Duplicate from BlinkDL/RWKV-World-7B

Co-authored-by: BlinkDL <[email protected]>

.gitattributes

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README.md

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+---
+title: Raven RWKV 7B
+emoji: 🚀
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 3.23.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: BlinkDL/RWKV-World-7B
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import os, gc, copy, torch, re
+from datetime import datetime
+from huggingface_hub import hf_hub_download
+from pynvml import *
+nvmlInit()
+gpu_h = nvmlDeviceGetHandleByIndex(0)
+ctx_limit = 1536
+title = "RWKV-4-World-7B-v1-20230626-ctx4096"
+
+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-world", filename=f"{title}.pth")
+model = RWKV(model=model_path, strategy='cuda fp16i8 *8 -> cuda fp16')
+from rwkv.utils import PIPELINE, PIPELINE_ARGS
+pipeline = PIPELINE(model, "rwkv_vocab_v20230424")
+
+def generate_prompt(instruction, input=None):
+    instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n').replace('\n\n','\n')
+    input = input.strip().replace('\r\n','\n').replace('\n\n','\n').replace('\n\n','\n')
+    if input:
+        return f"""Instruction: {instruction}
+
+Input: {input}
+
+Response:"""
+    else:
+        return f"""Question: {instruction}
+
+Answer:"""
+
+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 = re.sub(r'\n{2,}', '\n', instruction).strip().replace('\r\n','\n')
+    input = re.sub(r'\n{2,}', '\n', input).strip().replace('\r\n','\n')
+    ctx = generate_prompt(instruction, input)
+    
+    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]
+        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
+        if '\n\n' in out_str:
+            break
+
+    gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
+    print(f'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()
+
+examples = [
+    ["東京で訪れるべき素晴らしい場所とその紹介をいくつか挙げてください。", "", 300, 1.2, 0.5, 0.4, 0.4],
+    ["Écrivez un programme Python pour miner 1 Bitcoin, avec des commentaires.", "", 300, 1.2, 0.5, 0.4, 0.4],
+    ["Write a song about ravens.", "", 300, 1.2, 0.5, 0.4, 0.4],
+    ["Explain the following metaphor: Life is like cats.", "", 300, 1.2, 0.5, 0.4, 0.4],
+    ["Write a story using the following information", "A man named Alex chops a tree down", 300, 1.2, 0.5, 0.4, 0.4],
+    ["Generate a list of adjectives that describe a person as brave.", "", 300, 1.2, 0.5, 0.4, 0.4],
+    ["You have $100, and your goal is to turn that into as much money as possible with AI and Machine Learning. Please respond with detailed plan.", "", 300, 1.2, 0.5, 0.4, 0.4],
+]
+
+##########################################################################
+
+chat_intro = '''The following is a coherent verbose detailed conversation between <|user|> and an AI girl named <|bot|>.
+
+<|user|>: Hi <|bot|>, Would you like to chat with me for a while?
+
+<|bot|>: Hi <|user|>. Sure. What would you like to talk about? I'm listening.
+'''
+
+def user(message, chatbot):
+    chatbot = chatbot or []
+    # print(f"User: {message}")
+    return "", chatbot + [[message, None]]
+
+def alternative(chatbot, history):
+    if not chatbot or not history:
+        return chatbot, history
+    
+    chatbot[-1][1] = None
+    history[0] = copy.deepcopy(history[1])
+
+    return chatbot, history
+
+def chat(
+        prompt,
+        user,
+        bot,
+        chatbot,
+        history,
+        temperature=1.0,
+        top_p=0.8,
+        presence_penalty=0.1,
+        count_penalty=0.1,
+):
+    args = PIPELINE_ARGS(temperature=max(0.2, float(temperature)), top_p=float(top_p),
+                         alpha_frequency=float(count_penalty),
+                         alpha_presence=float(presence_penalty),
+                         token_ban=[],  # ban the generation of some tokens
+                         token_stop=[])  # stop generation whenever you see any token here
+    
+    if not chatbot:
+        return chatbot, history
+
+    message = chatbot[-1][0]
+    message = message.strip().replace('\r\n','\n').replace('\n\n','\n')
+    ctx = f"{user}: {message}\n\n{bot}:"
+
+    if not history:
+        prompt = prompt.replace("<|user|>", user.strip())
+        prompt = prompt.replace("<|bot|>", bot.strip())
+        prompt = prompt.strip()
+        prompt = f"\n{prompt}\n\n"
+
+        out, state = model.forward(pipeline.encode(prompt), None)
+        history = [state, None, []]  # [state, state_pre, tokens]
+        # print("History reloaded.")
+
+    [state, _, all_tokens] = history
+    state_pre_0 = copy.deepcopy(state)
+
+    out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:], state)
+    state_pre_1 = copy.deepcopy(state)    # For recovery
+
+    # print("Bot:", end='')
+
+    begin = len(all_tokens)
+    out_last = begin
+    out_str: str = ''
+    occurrence = {}
+    for i in range(300):
+        if i <= 0:
+            nl_bias = -float('inf')
+        elif i <= 30:
+            nl_bias = (i - 30) * 0.1
+        elif i <= 130:
+            nl_bias = 0
+        else:
+            nl_bias = (i - 130) * 0.25
+        out[11] += nl_bias
+        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)
+        next_tokens = [token]
+        if token == 0:
+            next_tokens = pipeline.encode('\n\n')
+        all_tokens += next_tokens
+        for xxx in occurrence:
+            occurrence[xxx] *= 0.996
+        if token not in occurrence:
+            occurrence[token] = 1
+        else:
+            occurrence[token] += 1
+
+        out, state = model.forward(next_tokens, state)
+
+        tmp = pipeline.decode(all_tokens[out_last:])
+        if '\ufffd' not in tmp:
+            # print(tmp, end='', flush=True)
+            out_last = begin + i + 1
+            out_str += tmp
+
+            chatbot[-1][1] = out_str.strip()
+            history = [state, all_tokens]
+            yield chatbot, history
+
