refactor(model): 使用注意力池化替换 span pooling 并支持 token_type_ids
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@ -56,7 +56,7 @@ class PinyinInputDataset(IterableDataset):
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self,
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data_dir: str,
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query_engine,
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tokenizer_name: str = "iic/nlp_structbert_backbone_tiny_std",
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tokenizer_name: str = "iic/nlp_structbert_backbone_lite_std",
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max_len: int = 88,
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text_field: str = "text",
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batch_query_size: int = 1000,
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@ -71,7 +71,7 @@ class PinyinInputDataset(IterableDataset):
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max_drop_prob: float = 0.8, # 最大丢弃概率
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max_repeat_expect: float = 50.0, # 最大重复期望
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sample_context_section=[0.90, 0.95, 1],
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drop_py_rate: float = 0.30,
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drop_py_rate: float = 0,
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):
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"""
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初始化数据集
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@ -434,15 +434,6 @@ class PinyinInputDataset(IterableDataset):
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# 拼音处理
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processed_pinyin = self.process_pinyin_sequence(next_pinyins)
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# Tokenize
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hint = self.tokenizer(
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sampled_context + processed_pinyin,
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max_length=self.max_len,
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padding="max_length",
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truncation=True,
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return_tensors="pt",
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)
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pg = self.pg_groups[processed_pinyin[0]] if processed_pinyin else 12
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prob = random.random()
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if prob < self.drop_py_rate:
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@ -450,6 +441,18 @@ class PinyinInputDataset(IterableDataset):
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else:
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py = processed_pinyin
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# Tokenize
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hint = self.tokenizer(
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sampled_context,
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py,
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max_length=self.max_len,
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padding="max_length",
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truncation=True,
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return_tensors="pt",
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return_token_type_ids=True,
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)
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# 生成样本
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sample = {
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"hint": hint,
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@ -592,6 +595,7 @@ def custom_collate_with_txt(batch):
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"hint": {
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"input_ids": torch.cat([h["input_ids"] for h in hints]),
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"attention_mask": torch.cat([h["attention_mask"] for h in hints]),
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"token_type_ids": torch.cat([h["token_type_ids"] for h in hints]),
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},
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"char_id": torch.cat([item["char_id"] for item in batch]),
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"char": [item["char"] for item in batch],
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@ -18,12 +18,12 @@ if __name__ == "__main__":
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dataset = PinyinInputDataset(
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data_dir="/home/songsenand/DataSet/data",
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query_engine=query_engine,
