### RWKV v1 源码详细解读 由于时间久远，没有找到对应的预训练脚本，对于v1主要做源码分析，v2-v6均可以找到载入模型实现的脚本。本文档详细解析 RWKV v1 版本的核心代码，具体包括初始化、时间混合模块、通道混合模块和多头注意力机制。代码使用 PyTorch 实现，具有良好的可读性和扩展性。 --- #### 模型初始化 首先，我们定义了一个用于初始化线性层和嵌入层的函数 `RWKV_Init`。 ```python def RWKV_Init(module, config): for m in module.modules(): if not isinstance(m, (nn.Linear, nn.Embedding)): continue with torch.no_grad(): name = '[unknown weight]' for name, parameter in module.named_parameters(): if id(m.weight) == id(parameter): break shape = m.weight.data.shape gain = 1.0 scale = 1.0 if isinstance(m, nn.Linear): if m.bias is not None: m.bias.data.zero_() if shape[0] > shape[1]: gain = math.sqrt(shape[0] / shape[1]) if shape[0] == config.vocab_size and shape[1] == config.n_embd: scale = config.rwkv_emb_scale if isinstance(m, nn.Embedding): gain = math.sqrt(max(shape[0], shape[1])) if shape[0] == config.vocab_size and shape[1] == config.n_embd: scale = config.rwkv_emb_scale if hasattr(m, 'scale_init'): scale = m.scale_init print(str(shape[0]).ljust(5), str(shape[1]).ljust(5), f'{round(scale,2):g}'.ljust(4), name) gain *= scale if gain == 0: nn.init.zeros_(m.weight) elif gain > 0: nn.init.orthogonal_(m.weight, gain=gain) else: nn.init.normal_(m.weight, mean=0, std=-gain) ``` 该函数遍历模块中的所有线性层和嵌入层，并根据特定条件初始化其权重。对于线性层，若偏置存在，则将其初始化为零；对于嵌入层，计算权重的增益和缩放因子。根据不同的条件使用不同的初始化方法，例如正交初始化或正态初始化。 --- #### 时间混合模块 `RWKV_TimeMix` 类实现了时间混合机制。 ```python class RWKV_TimeMix(nn.Module): def __init__(self, config, layer_id): super().__init__() assert config.n_attn % config.n_head == 0 self.layer_id = layer_id self.ctx_len = config.ctx_len self.n_head = config.n_head self.head_size = config.n_attn // config.n_head with torch.no_grad(): ww = torch.ones(config.n_head, config.ctx_len) curve = torch.tensor([-(config.ctx_len - 1 - i) for i in range(config.ctx_len)]) for h in range(config.n_head): if h < config.n_head - 1: decay_speed = math.pow(config.ctx_len, -(h+1)/(config.n_head-1)) else: decay_speed = 0.0 ww[h] = torch.exp(curve * decay_speed) self.time_w = nn.Parameter(ww) self.time_alpha = nn.Parameter(torch.ones(self.n_head, 1, config.ctx_len)) self.time_beta = nn.Parameter(torch.ones(self.n_head, config.ctx_len, 1)) self.time_gamma = nn.Parameter(torch.ones(config.ctx_len, 1)) self.time_shift = nn.ZeroPad2d((0,0,1,-1)) self.key = nn.Linear(config.n_embd, config.n_attn) self.value = nn.Linear(config.n_embd, config.n_attn) self.receptance = nn.Linear(config.n_embd, config.n_attn) self.output = nn.Linear(config.n_attn, config.n_embd) self.key.scale_init = 0 self.receptance.scale_init = 0 self.output.scale_init = 0 def forward(self, x): B, T, C = x.size() TT = self.ctx_len w = F.pad(self.time_w, (0, TT)) w = torch.tile(w, [TT]) w = w[:, :-TT].reshape(-1, TT, 2 * TT - 1) w = w[:, :, TT-1:] w = w[:, :T, :T] * self.time_alpha[:, :, :T] * self.time_beta[:, :T, :] x = torch.cat([self.time_shift(x[:, :, :C//2]), x[:, :, C//2:]], dim = -1) k = self.key(x) v = self.value(x) r = self.receptance(x) k = torch.clamp(k, max=30, min=-60) k = torch.exp(k) sum_k = torch.cumsum(k, dim=1) kv = (k * v).view(B, T, self.n_head, self.head_size) wkv = (torch.einsum('htu,buhc->bthc', w, kv)).contiguous().view(B, T, -1) rwkv = torch.sigmoid(r) * wkv / sum_k rwkv = self.output(rwkv) return rwkv * self.time_gamma[:T, :] ``` 该类实现了时间混合机制，通过时间权重矩阵 `time_w` 对输入进行变换。