"""Enhanced Transformer with Rotary Position Embedding. Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ transformers/rope/__init__.py. MIT License: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/lice...
# https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/rope/__init__.py """ --- title: Rotary Positional Embeddings (RoPE) summary: > Annotated implementation of RoPE from paper RoFormer: Enhanced Transformer with Rotary Position Embedding -...
旋转位置编码(Rotary Position Embedding, RoPE). RoPE 巧妙地使用了基于绝对位置信息的旋转矩阵来表示注意力中的相对位置信息。 RoPE 根据位置信息为序列中每个词元所对应的设置了独有的旋转矩阵,并和对应的查询和键进行相乘进行融合。 位置索引为 𝑡 对应的旋转矩阵定义如下所示: 利用旋转矩阵中三角函数的特性,位...
(device) / dim)) return inv_freq, attention_factor class Qwen2RotaryEmbedding(nn.Module): def __init__( self, config: Qwen2Config, device=None, ): super().__init__() self.rope_kwargs = {} # BC: "rope_type" was originally "type" self.rope_type = "default" self.max_seq_len...
抱着这个问题我仔细读了一下苏神关于Positional Embeddings的一系列博客,结论是都对,llama的实现和苏神原始的切分方式一致,且实测运算更快(可能是复数运算算子融合的缘故)。 Rotary Position Embedding回顾 苏神在参考1提到了Transformer中Position Embedding的意义:“破坏Transformer结构的完全对称性”。可以这样理解,假设做...
旋转位置编码(Rotary Position Embedding, RoPE). RoPE 巧妙地使用了基于绝对位置信息的旋转矩阵来表示注意力中的相对位置信息。 RoPE 根据位置信息为序列中每个词元所对应的设置了独有的旋转矩阵,并和对应的查询和键进行相乘进行融合。 位置索引为 对应的旋转矩阵定义如下所示: ...
Implementation of Rotary Position Embedding(RoPE) 更多的算法相关细节,前沿学术理论研究以及大模型微调专题实践代码,欢迎关注公众号:瓦力算法学研所 参考资料: https://spaces.ac.cn/archives/8130 https://www.zhihu.com/question/606813543/answer/3145466206 ...
https://normxu.github.io/Rethinking-Rotary-Position-Embedding-3/ (English by @NormXU) Idea Results Calculated the loss on llama2-13b with samples_15k.jsonl: Methodloss RoPE-4k(original llama2-13b) 1.4967 RoPE-8k(original llama2-13b) 8.8615 NTK-RoPE-4k(not dynamic) 1.6081 NTK-RoPE-8k(no...
EachMistralAttentioninstance can use the same instance of RoPE as RoPE follows the same rule for applying positional embedding for every layer. What is the impact of this PR? When loading the original Mistral model from pre-trained weights, this reduces the VRAM required for loading the model ...
In order to achieve the best performance on long context windows using non-uniform positional embeddings, LongRoPE:Exploit the best positional embedding rescaling parameters through an efficient search, providing a better initialization for fine-tuning and enabling an 8x extension in non-fine-tuning ...