534 lines
35 KiB
Python
Executable File
534 lines
35 KiB
Python
Executable File
import math, torch, torch.nn.functional as F
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from torch import nn
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from transformers.activations import ACT2FN
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from transformers import PreTrainedModel, GenerationMixin, PretrainedConfig
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from transformers.modeling_outputs import MoeCausalLMOutputWithPast
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# ════════════════════════════════════════════════════════════════════════════════
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# 整体架构协作关系
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# ════════════════════════════════════════════════════════════════════════════════
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#
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# MiniMindForCausalLM (最顶层，训练和推理的入口)
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# ├── MiniMindModel (Transformer 主干)
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# │ ├── embed_tokens (Embedding层: token_id → 向量)
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# │ ├── MiniMindBlock × N (N=8 层 Transformer 块，核心计算单元)
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# │ │ ├── input_layernorm (RMSNorm: 注意力前的归一化)
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# │ │ ├── Attention (GQA多头注意力 + RoPE位置编码)
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# │ │ │ ├── q_proj, k_proj, v_proj (线性投影)
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# │ │ │ ├── q_norm, k_norm (QK归一化，防止注意力分数爆炸)
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# │ │ │ ├── apply_rotary_pos_emb (RoPE旋转位置编码)
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# │ │ │ ├── repeat_kv (GQA: 复制KV头以匹配Q头数量)
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# │ │ │ ├── Flash Attention 或 手动注意力计算
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# │ │ │ └── o_proj (输出投影)
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# │ │ ├── post_attention_layernorm (RMSNorm: FFN前的归一化)
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# │ │ └── mlp (前馈网络，二选一):
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# │ │ ├── FeedForward (Dense版: SwiGLU激活)
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# │ │ └── MOEFeedForward (MoE版: Router + 多个Expert)
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# │ └── norm (最终RMSNorm)
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# └── lm_head (Linear: 隐藏向量 → 词表logits，与embed_tokens共享权重)
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#
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# 数据流 (训练时):
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# input_ids [B, S] → Embedding → [B, S, 768] → 8层Block → RMSNorm → lm_head
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# → logits [B, S, 6400] → CrossEntropy(logits[:-1], labels[1:]) → loss
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#
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# 数据流 (推理/generate时):
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# 每步只输入1个新token → 用KV Cache避免重复计算 → 取最后位置logits
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# → temperature/top_k/top_p采样 → 输出next_token → 循环直到EOS
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#
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# ════════════════════════════════════════════════════════════════════════════════
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# ════════════════════════════════════════════════════════════════════════════════
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# MiniMind Config
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# ════════════════════════════════════════════════════════════════════════════════
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# 模型的所有超参数配置，对齐 Qwen3 生态
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class MiniMindConfig(PretrainedConfig):
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model_type = "minimind"
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def __init__(self, hidden_size=768, num_hidden_layers=8, use_moe=False, **kwargs):
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super().__init__(**kwargs)
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# ---- 基础结构参数 ----
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self.hidden_size = hidden_size # 隐藏层维度 d_model，整个模型的"宽度"
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self.num_hidden_layers = num_hidden_layers # Transformer 层数，模型的"深度"
