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Update app.py
Browse files
app.py
CHANGED
@@ -5,17 +5,17 @@ from huggingface_hub import hf_hub_download
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from pynvml import *
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nvmlInit()
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gpu_h = nvmlDeviceGetHandleByIndex(0)
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ctx_limit =
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title = "RWKV-
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os.environ["RWKV_JIT_ON"] = '1'
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os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)
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from rwkv.model import RWKV
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model_path = hf_hub_download(repo_id="BlinkDL/
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model = RWKV(model=model_path, strategy='cuda
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from rwkv.utils import PIPELINE, PIPELINE_ARGS
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pipeline = PIPELINE(model, "
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def generate_prompt(instruction, input=None):
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instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
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@@ -95,165 +95,27 @@ def evaluate(
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yield out_str.strip()
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examples = [
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["Tell me about ravens.", "", 300, 1
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["Write a python function to mine 1 BTC, with details and comments.", "", 300, 1
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["Write a song about ravens.", "", 300, 1
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["Explain the following metaphor: Life is like cats.", "", 300, 1
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["Write a story using the following information", "A man named Alex chops a tree down", 300, 1
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["Generate a list of adjectives that describe a person as brave.", "", 300, 1
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["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
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]
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##########################################################################
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chat_intro = '''The following is a coherent verbose detailed conversation between <|user|> and an AI girl named <|bot|>.
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<|user|>: Hi <|bot|>, Would you like to chat with me for a while?
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<|bot|>: Hi <|user|>. Sure. What would you like to talk about? I'm listening.
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'''
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def user(message, chatbot):
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chatbot = chatbot or []
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# print(f"User: {message}")
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return "", chatbot + [[message, None]]
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def alternative(chatbot, history):
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if not chatbot or not history:
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return chatbot, history
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chatbot[-1][1] = None
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history[0] = copy.deepcopy(history[1])
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return chatbot, history
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def chat(
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prompt,
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user,
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bot,
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chatbot,
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history,
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temperature=1.0,
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top_p=0.8,
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presence_penalty=0.1,
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count_penalty=0.1,
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):
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args = PIPELINE_ARGS(temperature=max(0.2, float(temperature)), top_p=float(top_p),
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alpha_frequency=float(count_penalty),
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alpha_presence=float(presence_penalty),
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token_ban=[], # ban the generation of some tokens
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token_stop=[]) # stop generation whenever you see any token here
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if not chatbot:
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return chatbot, history
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message = chatbot[-1][0]
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message = message.strip().replace('\r\n','\n').replace('\n\n','\n')
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ctx = f"{user}: {message}\n\n{bot}:"
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if not history:
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prompt = prompt.replace("<|user|>", user.strip())
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prompt = prompt.replace("<|bot|>", bot.strip())
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prompt = prompt.strip()
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prompt = f"\n{prompt}\n\n"
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out, state = model.forward(pipeline.encode(prompt), None)
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history = [state, None, []] # [state, state_pre, tokens]
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# print("History reloaded.")
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[state, _, all_tokens] = history
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state_pre_0 = copy.deepcopy(state)
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out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:], state)
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state_pre_1 = copy.deepcopy(state) # For recovery
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# print("Bot:", end='')
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begin = len(all_tokens)
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out_last = begin
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out_str: str = ''
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occurrence = {}
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for i in range(300):
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if i <= 0:
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nl_bias = -float('inf')
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elif i <= 30:
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nl_bias = (i - 30) * 0.1
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elif i <= 130:
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nl_bias = 0
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else:
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nl_bias = (i - 130) * 0.25
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out[187] += nl_bias
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for n in occurrence:
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out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
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token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
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next_tokens = [token]
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if token == 0:
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next_tokens = pipeline.encode('\n\n')
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all_tokens += next_tokens
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for xxx in occurrence:
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occurrence[xxx] *= 0.996
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if token not in occurrence:
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occurrence[token] = 1
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else:
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occurrence[token] += 1
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out, state = model.forward(next_tokens, state)
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tmp = pipeline.decode(all_tokens[out_last:])
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if '\ufffd' not in tmp:
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# print(tmp, end='', flush=True)
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out_last = begin + i + 1
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out_str += tmp
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chatbot[-1][1] = out_str.strip()
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history = [state, all_tokens]
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yield chatbot, history
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out_str = pipeline.decode(all_tokens[begin:])
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out_str = out_str.replace("\r\n", '\n').replace('\\n', '\n')
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if '\n\n' in out_str:
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break
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# State recovery
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if f'{user}:' in out_str or f'{bot}:' in out_str:
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idx_user = out_str.find(f'{user}:')
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idx_user = len(out_str) if idx_user == -1 else idx_user
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idx_bot = out_str.find(f'{bot}:')
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idx_bot = len(out_str) if idx_bot == -1 else idx_bot
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idx = min(idx_user, idx_bot)
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if idx < len(out_str):
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out_str = f" {out_str[:idx].strip()}\n\n"
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tokens = pipeline.encode(out_str)
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all_tokens = all_tokens[:begin] + tokens
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out, state = model.forward(tokens, state_pre_1)
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break
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gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
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print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')
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gc.collect()
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torch.cuda.empty_cache()
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chatbot[-1][1] = out_str.strip()
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history = [state, state_pre_0, all_tokens]
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yield chatbot, history
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##########################################################################
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with gr.Blocks(title=title) as demo:
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gr.HTML(f"<div style=\"text-align: center;\">\n<h1
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with gr.Tab("Instruct mode"):
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gr.Markdown(f"
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with gr.Row():
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with gr.Column():
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instruction = gr.Textbox(lines=2, label="Instruction", value="Tell me about ravens.")
