import gradio as gr import os from datetime import datetime from huggingface_hub import hf_hub_download from pynvml import * nvmlInit() gpu_h = nvmlDeviceGetHandleByIndex(0) ctx_limit = 512 title = "RWKV-4 14B fp16" desc = f'''DEMO limited to ctxlen {ctx_limit}, and slow because A10g does not have enough VRAM (some layers are computed on CPU instead). Links: ChatRWKV RWKV-LM RWKV pip package ''' os.environ["RWKV_JIT_ON"] = '1' os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster) from rwkv.model import RWKV model_path = hf_hub_download(repo_id="BlinkDL/rwkv-4-pile-14b", filename="RWKV-4-Pile-14B-20230213-8019.pth") model = RWKV(model=model_path, strategy='cuda fp16 *33 -> cpu fp32') # model_path = hf_hub_download(repo_id="BlinkDL/rwkv-4-pile-169m", filename="RWKV-4-Pile-169M-20220807-8023.pth") # model = RWKV(model=model_path, strategy='cuda fp16') from rwkv.utils import PIPELINE, PIPELINE_ARGS pipeline = PIPELINE(model, "20B_tokenizer.json") def infer( ctx, token_count=10, temperature=1.0, top_p=0.85, 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 = [0], # ban the generation of some tokens token_stop = []) # stop generation whenever you see any token here ctx = ctx.strip(' ') if ctx.endswith('\n'): ctx = f'\n{ctx.strip()}\n' else: ctx = f'\n{ctx.strip()}' gpu_info = nvmlDeviceGetMemoryInfo(gpu_h) print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}') all_tokens = [] out_last = 0 out_str = '' occurrence = {} state = None for i in range(int(token_count)): out, state = model.forward(pipeline.encode(ctx)[:ctx_limit] if i == 0 else [token], state) for n in args.token_ban: out[n] = -float('inf') 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] 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 yield out_str.strip() examples = [ ["Ask Expert\n\nQuestion:\nWhat are some good plans for world peace?\n\nExpert Full Answer:\n", 100, 1.0, 0.85, 0.1, 0.1], ["Q & A\n\nQuestion:\nWhy is the sky blue?\n\nDetailed Expert Answer:\n", 100, 1.0, 0.85, 0.1, 0.1], ["Expert Questions & Helpful Answers\nAsk Research Experts\nQuestion:\nCan you write a short story about an elf maiden named Julia that meets a warrior named Rallio and they go on an adventure together?\n\nFull Answer:\n", 100, 1.0, 0.85, 0.1, 0.1], ] iface = gr.Interface( fn=infer, description=f'''{desc}''', allow_flagging="never", inputs=[ gr.Textbox(lines=20, label="Prompt"), # prompt gr.Slider(10, 200, step=10, value=100), # token_count gr.Slider(0.2, 2.0, step=0.1, value=1.0), # temperature gr.Slider(0.0, 1.0, step=0.05, value=0.85), # top_p gr.Slider(0.0, 1.0, step=0.1, value=0.1), # presencePenalty gr.Slider(0.0, 1.0, step=0.1, value=0.1), # countPenalty ], outputs=gr.Textbox(label="Generated Output", lines=35), examples=examples, cache_examples=False, ).queue() demo = gr.TabbedInterface( [iface], ["Generative"], title=title, ) demo.queue() demo.launch(share=False)