Billy D.
a16ffff73f
- Add Dockerfiles for nvidia, rdna2, strixhalo, and intel GPU targets - Add ray-serve modules (embeddings, whisper, tts, llm, reranker) - Add Gitea Actions workflow for automated builds - Add Makefile for local development - Update README with comprehensive documentation
143 lines
4.4 KiB
Python
143 lines
4.4 KiB
Python
"""
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Ray Serve deployment for sentence-transformers CrossEncoder reranking.
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Runs on: drizzt (Radeon 680M iGPU, ROCm) or danilo (Intel i915 iGPU, OpenVINO/IPEX)
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"""
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import os
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import time
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import uuid
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from typing import Any, Dict, List, Tuple
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from ray import serve
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@serve.deployment(name="RerankerDeployment", num_replicas=1)
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class RerankerDeployment:
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def __init__(self):
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from sentence_transformers import CrossEncoder
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import torch
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self.model_id = os.environ.get("MODEL_ID", "BAAI/bge-reranker-v2-m3")
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self.use_ipex = False
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self.device = "cpu"
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# Detect device - check for Intel GPU first via IPEX
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try:
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import intel_extension_for_pytorch as ipex
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self.use_ipex = True
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if hasattr(torch, "xpu") and torch.xpu.is_available():
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self.device = "xpu"
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print("Intel GPU detected via IPEX, using XPU device")
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else:
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print("IPEX available, will use CPU optimization")
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except ImportError:
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print("IPEX not available, checking for other GPUs")
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# Check for CUDA/ROCm if not using Intel
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if not self.use_ipex:
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if torch.cuda.is_available():
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self.device = "cuda"
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print(f"Using CUDA/ROCm device")
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else:
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print("No GPU detected, using CPU")
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print(f"Loading reranker model: {self.model_id}")
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print(f"Using device: {self.device}")
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# Load model
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self.model = CrossEncoder(self.model_id, device=self.device)
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# Apply IPEX optimization if available
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if self.use_ipex and self.device == "cpu":
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try:
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import intel_extension_for_pytorch as ipex
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self.model.model = ipex.optimize(self.model.model)
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print("IPEX CPU optimization applied")
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except Exception as e:
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print(f"IPEX optimization failed: {e}")
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print(f"Reranker model loaded successfully")
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async def __call__(self, request: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Handle reranking requests.
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Expected request format:
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{
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"query": "search query",
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"documents": ["doc1", "doc2", "doc3"],
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"top_k": 3,
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"return_documents": true
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}
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Alternative format (pairs):
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{
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"pairs": [["query", "doc1"], ["query", "doc2"]]
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}
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"""
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# Handle pairs format
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if "pairs" in request:
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pairs = request["pairs"]
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scores = self.model.predict(pairs)
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results = []
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for i, (pair, score) in enumerate(zip(pairs, scores)):
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results.append({
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"index": i,
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"score": float(score),
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})
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return {
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"object": "list",
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"results": results,
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"model": self.model_id,
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}
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# Handle query + documents format
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query = request.get("query", "")
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documents = request.get("documents", [])
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top_k = request.get("top_k", len(documents))
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return_documents = request.get("return_documents", True)
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if not documents:
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return {
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"object": "list",
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"results": [],
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"model": self.model_id,
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}
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# Create query-document pairs
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pairs = [[query, doc] for doc in documents]
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# Get scores
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scores = self.model.predict(pairs)
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# Create results with indices and scores
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results = []
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for i, (doc, score) in enumerate(zip(documents, scores)):
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result = {
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"index": i,
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"score": float(score),
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}
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if return_documents:
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result["document"] = doc
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results.append(result)
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# Sort by score descending
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results.sort(key=lambda x: x["score"], reverse=True)
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# Apply top_k
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results = results[:top_k]
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return {
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"object": "list",
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"results": results,
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"model": self.model_id,
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"usage": {
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"total_pairs": len(pairs),
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},
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}
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app = RerankerDeployment.bind()
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