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self.use_moe = use_moe # 是否使用 MoE (Mixture of Experts)
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self.dropout = kwargs.get("dropout", 0.0) # Dropout率，默认0(不丢弃)
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# ---- 词表与特殊token ----
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self.vocab_size = kwargs.get("vocab_size", 6400) # 词表大小(极小词表，压缩参数量)
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self.bos_token_id = kwargs.get("bos_token_id", 1) # 句子开始token
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self.eos_token_id = kwargs.get("eos_token_id", 2) # 句子结束token
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# ---- 注意力参数 ----
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self.flash_attn = kwargs.get("flash_attn", True) # 是否启用Flash Attention加速
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self.num_attention_heads = kwargs.get("num_attention_heads", 8) # Q头数量(8个)
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self.num_key_value_heads = kwargs.get("num_key_value_heads", 4) # KV头数量(4个，GQA模式: 每2个Q头共享1个KV头)
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self.head_dim = kwargs.get("head_dim", self.hidden_size // self.num_attention_heads) # 每个头的维度 = 768/8 = 96
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# ---- FFN参数 ----
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self.hidden_act = kwargs.get("hidden_act", 'silu') # 激活函数 (SiLU，用于SwiGLU)
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# intermediate_size = ceil(768 * π / 64) * 64 = 2432，FFN的中间维度(约hidden_size的3.14倍)
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self.intermediate_size = kwargs.get("intermediate_size", math.ceil(hidden_size * math.pi / 64) * 64)
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# ---- 位置编码参数 ----
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self.max_position_embeddings = kwargs.get("max_position_embeddings", 32768) # 最大位置数(32K)
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self.rms_norm_eps = kwargs.get("rms_norm_eps", 1e-6) # RMSNorm的epsilon防除零
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self.rope_theta = kwargs.get("rope_theta", 1e6) # RoPE基础频率(越大支持越长序列)
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# ---- 权重绑定 ----
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self.tie_word_embeddings = kwargs.get("tie_word_embeddings", True) # embedding和lm_head共享权重，省一份参数
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# ---- YaRN长文本外推 ----
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self.inference_rope_scaling = kwargs.get("inference_rope_scaling", False)
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self.rope_scaling = {
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"beta_fast": 32, # 高频维度边界(这些维度不缩放)
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"beta_slow": 1, # 低频维度边界(这些维度做最大缩放)
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"factor": 16, # 缩放因子(目标长度/原始长度)
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"original_max_position_embeddings": 2048, # 原始训练长度
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"attention_factor": 1.0, # 注意力缩放系数
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"type": "yarn"
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} if self.inference_rope_scaling else None
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# ---- MoE专用参数 (use_moe=False时忽略) ----
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self.num_experts = kwargs.get("num_experts", 4) # Expert总数
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self.num_experts_per_tok = kwargs.get("num_experts_per_tok", 1) # 每个token激活几个Expert (top-1)
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self.moe_intermediate_size = kwargs.get("moe_intermediate_size", self.intermediate_size) # 每个Expert的FFN中间维度
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self.norm_topk_prob = kwargs.get("norm_topk_prob", True) # 是否归一化路由概率
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self.router_aux_loss_coef = kwargs.get("router_aux_loss_coef", 5e-4) # 负载均衡loss系数
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# ════════════════════════════════════════════════════════════════════════════════
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# 基础组件
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# ════════════════════════════════════════════════════════════════════════════════
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# ---- RMSNorm: 比LayerNorm更简单高效的归一化 ----
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# LayerNorm: (x - mean) / std * γ + β (需要均值和方差)
