Update app.py

app.py

@@ -1,38 +1,241 @@
+"""
+RWKV RNN Model - Gradio Space for HuggingFace
+YT - Mean Gene Hacks - https://www.youtube.com/@MeanGeneHacks
+(C) Gene Ruebsamen - 2/7/2023
+License: GPL3
+"""
+
+import gradio as gr
+import codecs
+from ast import literal_eval
+from datetime import datetime
 from rwkvstic.load import RWKV
+from rwkvstic.agnostic.backends import TORCH, TORCH_QUANT, TORCH_STREAM
 import torch
-model = RWKV(
-    "https://huggingface.co/BlinkDL/rwkv-4-pile-1b5/resolve/main/RWKV-4-Pile-1B5-Instruct-test1-20230124.pth",
-    "pytorch(cpu/gpu)",
-    runtimedtype=torch.float32,
-    useGPU=torch.cuda.is_available(),
-    dtype=torch.float32
-)
-import gradio as gr
+import gc
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+def to_md(text):
+    return text.replace("\n", "<br />")
+
+
+def get_model():
+    model = None
+    model = RWKV(
+        "https://huggingface.co/BlinkDL/rwkv-4-pile-1b5/resolve/main/RWKV-4-Pile-1B5-Instruct-test1-20230124.pth",
+        "pytorch(cpu/gpu)",
+        runtimedtype=torch.float32,
+        useGPU=torch.cuda.is_available(),
+        dtype=torch.float32
+    )
+    return model
+
+model = None
 
+def infer(
+        prompt,
+        mode = "generative",
+        max_new_tokens=10,
+        temperature=0.1,
+        top_p=1.0,
+        stop="<|endoftext|>",
+        seed=42,
+):
+    global model
 
-def predict(input, history=None):
-    model.setState(history[1])
-    model.loadContext(newctx=f"Prompt: {input}\n\nExpert Long Detailed Response: ")
-    r = model.forward(number=100,stopStrings=["\n\nPrompt"])
-    rr = [(input,r["output"])]
-    return [*history[0],*rr], [[*history[0],*rr],r["state"]]
+    if model == None:
+        gc.collect()
+        if (DEVICE == "cuda"):
+            torch.cuda.empty_cache()
+        model = get_model()
+        
+    max_new_tokens = int(max_new_tokens)
+    temperature = float(temperature)
+    top_p = float(top_p)
+    stop =  [x.strip(' ') for x in stop.split(',')]
+    seed = seed
 
-def freegen(input):
+    assert 1 <= max_new_tokens <= 384
+    assert 0.0 <= temperature <= 1.0
+    assert 0.0 <= top_p <= 1.0
+
+    if temperature == 0.0:
+        temperature = 0.01
+    if prompt == "":
+        prompt = " "
+
+    # Clear model state for generative mode
     model.resetState()
-    model.loadContext(newctx=input)
-    return model.forward(number=100)["output"]
-with gr.Blocks() as demo:
-    with gr.Tab("Chatbot"):
-        chatbot = gr.Chatbot()
-        state = model.emptyState
-        state = gr.State([[],state])
-        with gr.Row():
-            txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
+    if (mode == "Q/A"):
+        prompt = f"Expert Questions & Helpful Answers\nAsk Research Experts\nQuestion:\n{prompt}\n\nFull Answer:"
+    
+    print(f"PROMPT ({datetime.now()}):\n-------\n{prompt}")
+    print(f"OUTPUT ({datetime.now()}):\n-------\n")
+    # Load prompt
+    model.loadContext(newctx=prompt)
+    generated_text = ""
+    done = False
+    with torch.no_grad():
+        for _ in range(max_new_tokens):
+            char = model.forward(stopStrings=stop,temp=temperature,top_p_usual=top_p)["output"]
+            print(char, end='', flush=True)
+            generated_text += char
+            generated_text = generated_text.lstrip("\n ")
+            
+            for stop_word in stop:
+                stop_word = codecs.getdecoder("unicode_escape")(stop_word)[0]
+                if stop_word != '' and stop_word in generated_text:
+                    done = True
+                    break
+            yield generated_text
+            if done:
+                print("<stopped>\n")
+                break
+
+    #print(f"{generated_text}")
+    
+    for stop_word in stop:
+        stop_word = codecs.getdecoder("unicode_escape")(stop_word)[0]
+        if stop_word != '' and stop_word in generated_text:
+            generated_text = generated_text[:generated_text.find(stop_word)]
+    
+    gc.collect()
+    yield generated_text
+
+
+def chat(
+        prompt,
+        history,
+        max_new_tokens=10,
+        temperature=0.1,
+        top_p=1.0,
+        stop="<|endoftext|>",
+        seed=42,
+):
+    global model
+    history = history or []
+        
+    if model == None:
+        gc.collect()
+        if (DEVICE == "cuda"):
+            torch.cuda.empty_cache()
+        model = get_model()
+        
+    if len(history) == 0:
+        # no history, so lets reset chat state
+        model.resetState()
+        
+    max_new_tokens = int(max_new_tokens)
+    temperature = float(temperature)
+    top_p = float(top_p)
+    stop =  [x.strip(' ') for x in stop.split(',')]
+    seed = seed
+
+    assert 1 <= max_new_tokens <= 384
+    assert 0.0 <= temperature <= 1.0
+    assert 0.0 <= top_p <= 1.0
+
+    if temperature == 0.0:
+        temperature = 0.01
+    if prompt == "":
+        prompt = " "
+    
+    print(f"CHAT ({datetime.now()}):\n-------\n{prompt}")
+    print(f"OUTPUT ({datetime.now()}):\n-------\n")
+    # Load prompt
+    model.loadContext(newctx=prompt)
+    generated_text = ""
+    done = False
+    generated_text = model.forward(number=max_new_tokens, stopStrings=stop,temp=temperature,top_p_usual=top_p)["output"]
+
+    generated_text = generated_text.lstrip("\n ")
+    print(f"{generated_text}")
+    
+    for stop_word in stop:
+        stop_word = codecs.getdecoder("unicode_escape")(stop_word)[0]
+        if stop_word != '' and stop_word in generated_text:
+            generated_text = generated_text[:generated_text.find(stop_word)]
     