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tokenizer_name="iic/nlp_structbert_backbone_tiny_std",
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tokenizer_name="iic/nlp_structbert_backbone_lite_std",
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max_len=88,
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batch_query_size=300,
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shuffle=True,
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shuffle_buffer_size=4000,
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drop_py_rate=0.7
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drop_py_rate=0
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)
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logger.info("数据集初始化")
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dataloader = DataLoader(
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@ -22,6 +22,28 @@ def eval_dataloader(path: Union[str, Path] = (files(__package__) / "eval_dataset
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return [pickle.load(file.open("rb")) for file in Path(path).glob("*.pkl")]
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# ---------------------------- 注意力池化模块（新增）----------------------------
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class AttentionPooling(nn.Module):
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def __init__(self, hidden_size):
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super().__init__()
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self.attn = nn.Linear(hidden_size, 1)
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# 三个可学习偏置：文本、拼音、个性化
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self.bias = nn.Parameter(torch.zeros(3)) # [text_bias, pinyin_bias, user_bias]
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def forward(self, x, mask=None, token_type_ids=None):
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scores = self.attn(x).squeeze(-1) # [batch, seq_len]
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if token_type_ids is not None:
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# 根据 token_type_ids 添加对应偏置
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# bias 形状 [3]，通过索引扩展为 [batch, seq_len]
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bias_per_token = self.bias[token_type_ids] # [batch, seq_len]
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scores = scores + bias_per_token
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if mask is not None:
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scores = scores.masked_fill(mask == 0, -1e9)
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weights = torch.softmax(scores, dim=-1)
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pooled = torch.sum(weights.unsqueeze(-1) * x, dim=1)
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return pooled
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# ---------------------------- 残差块 ----------------------------
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class ResidualBlock(nn.Module):
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def __init__(self, dim, dropout_prob=0.3):
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@ -35,6 +57,7 @@ class ResidualBlock(nn.Module):
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def forward(self, x):
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residual = x
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# 修复：使用 self.gelu 而不是未定义的 self.relu
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x = self.gelu(self.linear1(x))
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x = self.ln1(x)
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x = self.linear2(x)
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@ -62,7 +85,7 @@ class Expert(nn.Module):
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)
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self.output = nn.Sequential(
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nn.Linear(d_model, d_model),
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nn.GELU(inplace=True),
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nn.GELU(),
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nn.Dropout(dropout_prob),
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nn.Linear(d_model, self.output_dim),
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)
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@ -108,7 +131,10 @@ class MoEModel(nn.Module):
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)
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self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=4)
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# 3. 专家系统
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# 3. 注意力池化（新增）
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self.attn_pool = AttentionPooling(self.hidden_size)
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# 4. 专家系统
