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Transformer Encoder 模块 — Person B 负责实现
包含:
- TransformerEncoderLayer: 单层编码器
- TransformerEncoder: 多层编码器堆叠
架构 (Pre-LayerNorm):
x → LayerNorm → MultiHeadAttention → Residual → LayerNorm → FFN → Residual
"""
from __future__ import annotations
import torch
import torch.nn as nn
from typing import Optional
class TransformerEncoderLayer(nn.Module):
"""
单层 Transformer Encoder。
TODO [Person B]: 实现以下组件:
1. Self-Attention: MultiHeadAttention (支持 Flash Attention)
2. Feed-Forward Network: Linear → Activation → Dropout → Linear
3. 两个 LayerNorm
4. Residual connections
5. Dropout
注意: 使用 Pre-LayerNorm 架构 (先 norm 再 attention/ffn)
"""
def __init__(
self,
d_model: int = 512,
nhead: int = 8,
dim_feedforward: int = 2048,
dropout: float = 0.1,
activation: str = "gelu",
use_flash_attention: bool = True,
use_rotary_embedding: bool = True,
pre_norm: bool = True,
):
super().__init__()
raise NotImplementedError("TODO: Person B 实现 TransformerEncoderLayer.__init__")
def forward(
self,
src: torch.Tensor, # [B, S, D]
src_key_padding_mask: Optional[torch.BoolTensor] = None, # [B, S]
) -> torch.Tensor:
"""
TODO [Person B]: Pre-LayerNorm 前向传播:
1. residual = src
2. src = layer_norm_1(src)
3. src = self_attention(src, src, src, key_padding_mask=src_key_padding_mask)
4. src = residual + dropout(src)
5. residual = src
6. src = layer_norm_2(src)
7. src = ffn(src)
8. src = residual + dropout(src)
"""
raise NotImplementedError("TODO: Person B 实现 TransformerEncoderLayer.forward")
class TransformerEncoder(nn.Module):
"""
多层 Transformer Encoder。
TODO [Person B]:
1. 堆叠 N 个 TransformerEncoderLayer
2. 最终加一个 LayerNorm (Pre-Norm 架构需要)
"""
def __init__(self, encoder_layer: TransformerEncoderLayer, num_layers: int):
super().__init__()
raise NotImplementedError("TODO: Person B 实现 TransformerEncoder.__init__")
def forward(
self,
src: torch.Tensor,
src_key_padding_mask: Optional[torch.BoolTensor] = None,
) -> torch.Tensor:
raise NotImplementedError("TODO: Person B 实现 TransformerEncoder.forward")
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