From 1af85a36bcb13416c6aca6c241862e2be70f175b Mon Sep 17 00:00:00 2001 From: songsenand Date: Fri, 3 Apr 2026 17:04:35 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E6=9B=B4=E6=96=B0=E8=BE=93=E5=85=A5?= =?UTF-8?q?=E6=B3=95=E6=A8=A1=E5=9E=8B=E6=9E=B6=E6=9E=84=E8=AE=BE=E8=AE=A1?= =?UTF-8?q?=E6=96=87=E6=A1=A3=E5=B9=B6=E9=87=8D=E6=9E=84=E6=A0=B8=E5=BF=83?= =?UTF-8?q?=E7=BB=84=E4=BB=B6=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 4 +- README.md | 225 +- src/model/assets/tokenizer/config.json | 30 - src/model/components.py | 269 +- src/model/dataset.py | 196 +- src/model/model.py | 518 +--- test.py | 18 +- uv.lock | 3610 ++++++++++++------------ 8 files changed, 2445 insertions(+), 2425 deletions(-) delete mode 100644 src/model/assets/tokenizer/config.json diff --git a/.gitignore b/.gitignore index d7f3af1..b210324 100644 --- a/.gitignore +++ b/.gitignore @@ -174,4 +174,6 @@ cython_debug/ # Built Visual Studio Code Extensions *.vsix -uv.lock \ No newline at end of file +uv.lock + +data/* diff --git a/README.md b/README.md index 8549d90..9765343 100644 --- a/README.md +++ b/README.md @@ -1,95 +1,166 @@ -# SUimeModelTraner +# 输入法预测模型架构设计 (Input Method Prediction Model) -> 深度学习输入法引擎技术方案 +## 1. 概述 +本项目旨在构建一个轻量级、高精度的中文输入法预测模型。核心设计理念是通过**结构化槽位记忆**与**交叉注意力机制**，将当前语境（光标前后文本+拼音）与历史输入习惯深度融合。为了在有限的计算资源下保持高表达能力，模型引入了**混合专家网络 (MoE)** 模块。 -## 1. 任务目标 -设计一个基于上下文的输入法引擎模型，输入包括光标前文本、拼音、光标后文本及历史记录，输出候选词序列（文字ID序列），支持束搜索解码，实现精准的拼音到文字转换。 +## 2. 核心架构流程 +数据流遵循以下路径： +`输入编码` → `Transformer 上下文编码` → `槽位记忆嵌入` → `交叉注意力融合` → `门控+专家混合 (MoE)` → `分类预测` → `束搜索解码` -## 2. 输入表示 -- **四段文本**： - - 光标前文本 - - 拼音（如“bangdao”） - - 光标后文本 - - 符合条件的历史记录（如“绑到|邦道|…”） -- **编码方式**：使用BERT类Tokenizer，统一序列长度 **L=128**（或88）。 -- **段落区分**：通过 `token_type_ids` 标记段落，取值为 **0,1,2,3**（分别对应四类输入）。 -- **拼音处理**：暂将拼音作为普通文本输入，预留专用嵌入接口供后续优化。 +### 2.1 输入层设计 +模型接收三类输入，分别处理以保持语义清晰： +1. **当前文本上下文**：包含光标前文本（Prefix）和光标后文本（Suffix）。 +2. **拼音序列**：与当前文本对应的拼音信息，作为增强特征融入文本编码。 +3. **历史槽位序列**：最近 N 个历史输入词汇，作为结构化记忆输入。 -## 3. 模型架构概览 -整体结构分为：输入编码 → Transformer编码 → 槽位记忆 → 交叉注意力 → 门控+专家混合 → 分类头 → 束搜索解码。 +### 2.2 模块详解 -![模型架构示意图] +#### A. Transformer 编码器 (Context Encoder) +负责提取当前语境的深层语义表示。 +* **输入处理**：将 Prefix、Suffix 及拼音通过 Embedding 层映射。拼音采用**特征叠加**或**独立 Token** 方式融入，避免双流架构的复杂性。 +* **骨干网络**：使用标准的 Transformer Encoder。 + * **隐藏层维度**：512 [1] + * **Transformer 层数**：4 层（轻量级设计，从头训练） [1] + * **注意力头数**：4 头 [1] +* **输出**：上下文表示 $H$，形状为 `[batch, L, 512]` [1]。 -## 4. 核心模块设计 +#### B. 槽位记忆模块 (Slot Memory) +负责将非结构化的历史输入转化为结构化的记忆向量。 +* **嵌入方式**：历史词汇通过独立的 `Slot Embedding` 查找表映射。 +* **位置编码**：添加可学习的 `Positional Embedding` 以保留历史输入的时间顺序信息。 +* **输出**：槽位序列 $S$，形状为 `[batch, Num_Slots, 512]`。 -### 4.1 Embedding层与Transformer编码器 -- **Embedding维度**：512 -- **Transformer层数**：4层 -- **多头注意力头数**：4或8 -- **输出**：上下文表示 `H`（形状：`[batch, L, 512]`） -- **预训练**：可选加载structBERT轻量级骨干（链接已失效，当前从头训练）。 +#### C. 交叉注意力融合 (Cross-Attention Fusion) +这是模型的核心创新点，用于动态关联“历史记忆”与“当前语境”。 +* **Query (Q)**：当前步的槽位序列 $S$（经过位置编码后）。 +* **Key/Value (K/V)**：Transformer 编码器输出的上下文表示 $H$ [1]。 +* **机制**：让历史槽位主动关注当前文本语境，捕捉如“在‘班级第一名’语境下，‘王次香’比‘王慈祥’更相关”的逻辑。 +* **输出**：融合后的特征序列，形状为 `[batch, Num_Slots, 512]`。 -### 4.2 槽位记忆模块 -- **槽位结构**：共8个槽位，每个槽位最多可填充3步，总计最多24步。 -- **槽位嵌入**：每一步选择的文字ID通过共享的embedding层转换为512维向量。 -- **历史槽位表示**：将当前步之前的所有槽位嵌入**按顺序拼接**，形成动态增长的序列。 -- **位置编码**：为拼接后的槽位序列添加可学习的位置嵌入，帮助模型捕捉时序关系。 -- **初始状态**：第一步时历史为空，用特殊 `[START]` 嵌入向量作为占位。 +#### D. 门控与专家混合 (Gating + MoE) +实际测试表明，移除 MoE 会导致模型性能显著下降，因此该模块对于捕捉复杂分布至关重要。 +* **专家数量**：20 个专家 [1]。 +* **门控机制**：根据输入特征动态选择激活部分专家，实现稀疏激活，在增加模型容量的同时控制计算成本。 +* **输出**：经过专家网络增强后的特征向量。 -### 4.3 交叉注意力与注意力池化 -- **Query**：当前步的槽位序列（经过位置编码后） -- **Key/Value**：Transformer编码器输出 `H` -- **输出**：槽位相关的特征序列 -- **注意力池化**：对交叉注意力输出序列进行池化（如均值池化或可学习注意力池化），得到固定长度的特征向量 `f`（维度512）。 +#### E. 分类头与解码 +* **分类预测**：MoE 输出的特征向量通过全连接层映射到词表空间，输出下一个字/词的概率分布。 +* **解码策略**：推理阶段使用**束搜索 (Beam Search)**，束宽设为 5 [1]。 -### 4.4 门控网络与专家层（MoE） -- **门控网络**：输入 `f`，输出20个专家的权重，选择**top-3**专家。 -- **专家结构**：每个专家为残差网络，输出维度 **512**（与隐藏层一致）。 -- **专家组合**：对选中的3个专家输出进行加权求和，得到融合特征 `e`。 +## 3. 关键超参数配置 -### 4.5 分类头 -- **设计**：采用同维FFN，结构为 - `Linear(512, 512, bias=False) → LayerNorm → GELU → Linear(512, 10019)` - 输出10019维（ID范围0～10018，其中0表示终止符）。 -- **优点**：保留特征维度，引入非线性，参数量适中。 +为确保模型性能与效率的平衡，建议采用以下超参数 [1]： -## 5. 解码策略：束搜索 -- **搜索范围**：在每个槽位步内部执行束搜索，束宽设为 **k**（默认为3）。 -- **候选维护**：每个候选路径独立维护历史槽位序列（拼接后的嵌入）及累计概率。 -- **终止条件**： - 1. 所有槽位已填满（8×3=24步）； - 2. 当前步所有候选分支的最高概率词均为 **0（终止符）**，则强制退出。 -- **输出**：概率最高的完整槽位序列。 +| 参数项 | 推荐值 | 说明 | +| :--- | :--- | :--- | +| **序列长度 (L)** | 128 | 上下文窗口大小 [1] | +| **隐藏层维度** | 512 | Embedding 及 Transformer 内部维度 [1] | +| **Transformer 层数** | 4 | 轻量级骨干，降低延迟 [1] | +| **注意力头数** | 4 | 适配 512 维度的高效配置 [1] | +| **专家数量** | 20 | MoE 层中的专家总数，对性能至关重要 [1] | +| **束宽 (Beam Width)** | 5 | 推理时平衡速度与准确率 [1] | +| **学习率** | 1e-4 ~ 5e-4 | 建议配合 Warmup 策略 [1] | -## 6. 训练设置 -- **优化器**：AdamW -- **损失函数**：每一步的CrossEntropyLoss（仅计算非填充位置） -- **训练数据**：真实用户输入日志，构造（上下文，拼音，目标槽位序列）三元组 -- **标签处理**：每个槽位步的真实文字ID作为监督信号 +## 4. 训练策略 -## 7. 关键设计考量与潜在风险 +本模型采用标准的**序列到序列（Seq2Seq）监督学习**范式，直接对目标槽位序列进行逐步预测。 -| 设计点 | 考量 | 潜在风险/优化 | -|--------|------|----------------| -| 拼音编码 | 当前作为普通文本，实现简单 | 多字词场景可能对齐困难，后续可增加专用拼音嵌入层 | -| 槽位更新 | 拼接+位置编码，保留完整历史 | 序列长度动态增长（最多24），交叉注意力计算量可控 | -| 专家层 | 20专家，top-3组合，增强特征选择性 | 需确保门控网络负载均衡，避免专家“死”掉 | -| 分类头 | 同维FFN，兼顾容量与非线性 | 若过拟合可降维或增加Dropout | -| 束搜索 | 槽位内束搜索，工程复杂度适中 | 需维护多个候选路径的历史状态，内存管理需注意 | +### 4.1 数据构造与标签 +* **输入三元组**：训练数据由 `(上下文, 拼音, 目标槽位序列)` 构成 [1]。 + * **上下文**：光标前后的文本片段。 + * **拼音**：当前待输入字的拼音序列。 + * **目标槽位序列**：真实用户输入的文字 ID 序列，作为模型的监督信号 [1]。 +* **标签处理**：在每一个槽位步（Step），模型需要预测该步对应的真实文字 ID [1]。 -## 8. 后续优化方向 -- **拼音增强**：引入拼音专用嵌入（音节级编码） -- **槽位建模增强**：若拼接方式难以学习长距离依赖，可替换为轻量级GRU（但当前暂不采用） -- **预训练**：尝试加载开源中文BERT权重作为编码器初始化 -- **知识蒸馏**：若模型过大，可蒸馏为更小版本用于端侧部署 +### 4.2 损失函数与优化 +* **损失函数**：使用 **CrossEntropyLoss** 计算每一步预测结果与真实标签之间的差异 [1]。 + * **掩码机制**：仅计算非填充位置（Non-padding positions）的损失，忽略无效的时间步 [1]。 +* **优化器**：采用 **AdamW** 进行参数更新 [1]。 ---- +### 4.3 训练流程细节 +1. **前向传播**： + * 模型接收上下文和拼音，通过 Transformer 编码得到语境表示。 + * 结合历史槽位记忆，通过交叉注意力和 MoE 模块融合特征。 + * 分类头输出当前步所有候选字的概率分布。 +2. **Teacher Forcing**： + * 在训练过程中，**强制使用真实的上一槽位输出**作为下一步的输入条件。这意味着模型在训练时始终基于“正确的历史”进行预测，从而快速收敛。 +3. **反向传播**： + * 根据 CrossEntropyLoss [1] 计算梯度，并通过 AdamW [1] 更新模型权重。 -## 附录：关键超参数（待定） -- 序列长度：128 -- 隐藏层维度：512 -- Transformer层数：4 -- 注意力头数：4 -- 专家数量：20 -- 束宽：5 -- 学习率：待调（建议 1e-4 ～ 5e-4，带warmup） +### 4.4 推理与训练的差异 +* **训练时**：使用 Ground Truth（真实标签）作为槽位输入，确保模型学习到最优的条件概率分布。 +* **推理时**：由于无法获取真实标签，模型采用**束搜索（Beam Search）** [1]。 + * **束宽**：默认为 5 [1]。 + * **候选维护**：每个候选路径独立维护其历史槽位序列及累计概率 [1]。 + * **终止条件**：当所有槽位填满（如 8×3=24 步）或所有候选分支的最高概率词均为终止符时退出 [1]。 + + +## 5. 代码实现示意 (PyTorch) + +```python +import torch +import torch.nn as nn + +class Expert(nn.Module): + def __init__(self, dim=512): + super().__init__() + self.net = nn.Sequential( + nn.Linear(dim, dim * 4), + nn.GELU(), + nn.Linear(dim * 4, dim) + ) + def forward(self, x): + return self.net(x) + +class InputMethodModel(nn.Module): + def __init__(self, vocab_size, pinyin_vocab_size, slot_vocab_size, dim=512, n_layers=4, n_heads=4, num_experts=20): + super().__init__() + # 1. Context Encoder + self.text_emb = nn.Embedding(vocab_size, dim) + self.pinyin_emb = nn.Embedding(pinyin_vocab_size, dim) + self.pos_emb = nn.Embedding(128, dim) + encoder_layer = nn.TransformerEncoderLayer(d_model=dim, nhead=n_heads) + self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=n_layers) + + # 2. Slot Memory + self.slot_emb = nn.Embedding(slot_vocab_size, dim) + self.slot_pos_emb = nn.Embedding(5, dim) # 假设保留5个历史槽位 + + # 3. Cross-Attention + self.cross_attn = nn.MultiheadAttention(embed_dim=dim, num_heads=n_heads, batch_first=True) + + # 4. MoE Layer + self.num_experts = num_experts + self.experts = nn.ModuleList([Expert(dim) for _ in range(num_experts)]) + self.gate = nn.Linear(dim, num_experts) + + # 5. Classification Head + self.classifier = nn.Linear(dim, vocab_size) + + def forward(self, text_ids, pinyin_ids, history_slot_ids): + # Encode Context + x = self.text_emb(text_ids) + self.pinyin_emb(pinyin_ids) + x += self.pos_emb(torch.arange(x.size(1)).to(x.device)) + H = self.transformer(x) # [B, L, 512] + + # Encode Slots + S = self.slot_emb(history_slot_ids) + S += self.slot_pos_emb(torch.arange(S.size(1)).to(S.device)) + + # Cross-Attention: Q=Slots, K/V=Context + fused, _ = self.cross_attn(S, H, H) # [B, Slots, 512] + + # MoE Processing + # 简化版 MoE: 对所有专家输出进行加权平均 + gate_scores = torch.softmax(self.gate(fused), dim=-1) # [B, Slots, Num_Experts] + expert_outputs = torch.stack([expert(fused) for expert in self.experts], dim=-2) # [B, Slots, Num_Experts, Dim] + moe_out = torch.sum(gate_scores.unsqueeze(-1) * expert_outputs, dim=-2) # [B, Slots, Dim] + + # Pooling & Predict + pooled = moe_out.mean(dim=1) # [B, 512] + logits = self.classifier(pooled) + return logits +``` + +## 6. 总结 +本方案通过**单流 Transformer 编码**结合**结构化槽位交叉注意力**，并引入**20个专家的 MoE 模块** [1]，在保证模型轻量（4层 Transformer）的同时，有效利用了历史输入习惯并提升了模型表达上限。相比暴力拼接或双流架构，该设计在工程实现上更优雅，在推理效率上更高效，是轻量级输入法模型的局部最优解。 diff --git a/src/model/assets/tokenizer/config.json b/src/model/assets/tokenizer/config.json deleted file mode 100644 index 44f40fa..0000000 --- a/src/model/assets/tokenizer/config.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "add_cross_attention": false, - "attention_probs_dropout_prob": 0.1, - "bos_token_id": null, - "classifier_dropout": null, - "directionality": "bidi", - "eos_token_id": null, - "hidden_act": "gelu", - "hidden_dropout_prob": 0.1, - "hidden_size": 512, - "initializer_range": 0.02, - "intermediate_size": 2048, - "is_decoder": false, - "layer_norm_eps": 1e-12, - "max_position_embeddings": 512, - "model_type": "bert", - "num_attention_heads": 8, - "num_hidden_layers": 6, - "pad_token_id": 0, - "pooler_fc_size": 768, - "pooler_num_attention_heads": 12, - "pooler_num_fc_layers": 3, - "pooler_size_per_head": 128, - "pooler_type": "first_token_transform", - "tie_word_embeddings": true, - "transformers_version": "5.1.0", - "type_vocab_size": 4, - "use_cache": true, - "vocab_size": 21128 -} diff --git a/src/model/components.py b/src/model/components.py index 4f56d05..ee60e65 100644 --- a/src/model/components.py +++ b/src/model/components.py @@ -1,9 +1,7 @@ import torch -import torch.amp as amp import torch.nn as nn import torch.nn.functional as F import torch.optim as optim -from loguru import logger # ---------------------------- 注意力池化模块---------------------------- @@ -80,3 +78,270 @@ class Expert(nn.Module): for block in self.res_blocks: x = block(x) return self.output(x) + + +# ------------------------------------------------------------------ +# 1. 上下文编码器 (Context Encoder) +# 对应 README 4.1: 4层 Transformer, 512维, 输出 H [1] +# ------------------------------------------------------------------ +class ContextEncoder(nn.Module): + def __init__( + self, vocab_size, pinyin_vocab_size, dim=512, n_layers=4, n_heads=4, max_len=128 + ): + super().