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total_experts = num_domain_experts + num_shared_experts
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self.experts = nn.ModuleList()
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for i in range(total_experts):
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@ -127,7 +153,7 @@ class MoEModel(nn.Module):
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total_experts, self.hidden_size * self.output_multiplier
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)
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# 4. 分类头
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# 5. 分类头
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self.classifier = nn.Sequential(
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nn.LayerNorm(self.hidden_size * self.output_multiplier),
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nn.Dropout(0.4),
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@ -139,80 +165,41 @@ class MoEModel(nn.Module):
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self.device = device
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return super().to(device)
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def forward(self, input_ids, attention_mask, pg, p_start):
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def forward(self, input_ids, attention_mask, token_type_ids, pg):
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"""
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ONNX 兼容的 Forward 函数
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新版 Forward 函数，不再需要 p_start，改用 token_type_ids。
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Args:
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input_ids: [B, L]
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attention_mask: [B, L]
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token_type_ids: [B, L] (0=文本, 1=拼音)
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pg: [B] 拼音组 ID
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p_start: [B] 拼音起始索引位置 (整数 Tensor)
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"""
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# ----- 1. Embeddings -----
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embeddings = self.embedding(input_ids)
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# 注意：预训练的 embedding 层本身可能已经包含了 token_type_ids 的处理，
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# 但这里我们直接使用它的 embedding，并手动将 token_type_ids 的嵌入加到上面。
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# 由于 bert.embeddings 通常包含 token_type_embeddings，我们可以利用它。
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# 但为简化，我们直接使用 bert.embeddings(input_ids, token_type_ids=token_type_ids)
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# 如果当前 embedding 不支持传入 token_type_ids，可以手动相加：
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# embeddings = self.embedding(input_ids) + self.embedding.token_type_embeddings(token_type_ids)
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# 这里采用更通用的方式：假设 self.embedding 有 token_type_ids 参数
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embeddings = self.embedding(input_ids, token_type_ids=token_type_ids)
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# ----- 2. Transformer Encoder -----
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# padding mask: True 表示忽略该位置
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padding_mask = attention_mask == 0
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encoded = self.encoder(
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embeddings, src_key_padding_mask=padding_mask
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) # [B, S, H]
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# ----- 3. ONNX 兼容的 Span Pooling (向量化实现) -----
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"""
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思路：
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我们不能用循环去切片。我们要构造一个 Mask 矩阵。
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目标：对于每个样本 i，生成一个长度为 L 的向量，其中 p_start[i] < index < p_end[i] 的位置为 1，其余为 0。
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步骤：
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1. 生成位置索引轴：indices = [0, 1, 2, ..., L-1] (Shape: [L])
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2. 扩展维度以匹配 Batch：
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indices: [1, L]
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p_start: [B, 1]
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p_end: [B, 1]
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3. 逻辑比较 (Broadcasting)：
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mask = (indices > p_start) & (indices < p_end)
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结果 Shape: [B, L] (Boolean)
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4. 应用 Mask：
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masked_encoded = encoded * mask.unsqueeze(-1)
