From b6a677f15d92ddd9707cd11b07b8e1fa3ef572b1 Mon Sep 17 00:00:00 2001 From: songsenand Date: Mon, 23 Mar 2026 16:30:38 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E6=B7=BB=E5=8A=A0=E8=BE=93=E5=85=A5?= =?UTF-8?q?=E6=B3=95=E5=BC=95=E6=93=8E=E6=A8=A1=E5=9E=8B=E8=AE=BE=E8=AE=A1?= =?UTF-8?q?=E4=B8=8E=E5=AE=9E=E7=8E=B0=EF=BC=8C=E5=8C=85=E5=90=AB=E6=9E=B6?= =?UTF-8?q?=E6=9E=84=E3=80=81=E6=A0=B8=E5=BF=83=E6=A8=A1=E5=9D=97=E5=8F=8A?= =?UTF-8?q?=E8=AE=AD=E7=BB=83=E7=AD=96=E7=95=A5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .python-version | 1 + README.md | 97 ++++ hello.py | 6 + pyproject.toml | 37 ++ src/model/__init__.py | 0 src/model/components.py | 82 +++ src/model/model.py | 281 ++++++++++ uv.lock | 1181 +++++++++++++++++++++++++++++++++++++++ 8 files changed, 1685 insertions(+) create mode 100644 .python-version create mode 100644 hello.py create mode 100644 pyproject.toml create mode 100644 src/model/__init__.py create mode 100644 src/model/components.py create mode 100644 src/model/model.py create mode 100644 uv.lock diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/README.md b/README.md index 424ea1a..8939e95 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,99 @@ # SUimeModelTraner +> 深度学习输入法引擎技术方案 + +## 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. 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。 + +## 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。 + +![模型架构示意图] + +## 4. 核心模块设计 + +### 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）。 + +### 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。 + +### 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）。 + +### 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`。 + +### 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。 + +## 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**（如5）。 +- **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。 + +## 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 + +## 7. 关键设计考量与潜在风险 + +| 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 | + +## 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 + +--- + +## 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） + +--- + +此方案结构完整，模块间接口清晰，可立即进入原型实现阶段。建议先在小规模数据上验证前向与训练流程，再逐步扩展至全量数据调优。 diff --git a/hello.py b/hello.py new file mode 100644 index 0000000..162518b --- /dev/null +++ b/hello.py @@ -0,0 +1,6 @@ +def main(): + print("Hello from suimemodeltraner!") + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..c9599a1 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,37 @@ +[project] +name = "suimemodeltraner" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "modelscope>=1.35.1", + "onnx>=1.20.1", + "torch>=2.10.0", +] + +[tool.uv] +# 设置当前项目的默认索引源 +index-url = "https://pypi.tuna.tsinghua.edu.cn/simple" +[dependency-groups] +dev = [ + "autocommit", + "pytest>=9.0.2", +] + +[tool.uv.sources] +autocommit = { git = "https://gitea.winkinshly.site/songsenand/autocommit.git" } + +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[tool.setuptools] +# 👇 这是关键：指定包在 src/ 下 +package-dir = {"" = "src"} + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.setuptools.package-data] +model = ["assets/*"] diff --git a/src/model/__init__.py b/src/model/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/model/components.py b/src/model/components.py new file mode 100644 index 0000000..4f56d05 --- /dev/null +++ b/src/model/components.py @@ -0,0 +1,82 @@ +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---------------------------- +class AttentionPooling(nn.Module): + def __init__(self, hidden_size): + super().__init__() + self.attn = nn.Linear(hidden_size, 1) + # 三个可学习偏置：文本、拼音、个性化 + self.bias = nn.Parameter(torch.zeros(3)) # [text_bias, pinyin_bias, user_bias] + + def forward(self, x, mask=None, token_type_ids=None): + scores = self.attn(x).squeeze(-1) # [batch, seq_len] + if token_type_ids is not None: + # 根据 token_type_ids 添加对应偏置 + # bias 形状 [3]，通过索引扩展为 [batch, seq_len] + bias_per_token = self.bias[token_type_ids] # [batch, seq_len] + scores = scores + bias_per_token + if mask is not None: + scores = scores.masked_fill(mask == 0, -1e9) + weights = torch.softmax(scores, dim=-1) + pooled = torch.sum(weights.unsqueeze(-1) * x, dim=1) + return pooled + + # ---------------------------- 残差块 ---------------------------- + + +class ResidualBlock(nn.Module): + def __init__(self, dim, dropout_prob=0.3): + super().__init__() + self.linear1 = nn.Linear(dim, dim) + self.ln1 = nn.LayerNorm(dim) + self.linear2 = nn.Linear(dim, dim) + self.ln2 = nn.LayerNorm(dim) + self.gelu = nn.GELU() + self.dropout = nn.Dropout(dropout_prob) + + def forward(self, x): + residual = x + # 修复：使用 self.gelu 而不是未定义的 self.relu + x = self.gelu(self.linear1(x)) + x = self.ln1(x) + x = self.linear2(x) + x = self.ln2(x) + x = self.dropout(x) + x = x + residual + return self.gelu(x) + + +# ---------------------------- 专家网络 ---------------------------- +class Expert(nn.Module): + def __init__( + self, + input_dim, + d_model=512, + num_resblocks=4, + output_multiplier=1, + dropout_prob=0.3, + ): + super().__init__() + self.output_dim = input_dim * output_multiplier + self.linear_in = nn.Linear(input_dim, d_model) + self.res_blocks = nn.ModuleList( + [ResidualBlock(d_model, dropout_prob) for _ in range(num_resblocks)] + ) + self.output = nn.Sequential( + nn.Linear(d_model, d_model), + nn.GELU(), + nn.Dropout(dropout_prob), + nn.Linear(d_model, self.output_dim), + ) + + def forward(self, x): + x = self.linear_in(x) + for block in self.res_blocks: + x = block(x) + return self.output(x) diff --git a/src/model/model.py b/src/model/model.py new file mode 100644 index 0000000..13a0fae --- /dev/null +++ b/src/model/model.py @@ -0,0 +1,281 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .components import AttentionPooling, Expert # , ResidualBlock # 假设已实现 + + +class InputMethodEngine(nn.Module): + """ + 输入法引擎模型 + 输入：光标前/后文本、拼音、历史记录（四段）的编码序列 + 输出：槽位序列（最多24个文字ID）的概率分布 + """ + + def __init__( + self, + input_vocab_size: int, # 输入文本的词汇表大小（含特殊符号） + output_vocab_size: int, # 输出文字的词汇表大小（含终止符0） + hidden_size: int = 512, # 隐藏层维度 + num_layers: int = 4, # Transformer 层数 + num_heads: int = 4, # 多头注意力头数 + max_slot_steps: int = 24, # 最大槽位步数（8槽×3步） + num_experts: int = 20, # 专家数量 + top_k: int = 3, # 每个token选择的专家数 + expert_res_blocks: int = 4, # 每个专家内部的残差块数 + dropout: float = 0.3, + use_attention_pooling: bool = False, # 是否在槽位特征后加池化（暂未使用） + **kwargs, + ): + 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 + + # -------------------- 1. 文本编码器 -------------------- + self.token_embedding = nn.Embedding(input_vocab_size, hidden_size) + self.position_embedding = nn.Embedding( + 512, hidden_size + ) # 可学习位置编码，512足够长 + self.token_type_embedding = nn.Embedding(4, hidden_size) # 4种段落类型 + + encoder_layer = nn.TransformerEncoderLayer( + d_model=hidden_size, + nhead=num_heads, + dim_feedforward=hidden_size * 4, + dropout=dropout, + activation="gelu", + batch_first=True, + ) + self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers) + + # -------------------- 2. 槽位相关组件 -------------------- + # 槽位文字嵌入（输出词汇表） + self.slot_embedding = nn.Embedding(output_vocab_size, hidden_size) + # 槽位位置编码（可学习，最大步数+1用于起始符） + self.slot_position_embedding = nn.Embedding(max_slot_steps + 1, hidden_size) + + # 交叉注意力（Query = 槽位序列，Key/Value = 文本编码结果） + self.cross_attention = nn.MultiheadAttention( + embed_dim=hidden_size, + num_heads=num_heads, + dropout=dropout, + batch_first=True, + ) + + # 可选：注意力池化（若 use_attention_pooling=True，则用于聚合槽位序列特征） + if use_attention_pooling: + self.attention_pooling = AttentionPooling(hidden_size) + + # -------------------- 3. 门控网络 + 专家层 -------------------- + self.gate = nn.Linear(hidden_size, num_experts) # 输出 logits，用于选择专家 + + # 每个专家是一个独立的残差网络（输出维度=hidden_size） + 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) + ] + ) + + # -------------------- 4. 分类头（同维FFN）-------------------- + 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), + ) + + # 初始化 + self._init_weights() + + def _init_weights(self): + """初始化权重（简单示例）""" + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def encode_text(self, input_ids, token_type_ids, attention_mask): + """ + 编码输入文本（光标前、拼音、光标后、历史记录） + 返回: [batch, seq_len, hidden_size] 编码后的上下文表示 + """ + seq_len = input_ids.size(1) + # 位置编码 + positions = torch.arange(seq_len, device=input_ids.device).unsqueeze(0) + pos_emb = self.position_embedding(positions) + # token类型编码 + type_emb = self.token_type_embedding(token_type_ids) + # 词嵌入 + token_emb = self.token_embedding(input_ids) + # 相加 + x = token_emb + pos_emb + type_emb + # Transformer编码器（需要padding mask） + # attention_mask: [batch, seq_len] 1表示有效，0表示填充 + # TransformerEncoder 要求 mask 为 [batch, seq_len] 且 1 表示忽略 + src_key_padding_mask = attention_mask == 0 # True表示填充位置 + x = self.transformer(x, src_key_padding_mask=src_key_padding_mask) + return x + + 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 + + def forward( + self, + input_ids, + token_type_ids, + attention_mask, + slot_target_ids=None, + slot_target_mask=None, + mode="train", + ): + """ + 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 + """ + if mode == "train": + return self.forward_train( + input_ids, + token_type_ids, + attention_mask, + slot_target_ids, + slot_target_mask, + ) + else: + raise NotImplementedError( + "Inference 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