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# RMSNorm: x / RMS(x) * γ (只需均方根，省掉均值和偏置)
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class RMSNorm(torch.nn.Module):
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def __init__(self, dim: int, eps: float = 1e-5):
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super().__init__()
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(dim)) # 可学习的缩放参数 γ
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def norm(self, x):
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# RMS(x) = sqrt(mean(x²))，rsqrt = 1/sqrt
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x):
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# 先转float32算归一化(防精度损失)，再转回原dtype
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return (self.weight * self.norm(x.float())).type_as(x)
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# ---- RoPE: 旋转位置编码 (预计算频率表) ----
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# 核心思想: 不把位置"加"到embedding上，而是把Q和K向量"旋转"一个与位置相关的角度
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# 这样 Q_i · K_j 的点积自然编码了相对距离 |i-j|
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def precompute_freqs_cis(dim: int, end: int = int(32 * 1024), rope_base: float = 1e6, rope_scaling: dict = None):
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# 每个维度对应一个频率: freq_i = 1 / (θ^(2i/d))
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# 低维度 → 高频(捕捉局部位置)，高维度 → 低频(捕捉远距离位置)
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freqs, attn_factor = 1.0 / (rope_base ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)), 1.0
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# YaRN外推: 当推理长度 > 训练长度时，对不同维度做差异化频率缩放
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# 高频维度(局部位置信息): 保持不变
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# 低频维度(远距离位置信息): 频率除以factor缩放
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# 中间维度: 线性插值过渡
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if rope_scaling is not None:
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orig_max, factor, beta_fast, beta_slow, attn_factor = (
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rope_scaling.get("original_max_position_embeddings", 2048), rope_scaling.get("factor", 16),
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rope_scaling.get("beta_fast", 32.0), rope_scaling.get("beta_slow", 1.0), rope_scaling.get("attention_factor", 1.0)
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)
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if end / orig_max > 1.0: # 只有推理长度超出训练长度时才缩放
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inv_dim = lambda b: (dim * math.log(orig_max / (b * 2 * math.pi))) / (2 * math.log(rope_base))
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low, high = max(math.floor(inv_dim(beta_fast)), 0), min(math.ceil(inv_dim(beta_slow)), dim // 2 - 1)
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# ramp: 0→1的线性斜坡，控制缩放程度
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ramp = torch.clamp((torch.arange(dim // 2, device=freqs.device).float() - low) / max(high - low, 0.001), 0, 1)
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# 最终频率 = 原频率 * (1-ramp + ramp/factor)
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# ramp=0 → 保持原频率(高频维度)
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# ramp=1 → 频率/factor(低频维度)
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freqs = freqs * (1 - ramp + ramp / factor)
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# 生成位置-频率矩阵: [max_pos, dim//2]
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t = torch.arange(end, device=freqs.device) # 位置序列 [0, 1, 2, ..., end-1]
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freqs = torch.outer(t, freqs).float() # 外积: 每个位置 × 每个频率
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# 计算cos和sin，拼接成完整维度 [max_pos, dim]
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freqs_cos = torch.cat([torch.cos(freqs), torch.cos(freqs)], dim=-1) * attn_factor
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freqs_sin = torch.cat([torch.sin(freqs), torch.sin(freqs)], dim=-1) * attn_factor
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return freqs_cos, freqs_sin
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# ---- 应用RoPE旋转到Q和K ----
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# 公式: q_rot = q * cos(θ) + rotate_half(q) * sin(θ)
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# 其中 rotate_half 将向量前后半部分交换并取负: [a,b] → [-b,a] (相当于复数乘法)
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def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
