GQA(Grouped-Query Attention,GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints)是分组查询注意力,GQA将查询头分成G组,每个组共享一个Key 和 Value 矩阵。GQA-G是指具有G组的grouped-query attention。GQA-1具有单个组,因此具有单个Key 和 Value,等效于MQA。而GQA-H具有与头数...
核心贡献:优化 multi-head attention 为文中命名的 multi-query attention,减少多head相关运算,不降低精度 且 大幅提升解码速度。 具体对比如下: multi-head attention: multi-query attention:
hidden_size,num_heads):super(MutiHeadAttention,self).__init__()self.num_heads=num_headsself.head_dim=hidden_size//num_heads## 初始化Q、K、V投影矩阵self.q_linear=nn.Linear(hidden_size,hidden_size)self.k_linear=nn.Linear(hidden_size,hidden_size)self.v_linear=nn.Linear(hidden_size,hidden...
多查询注意力(Multi Query Attention，MQA)和分组查询注意力(Group Query Attention，GQA)是在近年来对Transformer模型的改进中引起关注的新技术。MQA最早于2019年的论文《Fast Transformer Decoding: One Write-Head is All You Need》中提出，旨在解决Transformer增量推理阶段效率低下的问题。虽然当时并没有引起广泛关注...
🐛 Bug MultiheadAttention should yield the same result if I split the key into several chunks and then concatenate chunk results back together. Now it does not work for some chunk sizes. To Reproduce Steps to reproduce the behavior: np.ra...
A multi-head attention module is added on top of a multi-layered GRU for effective feature extraction, and a normalized multi-head attention module is proposed for feature aggregation. We also adopt the softtriple loss - a combination of triplet loss and softmax loss - and showcase its ...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - Enhance torch.nn.MultiheadAttention to allow using different dimension for projected query and key · pytorch/pytorch@f9b9009
Query-by-Example Keyword Spotting system using Multi-head Attention and Softtriple LossEugene KimHan Suk ShimJinmiao HuangWaseem Gharbieh
• Decoder也由一系列相同的注意力层构成,但与Encoder不同的是,它包含两个自注意力模块:一个是 masked multi-head self-attention,确保在预测当前位置的词时不会看到未来的位置;另一个是 encoder-decoder attention,让解码器可以关注到编码器的所有位置信息。
Mark一下最近读的推理加速的paper | Training for KV-Cache Compression (2023.05) [GQA] GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints(@Google) [flaxformer] 302 Stars(2024.03) [DMC] Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference (@NVIDIA ...