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input = gr.Textbox(lines=2, label="Input", placeholder="none")
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token_count = gr.Slider(10,
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temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.
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top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.5)
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presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.4)
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count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.4)
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submit.click(evaluate, [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], [output])
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clear.click(lambda: None, [], [output])
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data.click(lambda x: x, [data], [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty])
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# with gr.Tab("Chat (Experimental - Might be buggy - use ChatRWKV for reference)"):
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# 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")
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# with gr.Row():
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# with gr.Column():
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# chatbot = gr.Chatbot()
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# state = gr.State()
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# message = gr.Textbox(label="Message", value="Write me a python code to land on moon.")
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# with gr.Row():
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# send = gr.Button("Send", variant="primary")
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# alt = gr.Button("Alternative", variant="secondary")
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# clear = gr.Button("Clear", variant="secondary")
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# with gr.Column():
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# with gr.Row():
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# user_name = gr.Textbox(lines=1, max_lines=1, label="User Name", value="Bob")
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# bot_name = gr.Textbox(lines=1, max_lines=1, label="Bot Name", value="Alice")
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# prompt = gr.Textbox(lines=10, max_lines=50, label="Scenario", value=chat_intro)
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# temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2)
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# top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.5)
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# presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.4)
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# count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.4)
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# chat_inputs = [
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# prompt,
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# user_name,
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# bot_name,
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# chatbot,
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# state,
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# temperature,
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# top_p,
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# presence_penalty,
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# count_penalty
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# ]
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# chat_outputs = [chatbot, state]
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# message.submit(user, [message, chatbot], [message, chatbot], queue=False).then(chat, chat_inputs, chat_outputs)
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# send.click(user, [message, chatbot], [message, chatbot], queue=False).then(chat, chat_inputs, chat_outputs)
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# alt.click(alternative, [chatbot, state], [chatbot, state], queue=False).then(chat, chat_inputs, chat_outputs)
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# clear.click(lambda: ([], None, ""), [], [chatbot, state, message], queue=False)
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demo.queue(concurrency_count=1, max_size=10)
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demo.launch(share=False)
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from pynvml import *
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nvmlInit()
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gpu_h = nvmlDeviceGetHandleByIndex(0)
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ctx_limit = 3000
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title = "RWKV-5-World-1.5B-v2-OnlyForTest_56%_trained-20231013-ctx4096"
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os.environ["RWKV_JIT_ON"] = '1'
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os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)
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from rwkv.model import RWKV
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model_path = hf_hub_download(repo_id="BlinkDL/temp", filename=f"{title}.pth")
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model = RWKV(model=model_path, strategy='cuda fp16')
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from rwkv.utils import PIPELINE, PIPELINE_ARGS
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pipeline = PIPELINE(model, "rwkv_vocab_v20230424")
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def generate_prompt(instruction, input=None):
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instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
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yield out_str.strip()
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examples = [
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["Tell me about ravens.", "", 300, 1, 0.5, 0.4, 0.4],
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["Write a python function to mine 1 BTC, with details and comments.", "", 300, 1, 0.5, 0.4, 0.4],
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["Write a song about ravens.", "", 300, 1, 0.5, 0.4, 0.4],
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["Explain the following metaphor: Life is like cats.", "", 300, 1, 0.5, 0.4, 0.4],
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["Write a story using the following information", "A man named Alex chops a tree down", 300, 1, 0.5, 0.4, 0.4],
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["Generate a list of adjectives that describe a person as brave.", "", 300, 1, 0.5, 0.4, 0.4],
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["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, 0.5, 0.4, 0.4],
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]
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##########################################################################
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with gr.Blocks(title=title) as demo:
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gr.HTML(f"<div style=\"text-align: center;\">\n<h1>RWKV-5 World v2 - {title}</h1>\n</div>")
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with gr.Tab("Instruct mode"):
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gr.Markdown(f"This is a 1.5B [RWKV-5 World v2](https://huggingface.co/BlinkDL/rwkv-5-world) 100% RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM). *** Please try examples first (bottom of page) *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}. For best results, *** keep you prompt short and clear ***.")
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with gr.Row():
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with gr.Column():
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instruction = gr.Textbox(lines=2, label="Instruction", value="Tell me about ravens.")
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input = gr.Textbox(lines=2, label="Input", placeholder="none")
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token_count = gr.Slider(10, 500, label="Max Tokens", step=10, value=500)
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temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.0)
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top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.5)
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presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.4)
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count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.4)
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submit.click(evaluate, [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], [output])
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clear.click(lambda: None, [], [output])
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data.click(lambda x: x, [data], [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty])
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demo.queue(concurrency_count=1, max_size=10)
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demo.launch(share=False)
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