transformer的embed

Transformer的Embed是指将输入的token序列转换为向量表示的过程。在Transformer中，Embedding层的作用是将输入的token序列中的每个token映射为一个d_model维的向量表示，这个向量表示会随着模型的训练而不断更新。

具体来说，Embedding层由一个大小为vocab_size x d_model的矩阵构成，其中vocab_size是词表大小，d_model是模型的维度。对于输入的token序列，每个token都会被映射为一个d_model维的向量，这些向量组成的矩阵就是输入序列的向量表示。

需要注意的是，在Transformer中，Embedding层的输出还会乘以一个sqrt(d_model)的系数，这是为了避免Embedding层输出的向量过大，影响模型训练的稳定性。

相关问题

transformer embed模型详解

### Transformer Embedding Model Detailed Explanation

In the context of transformers, embeddings serve as crucial components that convert discrete tokens into continuous vector spaces. Each token from an input sequence is transformed into a dense vector representation through this process[^1]. The embedding layer captures semantic meanings and relationships among words or patches (in case of images), which are then fed into subsequent layers.

For text-based tasks using Hugging Face’s `transformers` library, word embeddings typically include positional encodings to preserve order information since self-attention mechanisms do not inherently account for positionality[^2].

#### Positional Encoding
Positional encoding adds absolute or relative position information to each token's embedding so that the model can distinguish different positions even when identical tokens appear multiple times within one sentence. This approach ensures that the attention mechanism understands where each part belongs spatially without relying solely on content similarity[^3].

import numpy as np

def get_positional_encoding(max_len, d_model):
    pe = np.zeros((max_len, d_model))
    position = np.arange(0, max_len)[:, None]
    div_term = np.exp(np.arange(0, d_model, 2) * -(np.log(10000.0) / d_model))
    
    pe[:, 0::2] = np.sin(position * div_term)
    pe[:, 1::2] = np.cos(position * div_term)

    return pe

The above code snippet demonstrates how sinusoidal functions generate fixed positional encodings based on token indices and dimension sizes. These values remain constant during training but help maintain sequential structure awareness throughout processing stages.

#### Tokenization & Vocabulary Mapping
Before applying any transformation operations like those mentioned earlier, raw inputs must first undergo preprocessing steps such as tokenizing sentences into individual units suitable for numerical computation while mapping them against predefined vocabularies maintained inside specific models provided by libraries similar to huggingface/transformers.

--related questions--

1. How does positional encoding contribute to maintaining temporal dependencies in sequences?
2. What alternatives exist besides sine/cosine waves for generating positional codes?
3. Can you explain why adding rather than concatenating position-specific signals works better with transformer architectures?
4. In what scenarios might custom vocabulary mappings be necessary over default ones offered by popular NLP frameworks?

swin transformer中的embed_dim

在Swin Transformer中，embed_dim是指输入嵌入向量的维度大小。在Swin Transformer的实现中，embed_dim是通过参数input_dim和patch_dim计算得出的，即embed_dim = input_dim * patch_dim * patch_dim。其中，input_dim是输入图像的通道数，patch_dim是将输入图像分割成的小块的大小。这个值的大小会影响模型的性能和计算效率，通常情况下，较大的embed_dim会带来更好的性能，但也会增加计算量和内存消耗。