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5. 求和并归一化：
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sum_vec = masked_encoded.sum(dim=1)
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count = mask.sum(dim=1).clamp(min=1) # 防止除零
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pooled = sum_vec / count
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"""
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B, L, H = encoded.shape
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device = encoded.device
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# 生成位置轴 [0, 1, ..., L-1]
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positions = torch.arange(L, device=device).unsqueeze(0) # [1, L]
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# 调整 p_start 形状为 [B, 1] 以便广播
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p_start_exp = p_start.unsqueeze(1) # [B, 1]
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span_mask = positions >= p_start_exp
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# 转换为 Float 用于乘法
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span_mask_float = span_mask.float() # [B, L]
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# 应用 Mask
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# encoded: [B, L, H] -> mask: [B, L, 1]
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masked_encoded = encoded * span_mask_float.unsqueeze(-1)
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# 求和
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span_sum = masked_encoded.sum(dim=1) # [B, H]
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# 计算有效长度 (防止除以 0)
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span_count = span_mask_float.sum(dim=1, keepdim=True).clamp(min=1.0) # [B, 1]
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# 平均池化
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pooled = span_sum / span_count # [B, H]
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# ----- 3. 注意力池化（代替原来的 Span Pooling）-----
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# 使用 attention_mask 忽略 padding 位置
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pooled = self.attn_pool(encoded, attention_mask, token_type_ids) # [B, H]
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# ----- 4. 专家路由（硬路由）-----
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if torch.jit.is_tracing():
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# ------------------ ONNX 导出模式：条件分支（batch=1）------------------
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# 此时 pg 为标量 Tensor，转换为 Python int
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# ONNX 导出模式：batch=1，根据 pg 选择专家
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group_id = pg.item() if torch.is_tensor(pg) else pg
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# 注意：专家索引从 0 开始，确保所有 case 都覆盖且偏置正确
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# 使用字典映射或 if-elif（ONNX 需要静态图，此处保持原样但修正索引错误）
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if group_id == 0:
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expert_out = self.experts[0](pooled) + self.expert_bias(
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torch.tensor(0, device=pooled.device)
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@ -267,33 +254,36 @@ class MoEModel(nn.Module):
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)
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else:
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batch_size = pooled.size(0)
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# 并行计算所有专家输出
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expert_outputs = torch.stack(
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[e(pooled) for e in self.experts], dim=0
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) # [E, B, D]
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# 根据 pg 索引专家输出
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expert_out = expert_outputs[pg, torch.arange(batch_size)] # [B, D]
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# 添加专家偏置
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bias = self.expert_bias(pg) # [B, D]
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expert_out = expert_out + bias
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# ----- 5. 分类头 -----
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logits = self.classifier(expert_out) # [batch, num_classes]
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if not self.training: # 推理时加 Softmax
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probs = torch.softmax(logits, dim=-1)
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return probs
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return logits
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return self.classifier(expert_out) # [batch, num_classes]
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def model_eval(self, eval_dataloader, criterion):
