SelfAttention
代码语言：javascript
复制class SelfAttention(torch.nn.Module):
"""
自注意力的逻辑，包含四部分：
从输入计算 QKV，
对 QKV 分头，
从 QKV 计算 O（在`CoreAttention`里面），
从 O 计算输出
"""
def __init__(self, config: ChatGLMConfig, layer_number, device=None):
super(SelfAttention, self).__init__()
# 层的序号
self.layer_number = max(1, layer_number)
# ProjSize：就是没有开启 MQA 情况下的 QKV 的尺寸
# 等于 NHead * HeadSize，和原始的 HidSize 可能有不同·
self.projection_size = config.kv_channels * config.num_attention_heads
# HeadSize = ProjSize // NHead
self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads
# NHead
self.num_attention_heads_per_partition = config.num_attention_heads
# 控制是否启用MQA
self.multi_query_attention = config.multi_query_attention
# 如果不启用 MQA，QKVSize 就是三倍的 ProjSize
self.qkv_hidden_size = 3 * self.projection_size
if self.multi_query_attention:
# 如果启用了 MQA
# NGroup
self.num_multi_query_groups_per_partition = config.multi_query_group_num
# QKVSize 等于 ProjSize(Q) 2 * HeadSize * NGroup (KV)
self.qkv_hidden_size = (
self.projection_size 2 * self.hidden_size_per_attention_head * config.multi_query_group_num
)
# 将输入映射成 QKV 的线性层
self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size,
bias=config.add_bias_linear or config.add_qkv_bias,
device=device, **_config_to_kwargs(config)
)
# 用于从 QKV 计算 O 的核心模块
self.core_attention = CoreAttention(config, self.layer_number)
# 用于从 O 计算输出的线性层
self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear,
device=device, **_config_to_kwargs(config)
)
def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None):
if self.multi_query_attention:
num_attention_heads = self.num_multi_query_groups_per_partition
else:
num_attention_heads = self.num_attention_heads_per_partition
return torch.empty(
inference_max_sequence_len,
batch_size,
num_attention_heads,
self.hidden_size_per_attention_head,
dtype=dtype,
device=device,
)
def forward(
self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
):
# 输入隐藏状态尺寸为 [SeqLen, BatchSize, HidSize]
# 使用输入计算 QKV
mixed_x_layer = self.query_key_value(hidden_states)
if self.multi_query_attention:
# 如果开启了 MQA，将 QKV 按照最后一维分割
# 得到 Q [SeqLen, BatchSize, ProjSize]
# 和 K/V [SeqLen, BatchSize, NGroup * HeadSize]
(query_layer, key_layer, value_layer) = mixed_x_layer.split(
[
self.num_attention_heads_per_partition * self.hidden_size_per_attention_head,
self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
],
dim=-1,
)
# 对 Q 分头，变形为 [SeqLen, BatchSize, NHead, HeadSize]
query_layer = query_layer.view(
query_layer.size()[:-1] (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
)
# 对 K 分头，变形为 [SeqLen, BatchSize, NGroup, HeadSize]
key_layer = key_layer.view(
key_layer.size()[:-1] (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
)
# 对 V 分头，变形为 [SeqLen, BatchSize, NGroup, HeadSize]
value_layer = value_layer.view(
value_layer.size()[:-1]
(self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
)
else:
# 变形为 [SeqLen, BatchSize, NHead, 3 * HeadSize]
new_tensor_shape = mixed_x_layer.size()[:-1]
(self.num_attention_heads_per_partition,
3 * self.hidden_size_per_attention_head)
mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
# 将 QKV 最后一维平分三份，得到 Q/K/V
# 尺寸为 [SeqLen, BatchSize, NHead, HeadSize]
(query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
# 应用 ROPE
if rotary_pos_emb is not None:
query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb)
key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb)
# 如果传入了 KVCache
# 拆分为 KCache 和 VCache
# 每个形状为 [CacheLen, BatchSize, NGroup, HeadSize]
# 分别添加到 K 和 V 前面
if kv_cache is not None:
cache_k, cache_v = kv_cache
key_layer = torch.cat((cache_k, key_layer), dim=0)
value_layer = torch.cat((cache_v, value_layer), dim=0)
# 如果设置了 UseCache，则返回 KV
if use_cache:
kv_cache = (key_layer, value_layer)
else:
