Update app.py

app.py

@@ -2,7 +2,19 @@
 RWKV RNN Model - Gradio Space for HuggingFace
 YT - Mean Gene Hacks - https://www.youtube.com/@MeanGeneHacks
 (C) Gene Ruebsamen - 2/7/2023
-License: GPL3
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
 """
 
 import gradio as gr
@@ -10,12 +22,18 @@ 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
+from config import config, title
 import torch
 import gc
 
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
+desc = '''<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."'''
+
+thanks = '''<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>'''
+
+
 def to_md(text):
     return text.replace("\n", "<br />")
 
@@ -23,22 +41,21 @@ def to_md(text):
 def get_model():
     model = None
     model = RWKV(
-        "https://huggingface.co/BlinkDL/rwkv-4-pile-7b/resolve/main/RWKV-4-Pile-7B-Instruct-test1-20230124.pth",
-        "pytorch(cpu/gpu)",
-        runtimedtype=torch.bfloat16,
-        useGPU=torch.cuda.is_available(),
-        dtype=torch.bfloat16
+        **config
     )
     return model
 
+
 model = None
 
+
 def infer(
         prompt,
-        mode = "generative",
+        mode="generative",
         max_new_tokens=10,
         temperature=0.1,
         top_p=1.0,
+        end_adj=0.0,
         stop="<|endoftext|>",
         seed=42,
 ):
@@ -49,27 +66,27 @@ def infer(
         if (DEVICE == "cuda"):
             torch.cuda.empty_cache()
         model = get_model()
-        
+
     max_new_tokens = int(max_new_tokens)
     temperature = float(temperature)
+    end_adj = float(end_adj)
     top_p = float(top_p)
-    stop =  [x.strip(' ') for x in stop.split(',')]
+    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
+    temperature = max(0.05, temperature)
     if prompt == "":
         prompt = " "
 
     # Clear model state for generative mode
     model.resetState()
     if (mode == "Q/A"):
-        prompt = f"Q:\n{prompt}\n\nExpert Long Detailed Response:"
-    
+        prompt = f"Ask Expert\n\nQuestion:\n{prompt}\n\nExpert Full Answer:\n"
+
     print(f"PROMPT ({datetime.now()}):\n-------\n{prompt}")
     print(f"OUTPUT ({datetime.now()}):\n-------\n")
     # Load prompt
@@ -78,11 +95,12 @@ def infer(
     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"]
+            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:
@@ -93,13 +111,13 @@ def infer(
                 print("<stopped>\n")
                 break
 
-    #print(f"{generated_text}")
-    
+    # 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
 