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def rotate_half(x): return torch.cat((-x[..., x.shape[-1] // 2:], x[..., : x.shape[-1] // 2]), dim=-1)
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q_embed = ((q * cos.unsqueeze(unsqueeze_dim)) + (rotate_half(q) * sin.unsqueeze(unsqueeze_dim))).to(q.dtype)
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k_embed = ((k * cos.unsqueeze(unsqueeze_dim)) + (rotate_half(k) * sin.unsqueeze(unsqueeze_dim))).to(k.dtype)
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return q_embed, k_embed
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# ---- GQA辅助: 复制KV头以匹配Q头数量 ----
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# GQA (Grouped Query Attention): Q=8头, KV=4头，每2个Q头共享1对KV
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# 需要把4个KV头复制一份变成8个，才能和8个Q头做注意力计算
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# 相比MHA(Q=K=V=8头)节省50%的KV Cache显存
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def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
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bs, slen, num_key_value_heads, head_dim = x.shape
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if n_rep == 1: return x # MHA模式(Q头数=KV头数)，无需复制
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# [B, S, kv_heads, dim] → [B, S, kv_heads, n_rep, dim] → [B, S, q_heads, dim]
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return (x[:, :, :, None, :].expand(bs, slen, num_key_value_heads, n_rep, head_dim).reshape(bs, slen, num_key_value_heads * n_rep, head_dim))
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# ════════════════════════════════════════════════════════════════════════════════
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# 注意力层
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# ════════════════════════════════════════════════════════════════════════════════
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# 实现 GQA (Grouped Query Attention) + RoPE + KV Cache + Flash Attention
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#
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# 计算流程:
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# x → Q,K,V投影 → QK归一化 → RoPE旋转 → KV Cache拼接
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# → GQA复制KV → 注意力计算(Flash或手动) → 输出投影
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class Attention(nn.Module):
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def __init__(self, config: MiniMindConfig):
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super().__init__()
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self.num_key_value_heads = config.num_attention_heads if config.num_key_value_heads is None else config.num_key_value_heads
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self.n_local_heads = config.num_attention_heads # Q头数 = 8
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self.n_local_kv_heads = self.num_key_value_heads # KV头数 = 4
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self.n_rep = self.n_local_heads // self.n_local_kv_heads # 每个KV头对应几个Q头 = 2
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self.head_dim = config.head_dim # 每个头的维度 = 96
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self.is_causal = True # 因果注意力(只能看到前面的token)
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# Q/K/V/O 线性投影 (无偏置，现代LLM标配)
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self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False) # 768 → 8*96=768
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self.k_proj = nn.Linear(config.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) # 768 → 4*96=384
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self.v_proj = nn.Linear(config.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) # 768 → 4*96=384
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self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False) # 768 → 768
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# QK归一化 (Qwen3的设计，防止注意力分数随训练爆炸)
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self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.attn_dropout = nn.Dropout(config.dropout)
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self.resid_dropout = nn.Dropout(config.dropout)
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self.dropout = config.dropout
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# 检测是否支持Flash Attention (PyTorch 2.0+的SDPA)
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self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and config.flash_attn
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def forward(self, x, position_embeddings, past_key_value=None, use_cache=False, attention_mask=None):
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bsz, seq_len, _ = x.shape
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# Step 1: 线性投影得到Q, K, V
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xq, xk, xv = self.q_proj(x), self.k_proj(x), self.v_proj(x)
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# reshape成多头格式: [B, S, n_heads, head_dim]
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xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim) # [B, S, 8, 96]
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xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) # [B, S, 4, 96]
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xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) # [B, S, 4, 96]
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# Step 2: QK归一化 (防止注意力分数随维度增大而爆炸)
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xq, xk = self.q_norm(xq), self.k_norm(xk)
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# Step 3: 应用RoPE旋转位置编码
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cos, sin = position_embeddings
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xq, xk = apply_rotary_pos_emb(xq, xk, cos, sin)
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# Step 4: KV Cache (推理加速，拼接历史K/V)
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# 训练时: past_key_value=None，每次处理完整序列
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# 推理时: 拼接之前步骤缓存的K/V，只需计算新token
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if past_key_value is not None:
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xk = torch.cat([past_key_value[0], xk], dim=1) # 拼接历史K
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xv = torch.cat([past_key_value[1], xv], dim=1) # 拼接历史V
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past_kv = (xk, xv) if use_cache else None
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# Step 5: GQA头复制 + 转置为注意力计算格式
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# Q: [B, 8, S, 96], K: [B, 8, S, 96](从4头复制到8头), V: 同K
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xq, xk, xv = (xq.transpose(1, 2), repeat_kv(xk, self.n_rep).transpose(1, 2), repeat_kv(xv, self.n_rep).transpose(1, 2))
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# Step 6: 注意力计算 (两条路径)
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if self.flash and (seq_len > 1) and (not self.is_causal or past_key_value is None) and (attention_mask is None or torch.all(attention_mask == 1)):
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# 路径A: Flash Attention (O(N)显存，更快)
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# 条件: 非单token推理 + 无自定义mask时可用
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output = F.scaled_dot_product_attention(xq, xk, xv, dropout_p=self.dropout if self.training else 0.0, is_causal=self.is_causal)
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else:
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# 路径B: 手动注意力 (KV Cache推理 或 有padding mask时)
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# scores = Q @ K^T / sqrt(d)
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scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)
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# 因果mask: 上三角设为-inf，确保位置i只能attend到<=i的位置
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if self.is_causal: scores[:, :, :, -seq_len:] += torch.full((seq_len, seq_len), float("-inf"), device=scores.device).triu(1)
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# padding mask: 被padding的位置设为-1e9
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if attention_mask is not None: scores += (1.0 - attention_mask.unsqueeze(1).unsqueeze(2)) * -1e9
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# softmax → dropout → 与V相乘
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output = self.attn_dropout(F.softmax(scores.float(), dim=-1).type_as(xq)) @ xv
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# Step 7: 合并多头 + 输出投影
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output = output.transpose(1, 2).reshape(bsz, seq_len, -1) # [B, S, 768]
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output = self.resid_dropout(self.o_proj(output))
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return output, past_kv
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# ════════════════════════════════════════════════════════════════════════════════
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# 前馈网络 (FFN)
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# ════════════════════════════════════════════════════════════════════════════════