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"""
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在验证集上评估模型，返回准确率和平均损失。
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评估模型在验证集上的性能。
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参数：
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eval_dataloader: DataLoader，提供 'input_ids', 'attention_mask', 'pg', 'char_id'
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criterion: 损失函数，默认为 CrossEntropyLoss()
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返回：
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accuracy: float, 准确率
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avg_loss: float, 平均损失
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Args:
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eval_dataloader (DataLoader): 验证集的数据加载器，每个batch包含以下字段：
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- hint: 包含input_ids、attention_mask和token_type_ids的字典
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- pg: 程序图数据
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- char_id: 字符ID标签
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criterion (callable): 损失函数，用于计算模型输出与标签之间的损失
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Returns:
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tuple: 包含两个浮点数的元组 (accuracy, avg_loss)
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- accuracy (float): 模型在验证集上的准确率
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- avg_loss (float): 模型在验证集上的平均损失
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Note:
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该方法会自动将模型切换到评估模式(self.eval())，
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并使用torch.no_grad()上下文管理器来禁用梯度计算，
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以节省内存和计算资源。
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"""
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self.eval()
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total_loss = 0.0
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@ -302,20 +292,18 @@ class MoEModel(nn.Module):
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with torch.no_grad():
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for batch in eval_dataloader:
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# 移动数据到模型设备
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input_ids = batch["hint"]["input_ids"].to(self.device)
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attention_mask = batch["hint"]["attention_mask"].to(self.device)
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token_type_ids = batch["hint"]["token_type_ids"].to(self.device) # 新增
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pg = batch["pg"].to(self.device)
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p_start = batch["p_start"].to(self.device)
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labels = batch["char_id"].to(self.device)
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# 前向传播
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probs = self(input_ids, attention_mask, pg)
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loss = criterion(log_probs, labels)
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logits = self(input_ids, attention_mask, token_type_ids, pg)
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loss = criterion(logits, labels)
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total_loss += loss.item() * labels.size(0)
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# 计算准确率
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preds = probs.argmax(dim=-1)
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preds = torch.softmax(logits, dim=-1).argmax(dim=-1)
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correct += (preds == labels).sum().item()
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total += labels.size(0)
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@ -327,117 +315,117 @@ class MoEModel(nn.Module):
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"""
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生成用于预测的样本数据。
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参数:
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text (str): 输入的文本内容。
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py (list): 与文本对应的拼音列表。
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tokenizer (PreTrainedTokenizer, optional): 用于文本编码的分词器。如果未提供且实例中没有默认分词器，
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则会自动加载预训练的分词器。
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该方法将文本和拼音转换为模型所需的输入格式，包括input_ids、attention_mask和token_type_ids。
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如果没有提供tokenizer，会使用默认的AutoTokenizer。
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返回:
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dict: 包含以下键值的字典：
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- "hint": 包含编码后的输入特征，包括 "input_ids" 和 "attention_mask"。
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- "pg": 一个张量，表示拼音的第一个字符在 PG 映射中的索引。