__init__() + self.dim = dim + + # Embeddings + self.text_emb = nn.Embedding(vocab_size, dim) + self.pinyin_emb = nn.Embedding(pinyin_vocab_size, dim) + self.pos_emb = nn.Embedding(max_len, dim) + + # Transformer Encoder (4 layers, 4 heads) [1] + encoder_layer = nn.TransformerEncoderLayer( + d_model=dim, + nhead=n_heads, + dim_feedforward=dim * 4, + dropout=0.1, + batch_first=True, # 方便处理 [B, L, D] + ) + self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=n_layers) + + # LayerNorm for stability + self.ln = nn.LayerNorm(dim) + + def forward(self, text_ids, pinyin_ids, mask=None): + """ + Args: + text_ids: [batch, seq_len] + pinyin_ids: [batch, seq_len] (假设已对齐，若不对齐需预处理) + mask: [batch, seq_len] optional padding mask + Returns: + H: [batch, seq_len, 512] Context representation [1] + """ + # 1. Embedding Fusion: Text + Pinyin + Position + # 策略：拼音作为增强特征叠加到文本上，符合轻量级设计 + x = self.text_emb(text_ids) + self.pinyin_emb(pinyin_ids) + + seq_len = x.size(1) + pos_ids = ( + torch.arange(seq_len, device=x.device).unsqueeze(0).expand_as(text_ids) + ) + x += self.pos_emb(pos_ids) + + # 2. Transformer Encoding + # src_key_padding_mask expects True for padding positions + if mask is not None: + # Convert 0/1 mask to bool mask where True is padding + src_mask = mask == 0 + else: + src_mask = None + + H = self.transformer(x, src_key_padding_mask=src_mask) + return self.ln(H) + + +# ------------------------------------------------------------------ +# 2. 槽位记忆模块 (Slot Memory) +# 对应 README 4.2: 8个槽位, 每槽3步, 拼接+位置编码 [1] +# ------------------------------------------------------------------ +class SlotMemory(nn.Module): + def __init__(self, vocab_size, max_slots=8, steps_per_slot=3, dim=512): + super().__init__() + self.max_slots = max_slots + self.steps_per_slot = steps_per_slot + self.total_steps = max_slots * steps_per_slot # 24 steps [1] + + # Shared embedding layer for history tokens [1] + self.emb = nn.Embedding(vocab_size, dim) + + # Learnable positional embeddings for the flattened sequence [1] + self.pos_emb = nn.Embedding(self.total_steps, dim) + + # Start token embedding for empty slots [1] + self.start_emb = nn.Parameter(torch.randn(1, 1, dim)) + + def forward(self, history_ids): + """ + Args: + history_ids: [batch, total_steps] + Flattened sequence of history tokens. + Empty positions should be filled with a special PAD or handled via mask. + Returns: + S: [batch, total_steps, 512] Slot sequence representation [1] + """ + # Embed history tokens + S = self.emb(history_ids) # [B, 24, 512] + + # Add positional embeddings + pos_ids = ( + torch.arange(S.size(1), device=S.device).unsqueeze(0).expand_as(history_ids) + ) + S += self.pos_emb(pos_ids) + + return S + + +# ------------------------------------------------------------------ +# 3. 交叉注意力融合 (Cross-Attention Fusion) +# 对应 README: Query=Slots, Key/Value=Context H [1] +# ------------------------------------------------------------------ +class CrossAttentionFusion(nn.Module): + def __init__(self, dim=512, n_heads=4): + super().__init__() + self.dim = dim + self.n_heads = n_heads + self.head_dim = dim // n_heads + assert self.head_dim * n_heads == dim, "dim must be divisible by n_heads" + + # Linear projections for Q, K, V + self.q_proj = nn.Linear(dim, dim, bias=False) + self.k_proj = nn.Linear(dim, dim, bias=False) + self.v_proj = nn.Linear(dim, dim, bias=False) + self.out_proj = nn.Linear(dim, dim, bias=False) + self.ln = nn.LayerNorm(dim) + + def forward(self, slots_S, context_H, slot_mask=None, context_mask=None): + """ + Args: + slots_S: [batch, num_slots_steps, dim] Query + context_H: [batch, ctx_len, dim] Key/Value + slot_mask: [batch, num_slots_steps] Optional (not used in scaled_dot_product_attention) + context_mask: [batch, ctx_len] Optional padding mask + Returns: + Fused: [batch, num_slots_steps, dim] + """ + batch_size, num_slots, _ = slots_S.shape + _, ctx_len, _ = context_H.shape + + # Project queries, keys, values + Q = self.q_proj(slots_S) # [batch, num_slots, dim] + K = self.k_proj(context_H) # [batch, ctx_len, dim] + V = self.v_proj(context_H) # [batch, ctx_len, dim] + + # Reshape for multi-head attention: [batch, seq_len, n_heads, head_dim] -> [batch, n_heads, seq_len, head_dim] + Q = Q.view(batch_size, num_slots, self.n_heads, self.head_dim).transpose(1, 2) + K = K.view(batch_size, ctx_len, self.n_heads, self.head_dim).transpose(1, 2) + V = V.view(batch_size, ctx_len, self.n_heads, self.head_dim).transpose(1, 2) + + # Prepare attention mask if context_mask is provided + attn_mask = None + if context_mask is not None: + # context_mask: [batch, ctx_len] where 0 means padding + # Convert to bool mask and reshape for broadcasting + bool_mask = context_mask == 0 # [batch, ctx_len] + bool_mask = bool_mask[:, None, None, :] # [batch, 1, 1, ctx_len] + # Convert to float mask where True (padding) becomes -inf + attn_mask = bool_mask.float().masked_fill(bool_mask, float("-inf")) + + # Scaled dot-product attention + attn_output = F.scaled_dot_product_attention( + Q, + K, + V, + attn_mask=attn_mask, + dropout_p=0.0, # no dropout + ) + + # Reshape back: [batch, n_heads, num_slots, head_dim] -> [batch, num_slots, dim] + attn_output = ( + attn_output.transpose(1, 2) + .contiguous() + .view(batch_size, num_slots, self.dim) + ) + + # Project back + fused = self.out_proj(attn_output) + + # Residual connection and layer norm + fused = self.ln(fused + slots_S) + + return fused + + +# ------------------------------------------------------------------ +# 4. 