_tokens += [token]
 for xxx in occurrence:
 occurrence[xxx] *= 0.996 
 if token not in occurrence:
 occurrence[token] = 1
 else:
 occurrence[token] += 1
 
 tmp = pipeline.decode(all_tokens[out_last:])
 if '\ufffd' not in tmp:
 out_str += tmp
 yield out_str.strip()
 out_last = i + 1

 gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
 timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
 print(f'{timestamp} - vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')
 del out
 del state
 gc.collect()
 torch.cuda.empty_cache()
 yield out_str.strip()


##########################################################################
cur_dir = os.path.dirname(os.path.abspath(__file__))
examples = [
 [
 f"{cur_dir}/examples_pizza.jpg",
 "What are steps to cook it?"
 ],
 [
 f"{cur_dir}/examples_bluejay.jpg",
 "what is the name of this bird?",
 ],
 [
 f"{cur_dir}/examples_extreme_ironing.jpg",
 "What is unusual about this image?",
 ],
 [
 f"{cur_dir}/examples_waterview.jpg",
 "What are the things I should be cautious about when I visit here?",
 ],
]


def pil_image_to_base64(pil_image):
 buffered = BytesIO()
 pil_image.save(buffered, format="JPEG") # You can change the format as needed (JPEG, PNG, etc.)
 # Encodes the image data into base64 format as a bytes object
 base64_image = base64.b64encode(buffered.getvalue()).decode('utf-8')
 return base64_image

image_cache = {}
def compute_image_state(image):
 base64_image = pil_image_to_base64(image)
 if base64_image in image_cache:
 image_state = image_cache[base64_image]
 else:
 image = image_processor(images=image.convert('RGB'), return_tensors='pt')['pixel_values']
 image_features = visual_encoder.encode_images(image.unsqueeze(0)).squeeze(0) # [L, D]
 # apply layer norm to image feature, very important
 image_features = F.layer_norm(image_features, 
 (image_features.shape[-1],), 
 weight=model.w['blocks.0.ln0.weight'], 
 bias=model.w['blocks.0.ln0.bias'])
 _, image_state = model.forward(embs=image_features, state=None)
 image_cache[base64_image] = image_state
 return image_state

def chatbot(image, question):
 if image is None:
 yield "Please upload an image."
 return
 image_state = compute_image_state(image)
 input_text = generate_prompt(question)
 for output in generate(input_text, image_state):
 yield output

with gr.Blocks(title=title) as demo:
 with gr.Row():
 with gr.Column():
 image = gr.Image(type='pil', label="Image")
 with gr.Column():
 prompt = gr.Textbox(lines=8, label="Prompt", 
 value="Render a clear and concise summary of the photo.")
 with gr.Row():
 submit = gr.Button("Submit", variant="primary")
 clear = gr.Button("Clear", variant="secondary") 
 with gr.Column():
 output = gr.Textbox(label="Output", lines=10)
 data = gr.Dataset(components=[image, prompt], samples=examples, label="Examples", headers=["Image", "Prompt"])
 submit.click(chatbot, [image, prompt], [output])
 clear.click(lambda: None, [], [output])
 data.click(lambda x: x, [data], [image, prompt])

demo.queue(max_size=10)
demo.launch(share=False)