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Args:
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text (str): 输入文本，作为第一句输入。
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py (str): 拼音字符串，作为第二句输入。
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tokenizer (AutoTokenizer, optional): 分词器实例。如果为None且self.tokenizer不存在，
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则会创建默认的分词器。默认为None。
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功能说明:
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1. 如果未提供分词器且实例中不存在默认分词器，则从预训练模型加载分词器。
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2. 使用分词器对输入文本和拼音进行编码，设置最大长度为 88，并进行填充和截断。
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3. 构造样本字典，包含编码后的输入特征和拼音映射张量。
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Returns:
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dict: 包含模型输入的字典，格式为：
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{
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"hint": {
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"input_ids": tensor, # 文本和拼音的token IDs
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"attention_mask": tensor, # 注意力掩码
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"token_type_ids": tensor # 句子类型ID
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},
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"pg": tensor # 拼音组ID，根据拼音首字母生成
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}
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Notes:
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- 使用text_pair参数让分词器自动生成token_type_ids
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- 确保分词器支持return_token_type_ids=True
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- 最大长度(max_length)设置为88
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- 会自动进行padding和truncation处理
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- 拼音组ID当前根据拼音首字母生成，可根据实际需要改进
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"""
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# 如果未提供分词器且实例中没有默认分词器，则加载预训练分词器
|
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if tokenizer is None and not hasattr(self, "tokenizer"):
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(
|
||||
"iic/nlp_structbert_backbone_tiny_std"
|
||||
"iic/nlp_structbert_backbone_lite_std"
|
||||
)
|
||||
else:
|
||||
# 使用传入的分词器或实例中的默认分词器
|
||||
self.tokenizer = tokenizer or self.tokenizer
|
||||
|
||||
# 对输入文本和拼音进行编码，生成模型所需的输入格式
|
||||
hint = self.tokenizer(
|
||||
text,
|
||||
py,
|
||||
# 使用 text_pair 参数让分词器自动生成 token_type_ids
|
||||
# 注意：确保分词器支持 return_token_type_ids=True
|
||||
encoded = self.tokenizer(
|
||||
text, # 文本作为第一句
|
||||
py, # 拼音作为第二句
|
||||
max_length=88,
|
||||
padding="max_length",
|
||||
truncation=True,
|
||||
return_tensors="pt",
|
||||
return_token_type_ids=True, # 显式要求返回 token_type_ids
|
||||
)
|
||||
|
||||
# 构造样本字典
|
||||
sample = {}
|
||||
sample["hint"] = {
|
||||
"input_ids": hint["input_ids"],
|
||||
"attention_mask": hint["attention_mask"],
|
||||
sample = {
|
||||
"hint": {
|
||||
"input_ids": encoded["input_ids"],
|
||||
"attention_mask": encoded["attention_mask"],
|
||||
"token_type_ids": encoded["token_type_ids"], # 新增
|
||||
},
|
||||
"pg": torch.tensor(
|
||||
[PG[py[0]]]
|
||||
), # 拼音组 ID 仍根据首字母生成（可根据实际需要改进）
|
||||
}
|
||||
# 将拼音的第一个字符映射为 PG 中的索引并转换为张量
|
||||
sample["pg"] = torch.tensor([PG[py[0]]])
|
||||
sample["p_start"] = torch.tensor([len(text)])
|
||||
return sample
|
||||
|
||||
def predict(self, text, py, tokenizer=None):
|
||||
"""
|
||||
基于输入的文本和拼音，生成 sample 字典进行预测，支持批量/单样本，可选调试打印错误样本信息。
|
||||
预测函数，自动处理 batch 维度
|
||||
|
||||
参数：
|
||||
text : str
|
||||
输入的文本。
|
||||
py : str
|
||||
输入的拼音。
|
||||
tokenizer : Tokenizer, optional
|
||||
用于分词的分词器，默认为 None。
|
||||
debug : bool
|
||||
是否打印预测错误的样本信息。
|
||||
Args:
|
||||
text (str or List[str]): 输入文本或文本列表
|
||||
py (int or List[int]): 拼音特征，可以是单个值或列表
|
||||
tokenizer (object, optional): 分词器对象，用于文本预处理。默认为None
|
||||
|
||||
返回：
|
||||
preds : torch.Tensor
|
||||
[batch] 预测类别标签（若输入为单样本且无 batch 维度，则返回标量）
|
||||
Returns:
|
||||
torch.Tensor: 预测结果，如果是单个输入则返回一维张量，
|
||||
如果是批量输入则返回二维张量
|
||||
"""
|
||||
self.eval() # 将模型设置为评估模式，关闭dropout等训练时需要的层
|
||||
self.eval()
|
||||
sample = self.gen_predict_sample(text, py, tokenizer)
|
||||
input_ids = sample["hint"]["input_ids"]
|
||||
attention_mask = sample["hint"]["attention_mask"]
|
||||
token_type_ids = sample["hint"]["token_type_ids"]
|
||||
pg = sample["pg"]
|
||||
|
||||
# ------------------ 1. 提取并规范化输入 ------------------
|
||||
# 判断是否为单样本（input_ids 无 batch 维度）
|
||||
sample = self.gen_predict_sample(text, py, tokenizer) # 生成预测所需的样本数据
|
||||
input_ids = sample["hint"]["input_ids"] # 获取输入ID
|
||||
attention_mask = sample["hint"]["attention_mask"] # 获取注意力掩码
|
||||
pg = sample["pg"] # 获取拼音引导
|
||||
has_batch_dim = input_ids.dim() > 1 # 判断输入是否有batch维度
|
||||
|
||||
# 如果没有batch维度，则添加batch维度
|
||||
has_batch_dim = input_ids.dim() > 1
|
||||
