Update app.py

app.py

@@ -45,6 +45,239 @@ import string
 import argparse
 import json
 
+import gc, copy
+from datetime import datetime
+from huggingface_hub import hf_hub_download
+from pynvml import *
+nvmlInit()
+gpu_h = nvmlDeviceGetHandleByIndex(0)
+ctx_limit = 1536
+title = "RWKV-4-Raven-7B-v12-Eng98%-Other2%-20230521-ctx8192"
+
+os.environ["RWKV_JIT_ON"] = '1'
+os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)
+
+from rwkv.model import RWKV
+model_path1 = hf_hub_download(repo_id="BlinkDL/rwkv-4-raven", filename=f"{title}.pth")
+model1 = RWKV(model=model_path1, strategy='cuda fp16i8 *8 -> cuda fp16')
+from rwkv.utils import PIPELINE, PIPELINE_ARGS
+pipeline = PIPELINE(model1, "20B_tokenizer.json")
+
+def generate_prompt(instruction, input=None):
+    instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
+    input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
+    if input:
+        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+# Instruction:
+{instruction}
+# Input:
+{input}
+# Response:
+"""
+    else:
+        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+# Instruction:
+{instruction}
+# Response:
+"""
+
+def evaluate(
+    instruction,
+    input=None,
+    token_count=200,
+    temperature=1.0,
+    top_p=0.7,
+    presencePenalty = 0.1,
+    countPenalty = 0.1,
+):
+    args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
+                     alpha_frequency = countPenalty,
+                     alpha_presence = presencePenalty,
+                     token_ban = [], # ban the generation of some tokens
+                     token_stop = [0]) # stop generation whenever you see any token here
+
+    instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
+    input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
+    ctx = generate_prompt(instruction, input)
+    
+    all_tokens = []
+    out_last = 0
+    out_str = ''
+    occurrence = {}
+    state = None
+    for i in range(int(token_count)):
+        out, state = model1.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
+        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)
+        if token in args.token_stop:
+            break
+        all_tokens += [token]
+        if token not in occurrence:
+            occurrence[token] = 1
+        else:
+            occurrence[token] += 1
+        
+        tmp = pipeline.decode(all_tokens[out_last:])
+        if '\ufffd' not in tmp:
+            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}')  
+    del out
+    del state
+    gc.collect()
+    torch.cuda.empty_cache()
+    yield out_str.strip()
+
+examples = [
+    ["Tell me about ravens.", "", 300, 1.2, 0.5, 0.4, 0.4],
+    ["Write a python function to mine 1 BTC, with details and comments.", "", 300, 1.2, 0.5, 0.4, 0.4],
+    ["Write a song about ravens.", "", 300, 1.2, 0.5, 0.4, 0.4],
+    ["Explain the following metaphor: Life is like cats.", "", 300, 1.2, 0.5, 0.4, 0.4],
+    ["Write a story using the following information", "A man named Alex chops a tree down", 300, 1.2, 0.5, 0.4, 0.4],
+    ["Generate a list of adjectives that describe a person as brave.", "", 300, 1.2, 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.", "", 300, 1.2, 0.5, 0.4, 0.4],
+]
+
+##########################################################################
+
+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 = model1.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 = model1.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 = model1.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 = model1.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
+
 from TTS.tts.utils.synthesis import synthesis
 from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
 try: