Upload testapp.py

testapp.py

@@ -0,0 +1,138 @@
+import os
+from threading import Thread
+from typing import Iterator
+
+import gradio as gr
+#import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+MAX_MAX_NEW_TOKENS = 1024
+DEFAULT_MAX_NEW_TOKENS = 512
+total_count=0
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "2048"))
+
+DESCRIPTION = """\
+# DeepSeek-1.3B-Chat
+"""
+
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶.</p>"
+    model_id = "deepseek-ai/deepseek-coder-1.3b-instruct"
+    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32, device_map="auto")
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+
+if torch.cuda.is_available():
+    model_id = "deepseek-ai/deepseek-coder-1.3b-instruct"
+    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    tokenizer.use_default_system_prompt = False
+    
+
+
+#@spaces.GPU
+def generate(
+    message: str,
+    chat_history: list[tuple[str, str]],
+    system_prompt: str,
+    max_new_tokens: int = 1024,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1,
+) -> Iterator[str]:
+    global total_count
+    total_count += 1
+    print(total_count)
+    if total_count % 50 == 0 :
+        os.system("nvidia-smi")
+    conversation = []
+    if system_prompt:
+        conversation.append({"role": "system", "content": system_prompt})
+    for user, assistant in chat_history:
+        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
+    conversation.append({"role": "user", "content": message})
+
+    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt", add_generation_prompt=True)
+    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+    input_ids = input_ids.to(model.device)
+
+    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        {"input_ids": input_ids},
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=False,
+        top_p=top_p,
+        top_k=top_k,
+        num_beams=1,
+        # temperature=temperature,
+        repetition_penalty=repetition_penalty,
+        eos_token_id=32021
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs).replace("<|EOT|>","")
+
+
+chat_interface = gr.ChatInterface(
+    fn=generate,
+    additional_inputs=[
+        gr.Textbox(label="System prompt", lines=6),
+        gr.Slider(
+            label="Max new tokens",
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
+        ),
+        # gr.Slider(
+        #     label="Temperature",
+        #     minimum=0,
+        #     maximum=4.0,
+        #     step=0.1,
+        #     value=0,
+        # ),
+        gr.Slider(
+            label="Top-p (nucleus sampling)",
+            minimum=0.05,
+            maximum=1.0,
+            step=0.05,
+            value=0.9,
+        ),
+        gr.Slider(
+            label="Top-k",
+            minimum=1,
+            maximum=1000,
+            step=1,
+            value=50,
+        ),
+        gr.Slider(
+            label="Repetition penalty",
+            minimum=1.0,
+            maximum=2.0,
+            step=0.05,
+            value=1,
+        ),
+    ],
+    stop_btn=None,
+    examples=[
+        ["implement snake game using pygame"],
+        ["Can you explain briefly to me what is the Python programming language?"],
+        ["write a program to find the factorial of a number"],
+    ],
+)
+
+with gr.Blocks() as demo:
+    gr.Markdown(DESCRIPTION)
+    chat_interface.render()
+
+if __name__ == "__main__":
+    demo.queue(max_size=5).launch()