if not has_batch_dim:
|
||||
input_ids = input_ids.unsqueeze(0) # 在第0维添加batch维度
|
||||
attention_mask = attention_mask.unsqueeze(0) # 在第0维添加batch维度
|
||||
# 如果拼音引导是标量，则扩展为与输入ID相同的batch大小
|
||||
input_ids = input_ids.unsqueeze(0)
|
||||
attention_mask = attention_mask.unsqueeze(0)
|
||||
token_type_ids = token_type_ids.unsqueeze(0)
|
||||
if pg.dim() == 0:
|
||||
pg = pg.unsqueeze(0).expand(input_ids.size(0)) # 扩展拼音引导的batch维度
|
||||
pg = pg.unsqueeze(0).expand(input_ids.size(0))
|
||||
|
||||
# ------------------ 2. 移动设备 ------------------
|
||||
# 将输入数据移动到模型所在设备（GPU/CPU）
|
||||
input_ids = input_ids.to(self.device)
|
||||
attention_mask = attention_mask.to(self.device)
|
||||
token_type_ids = token_type_ids.to(self.device)
|
||||
pg = pg.to(self.device)
|
||||
|
||||
# ------------------ 3. 推理 ------------------
|
||||
# 使用torch.no_grad()上下文管理器，不计算梯度，节省内存
|
||||
with torch.no_grad():
|
||||
logits = self(input_ids, attention_mask, pg) # 前向传播获取logits
|
||||
preds = torch.softmax(logits, dim=-1).argmax(dim=-1) # [batch]
|
||||
logits = self(input_ids, attention_mask, token_type_ids, pg)
|
||||
preds = torch.softmax(logits, dim=-1).argmax(dim=-1)
|
||||
|
||||
# ------------------ 4. 返回结果（保持与输入维度一致） ------------------
|
||||
if not has_batch_dim:
|
||||
return preds.squeeze(0) # 返回标量
|
||||
return preds.squeeze(0)
|
||||
return preds
|
||||
|
||||
def fit(
|
||||
self,
|
||||
train_dataloader, # 训练数据加载器
|
||||
eval_dataloader=None, # 评估数据加载器，可选
|
||||
monitor: Optional[TrainingMonitor] = None, # 训练监控器，用于记录训练过程
|
||||
criterion=None, # 损失函数
|
||||
optimizer=None, # 优化器
|
||||
num_epochs=1, # 训练轮数
|
||||
stop_batch=1e6, # 最大训练批次数
|
||||
train_dataloader,
|
||||
eval_dataloader=None,
|
||||
monitor: Optional[TrainingMonitor] = None,
|
||||
criterion=None,
|
||||
optimizer=None,
|
||||
num_epochs=1,
|
||||
stop_batch=2e5,
|
||||
eval_frequency=500,
|
||||
grad_accum_steps=1, # 梯度累积步数
|
||||
clip_grad_norm=1.0, # 梯度裁剪的范数
|
||||
grad_accum_steps=1,
|
||||
clip_grad_norm=1.0,
|
||||
loss_weight=None,
|
||||
mixed_precision=True,
|
||||
weight_decay=0.1,
|
||||
|
|
@ -445,25 +433,16 @@ class MoEModel(nn.Module):
|
|||
label_smoothing=0.15,
|
||||
lr=1e-4,
|
||||
):
|
||||
"""
|
||||
训练模型，支持混合精度、梯度累积、学习率调度、实时监控。
|
||||
|
||||
参数：
|
||||
# TODO: 添加参数注释
|
||||
"""
|
||||
# 确保模型在正确的设备上（GPU或CPU）
|
||||
"""训练函数，调整了输入参数"""
|
||||
if self.device is None:
|
||||
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
self.to(self.device)
|
||||
|
||||
# 切换到训练模式
|
||||
self.train()
|
||||
|
||||
# 默认优化器设置
|
||||
if optimizer is None:
|
||||
optimizer = optim.AdamW(self.parameters(), lr=lr, weight_decay=weight_decay)
|
||||
|
||||
# 损失函数设置
|
||||
if criterion is None:
|
||||
if loss_weight is not None:
|
||||
criterion = nn.CrossEntropyLoss(
|
||||
|
|
@ -472,13 +451,13 @@ class MoEModel(nn.Module):
|
|||
else:
|
||||
criterion = nn.CrossEntropyLoss(label_smoothing=label_smoothing)
|
||||
|
||||
# 混合精度缩放器
|
||||
scaler = amp.GradScaler(enabled=mixed_precision)
|
||||
|
||||
total_steps = stop_batch
|
||||
total_steps = max(stop_batch, 2e5)
|
||||
warmup_steps = int(total_steps * warmup_ratio)
|
||||
logger.info(f"Training Start: Steps={total_steps}, Warmup={warmup_steps}")
|
||||
processed_batches = 0 # 新增：实际处理的 batch 数量计数器
|
||||
processed_batches = 0
|
||||
global_step = 0 # 初始化
|
||||
batch_loss_sum = 0.0
|
||||
optimizer.zero_grad()
|
||||
|
||||
|
|
@ -486,11 +465,9 @@ class MoEModel(nn.Module):
|
|||
for batch_idx, batch in enumerate(
|
||||
tqdm(train_dataloader, total=int(stop_batch))
|
||||
):
|
||||
processed_batches += 1
|
||||
|
||||
# LR Schedule
|
||||
if processed_batches < warmup_steps:
|
||||
current_lr = lr * (processed_batches / warmup_steps)
|
||||
current_lr = lr * (processed_batches/ warmup_steps)
|
||||
else:
|
||||
progress = (processed_batches - warmup_steps) / (
|
||||
total_steps - warmup_steps
|
||||
|
|
@ -499,26 +476,22 @@ class MoEModel(nn.Module):
|
|||
for param_group in optimizer.param_groups:
|
||||
param_group["lr"] = current_lr
|
||||
|
||||
# ---------- 移动数据 ----------
|
||||
# 移动数据（注意：batch 中现在包含 token_type_ids）
|
||||
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) # 新增
|
||||
pg = batch["pg"].to(self.device)
|
||||
p_start = batch["p_start"].to(self.device) # [B]
|
||||
labels = batch["char_id"].to(self.device)
|
||||
|
||||
# 混合精度前向
|
||||
# Forward
|
||||
with torch.amp.autocast(
|
||||
device_type=self.device.type, enabled=mixed_precision
|
||||
):
|
||||
logits = self(input_ids, attention_mask, pg, p_start)
|
||||
logits = self(input_ids, attention_mask, token_type_ids, pg)
|
||||
loss = criterion(logits, labels)
|
||||
loss = loss / grad_accum_steps
|
||||
|
||||
# 反向传播
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
# 梯度累积
|
||||
if (processed_batches) % grad_accum_steps == 0:
|
||||
scaler.unscale_(optimizer)
|
||||
torch.nn.utils.clip_grad_norm_(self.parameters(), clip_grad_norm)
|
||||
|
|
@ -536,34 +509,34 @@ class MoEModel(nn.Module):
|
|||
logger.warning("NaN detected, skipping step.")
