diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 00000000..f438a228 --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,24 @@ +FROM nvcr.io/nvidia/pytorch:23.02-py3 +LABEL org.opencontainers.image.authors="soulteary@gmail.com" + +RUN pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple && \ + pip install huggingface_hub +WORKDIR /app +RUN cat > /get-models.py <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 = f"./models/{title}.pth" +if os.path.isfile(model_path): + print(f"The pre-converted model exists.") +else: + from huggingface_hub import hf_hub_download + model_path = hf_hub_download(repo_id="BlinkDL/rwkv-4-pile-14b", filename=f"{title}.pth") + +model = RWKV(model=model_path, strategy='cuda fp16i8 *0+ -> cpu fp32 *1') +from rwkv.utils import PIPELINE, PIPELINE_ARGS +pipeline = PIPELINE(model, "20B_tokenizer.json") + +def infer( + ctx, + token_count=10, + temperature=1.0, + top_p=0.8, + 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 + gc.collect() + torch.cuda.empty_cache() + yield out_str.strip() + +examples = [ + ["Expert Questions & Helpful Answers\nAsk Research Experts\nQuestion:\nHow can we eliminate poverty?\n\nFull Answer:\n", 150, 1.0, 0.7, 0.2, 0.2], + ["Here's a short cyberpunk sci-fi adventure story. The story's main character is an artificial human created by a company called OpenBot.\n\nThe Story:\n", 150, 1.0, 0.7, 0.2, 0.2], + ['''Below is an instruction that describes a task. Write a response that appropriately completes the request. +### Instruction: +Generate a list of adjectives that describe a person as brave. +### Response: +''', 150, 1.0, 0.2, 0.5, 0.5], + ['''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: +Arrange the given numbers in ascending order. +### Input: +2, 4, 0, 8, 3 +### Response: +''', 150, 1.0, 0.2, 0.5, 0.5], + ["Ask Expert\n\nQuestion:\nWhat are some good plans for world peace?\n\nExpert Full Answer:\n", 150, 1.0, 0.7, 0.2, 0.2], + ["Q & A\n\nQuestion:\nWhy is the sky blue?\n\nDetailed Expert Answer:\n", 150, 1.0, 0.7, 0.2, 0.2], + ["Dear sir,\nI would like to express my boundless apologies for the recent nuclear war.", 150, 1.0, 0.7, 0.2, 0.2], + ["Here is a shell script to find all .hpp files in /home/workspace and delete the 3th row string of these files:", 150, 1.0, 0.7, 0.1, 0.1], + ["Building a website can be done in 10 simple steps:\n1.", 150, 1.0, 0.7, 0.2, 0.2], + ["A Chinese phrase is provided: 百闻不如一见。\nThe masterful Chinese translator flawlessly translates the phrase into English:", 150, 1.0, 0.5, 0.2, 0.2], + ["I believe the meaning of life is", 150, 1.0, 0.7, 0.2, 0.2], + ["Simply put, the theory of relativity states that", 150, 1.0, 0.5, 0.2, 0.2], +] + + +iface = gr.Interface( + fn=infer, + description=f'''{desc} *** Please try examples first (bottom of page) *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}.''', + allow_flagging="never", + inputs=[ + gr.Textbox(lines=10, label="Prompt", value="Here's a short cyberpunk sci-fi adventure story. The story's main character is an artificial human created by a company called OpenBot.