Spaces:
Running
on
Zero
Running
on
Zero
File size: 9,546 Bytes
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from __future__ import annotations
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import torch
from fastapi import HTTPException
from fastapi.responses import JSONResponse
from pydantic import BaseModel
try:
import spaces # type: ignore
except Exception: # pragma: no cover
class _SpacesShim:
@staticmethod
def GPU(*_args, **_kwargs):
def identity(fn):
return fn
return identity
spaces = _SpacesShim()
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
)
import gradio as gr
# Configuration
MAX_NEW_TOKENS = int(os.environ.get("MAX_NEW_TOKENS", "600"))
DEFAULT_TEMPERATURE = float(os.environ.get("DEFAULT_TEMPERATURE", "0.2"))
DEFAULT_TOP_P = float(os.environ.get("DEFAULT_TOP_P", "0.9"))
HF_TOKEN = os.environ.get("HF_TOKEN")
MODEL_ID = "Alovestocode/router-gemma3-merged"
# Model state
_MODEL = None
ACTIVE_STRATEGY: Optional[str] = None
# Initialize tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=False, token=HF_TOKEN)
print(f"Loaded tokenizer from {MODEL_ID}")
# Pydantic models
class GeneratePayload(BaseModel):
prompt: str
max_new_tokens: Optional[int] = None
temperature: Optional[float] = None
top_p: Optional[float] = None
class GenerateResponse(BaseModel):
text: str
# Detect ZeroGPU environment
IS_ZEROGPU = os.environ.get("SPACE_RUNTIME_STATELESS", "0") == "1"
if os.environ.get("SPACES_ZERO_GPU") is not None:
IS_ZEROGPU = True
# Model loading - ZeroGPU pattern: load on CPU, move to GPU inside @spaces.GPU functions
def get_model() -> AutoModelForCausalLM:
"""Load the model on CPU. GPU movement happens inside @spaces.GPU decorated function."""
global _MODEL, ACTIVE_STRATEGY
if _MODEL is None:
# In ZeroGPU: always load on CPU first, will use GPU only in @spaces.GPU functions
# For local runs: use CUDA if available
if IS_ZEROGPU:
# ZeroGPU: load on CPU with device_map=None
try:
kwargs = {
"device_map": None, # Stay on CPU for ZeroGPU
"quantization_config": BitsAndBytesConfig(load_in_8bit=True),
"trust_remote_code": True,
"low_cpu_mem_usage": True,
"token": HF_TOKEN,
}
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, **kwargs)
ACTIVE_STRATEGY = "8bit"
except Exception:
# Fallback to bf16 on CPU
kwargs = {
"device_map": None,
"torch_dtype": torch.bfloat16,
"trust_remote_code": True,
"low_cpu_mem_usage": True,
"token": HF_TOKEN,
}
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, **kwargs)
ACTIVE_STRATEGY = "bf16"
else:
# Local environment: use GPU if available
if torch.cuda.is_available():
try:
kwargs = {
"device_map": "auto",
"quantization_config": BitsAndBytesConfig(load_in_8bit=True),
"trust_remote_code": True,
"low_cpu_mem_usage": True,
"token": HF_TOKEN,
}
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, **kwargs)
ACTIVE_STRATEGY = "8bit"
except Exception:
kwargs = {
"device_map": "auto",
"torch_dtype": torch.bfloat16,
"trust_remote_code": True,
"low_cpu_mem_usage": True,
"token": HF_TOKEN,
}
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, **kwargs)
ACTIVE_STRATEGY = "bf16"
else:
kwargs = {
"device_map": "cpu",
"torch_dtype": torch.float32,
"trust_remote_code": True,
"low_cpu_mem_usage": True,
"token": HF_TOKEN,
}
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, **kwargs)
ACTIVE_STRATEGY = "cpu"
_MODEL = model.eval()
print(f"Loaded {MODEL_ID} with strategy='{ACTIVE_STRATEGY}' (ZeroGPU={IS_ZEROGPU})")
return _MODEL
# ZeroGPU decorated function - moves model to GPU inside this function
@spaces.GPU(duration=300)
def _generate_with_gpu(
prompt: str,
max_new_tokens: int = MAX_NEW_TOKENS,
temperature: float = DEFAULT_TEMPERATURE,
top_p: float = DEFAULT_TOP_P,
) -> str:
"""
GPU generation wrapper for ZeroGPU.