|
||||
optimizer.zero_grad()
|
||||
batch_loss_sum += loss.item() * grad_accum_steps
|
||||
|
||||
# 周期性评估
|
||||
if eval_dataloader and global_step % eval_frequency == 0:
|
||||
self.eval()
|
||||
acc, eval_loss = self.model_eval(eval_dataloader, criterion)
|
||||
if global_step == 0:
|
||||
avg_loss = eval_loss
|
||||
self.train()
|
||||
if monitor:
|
||||
monitor.add_step(
|
||||
global_step, {"loss": avg_loss, "acc": acc}
|
||||
if global_step % eval_frequency == 0:
|
||||
if eval_dataloader:
|
||||
self.eval()
|
||||
acc, eval_loss = self.model_eval(eval_dataloader, criterion)
|
||||
self.train()
|
||||
if monitor:
|
||||
# 使用 eval_loss 作为监控指标
|
||||
monitor.add_step(
|
||||
global_step, {"loss": batch_loss_sum, "acc": acc}
|
||||
)
|
||||
logger.info(
|
||||
f"step: {global_step}, eval_loss: {eval_loss:.4f}, acc: {acc:.4f}, 'batch_loss_sum': {batch_loss_sum / (eval_frequency if global_step > 0 else 1):.4f}, current_lr: {current_lr}"
|
||||
)
|
||||
logger.info(
|
||||
f"step: {global_step}, loss: {avg_loss:.4f}, acc: {acc:.4f}, eval_loss: {eval_loss:.4f}"
|
||||
)
|
||||
logger.info(f"step: {global_step}, 'batch_loss_sum': {batch_loss_sum / (eval_frequency if global_step > 0 else 1):.4f}, current_lr: {current_lr}")
|
||||
batch_loss_sum = 0.0
|
||||
if processed_batches - 1 >= stop_batch:
|
||||
if processed_batches >= stop_batch:
|
||||
break
|
||||
processed_batches += 1
|
||||
global_step += 1
|
||||
|
||||
# 训练结束发送通知
|
||||
try:
|
||||
res_acc, res_loss = self.model_eval(eval_dataloader, criterion)
|
||||
to_wechat_response = send_serverchan_message(
|
||||
send_serverchan_message(
|
||||
title="训练完成",
|
||||
content=f"训练完成，acc: {res_acc:.4f}, loss: {res_loss:.4f}",
|
||||
content=f"acc: {res_acc:.4f}, loss: {res_loss:.4f}",
|
||||
)
|
||||
logger.info(f"训练完成，acc: {res_acc:.4f}, loss: {res_loss:.4f}")
|
||||
logger.info(f"发送消息: {to_wechat_response}")
|
||||
except Exception as e:
|
||||
logger.error(f"发送消息失败: {e}")
|
||||
|
||||
|
|
@ -601,11 +574,11 @@ class MoEModel(nn.Module):
|
|||
# --- ONNX 导出辅助函数 ---
|
||||
def export_onnx(self, output_path, dummy_input):
|
||||
"""
|
||||
dummy_input 应该是一个字典或元组，包含:
|
||||
(input_ids, attention_mask, pg, p_start)
|
||||
dummy_input 应该是一个元组，包含:
|
||||
(input_ids, attention_mask, token_type_ids, pg)
|
||||
"""
|
||||
self.eval()
|
||||
input_names = ["input_ids", "attention_mask", "pg", "p_start"]
|
||||
input_names = ["input_ids", "attention_mask", "token_type_ids", "pg"]
|
||||
output_names = ["logits"]
|
||||
|
||||
torch.onnx.export(
|
||||
|
|
@ -617,11 +590,11 @@ class MoEModel(nn.Module):
|
|||
dynamic_axes={
|
||||
"input_ids": {0: "batch_size", 1: "seq_len"},
|
||||
"attention_mask": {0: "batch_size", 1: "seq_len"},
|
||||
"token_type_ids": {0: "batch_size", 1: "seq_len"},
|
||||
"pg": {0: "batch_size"},
|
||||
"p_start": {0: "batch_size"},
|
||||
"logits": {0: "batch_size"},
|
||||
},
|
||||
opset_version=14, # 推荐使用 14+ 以支持更好的算子
|
||||
opset_version=14,
|
||||
do_constant_folding=True,
|
||||
)
|
||||
logger.info(f"ONNX model exported to {output_path}")
|
||||
|
|
|
|||
Loading…
Reference in New Issue