attn.py

import torch
import torch.distributed as dist
from lightx2v.attentions import attention
from lightx2v.attentions.distributed.comm.all2all import all2all_seq2head, all2all_head2seq


def ulysses_attn(q, k, v, img_qkv_len, cu_seqlens_qkv, attention_type="flash_attn2"):
    '''
    执行 Ulysses 注意力机制，结合图像和文本的查询、键和值。

    参数:
        q (torch.Tensor): 查询张量，形状为 [shard_seqlen, heads, hidden_dims]
        k (torch.Tensor): 键张量，形状为 [shard_seqlen, heads, hidden_dims]
        v (torch.Tensor): 值张量，形状为 [shard_seqlen, heads, hidden_dims]
        img_qkv_len (int): 图像查询、键和值的长度
        cu_seqlens_qkv (torch.Tensor): 累积序列长度，包含文本和图像的长度信息
        attention_type (str): 注意力类型，默认为 "flash_attn2"

    返回:
        torch.Tensor: 计算得到的注意力结果
    '''
    # 获取当前进程的排名和全局进程数
    cur_rank = dist.get_rank()
    world_size = dist.get_world_size()
    
    # 获取序列长度和文本相关的长度
    seq_len = q.shape[0]
    if len(cu_seqlens_qkv) == 3:
        txt_qkv_len = cu_seqlens_qkv[1] - img_qkv_len  # 文本查询、键和值的长度
        txt_mask_len = cu_seqlens_qkv[2] - img_qkv_len  # 文本掩码长度
    elif len(cu_seqlens_qkv) == 2:
        txt_qkv_len = cu_seqlens_qkv[1] - img_qkv_len  # 文本查询、键和值的长度
        txt_mask_len = None
    
    # 获取查询张量的头数和隐藏维度
    _, heads, hidden_dims = q.shape
    shard_heads = heads // world_size  # 每个进程处理的头数
    shard_seqlen = img_qkv_len  # 每个进程处理的序列长度

    # 分割图像和文本的查询、键和值
    img_q, img_k, img_v = q[:img_qkv_len,:,:].contiguous(), k[:img_qkv_len,:,:].contiguous(), v[:img_qkv_len,:,:].contiguous()
    txt_q, txt_k, txt_v = q[img_qkv_len:,:,:].contiguous(), k[img_qkv_len:,:,:].contiguous(), v[img_qkv_len:,:,:].contiguous()

    # 将图像的查询、键和值转换为头的格式
    img_q = all2all_seq2head(img_q)
    img_k = all2all_seq2head(img_k)
    img_v = all2all_seq2head(img_v)
    torch.cuda.synchronize()  # 确保CUDA操作完成

    # 处理文本的查询、键和值，选择当前进程的头
    txt_q = txt_q[:,cur_rank*shard_heads:(cur_rank+1)*shard_heads,:]
    txt_k = txt_k[:,cur_rank*shard_heads:(cur_rank+1)*shard_heads,:]
    txt_v = txt_v[:,cur_rank*shard_heads:(cur_rank+1)*shard_heads,:]

    # 合并图像和文本的查询、键和值
    q = torch.cat((img_q, txt_q), dim=0)
    k = torch.cat((img_k, txt_k), dim=0)
    v = torch.cat((img_v, txt_v), dim=0)

    # 初始化累积序列长度张量
    cu_seqlens_qkv = torch.zeros([3], dtype=torch.int32, device="cuda")
    s = txt_qkv_len + img_q.shape[0]  # 计算文本和图像的总长度
    s1 = s  # 当前样本的结束位置
    cu_seqlens_qkv[1] = s1  # 设置累积序列长度
    if txt_mask_len:
        s2 = txt_mask_len + img_q.shape[0]  # 文本掩码的结束位置
        cu_seqlens_qkv[2] = s2  # 设置累积序列长度
    max_seqlen_qkv = img_q.shape[0] + txt_q.shape[0]  # 最大序列长度

    # 调用注意力函数计算注意力结果
    attn = attention(
        attention_type=attention_type,
        q=q,
        k=k,
        v=v,
        cu_seqlens_q=cu_seqlens_qkv,
        cu_seqlens_kv=cu_seqlens_qkv,
        max_seqlen_q=max_seqlen_qkv,
        max_seqlen_kv=max_seqlen_qkv
    )

    # 分割图像和文本的注意力结果
    img_attn, txt_attn = attn[:img_q.shape[0],:], attn[img_q.shape[0]:,]

    # 收集所有进程的文本注意力结果
    gathered_txt_attn = [torch.empty_like(txt_attn) for _ in range(world_size)]
    dist.all_gather(gathered_txt_attn, txt_attn)

    # 处理图像注意力结果
    img_attn = img_attn.reshape(world_size*shard_seqlen, shard_heads, hidden_dims)  # 重塑图像注意力结果
    img_attn = all2all_head2seq(img_attn)  # 将头的格式转换回序列格式
    img_attn = img_attn.reshape(shard_seqlen, -1)  # 重塑为 [shard_seqlen, -1] 形状

    torch.cuda.synchronize()  # 确保CUDA操作完成
    txt_attn = torch.cat(gathered_txt_attn, dim=1)  # 合并所有进程的文本注意力结果

    # 合并图像和文本的注意力结果
    attn = torch.cat([img_attn, txt_attn], dim=0)

    return attn  # 返回最终的注意力结果