Blockwise Parallel Transformer for Long Context Large Models

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本文提出分块并行Transformer(BPT),解决Transformer处理长序列时的内存瓶颈问题。BPT能处理比普通Transformer长32倍的序列,内存效率提升,适用于需要处理长序列和长期依赖的任务。实验表明,BPT在减少内存需求的同时,性能也得到提高。

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本文是LLM系列文章,针对《Blockwise Parallel Transformer for Long Context Large Models》的翻译。

摘要

Transformer已经成为最先进的自然语言处理模型的基石,在广泛的人工智能应用中展示了卓越的性能。然而,Transformer中的自注意机制和大型前馈网络带来的内存需求限制了它们处理长序列的能力,从而给涉及多个长序列或长期依赖性的任务带来了挑战。我们提出了一种独特的方法,分块并行Transformer(BPT),它利用自注意和前馈网络融合的分块计算来最大限度地降低内存成本。通过处理较长的输入序列,同时保持内存效率,BPT使训练序列比普通Transformers长32倍,比以前的内存高效方法长4倍。对语言建模和强化学习任务的大量实验证明了BPT在减少记忆需求和提高性能方面的有效性。

1 引言

2 Transformer的内存瓶颈

3 大型上下文模型的分块并行

4 内存消耗

5 设置

6 结果

7 相关工作

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### Cross Attention in Deep Learning Cross attention is a mechanism that allows one sequence to attend over another different sequence. This concept has been widely used within transformer architectures where it enables models to capture relationships between two distinct sequences of tokens or features. In the context of transformers, cross attention layers are typically employed during tasks such as machine translation, multimodal learning (e.g., image captioning), and other scenarios involving interactions across multiple modalities or domains[^1]. #### Implementation Details The core idea behind implementing cross attention involves computing weighted sums based on similarity scores derived from queries associated with one input sequence and keys/values related to another separate but relevant sequence. Here's an example implementation using PyTorch: ```python import torch from torch import nn class CrossAttention(nn.Module): def __init__(self, embed_dim, num_heads=8): super(CrossAttention, self).__init__() self.attention = nn.MultiheadAttention(embed_dim, num_heads) def forward(self, query, key_value_pair): """ Args: query: Tensor of shape [target_seq_len, batch_size, embed_dim] key_value_pair: Tuple containing tensors for keys and values, each tensor should have shape [source_seq_len, batch_size, embed_dim] Returns: output: Tensor after applying cross-attention operation. Shape will be [target_seq_len, batch_size, embed_dim]. """ key, value = key_value_pair attn_output, _ = self.attention(query=query, key=key, value=value) return attn_output ``` This code snippet defines a simple `CrossAttention` module which can process pairs of sequences by attending over them according to their respective embeddings dimensions provided at initialization time (`embed_dim`). The multi-head variant enhances expressiveness through parallel processing paths while maintaining computational efficiency via shared parameters among heads. #### Use Cases One prominent application area includes **multimodal fusion**, particularly when combining textual information alongside visual inputs like images or videos. For instance, in video question answering systems, cross attention helps align questions posed about certain clips directly against specific frames or segments therein, thereby improving overall performance metrics significantly compared to uni-modal approaches alone[^2]. Another notable scenario pertains to **sequence-to-sequence modeling**—especially beneficial under conditions characterized by long-range dependencies spanning source-target mappings beyond traditional recurrent neural networks' capabilities due to vanishing gradient issues inherent thereto. --related problems-- 1. How does cross attention differ fundamentally from self-attention mechanisms? 2. Can you provide examples illustrating how cross attention improves upon conventional methods in natural language understanding tasks? 3. What challenges might arise when deploying cross attention modules within large-scale industrial applications?
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