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# ---- SwiGLU FFN (Dense版) ----
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# 标准FFN: output = down(ReLU(up(x))) 两个矩阵
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# SwiGLU: output = down(SiLU(gate(x)) * up(x)) 三个矩阵，效果更好
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# gate_proj 和 up_proj 分别投影到中间维度，gate走激活函数后与up逐元素相乘，再down投影回来
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class FeedForward(nn.Module):
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def __init__(self, config: MiniMindConfig, intermediate_size: int = None):
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super().__init__()
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intermediate_size = intermediate_size or config.intermediate_size # 默认2432
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self.gate_proj = nn.Linear(config.hidden_size, intermediate_size, bias=False) # 768 → 2432 (门控)
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self.down_proj = nn.Linear(intermediate_size, config.hidden_size, bias=False) # 2432 → 768 (降维)
|
||
self.up_proj = nn.Linear(config.hidden_size, intermediate_size, bias=False) # 768 → 2432 (上投影)
|
||
self.act_fn = ACT2FN[config.hidden_act] # SiLU = x * sigmoid(x)
|
||
|
||
def forward(self, x):
|
||
# SwiGLU: down(act(gate(x)) ⊙ up(x))
|
||
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
||
# ---- MoE FFN (稀疏版) ----
|
||
# 每个token被Router分配到top-k个Expert(这里k=1)处理
|
||
# 相比Dense: 总参数量大(4个Expert)，但每个token只激活1个Expert
|
||
# 效果: 用更多参数量换更强表达力，而计算量(FLOPs)基本不变
|
||
#
|
||
# 流程: x → Router(gate) → 选出top-1 Expert → Expert(x) * weight → 输出
|
||
class MOEFeedForward(nn.Module):
|
||
def __init__(self, config: MiniMindConfig):
|
||
super().__init__()
|
||
self.config = config
|
||
self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False) # Router: 768 → 4
|
||
self.experts = nn.ModuleList([FeedForward(config, intermediate_size=config.moe_intermediate_size) for _ in range(config.num_experts)]) # 4个独立的FFN
|
||
self.act_fn = ACT2FN[config.hidden_act]
|
||
|
||
def forward(self, x):
|
||
batch_size, seq_len, hidden_dim = x.shape
|
||
x_flat = x.view(-1, hidden_dim) # [B*S, 768] 展平为token序列
|
||
|
||
# Step 1: Router计算每个token对每个Expert的分数
|
||
scores = F.softmax(self.gate(x_flat), dim=-1) # [B*S, 4] 概率分布
|
||
|
||
# Step 2: 选出每个token的top-k Expert (k=1)
|
||
topk_weight, topk_idx = torch.topk(scores, k=self.config.num_experts_per_tok, dim=-1, sorted=False) # [B*S, 1]
|
||
# 归一化权重 (top-1时其实就是1.0，top-k>1时确保权重和为1)
|
||
if self.config.norm_topk_prob: topk_weight = topk_weight / (topk_weight.sum(dim=-1, keepdim=True) + 1e-20)
|
||
|
||
# Step 3: 分发token到各Expert并加权聚合
|
||
y = torch.zeros_like(x_flat)
|
||
for i, expert in enumerate(self.experts):
|
||
mask = (topk_idx == i) # 哪些token被分配到Expert i
|
||
if mask.any():
|
||
token_idx = mask.any(dim=-1).nonzero().flatten() # 被选中的token索引
|
||
weight = topk_weight[mask].view(-1, 1) # 对应的路由权重
|
||
# Expert处理 → 乘权重 → 累加到输出
|
||
y.index_add_(0, token_idx, (expert(x_flat[token_idx]) * weight).to(y.dtype))
|
||
elif self.training:
|
||
# 训练时: 即使Expert未被选中，也让它参与计算图(0*params)
|
||
# 否则DDP会报错"某些参数没有梯度"
|
||
y[0, 0] += 0 * sum(p.sum() for p in expert.parameters())
|
||
|
||
# Step 4: 负载均衡辅助loss (鼓励token均匀分布到各Expert)
|
||
# 如果没有这个loss，Router倾向于把所有token都发给同一个Expert("赢者通吃")
|
||
if self.training and self.config.router_aux_loss_coef > 0:
|
||
# load: 每个Expert实际接收的token比例 [num_experts]
|
||
load = F.one_hot(topk_idx, self.config.num_experts).float().mean(0)
|
||
# aux_loss = (实际负载分布 · 路由概率分布) * num_experts * coef
|
||
# 当所有Expert负载均匀时，此loss最小
|
||
self.aux_loss = (load * scores.mean(0)).sum() * self.config.num_experts * self.config.router_aux_loss_coef
|
||
else:
|
||
self.aux_loss = scores.new_zeros(1).squeeze()
|
||
|
||
return y.view(batch_size, seq_len, hidden_dim)
|
||
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════════
|
||
# Transformer Block (单层)
|
||
# ════════════════════════════════════════════════════════════════════════════════
|
||
# 一个标准的 Pre-Norm Transformer Decoder Block:
|
||
#
|
||
# x ─────────────────────┐
|
||
# │ │ (残差连接1)
|
||
# ├→ RMSNorm → Attention ─┘
|
||
# │ │
|
||
# ├──────────────────────┤ (残差连接2)
|
||
# └→ RMSNorm → FFN/MoE ──┘
|
||
# │
|
||
# output
|
||
class MiniMindBlock(nn.Module):
|
||
def __init__(self, layer_id: int, config: MiniMindConfig):
|
||
super().__init__()
|
||
self.self_attn = Attention(config)
|
||
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) # 注意力前归一化
|
||
self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) # FFN前归一化