kv_cache = None
# MQA 模式下，给 K 和 V 广播到 Q 的形状
# [..., NGroup, ...] => [..., NGroup, 1, ...] =>
# [..., NGroup, NHead // NGroup, ...] =>
# [..., NHead, ...]
if self.multi_query_attention:
# K 变形为 [CacheSeqLen, BatchSize, NGroup, 1, HeadSize]
key_layer = key_layer.unsqueeze(-2)
# K 广播为 [CacheSeqLen, BatchSize, NGroup, NHead // NGroup, HeadSize]
# NHead // NGroup 是每一组的头部数量
# 相当于把最后一维复制了 NHead // NGroup 等份
key_layer = key_layer.expand(
-1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1
)
# K 变形为 [CacheSeqLen, BatchSize, NHead, HeadSize]
key_layer = key_layer.contiguous().view(
key_layer.size()[:2] (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
)
# V 变形为 [CacheSeqLen, BatchSize, NGroup, 1, HeadSize]
value_layer = value_layer.unsqueeze(-2)
# V 广播为 [CacheSeqLen, BatchSize, NGroup, NHead // NGroup, HeadSize]
value_layer = value_layer.expand(
-1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1
)
# V 变形为 [CacheSeqLen, BatchSize, NHead, HeadSize]
value_layer = value_layer.contiguous().view(
value_layer.size()[:2] (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
)
# 将 Q K V 和掩码数组传入核心模块，得到 O
# 尺寸为 [SeqLen, BatchSize, ProjSize]
context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask)
# 使用 O 计算输出，尺寸为 [SeqLen, BatchSize, HidSize]
output = self.dense(context_layer)
return output, kv_cache
CoreAttention
代码语言：javascript
复制class CoreAttention(torch.nn.Module):
'''
包含了从分头的 QKV 计算 O 的逻辑
'''
def __init__(self, config: ChatGLMConfig, layer_number):
super(CoreAttention, self).__init__()
# 控制 QK 是否缩放
self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling
# 控制注意力矩阵是否转为 FP32
self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
# 缩放模式下必须为 FP32
if self.apply_query_key_layer_scaling:
self.attention_softmax_in_fp32 = True
# 确保层序号大于等于 1
self.layer_number = max(1, layer_number)
# ProjSize = HeadSize * NHead
projection_size = config.kv_channels * config.num_attention_heads
# ProjSize
self.hidden_size_per_partition = projection_size
# HeadSize = HeadSize // NHead
self.hidden_size_per_attention_head = projection_size // config.num_attention_heads
# NHead
self.num_attention_heads_per_partition = config.num_attention_heads
# 如果定义了 QK 缩放
# 系数就是层序号
# d = 系数 * HeadSize
# 否则 d = HeadSize
coeff = None
self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
if self.apply_query_key_layer_scaling:
coeff = self.layer_number
self.norm_factor *= coeff
self.coeff = coeff
# 用于注意力矩阵的 Dropout
self.attention_dropout = torch.nn.Dropout(config.attention_dropout)
def forward(self, query_layer, key_layer, value_layer, attention_mask):
# Q：[SeqLen, BatchSize, NHead, HeadSize]
# K：[CacheSeqLen, BatchSize, NHead, HeadSize]
# V：[CacheSeqLen, BatchSize, NHead, HeadSize]
# 如果 PyTorch 版本大于 2，直接调用内置函数
pytorch_major_version = int(torch.__version__.split('.')[0])
if pytorch_major_version >= 2:
query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]]
if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
is_causal=True)
else:
if attention_mask is not None:
attention_mask = ~attention_mask
context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
attention_mask)
context_layer = context_layer.permute(2, 0, 1, 3)
new_context_layer_shape = context_layer.size()[:-2] (self.hidden_size_per_partition,)
context_layer = context_layer.reshape(*new_context_layer_shape)
else:
# 否则自己实现计算逻辑
# 定义注意力矩阵的尺寸
# [BatchSize, NHead, Seqlen, CacheSeqLen]
output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0))
# 合并 Q 中间两维，[Seqlen, BatchSize * NHead, HeadSize]
query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1)
# 合并 K 中间两维，[CacheSeqlen, BatchSize * NHead, HeadSize]
key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1)
# 定义缓冲张量，形状和注意力矩阵相同
# [BatchSize * NHead, SeqLen, CacheSeqLen]
matmul_input_buffer = torch.empty(
output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype,
device=query_layer.device
)
# 交换 Q 前两维，[BatchSize * NHead, SeqLen, HeadSize]
# 交换 K 前两维和后两维，[BatchSize * NHead, HeadSize, CacheSeqLen]
# 计算原始注意力矩阵 A = Q @ K / d
# beta=0 所以不受缓冲张量的影响
matmul_result = torch.baddbmm(
matmul_input_buffer,
query_layer.transpose(0, 1), # [b * np, sq, hn]
key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk]
beta=0.0,
alpha=(1.0 / self.norm_factor),
)
# 拆分 A 第一维，[BatchSize, NHead, Seqlen, CacheSeqLen]
attention_scores = matmul_result.view(*output_size)
# 如果定义了...，将其转为 FP32
if self.attention_softmax_in_fp32:
attention_scores = attention_scores.float()
# 如果定义了系数，将其相乘
if self.coeff is not None:
attention_scores = attention_scores * self.coeff
# 如果传入了掩码矩阵，并且注意力矩阵后两维相等（也就是没有KVCache）
if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]:
# 将掩码矩阵初始化为全1矩阵
# 形状为 [BatchSize, 1, Seqlen, CacheSeqLen]
attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3],
device=attention_scores.device, dtype=torch.bool)
# 只保留下三角元素，上三角置 0
attention_mask.tril_()
# 翻转矩阵，使上三角为 True，下三角为 False
attention_mask = ~attention_mask
# 如果传入了掩码矩阵，将其非零位置的元素设为 -inf
if attention_mask is not None:
attention_scores = attention_scores.masked_fill(attention_mask, float("-inf"))
# 注意力矩阵应用 SoftMax
attention_probs = F.softmax(attention_scores, dim=-1)
# 转回输入的数据类型
attention_probs = attention_probs.type_as(value_layer)
# 对注意力矩阵应用 Dropout
attention_probs = self.attention_dropout(attention_probs)
# 定义 O 的尺寸 [BatchSize, NHead, SeqLen, HeadSize]
output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3))
# 合并 V 中间两维，[CacheSeqLen, BatchSize * NHead, HeadSize]
value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1)
# 合并 A 前两维，[BatchSize * NHead, SeqLen, CacheSeqLen]
attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
# 交换 V 前两维，[BatchSize * NHead, CacheSeqLen, HeadSize]
# 计算 O = A @ V
context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))
# 拆分 O 前两维， [BatchSize, NHead, SeqLen, HeadSize]
context_layer = context_layer.view(*output_size)
# 将 O 转置为 [SeqLen, BatchSize, NHead, HeadSize]
context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
# 合并 O 后两维，[SeqLen, BatchSize, ProjSize]
new_context_layer_shape = context_layer.size()[:-2] (self.hidden_size_per_partition,)
context_layer = context_layer.view(*new_context_layer_shape)
# 返回 O
return context_layer