@@ -107,137 +125,161 @@ def infer(
 def chat(
         prompt,
         history,
+        username,
         max_new_tokens=10,
         temperature=0.1,
         top_p=1.0,
-        stop="<|endoftext|>",
         seed=42,
 ):
     global model
     history = history or []
-        
+
+    intro = ""
+
     if model == None:
         gc.collect()
         if (DEVICE == "cuda"):
             torch.cuda.empty_cache()
         model = get_model()
-        
+
+    username = username.strip()
+    username = username or "USER"
+
+    intro = f'''The following is a verbose and detailed conversation between an AI assistant called FRITZ, and a human user called USER. FRITZ is intelligent, knowledgeable, wise and polite.
+
+    {username}: What year was the french revolution?
+    FRITZ: The French Revolution started in 1789, and lasted 10 years until 1799.
+    {username}: 3+5=?
+    FRITZ: The answer is 8.
+    {username}: What year did the Berlin Wall fall?
+    FRITZ: The Berlin wall stood for 28 years and fell in 1989.
+    {username}: solve for a: 9-a=2
+    FRITZ: The answer is a=7, because 9-7 = 2.
+    {username}: wat is lhc
+    FRITZ: The Large Hadron Collider (LHC) is a high-energy particle collider, built by CERN, and completed in 2008. It was used to confirm the existence of the Higgs boson in 2012.
+    {username}: Tell me about yourself.
+    FRITZ: My name is Fritz. I am an RNN based Large Language Model (LLM).
+    '''
+
     if len(history) == 0:
         # no history, so lets reset chat state
         model.resetState()
-        history = [[],model.emptyState]
+        history = [[], model.emptyState]
+        print("reset chat state")
     else:
-        model.setState(history[1])
+        if (history[0][0][0].split(':')[0] != username):
+            model.resetState()
+            history = [[], model.emptyState]
+            print("username changed, reset state")
+        else:
+            model.setState(history[1])
+            intro = ""
+
     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 = " "
-    
+    temperature = max(0.05, temperature)
+
+    prompt = f"{username}: " + prompt + "\n"
     print(f"CHAT ({datetime.now()}):\n-------\n{prompt}")
     print(f"OUTPUT ({datetime.now()}):\n-------\n")
     # Load prompt
-    model.loadContext(newctx="\nUser: "+prompt+"\n\nBot: ")
-    generated_text = ""
-    done = False
-    gen = model.forward(number=max_new_tokens, stopStrings=stop+["User"],temp=temperature,top_p_usual=top_p)
-    generated_text = gen["output"]
-    generated_text = generated_text.lstrip("\n ")
+
+    model.loadContext(newctx=intro+prompt)
+
+    out = model.forward(number=max_new_tokens, stopStrings=[
+                        "<|endoftext|>", username+":"], temp=temperature, top_p_usual=top_p)
+
+    generated_text = out["output"].lstrip("\n ")
+    generated_text = generated_text.rstrip("USER:")
     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[0].append((prompt, generated_text))
-    return history[0],[history[0],gen["state"]]
+    return history[0], [history[0], out["state"]]
 
 
 examples = [
     [
         # Question Answering
-        '''What is the capital of Germany?''',"Q/A", 25, 0.2, 1.0, "<|endoftext|>"],
+        '''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|>"],
+        '''Are humans good or bad?''', "Q/A", 150, 0.8, 0.8, "<|endoftext|>"],
+    [
+        # Question Answering
+        '''What is the purpose of Vitamin A?''', "Q/A", 50, 0.2, 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|>"],
+        '''Task given:
+
+Please Write a Short story about a cat learning python
+
+Best Full Response:
+''', "generative", 140, 0.85, 0.8, "<|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|>"]
+        '''Here is a short story (in the style of Tolkien) in which Aiden attacks a robot with a sword:
+        ''', "generative", 140, 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 https://huggingface.co/spaces/yahma/rwkv for the basis for this space</p>''',
+    description=f'''<h3>Generative and Question/Answer</h3>{desc}{thanks}''',
     allow_flagging="never",
     inputs=[
         gr.Textbox(lines=20, label="Prompt"),  # prompt
-        gr.Radio(["generative","Q/A"], value="generative", label="Choose Mode"),
+        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
+        gr.Slider(-999, 0.0, value=0.0)  # end_adj
+        gr.Textbox(lines=1, value="<|endoftext|>")  # stop
     ],
-    outputs=gr.Textbox(lines=25),
+    outputs=gr.Textbox(label="Generated Output", 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 https://huggingface.co/spaces/yahma/rwkv for the basis for this space</p>''',
+    description=f'''<h3>Chatbot</h3><h4>Refresh page or change name to reset memory context</h4>{desc}{thanks}''',
     allow_flagging="never",
     inputs=[
         gr.Textbox(lines=5, label="Message"),  # prompt
         "state",
+        gr.Text(lines=1, value="USER", label="Your Name",
+                placeholder="Enter your Name"),
         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
+        gr.Slider(0.0, 1.0, value=0.85)  # top_p
     ],
-    outputs=[gr.Chatbot(color_map=("green", "pink")),"state"],
+    outputs=[gr.Chatbot(label="Chat Log", color_map=(
+        "green", "pink")), "state"],
 ).queue()
 
 demo = gr.TabbedInterface(
 
-    [iface,chatiface],["Generative","Chatbot"],
-    title="RWKV-4 (7b Instruct)",
-    
-    )
+    [iface, chatiface], ["Generative", "Chatbot"],
+    title=title,
+
+)
 
 demo.queue()
-demo.launch(share=False)
+demo.launch(share=False)