+        out_str = pipeline.decode(all_tokens[begin:])
+        out_str = out_str.replace("\r\n", '\n')
+
+        if '\n\n' in out_str:
+            break
+
+        # State recovery
+        if f'{user}:' in out_str or f'{bot}:' in out_str:
+            idx_user = out_str.find(f'{user}:')
+            idx_user = len(out_str) if idx_user == -1 else idx_user
+            idx_bot = out_str.find(f'{bot}:')
+            idx_bot = len(out_str) if idx_bot == -1 else idx_bot
+            idx = min(idx_user, idx_bot)
+
+            if idx < len(out_str):
+                out_str = f" {out_str[:idx].strip()}\n\n"
+                tokens = pipeline.encode(out_str)
+
+                all_tokens = all_tokens[:begin] + tokens
+                out, state = model.forward(tokens, state_pre_1)
+                break
+
+    gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
+    print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')  
+
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    chatbot[-1][1] = out_str.strip()
+    history = [state, state_pre_0, all_tokens]
+    yield chatbot, history
+
+##########################################################################
+
+with gr.Blocks(title=title) as demo:
+    gr.HTML(f"<div style=\"text-align: center;\">\n<h1>🌍World - {title}</h1>\n</div>")
+    with gr.Tab("Instruct mode"):
+        gr.Markdown(f"World is [RWKV 7B](https://github.com/BlinkDL/ChatRWKV) 100% RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM) ***trained on 100+ world languages***. *** Please try examples first (bottom of page) *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}. Finetuned on alpaca, gpt4all, codealpaca and more. For best results, *** keep you prompt short and clear ***.</b>.") # <b>UPDATE: now with Chat (see above, as a tab) ==> turn off as of now due to VRAM leak caused by buggy code.
+        with gr.Row():
+            with gr.Column():
+                instruction = gr.Textbox(lines=2, label="Instruction", value='東京で訪れるべき素晴らしい場所とその紹介をいくつか挙げてください。')
+                input = gr.Textbox(lines=2, label="Input", placeholder="none")
+                token_count = gr.Slider(10, 300, label="Max Tokens", step=10, value=300)
+                temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2)
+                top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.5)
+                presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.4)
+                count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.4)
+            with gr.Column():
+                with gr.Row():
+                    submit = gr.Button("Submit", variant="primary")
+                    clear = gr.Button("Clear", variant="secondary")
+                output = gr.Textbox(label="Output", lines=5)
+        data = gr.Dataset(components=[instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, label="Example Instructions", headers=["Instruction", "Input", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
+        submit.click(evaluate, [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], [output])
+        clear.click(lambda: None, [], [output])
+        data.click(lambda x: x, [data], [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty])
+    
+    # with gr.Tab("Chat (Experimental - Might be buggy - use ChatRWKV for reference)"):
+    #     gr.Markdown(f'''<b>*** The length of response is restricted in this demo. Use ChatRWKV for longer generations. ***</b> Say "go on" or "continue" can sometimes continue the response. If you'd like to edit the scenario, make sure to follow the exact same format: empty lines between (and only between) different speakers. Changes only take effect after you press [Clear]. <b>The default "Bob" & "Alice" names work the best.</b>''', label="Description")
+    #     with gr.Row():
+    #         with gr.Column():
+    #             chatbot = gr.Chatbot()
+    #             state = gr.State()
+    #             message = gr.Textbox(label="Message", value="Write me a python code to land on moon.")
+    #             with gr.Row():
+    #                 send = gr.Button("Send", variant="primary")
+    #                 alt = gr.Button("Alternative", variant="secondary")
+    #                 clear = gr.Button("Clear", variant="secondary")
+    #         with gr.Column():
+    #             with gr.Row():
+    #                 user_name = gr.Textbox(lines=1, max_lines=1, label="User Name", value="Bob")
+    #                 bot_name = gr.Textbox(lines=1, max_lines=1, label="Bot Name", value="Alice")
+    #             prompt = gr.Textbox(lines=10, max_lines=50, label="Scenario", value=chat_intro)
+    #             temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2)
+    #             top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.5)
+    #             presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.4)
+    #             count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.4)
+    #     chat_inputs = [
+    #         prompt,
+    #         user_name,
+    #         bot_name,
+    #         chatbot,
+    #         state,
+    #         temperature,
+    #         top_p,
+    #         presence_penalty,
+    #         count_penalty
+    #     ]
+    #     chat_outputs = [chatbot, state]
+    #     message.submit(user, [message, chatbot], [message, chatbot], queue=False).then(chat, chat_inputs, chat_outputs)
+    #     send.click(user, [message, chatbot], [message, chatbot], queue=False).then(chat, chat_inputs, chat_outputs)
+    #     alt.click(alternative, [chatbot, state], [chatbot, state], queue=False).then(chat, chat_inputs, chat_outputs)
+    #     clear.click(lambda: ([], None, ""), [], [chatbot, state, message], queue=False)
+
+demo.queue(concurrency_count=1, max_size=10)
+demo.launch(share=False)

requirements.txt

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+torch
+ninja
+tokenizers
+rwkv==0.7.5
+pynvml
+huggingface_hub
+gradio>=3.17.1