-        txt.submit(predict, [txt, state], [chatbot, state])
-    with gr.Tab("Free Gen"):
-        with gr.Row():
-            input = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
-            outtext = gr.Textbox(show_label=False)
-        input.submit(freegen,input,outtext)
-demo.launch()
+    gc.collect()
+    history.append((prompt, generated_text))
+    return history,history
+
+
+examples = [
+    [
+        # Question Answering
+        '''What is the capital of Germany?''',"Q/A", 25, 0.2, 1.0, "<|endoftext|>"],
+    [
+        # Question Answering
+        '''Are humans good or bad?''',"Q/A", 150, 0.8, 0.8, "<|endoftext|>"],
+    [
+        # Chatbot
+        '''This is a conversation between two AI large language models named Alex and Fritz. They are exploring each other's capabilities, and trying to ask interesting questions of one another to explore the limits of each others AI.
+Conversation:
+Alex: Good morning, Fritz, what type of LLM are you based upon?
+Fritz: Morning Alex, I am an RNN with transformer level performance. My language model is 100% attention free.
+Alex:''', "generative", 220, 0.9, 0.9, "\\n\\n,<|endoftext|>"],
+    [
+        # Generate List
+        '''Q. Give me list of fiction books. 
+1. Harry Potter
+2. Lord of the Rings
+3. Game of Thrones
+Q. Give me a list of vegetables.
+1. Broccoli
+2. Celery
+3. Tomatoes
+Q. Give me a list of car manufacturers.''', "generative", 80, 0.2, 1.0, "\\n\\n,<|endoftext|>"],
+    [
+        # Natural Language Interface
+        '''You are the writing assistant for Stephen King. You have worked in the fiction/horror genre for 30 years. You are a Pulitzer Prize-winning author, and now you are tasked with developing a skeletal outline for his newest horror novel, set to be completed in the spring of 2024. Create a summary of this work.
+Summary:''',"generative", 200, 0.85, 0.8, "<|endoftext|>"]
+]
+
+
+iface = gr.Interface(
+    fn=infer,
+    description='''<p>RNN With Transformer-level LLM Performance. (<a href='https://github.com/BlinkDL/RWKV-LM'>github</a>)
+    According to the author: "It combines the best of RNN and transformers - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding"
+    <p>Thanks to <a href='https://www.rftcapital.com'>RFT Capital</a> for donating compute capability for our experiments. Additional thanks to the author of the <a href="https://github.com/harrisonvanderbyl/rwkvstic">rwkvstic</a> library.</p>''',
+    allow_flagging="never",
+    inputs=[
+        gr.Textbox(lines=20, label="Prompt"),  # prompt
+        gr.Radio(["generative","Q/A"], value="generative", label="Choose Mode"),
+        gr.Slider(1, 256, value=40),  # max_tokens
+        gr.Slider(0.0, 1.0, value=0.8),  # temperature
+        gr.Slider(0.0, 1.0, value=0.85),  # top_p
+        gr.Textbox(lines=1, value="<|endoftext|>") # stop
+    ],
+    outputs=gr.Textbox(lines=25),
+    examples=examples,
+    cache_examples=False,
+).queue()
+
+chatiface = gr.Interface(
+    fn=chat,
+    description='''<p>RNN With Transformer-level LLM Performance. (<a href='https://github.com/BlinkDL/RWKV-LM'>github</a>)
+    According to the author: "It combines the best of RNN and transformers - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding"
+    <p>Thanks to <a href='https://www.rftcapital.com'>RFT Capital</a> for donating compute capability for our experiments. Additional thanks to the author of the <a href="https://github.com/harrisonvanderbyl/rwkvstic">rwkvstic</a> library.</p>''',
+    allow_flagging="never",
+    inputs=[
+        gr.Textbox(lines=5, label="Message"),  # prompt
+        "state",
+        gr.Slider(1, 256, value=60),  # max_tokens
+        gr.Slider(0.0, 1.0, value=0.8),  # temperature
+        gr.Slider(0.0, 1.0, value=0.85),  # top_p
+        gr.Textbox(lines=1, value="<|endoftext|>") # stop
+    ],
+    outputs=[gr.Chatbot(color_map=("green", "pink")),"state"],
+).queue()
+
+demo = gr.TabbedInterface(
+
+    [iface,chatiface],["Generative","Chatbot"],
+    title="RWKV-4 (1.5b Instruct)",
+    
+    )
+
+demo.queue()
+demo.launch(share=False)