专家混合层 (MoE Layer) +# 对应 README: 20个专家 [1], 使用 components.py 中的 Expert 类 +# ------------------------------------------------------------------ +class MoELayer(nn.Module): + def __init__(self, dim=512, num_experts=20, top_k=2, export_resblocks=4): + super().__init__() + self.num_experts = num_experts + self.top_k = top_k + self.dim = dim + + # Import Expert from your existing components + # Assuming Expert class is defined as in components.py [2] + self.experts = nn.ModuleList( + [ + Expert( + input_dim=dim, + d_model=dim, + num_resblocks=export_resblocks, + output_multiplier=1, + ) + for _ in range(num_experts) + ] + ) + + # Gating Network [2] + self.gate = nn.Linear(dim, num_experts) + + def forward(self, x): + """ + Args: + x: [batch, seq_len, dim] + Returns: + out: [batch, seq_len, dim] + """ + B, L, D = x.shape + + # 1. Compute Gating Scores + gates = self.gate(x) # [B, L, num_experts] + + # 2. Select Top-K Experts + topk_vals, topk_indices = torch.topk(gates, self.top_k, dim=-1) # [B, L, K] + + # Normalize weights for selected experts + weights = F.softmax(topk_vals, dim=-1) # [B, L, K] + + # 3. Dispatch and Compute + # Initialize output + out = torch.zeros_like(x) + + # Reshape for easier processing: flatten batch and sequence dimensions + x_flat = x.view(-1, D) # [B*L, D] + weights_flat = weights.view(-1, self.top_k) # [B*L, K] + topk_indices_flat = topk_indices.view(-1, self.top_k) # [B*L, K] + + # For each of the top-k positions + for k in range(self.top_k): + # Get expert indices and weights for this position + expert_indices = topk_indices_flat[:, k] # [B*L] + expert_weights = weights_flat[:, k].unsqueeze(-1) # [B*L, 1] + + # Process each expert separately + for e_idx in range(self.num_experts): + # Mask for tokens assigned to this expert at position k + mask = expert_indices == e_idx # [B*L] + if not mask.any(): + continue + + # Extract tokens for this expert + x_selected = x_flat[mask] # [N_selected, D] + if x_selected.numel() == 0: + continue + + # Pass through expert + expert_out = self.experts[e_idx](x_selected) # [N_selected, D] + + # Apply expert weights and add to output + weighted_out = expert_out * expert_weights[mask] + + # Scatter back to flat output + out_flat = out.view(-1, D) + out_flat[mask] += weighted_out + + # Reshape back to original shape + out = out.view(B, L, D) + + return out diff --git a/src/model/dataset.py b/src/model/dataset.py index 91c0c72..98caf23 100644 --- a/src/model/dataset.py +++ b/src/model/dataset.py @@ -1,13 +1,13 @@ import random -from typing import Any, Dict, List, Optional, Tuple from importlib.resources import files from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple import numpy as np import torch from datasets import load_dataset from loguru import logger -from modelscope import AutoModel, AutoTokenizer +from modelscope import AutoTokenizer from pypinyin import Style, lazy_pinyin from pypinyin.contrib.tone_convert import to_initials from torch.utils.data import DataLoader, IterableDataset @@ -20,7 +20,8 @@ class PinyinInputDataset(IterableDataset): self, data_path: str, max_workes: int = -1, - max_length=128, + max_iter_length=1e6, + max_seq_length=128, text_field: str = "text", py_style_weight=(9, 2, 1), shuffle_buffer_size: int = 5000, @@ -37,7 +38,9 @@ class PinyinInputDataset(IterableDataset): Path(files(__package__) / "assets" / "tokenizer") ) self.data_path = data_path - self.max_length = max_length + + self.max_iter_length = max_iter_length + self.max_seq_length = max_seq_length self.text_field = text_field self.dataset = load_dataset(data_path, split="train", streaming=True) self.max_workers = max_workes @@ -48,8 +51,6 @@ class PinyinInputDataset(IterableDataset): self.query_engine = QueryEngine() self.query_engine.load() - self.shuffle_buffer_size = shuffle_buffer_size - self.buffer = [] # 提取每个样本的目标字符及其频率 self.sample_freqs = self.query_engine.get_all_weights() @@ -95,76 +96,14 @@ class PinyinInputDataset(IterableDataset): else: return 1 - def tokenize_with_four_seg(self, parts: List[str]) -> Dict[str, Any]: - input_ids = [] - token_type_ids = [] - - # 添加 [CLS] (Type 0) - cls_id = self.tokenizer.cls_token_id - input_ids.append(cls_id) - token_type_ids.append(0) - - for seg_idx, part in enumerate(parts): - if not part: - continue - - # Tokenize 单个部分，不加特殊符号 - # 注意：这里先不截断，最后统一截断，保证优先级高的段落（如part1）完整 - encoded_part = self.tokenizer( - part, add_special_tokens=False, truncation=False - ) - - part_ids = encoded_part["input_ids"] - - # 如果加上当前部分会超过 MAX_LEN - 1 (留一个位置给最后的SEP或截断)，则截断当前部分 - remaining_space = ( - self.max_length - len(input_ids) - 1 - ) # -1 for final SEP or safety - if len(part_ids) > remaining_space: - part_ids = part_ids[:remaining_space] - - if not part_ids: - continue - - input_ids.extend(part_ids) - # 当前段落的 type_id 即为 seg_idx (0, 1, 2, 3) - token_type_ids.extend([seg_idx] * len(part_ids)) - - # 添加 [SEP] (Type 跟随当前段落) - sep_id = self.tokenizer.sep_token_id - input_ids.append(sep_id) - token_type_ids.append(seg_idx) - - # 如果已经达到最大长度，提前退出 - if len(input_ids) >= self.max_length: - break - - # 4. 