Add application file

app.py

@@ -0,0 +1,229 @@
+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
+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
+
+    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
+
+    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 = " "
+
+    if (mode == "generative"):
+        # Clear model state for generative mode
+        model.resetState()
+    else: # Q/A
+        model.resetState()
+        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()
+        
+    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"PROMPT ({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)]
+    
+    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 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!
+Fritz:''', "generative", 200, 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 novel, set to be completed in the spring of 2024. Create a title and brief description for the first 5 chapters of this work.\n\nTitle:''',"generative", 250, 0.85, 0.85, "<|endoftext|>"]
+]
+
+
+iface = gr.Interface(
+    fn=infer,
+    description='''<p><a href='https://github.com/BlinkDL/RWKV-LM'>RWKV Language Model</a> - RNN With Transformer-level LLM Performance</p>
+    <p>Big thank you to <a href='https://www.rftcapital.com'>RFT Capital</a> for providing compute capability for our experiments.</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, 384, value=20),  # max_tokens
+        gr.Slider(0.0, 1.0, value=0.2),  # temperature
+        gr.Slider(0.0, 1.0, value=0.9),  # top_p
+        gr.Textbox(lines=1, value="<|endoftext|>") # stop
+    ],
+    outputs=gr.Textbox(lines=25),
+    examples=examples,
+)
+
+chatiface = gr.Interface(
+    fn=chat,
+    description='''<p><a href='https://github.com/BlinkDL/RWKV-LM'>RWKV Language Model</a> - RNN With Transformer-level LLM Performance</p>
+    <p>Big thank you to <a href='https://www.rftcapital.com'>RFT Capital</a> for providing compute capability for our experiments.</p>''',
+    allow_flagging="never",
+    inputs=[
+        gr.Textbox(lines=5, label="Message"),  # prompt
+        "state",
+        gr.Slider(1, 384, value=20),  # max_tokens
+        gr.Slider(0.0, 1.0, value=0.2),  # temperature
+        gr.Slider(0.0, 1.0, value=0.9),  # top_p
+        gr.Textbox(lines=1, value="<|endoftext|>,\\n") # stop
+    ],
+    outputs=[gr.Chatbot(color_map=("green", "pink")),"state"],
+)
+
+demo = gr.TabbedInterface(
+
+    [iface,chatiface],["Generative","Chatbot"],
+    title="RWKV-4 (1.5b Instruct)",
+    
+    ).queue()
+
+demo.launch(share=False)

requirements.txt

@@ -0,0 +1,5 @@
+transformers
+scipy
+torch
+inquirer
+rwkvstic