app.py

import gradio as gr
import os, gc
import torch
from transformers import CLIPImageProcessor
from huggingface_hub import hf_hub_download

ctx_limit = 3500
num_image_embeddings = 4096
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'

os.environ["RWKV_JIT_ON"] = '1'
os.environ["RWKV_CUDA_ON"] = '0' # if '1' then use CUDA kernel for seq mode (much faster)

from modeling_vision import VisionEncoder, VisionEncoderConfig
from modeling_rwkv import RWKV
model_path = hf_hub_download(repo_id="howard-hou/visualrwkv-5", filename=rwkv_remote_path)
model = RWKV(model=model_path, strategy='cpu fp32')
from rwkv.utils import PIPELINE, PIPELINE_ARGS
pipeline = PIPELINE(model, "rwkv_vocab_v20230424")

##########################################################################
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)
##########################################################################
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_features,
 token_count=200,
 temperature=1.0,
 top_p=0.3,
 presencePenalty = 0.1,
 countPenalty = 0.1,
):
 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:
 prefix_ids = pipeline.encode("User: ")
 prefix_embs = model.w['emb.weight'][prefix_ids]
 input_ids = pipeline.encode(ctx)
 text_embs = model.w['emb.weight'][input_ids]
 input_embs = torch.cat((prefix_embs, image_features, text_embs), dim=0)[-ctx_limit:]
 out, state = model.forward(embs=input_embs, state=None)
 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

 del out
 del state
 gc.collect()
 yield out_str.strip()


##########################################################################
cur_dir = os.path.dirname(os.path.abspath(__file__))
examples = [
 [
 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 chatbot(image, question):
 if image is None:
 yield "Please upload an image."
 return
 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]
 input_text = generate_prompt(question)
 for output in generate(input_text, image_features):
 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=5, label="Prompt", 
 value="Please upload an image and ask a question.")
 with gr.Row():
 submit = gr.Button("Submit", variant="primary")
 clear = gr.Button("Clear", variant="secondary") 
 with gr.Column():
 output = gr.Textbox(label="Output", lines=7)
 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)