|
||
# 根据配置选择Dense FFN或MoE FFN
|
||
self.mlp = FeedForward(config) if not config.use_moe else MOEFeedForward(config)
|
||
|
||
def forward(self, hidden_states, position_embeddings, past_key_value=None, use_cache=False, attention_mask=None):
|
||
# 残差连接1: Attention
|
||
residual = hidden_states
|
||
hidden_states, present_key_value = self.self_attn(
|
||
self.input_layernorm(hidden_states), position_embeddings, # Pre-Norm: 先归一化再进注意力
|
||
past_key_value, use_cache, attention_mask
|
||
)
|
||
hidden_states += residual # x + Attention(Norm(x))
|
||
|
||
# 残差连接2: FFN
|
||
hidden_states = hidden_states + self.mlp(self.post_attention_layernorm(hidden_states)) # x + FFN(Norm(x))
|
||
return hidden_states, present_key_value
|
||
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════════
|
||
# MiniMindModel (Transformer 主干)
|
||
# ════════════════════════════════════════════════════════════════════════════════
|
||
# 职责: token_ids → hidden_states (不含最终的lm_head投影)
|
||
#
|
||
# 流程: Embedding → Dropout → N层Block → 最终RMSNorm → hidden_states
|
||
class MiniMindModel(nn.Module):
|
||
def __init__(self, config: MiniMindConfig):
|
||
super().__init__()
|
||
self.config = config
|
||
self.vocab_size, self.num_hidden_layers = config.vocab_size, config.num_hidden_layers
|
||
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) # 6400 × 768
|
||
self.dropout = nn.Dropout(config.dropout)
|
||
self.layers = nn.ModuleList([MiniMindBlock(l, config) for l in range(self.num_hidden_layers)]) # 8层
|
||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) # 最终归一化
|
||
|
||
# 预计算RoPE频率表(注册为buffer，不参与训练但随模型保存/移动)
|
||
freqs_cos, freqs_sin = precompute_freqs_cis(dim=config.head_dim, end=config.max_position_embeddings, rope_base=config.rope_theta, rope_scaling=config.rope_scaling)
|
||
self.register_buffer("freqs_cos", freqs_cos, persistent=False)
|
||
self.register_buffer("freqs_sin", freqs_sin, persistent=False)
|
||
|
||
def forward(self, input_ids, attention_mask=None, past_key_values=None, use_cache=False, **kwargs):
|
||
batch_size, seq_length = input_ids.shape
|
||
|
||
# 兼容transformers 5.x的cache格式
|
||
if hasattr(past_key_values, 'layers'): past_key_values = None
|
||
past_key_values = past_key_values or [None] * len(self.layers)
|
||
|
||
# 确定当前序列的起始位置 (用于KV Cache场景下取正确的RoPE)
|
||
# 训练时: start_pos=0 (处理完整序列)
|
||
# 推理时: start_pos=已生成token数 (只处理新token)
|
||
start_pos = past_key_values[0][0].shape[1] if past_key_values[0] is not None else 0
|
||
|
||
# Embedding: token_id → 向量
|
||
hidden_states = self.dropout(self.embed_tokens(input_ids)) # [B, S] → [B, S, 768]
|
||
|
||
# 获取当前位置段的RoPE (处理transformers>=5.x meta-device初始化导致buffer丢失)
|
||
if self.freqs_cos[0, 0] == 0:
|
||
freqs_cos, freqs_sin = precompute_freqs_cis(dim=self.config.head_dim, end=self.config.max_position_embeddings, rope_base=self.config.rope_theta, rope_scaling=self.config.rope_scaling)
|
||
self.freqs_cos, self.freqs_sin = freqs_cos.to(hidden_states.device), freqs_sin.to(hidden_states.device)
|
||
position_embeddings = (self.freqs_cos[start_pos:start_pos + seq_length], self.freqs_sin[start_pos:start_pos + seq_length])
|
||
|
||
# 逐层前向传播
|
||
presents = []
|
||
for layer, past_key_value in zip(self.layers, past_key_values):
|
||
hidden_states, present = layer(
|
||
hidden_states,
|
||
position_embeddings,
|
||
past_key_value=past_key_value,
|
||
use_cache=use_cache,
|
||
attention_mask=attention_mask
|
||
)
|
||
presents.append(present)
|
||
|
||
# 最终归一化
|
||
hidden_states = self.norm(hidden_states)
|
||
|
||
# 汇总所有MoE层的辅助loss
|
||
aux_loss = sum([l.mlp.aux_loss for l in self.layers if isinstance(l.mlp, MOEFeedForward)], hidden_states.new_zeros(1).squeeze())
|
||
return hidden_states, presents, aux_loss
|
||
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════════
|
||
# MiniMindForCausalLM (最顶层: 训练+推理入口)
|
||
# ════════════════════════════════════════════════════════════════════════════════
|
||
# 在MiniMindModel基础上加了lm_head(hidden → vocab logits)和loss计算
|
||
# 继承GenerationMixin以兼容HuggingFace的generate接口
|
||
class MiniMindForCausalLM(PreTrainedModel, GenerationMixin):
|
||
config_class = MiniMindConfig
|
||
_tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} # 声明权重绑定关系
|
||
|
||
def __init__(self, config: MiniMindConfig = None):
|
||
self.config = config or MiniMindConfig()
|
||
super().__init__(self.config)
|
||
self.model = MiniMindModel(self.config) # Transformer主干
|
||
self.lm_head = nn.Linear(self.config.hidden_size, self.config.vocab_size, bias=False) # 768 → 6400
|
||
# 权重绑定: lm_head和embedding共享同一份权重矩阵，减少参数量
|
||
if self.config.tie_word_embeddings: self.model.embed_tokens.weight = self.lm_head.weight