处理 Padding 或 最终截断 - if len(input_ids) > self.max_length: - input_ids = input_ids[: self.max_length] - token_type_ids = token_type_ids[: self.max_length] - else: - pad_len = self.max_length - len(input_ids) - input_ids += [self.tokenizer.pad_token_id] * pad_len - token_type_ids += [0] * pad_len # Padding mask type 通常为 0 - - # 5. 生成 Attention Mask - attention_mask = [ - 1 if i != self.tokenizer.pad_token_id else 0 for i in input_ids - ] - - return { - "input_ids": torch.tensor(input_ids, dtype=torch.long), - "token_type_ids": torch.tensor(token_type_ids, dtype=torch.long), - "attention_mask": torch.tensor(attention_mask, dtype=torch.long), - } - # 生成对应文本的拼音 - def generate_pinyin(self, text: str) -> List[List[str]]: + def generate_pinyin(self, text: str) -> List[str]: return lazy_pinyin(text, errors=lambda x: [c for c in x]) # 生成需要预测汉字对应的拼音，并进行加强 - def get_mask_pinyin(self, text: str, pinyin_list: List[str]) -> (int, List[str]): + def get_mask_pinyin( + self, text: str, pinyin_list: List[str] + ) -> Tuple[int, List[str]]: mask_pinyin = [] for i in range(len(text)): if not self.query_engine.is_chinese_char(text[i]): @@ -193,14 +132,41 @@ class PinyinInputDataset(IterableDataset): self.dataset = self.dataset.shard(num_shards=num_workers, index=worker_id) + # 计算每个worker的配额 + # 将 max_iter_length 转换为整数以确保整数除法 + total_quota = int(self.max_iter_length) + base_quota = total_quota // num_workers + remainder = total_quota % num_workers + + # 最后一个worker处理剩余的样本（如果有余数） + if worker_id == num_workers - 1: + worker_quota = base_quota + remainder + else: + worker_quota = base_quota + else: + # 单worker情况，使用全部配额 + worker_quota = int(self.max_iter_length) + num_workers = 1 + + # 每个worker有自己的迭代计数器 + current_iter_index = 0 + batch_samples = [] for sample in self.dataset: + # 检查是否达到最大迭代次数 + if current_iter_index >= worker_quota: + break + text = sample.get(self.text_field, "") if text: pinyin_list = self.generate_pinyin(text) for i in range(len(text)): - labels = [] - # 如果text[i]不再字符库中，则跳过 + # 在开始处理每个字符前检查配额 + if current_iter_index >= worker_quota: + break + + labels = [] # 添加起始符 + # 如果text[i]不在字符库中，则跳过 # 当i小于48时候，则将part1取text[0:i] # 当i大于48时候，则将part1取text[i-48:i] if not self.query_engine.is_chinese_char(text[i]): @@ -256,34 +222,66 @@ class PinyinInputDataset(IterableDataset): labels = [ self.query_engine.get_char_info_by_char_pinyin(c, p).id - for c, p in zip(text[i:i + pinyin_len], pinyin_list[i:i + pinyin_len]) + for c, p in zip( + text[i : i + pinyin_len], + pinyin_list[i : i + pinyin_len], + ) ] - labels.append(0) - encoded = self.tokenize_with_four_seg( - [ - part1, - part2, - part3, - part4, - ] + encoded = self.tokenizer( + f"{part4}|{part1}", + part3, + max_length=self.max_seq_length, + padding="max_length", + truncation=True, + return_tensors="pt", + return_token_type_ids=True, ) - repeats = self.adjust_frequency( - min([self.sample_freqs[i] for i in labels]) - ) - sample = { - "input_ids": encoded["input_ids"], - "token_type_ids": encoded["token_type_ids"], - "attention_mask": encoded["attention_mask"], - "labels": torch.tensor(labels, dtype=torch.long), - "part1": part1, - "part2": part2, - "part3": part3, - "part4": part4, - } - batch_samples.extend([sample] * repeats) + samples = [] + for i, label in enumerate(labels): + repeats = self.adjust_frequency(label) + l = labels[:i] + len_l = len(l) + l.extend([0] * (8 - len_l)) + + samples.extend( + [ + { + "input_ids": encoded["input_ids"], + "token_type_ids": encoded["token_type_ids"], + "attention_mask": encoded["attention_mask"], + "label": torch.tensor([label], dtype=torch.long), + "history_slot_ids": torch.tensor( + l, dtype=torch.long + ), + "prefix": f"{part4}^{part1}", + "suffix": part3, + "pinyin": part2, + } + ] + * repeats + ) + + # 添加到缓冲区 + batch_samples.extend(samples) + + # 处理shuffle buffer if len(batch_samples) >= self.shuffle_buffer_size: indices = np.random.permutation(len(batch_samples)) - self.buffer.extend([batch_samples[i] for i in indices]) + for idx in indices: + if current_iter_index >= worker_quota: + # 清空batch_samples并返回 + batch_samples = [] + return # 使用return而不是break，因为我们在生成器函数中 + yield batch_samples[idx] + current_iter_index += 1 batch_samples = [] - yield from self.buffer + + # 处理剩余的样本 + if batch_samples: + indices = np.random.permutation(len(batch_samples)) + for idx in indices: + if current_iter_index >= worker_quota: + return + yield batch_samples[idx] + current_iter_index += 1 diff --git a/src/model/model.py b/src/model/model.py index 59fdd31..be854d8 100644 --- a/src/model/model.py +++ b/src/model/model.py @@ -1,448 +1,134 @@ -import math -from pathlib import Path -from typing import Optional, Union +from typing import Optional import torch import torch.nn as nn -import torch.amp as amp -import torch.optim as optim import torch.nn.functional as F -from loguru import logger -from modelscope import AutoTokenizer -from tqdm.notebook import tqdm - - -from .components import AttentionPooling, Expert # , ResidualBlock # 假设已实现 +# 导入 components.py 中的组件 +from .components import ( + AttentionPooling, # 可选，暂不使用 + ContextEncoder, + CrossAttentionFusion, + MoELayer, + SlotMemory, +) class InputMethodEngine(nn.Module): + """ + 输入法预测引擎模型。 + 基于 README 设计的轻量级输入法预测模型，整合了上下文编码、槽位记忆、 + 交叉注意力融合、混合专家网络 (MoE) 以及分类预测。 + + 输入参数: + input_ids (torch.Tensor): [batch_size, seq_len] 文本 token ids。 + token_type_ids (torch.Tensor): [batch_size, seq_len] 标识前缀/后缀等类型，用于偏置。 + attention_mask (torch.Tensor): [batch_size, seq_len] 注意力掩码，1 表示有效位置。 + history_slot_ids (torch.Tensor): [batch_size, num_slots] 或 [num_slots] 历史槽位 ID。 + 如果输入为 [num_slots]，内部会自动扩展 batch 维度。 + + 输出: + logits (torch.Tensor): [batch_size, vocab_size] 下一个字符的概率分布（未经过 softmax）。 + """ + def __init__( self, - pretrained_encoder, # 已加载并扩展好的预训练编码器 - output_vocab_size: int, - hidden_size: int = 512, # 需与预训练模型隐藏维度一致 - max_slot_steps: int = 24, - num_experts: int = 20, - top_k: int = 3, - expert_res_blocks: int = 4, - dropout: float = 0.3, - use_attention_pooling: bool = False, - **kwargs, + vocab_size: int = 10019, + pinyin_vocab_size: int = 28, + dim: int = 512, + num_slots: int = 8, # 历史槽位数量 (对应 README 中的 8 个槽位) + n_layers: int = 4, # Transformer 层数 + n_heads: int = 4, # 注意力头数 + num_experts: int = 20, # MoE 专家数量 + max_seq_len: int = 128, # 最大上下文长度 + use_pinyin: bool = False, # 是否使用拼音特征（若为 False，拼音嵌入恒为零） ): super().__init__() - self.hidden_size = hidden_size - self.output_vocab_size = output_vocab_size - self.max_slot_steps = max_slot_steps - self.num_experts = num_experts - self.top_k = top_k - self.use_attention_pooling = use_attention_pooling + self.dim = dim + self.num_slots = num_slots + self.use_pinyin = use_pinyin + self.vocab_size = vocab_size - # 预训练编码器 - self.encoder = pretrained_encoder - - self.slot_embedding = nn.Embedding(output_vocab_size, hidden_size) - self.slot_position_embedding = nn.Embedding(max_slot_steps + 1, hidden_size) - - self.cross_attention = nn.MultiheadAttention( - embed_dim=hidden_size, - num_heads=4, # 可配置 - dropout=dropout, - batch_first=True, + # 1. 上下文编码器 (ContextEncoder) + # 若 use_pinyin=False，则传入 pinyin_vocab_size=1 并固定嵌入为零 + self.context_encoder = ContextEncoder( + vocab_size=vocab_size, + pinyin_vocab_size=pinyin_vocab_size if use_pinyin else 1, + dim=dim, + n_layers=n_layers, + n_heads=n_heads, + max_len=max_seq_len, ) - if use_attention_pooling: - self.attention_pooling = AttentionPooling(hidden_size) - - self.gate = nn.Linear(hidden_size, num_experts) - self.experts = nn.ModuleList( - [ - Expert( - input_dim=hidden_size, - d_model=hidden_size, - num_resblocks=expert_res_blocks, - output_multiplier=1, - dropout_prob=dropout, - ) - for _ in range(num_experts) - ] + # 2. 槽位记忆模块 (SlotMemory) + # 适配历史槽位数量为 num_slots（每个槽位对应一个词，而非多步） + self.slot_memory = SlotMemory( + vocab_size=vocab_size, + max_slots=num_slots, + steps_per_slot=1, # 每个槽位只占一步 + dim=dim, ) - self.classifier = nn.Sequential( - nn.Linear(hidden_size, hidden_size, bias=False), - nn.LayerNorm(hidden_size), - nn.GELU(), - nn.Linear(hidden_size, output_vocab_size), - ) + # 3. 交叉注意力融合 (CrossAttentionFusion) + # 使用 F.scaled_dot_product_attention 实现的版本 + self.cross_attn = CrossAttentionFusion(dim=dim, n_heads=n_heads) - self._init_weights() + # 4. 混合专家层 (MoE) + self.moe = MoELayer(dim=dim, num_experts=num_experts, top_k=2) - def encode_text(self, input_ids, token_type_ids, attention_mask): - """ - 使用预训练编码器编码文本 - 注意：预训练模型输出可能包含 last_hidden_state 和 pooler_output - """ - outputs = self.encoder( - input_ids=input_ids, - token_type_ids=token_type_ids, - attention_mask=attention_mask, - return_dict=True, - ) - # 取 last_hidden_state [batch, seq_len, hidden] - return outputs.last_hidden_state + # 5. 分类头 + self.classifier = nn.Linear(dim, vocab_size) - def forward_single_step(self, context, slot_seq_emb, slot_seq_mask=None): - """ - 单步预测：根据当前槽位序列（已拼接的嵌入），预测下一个文字的概率分布 - context: [batch, seq_len, hidden] 文本编码结果 - slot_seq_emb: [batch, current_len, hidden] 当前槽位序列的嵌入（已拼接） - slot_seq_mask: [batch, current_len] 有效位置mask（1有效） - 返回: [batch, output_vocab_size] 概率分布 - """ - batch_size = slot_seq_emb.size(0) - # 交叉注意力：Query是槽位序列（通常只取最后一个步的嵌入作为Query，但这里我们使用整个序列） - # 为了简单，我们使用整个序列作为Query，然后取最后一个位置的输出（因为自回归） - # 方法1：Query = 最后一个位置的嵌入（单个向量） - last_query = slot_seq_emb[:, -1:, :] # [batch, 1, hidden] - # 交叉注意力 - attn_out, _ = self.cross_attention( - query=last_query, - key=context, - value=context, - key_padding_mask=( - context.sum(-1) == 0 - ), # 忽略填充位置，实际应传入attention_mask - ) # [batch, 1, hidden] - attn_out = attn_out.squeeze(1) # [batch, hidden] - - # 门控网络：选择top-k专家 - gate_logits = self.gate(attn_out) # [batch, num_experts] - topk_weights, topk_indices = torch.topk( - F.softmax(gate_logits, dim=-1), self.top_k, dim=-1 - ) - # 归一化权重 - topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True) - - # 计算专家输出加权和 - expert_outputs = torch.zeros_like(attn_out) # [batch, hidden] - for i in range(self.top_k): - expert_idx = topk_indices[:, i] # [batch] - weight = topk_weights[:, i].unsqueeze(1) # [batch, 1] - # 批量获取专家输出（需对每个样本分别处理，或用循环） - # 这里简单循环，实际可优化为 gather - for b in range(batch_size): - expert_out = self.experts[expert_idx[b]](attn_out[b : b + 1]) - expert_outputs[b] += weight[b] * expert_out.squeeze(0) - - # 分类头 - logits = self.classifier(expert_outputs) # [batch, output_vocab_size] - return logits - - def forward_train( - self, - input_ids, - token_type_ids, - attention_mask, - slot_target_ids, - slot_target_mask=None, - ): - """ - 训练模式：一次性并行计算所有槽位步的预测 - slot_target_ids: [batch, max_slot_steps] 真实标签（teacher forcing） - slot_target_mask: [batch, max_slot_steps] 有效步mask（1表示该步存在） - 返回: 各步logits [batch, max_slot_steps, output_vocab_size] - """ - batch_size, max_steps = slot_target_ids.shape - device = input_ids.device - - # 1. 编码文本 - context = self.encode_text( - input_ids, token_type_ids, attention_mask - ) # [b, L, h] - - # 2. 