\n\nThe Story:\n"), # prompt + gr.Slider(10, 200, step=10, value=150), # 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=0.7), # top_p + gr.Slider(0.0, 1.0, step=0.1, value=0.2), # presencePenalty + gr.Slider(0.0, 1.0, step=0.1, value=0.2), # countPenalty + ], + outputs=gr.Textbox(label="Generated Output", lines=28), + examples=examples, + cache_examples=False, +).queue() + +demo = gr.TabbedInterface( + [iface], ["Generative"], + title=title, +) + +demo.queue(max_size=10) +demo.launch(share=False, server_name="0.0.0.0") diff --git a/docker/webui.py b/docker/webui.py new file mode 100644 index 00000000..f198fff3 --- /dev/null +++ b/docker/webui.py @@ -0,0 +1,134 @@ +# modify https://huggingface.co/spaces/BlinkDL/ChatRWKV-gradio/blob/main/app.py +import gradio as gr +import os, gc, torch +from datetime import datetime +from pynvml import * +nvmlInit() +gpu_h = nvmlDeviceGetHandleByIndex(0) +ctx_limit = 1024 +title = "RWKV-4-Pile-14B-20230313-ctx8192-test1050" +desc = f'''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 = f"./models/{title}.pth" +if os.path.isfile(model_path): + print(f"The pre-converted model exists.") +else: + from huggingface_hub import hf_hub_download + model_path = hf_hub_download(repo_id="BlinkDL/rwkv-4-pile-14b", 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 infer( + ctx, + token_count=10, + temperature=1.0, + top_p=0.8, + 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 + gc.collect() + torch.cuda.empty_cache() + yield out_str.strip() + +examples = [ + ["Expert Questions & Helpful Answers\nAsk Research Experts\nQuestion:\nHow can we eliminate poverty?\n\nFull Answer:\n", 150, 1.0, 0.7, 0.2, 0.2], + ["Here's a short cyberpunk sci-fi adventure story. The story's main character is an artificial human created by a company called OpenBot.\n\nThe Story:\n", 150, 1.0, 0.7, 0.2, 0.2], + ['''Below is an instruction that describes a task. Write a response that appropriately completes the request. +### Instruction: +Generate a list of adjectives that describe a person as brave. +### Response: +''', 150, 1.0, 0.2, 0.5, 0.5], + ['''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: +Arrange the given numbers in ascending order. +### Input: +2, 4, 0, 8, 3 +### Response: +''', 150, 1.0, 0.2, 0.5, 0.5], + ["Ask Expert\n\nQuestion:\nWhat are some good plans for world peace?\n\nExpert Full Answer:\n", 150, 1.0, 0.7, 0.2, 0.2], + ["Q & A\n\nQuestion:\nWhy is the sky blue?\n\nDetailed Expert Answer:\n", 150, 1.0, 0.7, 0.2, 0.2], + ["Dear sir,\nI would like to express my boundless apologies for the recent nuclear war.", 150, 1.0, 0.7, 0.2, 0.2], + ["Here is a shell script to find all .hpp files in /home/workspace and delete the 3th row string of these files:", 150, 1.0, 0.7, 0.1, 0.1], + ["Building a website can be done in 10 simple steps:\n1.", 150, 1.0, 0.7, 0.2, 0.2], + ["A Chinese phrase is provided: 百闻不如一见。\nThe masterful Chinese translator flawlessly translates the phrase into English:", 150, 1.0, 0.5, 0.2, 0.2], + ["I believe the meaning of life is", 150, 1.0, 0.7, 0.2, 0.2], + ["Simply put, the theory of relativity states that", 150, 1.0, 0.5, 0.2, 0.2], +] + + +iface = gr.Interface( + fn=infer, + description=f'''{desc} *** Please try examples first (bottom of page) *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}.''', + allow_flagging="never", + inputs=[ + gr.Textbox(lines=10, label="Prompt", value="Here's a short cyberpunk sci-fi adventure story. The story's main character is an artificial human created by a company called OpenBot.\n\nThe Story:\n"), # prompt + gr.Slider(10, 200, step=10, value=150), # 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=0.7), # top_p + gr.Slider(0.0, 1.0, step=0.1, value=0.2), # presencePenalty + gr.Slider(0.0, 1.0, step=0.1, value=0.2), # countPenalty + ], + outputs=gr.Textbox(label="Generated Output", lines=28), + examples=examples, + cache_examples=False, +).queue() + +demo = gr.TabbedInterface( + [iface], ["Generative"], + title=title, +) + +demo.queue(max_size=10) +demo.launch(share=False, server_name="0.0.0.0")