|
||
self.post_init() # HuggingFace的初始化后处理
|
||
|
||
def forward(self, input_ids, attention_mask=None, past_key_values=None, use_cache=False, logits_to_keep=0, labels=None, **kwargs):
|
||
# Step 1: 通过Transformer主干得到hidden_states
|
||
hidden_states, past_key_values, aux_loss = self.model(input_ids, attention_mask, past_key_values, use_cache, **kwargs)
|
||
|
||
# Step 2: lm_head投影到词表空间
|
||
# logits_to_keep: 优化——只计算最后N个位置的logits(节省显存)
|
||
# 推理时只需最后1个位置，RL训练时只需completion部分
|
||
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
||
logits = self.lm_head(hidden_states[:, slice_indices, :]) # [B, S, 6400]
|
||
|
||
# Step 3: 计算loss (仅训练时有labels)
|
||
loss = None
|
||
if labels is not None:
|
||
# 自回归: 用位置t的logits预测位置t+1的token
|
||
# logits[:-1] 预测 labels[1:]
|
||
x, y = logits[..., :-1, :].contiguous(), labels[..., 1:].contiguous()
|
||
loss = F.cross_entropy(x.view(-1, x.size(-1)), y.view(-1), ignore_index=-100) # -100位置不算loss
|
||
|
||
return MoeCausalLMOutputWithPast(loss=loss, aux_loss=aux_loss, logits=logits, past_key_values=past_key_values, hidden_states=hidden_states)
|
||
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# 自回归生成 (推理)
|
||
# ════════════════════════════════════════════════════════════════════════════
|
||
# 自定义generate，不用HuggingFace的默认实现
|
||
# 支持: KV Cache / temperature / top_k / top_p / repetition_penalty / streaming
|
||
#
|
||
# 每步循环:
|
||
# 1. 只输入新token(利用KV Cache)
|
||
# 2. 取最后位置logits
|
||
# 3. temperature缩放 → repetition penalty → top_k截断 → top_p截断
|
||
# 4. 采样得到next_token
|
||
# 5. 拼接到input_ids，重复直到EOS或达到max长度
|
||
@torch.inference_mode()
|
||
def generate(self, inputs=None, attention_mask=None, max_new_tokens=8192, temperature=0.85, top_p=0.85, top_k=50, eos_token_id=2, streamer=None, use_cache=True, num_return_sequences=1, do_sample=True, repetition_penalty=1.0, **kwargs):
|
||
# 支持多条序列并行生成
|
||
input_ids = kwargs.pop("input_ids", inputs).repeat(num_return_sequences, 1)
|
||
attention_mask = attention_mask.repeat(num_return_sequences, 1) if attention_mask is not None else None
|
||
past_key_values = kwargs.pop("past_key_values", None)
|
||
finished = torch.zeros(input_ids.shape[0], dtype=torch.bool, device=input_ids.device) # 跟踪每条序列是否已结束
|
||
if streamer: streamer.put(input_ids.cpu())
|
||
|
||
for _ in range(max_new_tokens):
|
||
# 利用KV Cache: 只输入past_len之后的新token
|
||
past_len = past_key_values[0][0].shape[1] if past_key_values else 0
|
||
outputs = self.forward(input_ids[:, past_len:], attention_mask, past_key_values, use_cache=use_cache, **kwargs)
|
||
# 扩展attention_mask以包含新生成的位置
|
||
attention_mask = torch.cat([attention_mask, attention_mask.new_ones(attention_mask.shape[0], 1)], -1) if attention_mask is not None else None
|
||
|
||
# 取最后一个位置的logits，除以temperature
|
||
# temperature < 1 → 更确定(偏向高概率token)
|
||
# temperature > 1 → 更随机(概率分布更平坦)
|
||
logits = outputs.logits[:, -1, :] / temperature
|
||
|
||
# Repetition Penalty: 降低已出现token的概率
|
||
# 正分数的token: score / penalty (概率降低)
|
||
# 负分数的token: score * penalty (概率更低)
|
||
if repetition_penalty != 1.0:
|
||
for i in range(input_ids.shape[0]):
|
||
seen = torch.unique(input_ids[i]); score = logits[i, seen]; logits[i, seen] = torch.where(score > 0, score / repetition_penalty, score * repetition_penalty)
|
||
|
||
# Top-K 截断: 只保留概率最高的K个token
|
||
if top_k > 0:
|
||
logits[logits < torch.topk(logits, top_k)[0][..., -1, None]] = -float('inf')
|
||
|
||
# Top-P (Nucleus) 截断: 保留累积概率刚好超过P的最小token集合
|
||
# 动态调整候选数量——高置信时候选少，低置信时候选多
|
||
if top_p < 1.0:
|
||
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
|
||
mask = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1) > top_p
|
||
mask[..., 1:], mask[..., 0] = mask[..., :-1].clone(), 0 # 保证至少保留1个token
|
||
logits[mask.scatter(1, sorted_indices, mask)] = -float('inf')
|
||
|
||
# 采样或贪心选择
|
||
next_token = torch.multinomial(torch.softmax(logits, dim=-1), num_samples=1) if do_sample else torch.argmax(logits, dim=-1, keepdim=True)
|
||
|
||
# 已结束的序列强制输出EOS (防止padding位置产生垃圾token)
|
||
if eos_token_id is not None: next_token = torch.where(finished.unsqueeze(-1), next_token.new_full((next_token.shape[0], 1), eos_token_id), next_token)
|
||
|
||
# 拼接新token
|
||
input_ids = torch.cat([input_ids, next_token], dim=-1)
|
||
past_key_values = outputs.past_key_values if use_cache else None
|
||
if streamer: streamer.put(next_token.cpu())
|
||
|
||
# 检查是否所有序列都已生成EOS
|
||
if eos_token_id is not None:
|
||
finished |= next_token.squeeze(-1).eq(eos_token_id)
|
||
if finished.all(): break
|
||
|
||
if streamer: streamer.end()
|
||
if kwargs.get("return_kv"): return {'generated_ids': input_ids, 'past_kv': past_key_values}
|
||
return input_ids
|