In ZeroGPU mode: model is loaded on CPU, moved to GPU here, then back to CPU after.
"""
if not prompt.strip():
raise ValueError("Prompt must not be empty.")
global _MODEL
model = get_model()
# In ZeroGPU: move model to GPU inside this @spaces.GPU function
# For local: model might already be on GPU
current_device = torch.device("cpu")
if IS_ZEROGPU and torch.cuda.is_available():
current_device = torch.device("cuda")
model = model.to(current_device)
elif torch.cuda.is_available() and not IS_ZEROGPU:
current_device = torch.device("cuda")
inputs = tokenizer(prompt, return_tensors="pt").to(current_device)
eos = tokenizer.eos_token_id
try:
with torch.inference_mode():
output_ids = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
eos_token_id=eos,
pad_token_id=eos,
)
text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
result = text[len(prompt):].strip() or text.strip()
finally:
# In ZeroGPU: move model back to CPU to free GPU memory
if IS_ZEROGPU and torch.cuda.is_available():
_MODEL = model.to(torch.device("cpu"))
torch.cuda.empty_cache()
return result
# Gradio UI
def gradio_generate(
prompt: str,
max_new_tokens: int,
temperature: float,
top_p: float,
) -> tuple[str, str]:
"""Gradio handler for generation."""
if not prompt.strip():
return "ERROR: Prompt must not be empty.", "β Prompt required."
try:
text = _generate_with_gpu(prompt, max_new_tokens, temperature, top_p)
return text, "β
Generation successful."
except Exception as exc:
return f"ERROR: {exc}", f"β Generation failed: {exc}"
# Create Gradio interface
with gr.Blocks(title="Router Model API - ZeroGPU", theme=gr.themes.Soft()) as gradio_app:
gr.Markdown("# π Router Model API - ZeroGPU")
gr.Markdown("Intelligent routing agent for coordinating specialized AI agents")
with gr.Row():
with gr.Column():
prompt_input = gr.Textbox(
label="Router Prompt",
lines=8,
placeholder="Enter your router prompt here...",
)
max_tokens_input = gr.Slider(
minimum=64,
maximum=2048,
value=MAX_NEW_TOKENS,
step=16,
label="Max New Tokens",
)
temp_input = gr.Slider(
minimum=0.0,
maximum=2.0,
value=DEFAULT_TEMPERATURE,
step=0.05,
label="Temperature",
)
top_p_input = gr.Slider(
minimum=0.0,
maximum=1.0,
value=DEFAULT_TOP_P,
step=0.05,
label="Top-p",
)
generate_btn = gr.Button("π Generate", variant="primary")
with gr.Column():
output = gr.Textbox(
label="Generated Response",
lines=20,
show_copy_button=True,
)
status = gr.Markdown("Status: Ready")
generate_btn.click(
fn=gradio_generate,
inputs=[prompt_input, max_tokens_input, temp_input, top_p_input],
outputs=[output, status],
)
# API routes - add directly to Gradio's FastAPI app
@gradio_app.app.get("/health")
def api_health():
"""Health check endpoint."""
return {
"status": "ok",
"model": MODEL_ID,
"strategy": ACTIVE_STRATEGY or "pending",
}
@gradio_app.app.post("/v1/generate")
async def api_generate(payload: GeneratePayload):
"""Generate endpoint."""
try:
text = _generate_with_gpu(
prompt=payload.prompt,
max_new_tokens=payload.max_new_tokens or MAX_NEW_TOKENS,
temperature=payload.temperature or DEFAULT_TEMPERATURE,
top_p=payload.top_p or DEFAULT_TOP_P,
)
return {"text": text}
except Exception as exc:
raise HTTPException(status_code=500, detail=str(exc))
# Setup
print("Warm start skipped for ZeroGPU. Model will load on first request.")
gradio_app.queue(max_size=8)
app = gradio_app
if __name__ == "__main__": # pragma: no cover
app.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))
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