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| 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) | |
| # make sure cuda dir is in the same level as modeling_rwkv.py | |
| from modeling_rwkv import RWKV | |
| import gc | |
| import gradio as gr | |
| import base64 | |
| from io import BytesIO | |
| from pathlib import Path | |
| import torch | |
| import torch.nn.functional as F | |
| from datetime import datetime | |
| from transformers import CLIPImageProcessor | |
| from huggingface_hub import hf_hub_download | |
| from pynvml import * | |
| nvmlInit() | |
| gpu_h = nvmlDeviceGetHandleByIndex(0) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| ctx_limit = 3500 | |
| title = 'ViusualRWKV-v6.0' | |
| visualrwkv_remote_path = "VisualRWKV-v060-1B6-v1.0-20240612.pth" | |
| model_path = hf_hub_download(repo_id="howard-hou/visualrwkv-6", filename=visualrwkv_remote_path) | |
| # convert visualrwkv to RWKV and vision encoder ####################### | |
| output_dir = Path(model_path).parent | |
| state_dict = torch.load(model_path, map_location="cpu") | |
| rwkv_state_dict = {} | |
| visual_state_dict = {} | |
| for key in state_dict: | |
| if key.startswith("rwkv"): | |
| rwkv_state_dict[key[5:]] = state_dict[key].half() | |
| else: | |
| visual_state_dict[key] = state_dict[key].half() | |
| # save | |
| vision_local_path = output_dir / f"visual.pth" | |
| rwkv_local_path = output_dir / f"rwkv.pth" | |
| torch.save(rwkv_state_dict, rwkv_local_path) | |
| torch.save(visual_state_dict, vision_local_path) | |
| print("rwkv state dict has keys: ", len(rwkv_state_dict), "saved to ", rwkv_local_path) | |
| print("visual state dict has keys: ", len(visual_state_dict), "saved to ", vision_local_path) | |
| ########################################################################## | |
| vision_tower_name = 'openai/clip-vit-large-patch14-336' | |
| model = RWKV(model=str(rwkv_local_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_state_dict = torch.load(vision_local_path, map_location='cpu') | |
| visual_encoder.load_state_dict(vision_state_dict, strict=False) | |
| image_processor = CLIPImageProcessor.from_pretrained(vision_tower_name) | |
| visual_encoder = visual_encoder.to(device) | |
| ########################################################################## | |
| 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=512, | |
| 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 | |
| if '\n\n' in out_str: | |
| 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('sampled tokens:', all_tokens) | |
| 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 | |
| ln0_weight = model.w['blocks.0.ln0.weight'].to(torch.float32).to(device) | |
| ln0_bias = model.w['blocks.0.ln0.bias'].to(torch.float32).to(device) | |
| def compute_image_state(image, prefix_tokens): | |
| image = image_processor(images=image.convert('RGB'), return_tensors='pt')['pixel_values'] | |
| image = image.to(device) | |
| 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=ln0_weight, | |
| bias=ln0_bias) | |
| _, image_state = model.forward(tokens=prefix_tokens, embs=image_features, state=None) | |
| return image_state | |
| def chatbot(image, question): | |
| if image is None: | |
| yield "Please upload an image." | |
| return | |
| input_text = generate_prompt(question) | |
| prefix_tokens = pipeline.encode(input_text)[-ctx_limit:] | |
| image_state = compute_image_state(image, prefix_tokens) | |
| 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=10, 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=20) | |
| 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) |