Update app.py

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-import os, gc, torch
+import os, gc, copy, torch
 from datetime import datetime
 from huggingface_hub import hf_hub_download
 from pynvml import *
@@ -14,6 +14,7 @@ os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (muc
 from rwkv.model import RWKV
 model_path = hf_hub_download(repo_id="BlinkDL/rwkv-4-raven", filename=f"{title}.pth")
 model = RWKV(model=model_path, strategy='cuda fp16i8 *8 -> cuda fp16')
+# model = RWKV(model='D:/ChatRWKV/RWKV-4-Raven-7B-v9-Eng99%-Other1%-20230412-ctx8192.pth', strategy='cuda fp16i8 *10 -> cuda fp16')
 from rwkv.utils import PIPELINE, PIPELINE_ARGS
 pipeline = PIPELINE(model, "20B_tokenizer.json")
 
@@ -57,9 +58,6 @@ def evaluate(
  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 = ''
@@ -84,41 +82,222 @@ def evaluate(
  out_str += tmp
  yield out_str.strip()
  out_last = i + 1
+
+ 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()
  yield out_str.strip()
 
 examples = [
- ["Tell me about ravens.", "", 150, 1.0, 0.5, 0.4, 0.4],
- ["Write a python function to mine 1 BTC, with details and comments.", "", 150, 1.0, 0.5, 0.2, 0.2],
- ["Write a song about ravens.", "", 150, 1.0, 0.5, 0.4, 0.4],
- ["Explain the following metaphor: Life is like cats.", "", 150, 1.0, 0.5, 0.4, 0.4],
- ["Write a story using the following information", "A man named Alex chops a tree down", 150, 1.0, 0.5, 0.4, 0.4],
- ["Generate a list of adjectives that describe a person as brave.", "", 150, 1.0, 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.", "", 150, 1.0, 0.5, 0.4, 0.4],
+ ["Tell me about ravens.", "", 150, 1.2, 0.5, 0.3, 0.3],
+ ["Write a python function to mine 1 BTC, with details and comments.", "", 150, 1.2, 0.5, 0.3, 0.3],
+ ["Write a song about ravens.", "", 150, 1.2, 0.5, 0.3, 0.3],
+ ["Explain the following metaphor: Life is like cats.", "", 150, 1.2, 0.5, 0.3, 0.3],
+ ["Write a story using the following information", "A man named Alex chops a tree down", 150, 1.2, 0.5, 0.3, 0.3],
+ ["Generate a list of adjectives that describe a person as brave.", "", 150, 1.2, 0.5, 0.3, 0.3],
+ ["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.", "", 150, 1.2, 0.5, 0.3, 0.3],
 ]
 
-g = gr.Interface(
- fn=evaluate,
- inputs=[
- gr.components.Textbox(lines=2, label="Instruction", value="Tell me about ravens."),
- gr.components.Textbox(lines=2, label="Input", placeholder="none"),
- gr.components.Slider(minimum=10, maximum=200, step=10, value=150), # 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=0.5), # top_p
- gr.components.Slider(0.0, 1.0, step=0.1, value=0.4), # presencePenalty
- gr.components.Slider(0.0, 1.0, step=0.1, value=0.4), # countPenalty 
- ],
- outputs=[
- gr.inputs.Textbox(
- lines=5,
- label="Output",
- )
- ],
- title=f"🐦Raven - {title}",
- description="Raven is [RWKV 7B](https://github.com/BlinkDL/ChatRWKV) 100% RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM) finetuned to follow instructions. *** Please try examples first (bottom of page) *** (edit them to use your question). Demo limited to ctxlen 1024. It is finetuned on [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca), codealpaca and more. For best results, *** keep you prompt short and clear ***.",
- examples=examples,
- cache_examples=False,
-)
-g.queue(concurrency_count=1, max_size=10)
-g.launch(share=False)
+##########################################################################
+
+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[187] += 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
+
+ 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').replace('\\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>🐦Raven - {title}</h1>\n</div>")
+ with gr.Tab("Instruct mode"):
+ gr.Markdown(f"Raven is [RWKV 7B](https://github.com/BlinkDL/ChatRWKV) 100% RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM) finetuned to follow instructions. *** 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>UPDATE: now with Chat (see above, as a tab)</b>.")
+ with gr.Row():
+ with gr.Column():
+ instruction = gr.Textbox(lines=2, label="Instruction", value="Tell me about ravens.")
+ input = gr.Textbox(lines=2, label="Input", placeholder="none")
+ token_count = gr.Slider(10, 200, label="Max Tokens", step=10, value=150)
+ 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=0.3)
+ count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.3)
+ 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=0.3)
+ count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.3)
+ 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)