构建槽位序列嵌入（teacher forcing：使用真实标签构建） - # 将每个样本的槽位ID转为embedding，并添加位置编码 - # slot_target_ids 形状 [b, T] - slot_emb = self.slot_embedding(slot_target_ids) # [b, T, h] - # 位置编码（位置从0开始） - positions = torch.arange(max_steps, device=device).unsqueeze(0) # [1, T] - pos_emb = self.slot_position_embedding(positions) # [1, T, h] - slot_emb = slot_emb + pos_emb - # 可选：加入mask（无效位置置0） - if slot_target_mask is not None: - slot_emb = slot_emb * slot_target_mask.unsqueeze(-1) - - # 3. 交叉注意力：Query为整个槽位序列（每个位置独立预测） - # 注意：我们这里使用 self-attention 的方式？实际上应该是 cross-attention 且 Query 是槽位序列 - # 但常规做法是每个位置的 Query 只与文本编码交互，不与其他槽位交互，所以不需要掩码。 - # 使用 MultiheadAttention 的 query 和 key/value 不同即可。 - attn_out, _ = self.cross_attention( - query=slot_emb, # [b, T, h] - key=context, # [b, L, h] - value=context, - key_padding_mask=(attention_mask == 0), # 忽略文本填充位置 - ) # [b, T, h] - - # 4. 对每个槽位步分别通过门控+专家+分类头 - # 由于不同步之间共享参数，我们可以将 batch 和 steps 合并处理 - b, T, h = attn_out.shape - attn_flat = attn_out.view(b * T, h) # [b*T, h] - - # 门控网络 - gate_logits = self.gate(attn_flat) # [b*T, num_experts] - gate_probs = F.softmax(gate_logits, dim=-1) - topk_probs, topk_indices = torch.topk( - gate_probs, self.top_k, dim=-1 - ) # [b*T, top_k] - topk_probs = topk_probs / topk_probs.sum(dim=-1, keepdim=True) # 归一化 - - # 计算专家输出（并行化较复杂，这里用循环简化，实际可用 scatter 优化） - expert_out_flat = torch.zeros_like(attn_flat) # [b*T, h] - for i in range(self.top_k): - weight = topk_probs[:, i].unsqueeze(1) # [b*T, 1] - idx = topk_indices[:, i] # [b*T] - # 批量获取专家输出 - # 注意：每个专家处理所有样本，这里用循环专家，效率较低 - # 实际生产环境应优化为组卷积或使用 einsum，此处保持清晰 - for expert_id in range(self.num_experts): - mask = idx == expert_id - if mask.any(): - # 取出属于该专家的样本 - sub_input = attn_flat[mask] # [k, h] - sub_output = self.experts[expert_id](sub_input) # [k, h] - expert_out_flat[mask] += weight[mask] * sub_output - - # 分类头 - logits_flat = self.classifier(expert_out_flat) # [b*T, output_vocab_size] - logits = logits_flat.view(b, T, -1) # [b, T, output_vocab_size] - - return logits + # 可选：如果不需要拼音，将拼音嵌入矩阵固定为零 + if not use_pinyin and hasattr(self.context_encoder, "pinyin_emb"): + # 将拼音嵌入权重置零，确保对输出无影响 + nn.init.zeros_(self.context_encoder.pinyin_emb.weight) def forward( self, - input_ids, - token_type_ids, - attention_mask, - slot_target_ids=None, - slot_target_mask=None, - mode="train", - ): + input_ids: torch.Tensor, + token_type_ids: torch.Tensor, + attention_mask: torch.Tensor, + history_slot_ids: torch.Tensor, + ) -> torch.Tensor: """ - 统一接口 - mode='train': 使用 teacher forcing 并行计算所有步 logits，返回 [b, T, vocab] - mode='infer': 需结合外部循环，使用 forward_single_step + 前向传播。 """ - if mode == "train": - return self.forward_train( - input_ids, - token_type_ids, - attention_mask, - slot_target_ids, - slot_target_mask, - ) + batch_size = input_ids.size(0) + + # 处理 history_slot_ids：若为 [num_slots] 则扩展 batch 维度 + if history_slot_ids.dim() == 1: + history_slot_ids = history_slot_ids.unsqueeze(0).expand(batch_size, -1) + + # 1. 构造拼音输入（如果 use_pinyin=False，则使用全零占位符） + if self.use_pinyin: + # 注意：这里需要真实的拼音 ids，但当前输入未提供，故用零占位（实际应用中应从外部获取） + # 为简化演示，此处使用全零张量，并假设拼音词汇表中 0 为 padding。 + pinyin_ids = torch.zeros_like(input_ids) else: - raise NotImplementedError( - "Inference mode should call forward_single_step directly" - ) + pinyin_ids = torch.zeros_like(input_ids) - def to(self, device): - """重写 to 方法，记录设备""" - self.device = device - return super().to(device) - - def fit( - self, - train_dataloader, - eval_dataloader=None, - monitor=None, - criterion=None, - optimizer=None, - num_epochs=1, - stop_batch=2e5, - eval_frequency=500, - grad_accum_steps=1, - clip_grad_norm=1.0, - loss_weight=None, - mixed_precision=True, - weight_decay=0.1, - warmup_ratio=0.1, - label_smoothing=0.15, - lr=1e-4, - lr_schedule=None, - save_dir=None, - save_frequency=1000, - ): - """ - 训练模型 - """ - def default_lr_schedule(_lr, _processed_batches, _stop_batch, _warmup_steps): - if _processed_batches < _warmup_steps: - current_lr = _lr * (_processed_batches / _warmup_steps) - else: - progress = (_processed_batches - _warmup_steps) / ( - _stop_batch - _warmup_steps - ) - current_lr = _lr * (0.5 * (1.0 + math.cos(math.pi * progress))) - return current_lr + # 2. 上下文编码 -> H [batch, seq_len, dim] + # 注意：ContextEncoder.forward 接受 text_ids, pinyin_ids, mask + H = self.context_encoder(input_ids, pinyin_ids, mask=attention_mask) - if self.device is None: - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - self.to(self.device) - if lr_schedule is None: - lr_schedule = default_lr_schedule + # 3. 槽位记忆编码 -> S [batch, num_slots, dim] + S = self.slot_memory(history_slot_ids) # history_slot_ids: [batch, num_slots] - self.train() + # 4. 交叉注意力融合 (使用 CrossAttentionFusion) + fused = self.cross_attn(S, H, context_mask=attention_mask) - if optimizer is None: - optimizer = optim.AdamW(self.parameters(), lr=lr, weight_decay=weight_decay) + # 5. MoE 处理 -> [batch, num_slots, dim] + moe_out = self.moe(fused) - # 损失函数：需要设置 ignore_index=-1，因为标签中无效位置用 -1 表示 - if criterion is None: - if loss_weight is not None: - criterion = nn.CrossEntropyLoss( - weight=loss_weight, label_smoothing=label_smoothing, ignore_index=-1 - ) - else: - criterion = nn.CrossEntropyLoss( - label_smoothing=label_smoothing, ignore_index=-1 - ) - - scaler = amp.GradScaler(enabled=mixed_precision) - - total_steps = stop_batch - warmup_steps = int(total_steps * warmup_ratio) - logger.info(f"Training Start: Steps={total_steps}, Warmup={warmup_steps}") - processed_batches = 0 - batch_loss_sum = 0.0 - optimizer.zero_grad() - - try: - for epoch in range(num_epochs): - for batch_idx, batch in enumerate( - tqdm(train_dataloader, total=int(stop_batch)) - ): - # 学习率调度 - current_lr = lr_schedule( - lr, stop_batch, processed_batches, warmup_steps - ) - for param_group in optimizer.param_groups: - param_group["lr"] = current_lr - - # 从 batch 中获取数据 - input_ids = batch["hint"]["input_ids"].to(self.device) - attention_mask = batch["hint"]["attention_mask"].to(self.device) - token_type_ids = batch["hint"]["token_type_ids"].to(self.device) - labels = batch["char_id"].to(self.device) # [batch, max_slot_steps] - - # 构建 slot_target_mask：有效位置为 1，无效位置为 0（假设无效标签为 -1） - slot_target_mask = (labels != -1).float() # [batch, max_slot_steps] - - with torch.amp.autocast( - device_type=self.device.type, enabled=mixed_precision - ): - # 调用模型（训练模式） - logits = self( - input_ids=input_ids, - token_type_ids=token_type_ids, - attention_mask=attention_mask, - slot_target_ids=labels, - slot_target_mask=slot_target_mask, - mode="train", - ) # logits: [batch, max_slot_steps, output_vocab_size] - - # 计算损失（忽略填充位置，ignore_index=-1 已在 criterion 中设置） - loss = criterion( - logits.view(-1, self.output_vocab_size), labels.view(-1) - ) - loss = loss / grad_accum_steps - - scaler.scale(loss).backward() - - # 梯度累积更新 - if (processed_batches + 1) % grad_accum_steps == 0: - scaler.unscale_(optimizer) - torch.nn.utils.clip_grad_norm_(self.parameters(), clip_grad_norm) - scaler.step(optimizer) - scaler.update() - optimizer.zero_grad() - batch_loss_sum += loss.item() * grad_accum_steps - - # 定期评估 - if processed_batches % eval_frequency == 0: - if eval_dataloader: - self.eval() - acc, eval_loss = self.model_eval(eval_dataloader, criterion) - self.train() - if monitor: - monitor.add_step( - processed_batches, - { - "train_loss": batch_loss_sum - / (eval_frequency if processed_batches > 0 else 1), - "acc": acc, - "loss": eval_loss, - "lr": current_lr, - }, - ) - logger.info( - f"step: {processed_batches}, eval_loss: {eval_loss:.4f}, acc: {acc:.4f}, " - f"batch_loss_sum: {batch_loss_sum / (eval_frequency if processed_batches > 0 else 1):.4f}, " - f"current_lr: {current_lr}" - ) - else: - logger.info( - f"step: {processed_batches}, batch_loss_sum: {batch_loss_sum / (eval_frequency if processed_batches > 0 else 1):.4f}, " - f"current_lr: {current_lr}" - ) - batch_loss_sum = 0.0 - - processed_batches += 1 - if processed_batches >= stop_batch: - break - - else: - # 未达到梯度累积步数，只累加损失值，但不更新计数器（因为 processed_batches 在梯度更新时才增加） - # 注意：这里需要小心，原代码中 processed_batches 是在梯度更新后才增加，所以上面已经统一在更新后增加 - # 但为了兼容原有逻辑，这里不做额外处理 - pass - - # 训练结束通知 - if monitor: - monitor.finish() - - except KeyboardInterrupt: - logger.info("Training interrupted by user") - - - - def load_from_state_dict(self, state_dict_path: Union[str, Path]): - state_dict = torch.load( - state_dict_path, weights_only=True, map_location=self.device - ) - self.load_state_dict(state_dict) - - def load_from_pretrained_base_model( - self, - BaseModel, - snapshot_path: Union[str, Path], - device=torch.device("cuda" if torch.cuda.is_available() else "cpu"), - *args, - **kwargs, - ): - base_model = BaseModel(*args, **kwargs) - base_model.load_state_dict(torch.load(snapshot_path, map_location=device)) - self_static_dict = self.state_dict() - pretrained_dict = base_model.state_dict() - - freeze_layers = [] - - for key in self_static_dict.keys(): - if key in pretrained_dict.keys(): - if self_static_dict[key].shape == pretrained_dict[key].shape: - self_static_dict[key] = pretrained_dict[key].to(self.device) - freeze_layers.append(key) - self.load_state_dict(self_static_dict) - for name, param in self.named_parameters(): - if name in freeze_layers: - param.requires_grad = False + # 6. 池化与分类：对槽位维度求平均（或使用 mask 池化） + # 这里简单平均，若需要忽略 padding 槽位，可根据 history_slot_ids 是否为 0 构造 mask + slot_mask = (history_slot_ids != 0).float() # [batch, num_slots] + slot_mask = slot_mask.unsqueeze(-1) # [batch, num_slots, 1] + pooled = (moe_out * slot_mask).sum(dim=1) / (slot_mask.sum(dim=1) + 1e-8) + # 如果所有槽位均为 padding，则降级为全局平均 + if torch.isnan(pooled).any(): + pooled = moe_out.mean(dim=1) + logits = self.classifier(pooled) # [batch, vocab_size] + return logits diff --git a/test.py b/test.py index c74bb37..6b6f929 100644 --- a/test.py +++ b/test.py @@ -1,7 +1,17 @@ +import sys + +from tqdm import tqdm + from model.dataset import PinyinInputDataset -dataset = PinyinInputDataset('/home/songsenand/Data/corpus/CCI-Data/') +if sys.platform == "win32": + dataset_path = "data" +else: + dataset_path = "/home/songsenand/Data/corpus/CCI-Data/" + +dataset = PinyinInputDataset(dataset_path, max_iter_length=20, max_workes=3) for i, line in enumerate(dataset): - print(line['labels']) - if i > 10: - break \ No newline at end of file + for k, v in line.items(): + if isinstance(v, str): + continue + print(k, v.shape) diff --git a/uv.lock b/uv.lock index cabb661..8098c28 100644 --- a/uv.lock +++ b/uv.lock @@ -1,44 +1,54 @@ version = 1 -revision = 3 requires-python = ">=3.12" resolution-markers = [ - "python_version < '0'", - "python_full_version < '3.13' and sys_platform == 'win32'", - "python_full_version < '3.13' and sys_platform == 'emscripten'", - "python_full_version < '3.13' and sys_platform == 'linux'", - "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'linux' and 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