`time_w` 矩阵根据头数和上下文长度初始化，随后对输入进行时间维度上的变换和混合。`key`、`value` 和 `receptance` 三个线性层分别生成键、值和接收信号，并通过 sigmoid 函数计算输出。 --- #### 通道混合模块 `RWKV_ChannelMix` 类实现了通道混合机制。 ```python class RWKV_ChannelMix(nn.Module): def __init__(self, config, layer_id): super().__init__() self.layer_id = layer_id self.time_shift = nn.ZeroPad2d((0,0,1,-1)) hidden_sz = 5 * config.n_ffn // 2 self.key = nn.Linear(config.n_embd, hidden_sz) self.value = nn.Linear(config.n_embd, hidden_sz) self.weight = nn.Linear(hidden_sz, config.n_embd) self.receptance = nn.Linear(config.n_embd, config.n_embd) self.receptance.scale_init = 0 self.weight.scale_init = 0 def forward(self, x): B, T, C = x.size() x = torch.cat([self.time_shift(x[:, :, :C//2]), x[:, :, C//2:]], dim = -1) k = self.key(x) v = self.value(x) r = self.receptance(x) wkv = self.weight(F.mish(k) * v) rwkv = torch.sigmoid(r) * wkv return rwkv ``` 该类实现了通道混合机制，通过 `key`、`value` 和 `receptance` 三个线性层对输入进行变换。`key` 和 `value` 层生成的张量经过 `mish` 激活函数变换后再通过 `weight` 层进行加权变换，最后与 `receptance` 层生成的接收信号相乘得到最终输出。 --- #### 多头注意力机制 `MHA_rotary` 类实现了多头注意力机制，并引入了旋转位置编码。 ```python class MHA_rotary(nn.Module): def __init__(self, config, layer_id, time_shift = False): super().__init__() self.layer_id = layer_id assert config.n_attn % config.n_head == 0 self.n_head = config.n_head self.ctx_len = config.ctx_len self.head_size = config.n_attn // config.n_head if time_shift: self.time_shift = nn.ZeroPad2d((0,0,1,-1)) self.query = nn.Linear(config.n_embd, config.n_attn) self.key = nn.Linear(config.n_embd, config.n_attn) self.value = nn.Linear(config.n_embd, config.n_attn) self.register_buffer("mask", torch.tril(torch.ones(config.ctx_len, config.ctx_len))) self.rotary_ndims = int(self.head_size * 0.5) self.rotary_emb = RotaryEmbedding(self.rotary_ndims) self.output = nn.Linear(config.n_attn, config.n_embd) def forward(self, x): B, T, C = x.size() if hasattr(self, 'time_shift'): x = torch.cat([self.time_shift(x[:, :, :C//2]), x[:, :, C//2:]], dim = -1) q = self.query(x).view(B, T, self.n_head, self.head_size).transpose(1, 2) k = self.key(x).view(B, T, self.n_head, self.head_size).transpose(1, 2 ) v = self.value(x).view(B, T, self.n_head, self.head_size).transpose(1, 2) q, query_pass = q[..., :self.rotary_ndims], q[..., self.rotary_ndims:] k, key_pass = k[..., :self.rotary_ndims], k[..., self.rotary_ndims:] cos, sin = self.rotary_emb(q, seq_len=T) q, k = apply_rotary_pos_emb(q, k, cos, sin) q = torch.cat((q, query_pass), dim=-1) k = torch.cat((k, key_pass), dim=-1) att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) att = att.masked_fill(self.mask[:T,:T] == 0, float('-inf')) att = F.softmax(att, dim = -1) x = att @ v x = x.transpose(1, 2).contiguous().view(B, T, -1) x = self.output(x) return x ``` 该类实现了多头注意力机制，并引入了旋转位置编码以增强模型的位置信息表示。通过 `query`、`key` 和 `value` 三个线性层生成查询、键和值，再通过旋转编码和自注意力机制计算最终输出。 --- #### 总结 RWKV v1 模型实现了基础的时间混合、通道混合和多头注意力机制。通过对输入进行多维度的变换和混合，实现了对复杂特征的提取和表示。上述代码段展示了模型的核心组件和主要计算过程，为后续版本的优化和改进提供了坚实的基础。