✨ add multi card run
This commit is contained in:
eust-w
parent
cadbab7541
commit
73ce51c611
@ -9,6 +9,7 @@ import torch
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import numpy as np
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from PIL import Image
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from ops_mm_embedding_v1 import OpsMMEmbeddingV1
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from concurrent.futures import ThreadPoolExecutor
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from typing import List, Union, Tuple, Dict, Any, Optional
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import os
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import json
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@ -33,7 +34,11 @@ class MultimodalRetrievalLocal:
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use_all_gpus: bool = True,
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gpu_ids: List[int] = None,
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min_memory_gb: int = 12,
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index_path: str = "local_faiss_index"):
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index_path: str = "local_faiss_index",
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shard_model_across_gpus: bool = False,
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load_in_4bit: bool = False,
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load_in_8bit: bool = False,
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torch_dtype: Optional[torch.dtype] = torch.bfloat16):
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"""
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初始化多模态检索系统
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@ -46,6 +51,10 @@ class MultimodalRetrievalLocal:
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"""
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self.model_path = model_path
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self.index_path = index_path
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self.shard_model_across_gpus = shard_model_across_gpus
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self.load_in_4bit = load_in_4bit
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self.load_in_8bit = load_in_8bit
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self.torch_dtype = torch_dtype
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# 检查模型路径
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if not os.path.exists(model_path):
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@ -116,14 +125,53 @@ class MultimodalRetrievalLocal:
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"""加载多模态嵌入模型 OpsMMEmbeddingV1"""
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logger.info(f"加载本地多模态嵌入模型: {self.model_path}")
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try:
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device_str = "cuda" if self.use_gpu else "cpu"
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self.model = OpsMMEmbeddingV1(
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self.models: List[OpsMMEmbeddingV1] = []
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if self.use_gpu and len(self.gpu_ids) > 1 and self.shard_model_across_gpus:
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# Tensor-parallel sharding using device_map='auto' across visible GPUs
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logger.info(f"启用模型跨GPU切片(shard)，使用设备: {self.gpu_ids}")
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# Rely on CUDA_VISIBLE_DEVICES to constrain which GPUs are visible, or use accelerate's auto mapping
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ref_model = OpsMMEmbeddingV1(
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self.model_path,
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device="cuda",
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attn_implementation=None,
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device_map="auto",
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load_in_4bit=self.load_in_4bit,
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load_in_8bit=self.load_in_8bit,
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torch_dtype=self.torch_dtype,
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)
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# For sharded model, we keep a single logical model reference
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self.models.append(ref_model)
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else:
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if self.use_gpu and len(self.gpu_ids) > 1:
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logger.info(f"检测到多GPU，可用: {self.gpu_ids}，为每张卡加载一个模型副本（数据并行）")
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for gid in self.gpu_ids:
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device_str = f"cuda:{gid}"
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self.models.append(
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OpsMMEmbeddingV1(
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self.model_path,
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device=device_str,
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attn_implementation=None,
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load_in_4bit=self.load_in_4bit,
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load_in_8bit=self.load_in_8bit,
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torch_dtype=self.torch_dtype,
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)
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)
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ref_model = self.models[0]
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else:
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device_str = "cuda" if self.use_gpu else "cpu"
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logger.info(f"使用单设备: {device_str}")
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ref_model = OpsMMEmbeddingV1(
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self.model_path,
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device=device_str,
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attn_implementation=None,
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load_in_4bit=self.load_in_4bit,
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load_in_8bit=self.load_in_8bit,
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torch_dtype=self.torch_dtype,
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)
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self.models.append(ref_model)
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# 获取向量维度
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self.vector_dim = int(getattr(self.model.base_model.config, "hidden_size"))
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self.vector_dim = int(getattr(ref_model.base_model.config, "hidden_size"))
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logger.info(f"向量维度: {self.vector_dim}")
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logger.info("嵌入模型加载成功")
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except Exception as e:
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@ -158,33 +206,103 @@ class MultimodalRetrievalLocal:
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logger.error(f"元数据加载失败: {str(e)}")
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def encode_text(self, text: Union[str, List[str]]) -> np.ndarray:
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"""编码文本为向量（使用 OpsMMEmbeddingV1）"""
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"""编码文本为向量（使用 OpsMMEmbeddingV1，多GPU并行）"""
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if isinstance(text, str):
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text = [text]
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if len(text) == 0:
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return np.zeros((0, self.vector_dim))
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# 单条或单模型直接处理
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if len(self.models) == 1 or len(text) == 1:
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with torch.inference_mode():
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emb = self.model.get_text_embeddings(texts=text)
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text_embeddings = emb.detach().float().cpu().numpy()
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# emb 已经做过 L2 归一化，这里保持一致
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return text_embeddings[0] if len(text) == 1 else text_embeddings
