import gradio as gr import os, gc, torch from datetime import datetime from huggingface_hub import hf_hub_download from pynvml import * nvmlInit() gpu_h = nvmlDeviceGetHandleByIndex(0) ctx_limit = 1024 title = "RWKV-4-Pile-7B-Instruct-test4-20230326" 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-7b", filename=f"{title}.pth") model = RWKV(model=model_path, strategy='cuda fp16i8 *20 -> cuda fp16') from rwkv.utils import PIPELINE, PIPELINE_ARGS pipeline = PIPELINE(model, "20B_tokenizer.json") def generate_prompt(instruction, input=None): if input: return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. # Instruction: {instruction} # Input: {input} # Response: """ else: return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. # Instruction: {instruction} # Response: """ def evaluate( instruction, input=None, token_count=200, temperature=1.0, top_p=0.7, 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]) # stop generation whenever you see any token here instruction = instruction.strip() input = input.strip() ctx = generate_prompt(instruction, input) 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 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 gc.collect() torch.cuda.empty_cache() yield out_str.strip() g = gr.Interface( fn=evaluate, inputs=[ gr.components.Textbox(lines=2, label="Instruction", value="Tell me about alpacas."), gr.components.Textbox(lines=2, label="Input", placeholder="none"), gr.components.Slider(minimum=10, maximum=250, step=10, value=200), # token_count gr.components.Slider(minimum=0.2, maximum=2.0, step=0.1, value=1.0), # temperature gr.components.Slider(minimum=0, maximum=1, step=0.05, value=0.7), # top_p gr.components.Slider(0.0, 1.0, step=0.1, value=0.2), # presencePenalty gr.components.Slider(0.0, 1.0, step=0.1, value=0.2), # countPenalty ], outputs=[ gr.inputs.Textbox( lines=5, label="Output", ) ], title=f"🐦Raven {title}", description="Raven is [RWKV 7B](https://github.com/BlinkDL/ChatRWKV) finetuned to follow instructions. It is trained on the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) dataset and more.", ) g.queue(concurrency_count=1, max_size=10) g.launch(share=False)