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emb = self.models[0].get_text_embeddings(texts=text)
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arr = emb.detach().float().cpu().numpy()
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return arr[0] if len(text) == 1 else arr
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# 多GPU并行：按设备平均切分
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shard_count = len(self.models)
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shards: List[List[str]] = []
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for i in range(shard_count):
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shards.append([])
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for idx, t in enumerate(text):
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shards[idx % shard_count].append(t)
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results: List[np.ndarray] = [None] * shard_count # type: ignore
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def run_shard(model: OpsMMEmbeddingV1, shard_texts: List[str], shard_idx: int):
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if len(shard_texts) == 0:
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results[shard_idx] = np.zeros((0, self.vector_dim))
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return
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with torch.inference_mode():
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emb = model.get_text_embeddings(texts=shard_texts)
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results[shard_idx] = emb.detach().float().cpu().numpy()
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with ThreadPoolExecutor(max_workers=shard_count) as ex:
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for i, (model, shard_texts) in enumerate(zip(self.models, shards)):
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ex.submit(run_shard, model, shard_texts, i)
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# 还原原始顺序（按 round-robin 拼接）
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merged: List[np.ndarray] = []
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max_len = max(len(s) for s in shards)
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for k in range(max_len):
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for i in range(shard_count):
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if k < len(shards[i]) and results[i].shape[0] > 0:
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merged.append(results[i][k:k+1])
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if len(merged) == 0:
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return np.zeros((0, self.vector_dim))
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return np.vstack(merged)
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def encode_image(self, image: Union[Image.Image, List[Image.Image]]) -> np.ndarray:
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"""编码图像为向量（使用 OpsMMEmbeddingV1）"""
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"""编码图像为向量（使用 OpsMMEmbeddingV1，多GPU并行）"""
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try:
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# 规范为列表
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images: List[Image.Image]
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if isinstance(image, Image.Image):
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images = [image]
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images: List[Image.Image] = [image]
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else:
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images = image
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if not images:
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logger.error("encode_image: 图像列表为空")
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return np.zeros((0, self.vector_dim))
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# 强制为 RGB
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rgb_images = [img.convert('RGB') if img.mode != 'RGB' else img for img in images]
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# 单张或单模型直接处理
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if len(self.models) == 1 or len(rgb_images) == 1:
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with torch.inference_mode():
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emb = self.model.get_image_embeddings(images=rgb_images)
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image_embeddings = emb.detach().float().cpu().numpy()
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return image_embeddings
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emb = self.models[0].get_image_embeddings(images=rgb_images)
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return emb.detach().float().cpu().numpy()
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# 多GPU并行：按设备平均切分
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shard_count = len(self.models)
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shards: List[List[Image.Image]] = [[] for _ in range(shard_count)]
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for idx, img in enumerate(rgb_images):
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shards[idx % shard_count].append(img)
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results: List[np.ndarray] = [None] * shard_count # type: ignore
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def run_shard(model: OpsMMEmbeddingV1, shard_imgs: List[Image.Image], shard_idx: int):
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if len(shard_imgs) == 0:
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results[shard_idx] = np.zeros((0, self.vector_dim))
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return
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with torch.inference_mode():
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emb = model.get_image_embeddings(images=shard_imgs)
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results[shard_idx] = emb.detach().float().cpu().numpy()
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with ThreadPoolExecutor(max_workers=shard_count) as ex:
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for i, (model, shard_imgs) in enumerate(zip(self.models, shards)):
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ex.submit(run_shard, model, shard_imgs, i)
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# 还原原始顺序（按 round-robin 拼接）
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merged: List[np.ndarray] = []
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max_len = max(len(s) for s in shards)
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for k in range(max_len):
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for i in range(shard_count):
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if k < len(shards[i]) and results[i].shape[0] > 0:
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merged.append(results[i][k:k+1])
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if len(merged) == 0:
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return np.zeros((0, self.vector_dim))
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return np.vstack(merged)
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except Exception as e:
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logger.error(f"encode_image: 异常: {str(e)}")
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return np.zeros((0, self.vector_dim))
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@ -18,17 +18,36 @@ class OpsMMEmbeddingV1(nn.Module):
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device: str = "cuda",
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max_length: Optional[int] = None,
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attn_implementation: Optional[str] = None,
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device_map: Optional[str] = None,
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load_in_4bit: bool = False,
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load_in_8bit: bool = False,
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torch_dtype: Optional[torch.dtype] = torch.bfloat16,
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):
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super().__init__()
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self.device = device
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self.max_length = max_length
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self.default_instruction = "You are a helpful assistant."
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self.base_model = AutoModelForImageTextToText.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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from transformers import AutoModelForImageTextToText
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load_kwargs = dict(
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torch_dtype=torch_dtype,
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low_cpu_mem_usage=True,
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attn_implementation=attn_implementation,
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).to(self.device)
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)
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# Quantization options (requires bitsandbytes for 4/8-bit)
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if load_in_4bit:
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load_kwargs["load_in_4bit"] = True
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if load_in_8bit:
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load_kwargs["load_in_8bit"] = True
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if device_map is not None:
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load_kwargs["device_map"] = device_map
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self.base_model = AutoModelForImageTextToText.from_pretrained(
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model_name,
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**load_kwargs,
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)
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# Only move to a single device when not using tensor-parallel sharding
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if device_map is None:
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self.base_model = self.base_model.to(self.device)
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self.processor = AutoProcessor.from_pretrained(model_name, min_pixels=256 * 28 * 28, max_pixels=1280 * 28 * 28)
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self.processor.tokenizer.padding_side = "left"
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11
run_server.sh
Normal file
11
run_server.sh
Normal file
@ -0,0 +1,11 @@
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#!/usr/bin/env bash
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set -euo pipefail
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# Select GPUs 0 and 1 for tensor-parallel sharding
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export CUDA_VISIBLE_DEVICES=0,1
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# Unbuffered stdout for real-time logs
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export PYTHONUNBUFFERED=1
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# Start the local web app
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exec python3 web_app_local.py "$@"
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@ -824,7 +824,7 @@
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updateDataStats();
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}
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// 批量上传图片
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// 批量上传图片（调用批量API以利用多卡并行）
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async function uploadBatchImages(files) {
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try {
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const progressDiv = document.getElementById('imageUploadProgress');
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@ -833,36 +833,26 @@
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progressDiv.style.display = 'block';
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progressText.textContent = `0/${files.length}`;
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progressBar.style.width = '0%';
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progressBar.style.width = '10%';
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showAlert('info', `正在上传${files.length}张图片...`);
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showAlert('info', `正在批量上传 ${files.length} 张图片...`);
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let successCount = 0;
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for (let i = 0; i < files.length; i++) {
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const formData = new FormData();
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formData.append('image', files[i]);
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for (let i = 0; i < files.length; i++) {
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formData.append('images', files[i]);
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}
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const response = await fetch('/api/add_image', {
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const response = await fetch('/api/add_images_batch', {
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method: 'POST',
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body: formData
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});
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const data = await response.json();
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if (data.success) {
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successCount++;
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} else {
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console.error(`图片 ${files[i].name} 上传失败: ${data.error}`);
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if (!data.success) {
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throw new Error(data.error || '批量上传失败');
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}
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// 更新进度
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const progress = Math.round(((i + 1) / files.length) * 100);
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progressBar.style.width = `${progress}%`;
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progressText.textContent = `${i + 1}/${files.length}`;
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}
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showAlert('success', `成功上传 ${successCount}/${files.length} 张图片`);
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// 自动保存索引
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progressBar.style.width = '100%';
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progressText.textContent = `${files.length}/${files.length}`;
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showAlert('success', data.message || `成功上传 ${files.length} 张图片`);
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await autoSaveIndex();
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updateDataStats();
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} catch (error) {
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@ -870,40 +860,29 @@
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} finally {
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setTimeout(() => {
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document.getElementById('imageUploadProgress').style.display = 'none';
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}, 2000);
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}, 1500);
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}
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// 旧代码已删除
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// 旧代码已删除
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}
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// 批量上传文本
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// 批量上传文本（调用批量API以利用多卡并行）
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async function uploadBatchTexts(texts) {
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try {
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showAlert('info', `正在上传${texts.length}条文本...`);
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for (let i = 0; i < texts.length; i++) {
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const response = await fetch('/api/add_text', {
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showAlert('info', `正在批量上传 ${texts.length} 条文本...`);
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const response = await fetch('/api/add_texts_batch', {
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method: 'POST',
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headers: {'Content-Type': 'application/json'},
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body: JSON.stringify({text: texts[i]})
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body: JSON.stringify({texts})
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});
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const data = await response.json();
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if (!data.success) {
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throw new Error(`第${i+1}条文本上传失败: ${data.error}`);
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throw new Error(data.error || '批量上传失败');
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}
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}
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showAlert('success', `成功上传${texts.length}条文本`);
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// 自动保存索引
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showAlert('success', data.message || `成功上传 ${texts.length} 条文本`);
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await autoSaveIndex();
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updateDataStats();
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} catch (error) {
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showAlert('danger', `文本上传失败: ${error.message}`);
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}
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// 已替换为新的API调用
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// 旧代码已删除
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// 已删除
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}
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// 自动保存索引函数
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@ -94,7 +94,8 @@ def init_retrieval_system():
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retrieval_system = MultimodalRetrievalLocal(
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model_path=model_path,
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use_all_gpus=True,
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index_path=app.config['INDEX_PATH']
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index_path=app.config['INDEX_PATH'],
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shard_model_across_gpus=True
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)
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logger.info("多模态检索系统初始化完成")
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@ -181,6 +182,97 @@ def add_text():
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# 清理临时文件
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file_handler.cleanup_all_temp_files()
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@app.route('/api/add_images_batch', methods=['POST'])
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def add_images_batch():
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"""批量添加图像（多卡并行友好）"""
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try:
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# 读取多文件
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files = request.files.getlist('images')
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if not files:
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return jsonify({"success": False, "error": "没有上传文件"}), 400
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retrieval = init_retrieval_system()
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images = []
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metadatas = []
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image_paths = []
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for file in files:
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if file and allowed_file(file.filename):
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image_data = file.read()
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filename = secure_filename(file.filename)
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image_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
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with open(image_path, 'wb') as f:
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f.write(image_data)
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try:
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img = Image.open(BytesIO(image_data))
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if img.mode != 'RGB':
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img = img.convert('RGB')
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except Exception as e:
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return jsonify({"success": False, "error": f"图像格式不支持: {filename}: {str(e)}"}), 400
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images.append(img)
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||||
metadatas.append({
|
||||
"filename": filename,
|
||||
"timestamp": time.time(),
|
||||
"source": "web_upload_batch",
|
||||
"size": len(image_data),
|
||||
"local_path": image_path
|
||||
})
|
||||
image_paths.append(image_path)
|
||||
else:
|
||||
return jsonify({"success": False, "error": f"不支持的文件类型: {file.filename}"}), 400
|
||||
|
||||
image_ids = retrieval.add_images(images, metadatas, image_paths)
|
||||
retrieval.save_index()
|
||||
|
||||
stats = retrieval.get_stats()
|
||||
return jsonify({
|
||||
"success": True,
|
||||
"message": f"批量添加成功: {len(image_ids)}/{len(images)}",
|
||||
"image_ids": image_ids,
|
||||
"debug": {
|
||||
"server_time": datetime.now(timezone.utc).isoformat(),
|
||||
"vector_dimension": stats.get("vector_dimension"),
|
||||
"total_vectors": stats.get("total_vectors"),
|
||||
}
|
||||
})
|
||||
except Exception as e:
|
||||
logger.error(f"批量添加图像失败: {str(e)}")
|
||||
return jsonify({"success": False, "error": str(e)}), 500
|
||||
finally:
|
||||
file_handler.cleanup_all_temp_files()
|
||||
|
||||
@app.route('/api/add_texts_batch', methods=['POST'])
|
||||
def add_texts_batch():
|
||||
"""批量添加文本（多卡并行友好）"""
|
||||
try:
|
||||
data = request.json
|
||||
if not data or 'texts' not in data:
|
||||
return jsonify({"success": False, "error": "缺少 texts 数组"}), 400
|
||||
texts = [t for t in data.get('texts', []) if isinstance(t, str) and t.strip()]
|
||||
if not texts:
|
||||
return jsonify({"success": False, "error": "文本数组为空"}), 400
|
||||
|
||||
retrieval = init_retrieval_system()
|
||||
metadatas = [{"timestamp": time.time(), "source": "web_upload_batch"} for _ in texts]
|
||||
text_ids = retrieval.add_texts(texts, metadatas)
|
||||
retrieval.save_index()
|
||||
|
||||
stats = retrieval.get_stats()
|
||||
return jsonify({
|
||||
"success": True,
|
||||
"message": f"批量添加成功: {len(text_ids)}/{len(texts)}",
|
||||
"text_ids": text_ids,
|
||||
"debug": {
|
||||
"server_time": datetime.now(timezone.utc).isoformat(),
|
||||
"vector_dimension": stats.get("vector_dimension"),
|
||||
"total_vectors": stats.get("total_vectors"),
|
||||
}
|
||||
})
|
||||
except Exception as e:
|
||||
logger.error(f"批量添加文本失败: {str(e)}")
|
||||
return jsonify({"success": False, "error": str(e)}), 500
|
||||
|
||||
@app.route('/api/add_image', methods=['POST'])
|
||||
def add_image():
|
||||
"""添加图像"""
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user