spassage_attn.py

import os

import torch

try:
    import spas_sage_attn
except ImportError:
    spas_sage_attn = None

from lightx2v.utils.registry_factory import ATTN_WEIGHT_REGISTER

from .template import AttnWeightTemplate


@ATTN_WEIGHT_REGISTER("spas_sage_attn")
class SageAttnWeight(AttnWeightTemplate):
    def __init__(self):
        self.config = {}

    @classmethod
    def apply(self, q, k, v, cu_seqlens_q=None, cu_seqlens_kv=None, max_seqlen_q=None, max_seqlen_kv=None, model_cls=None, tensor_layout="HND"):
        q = q.unsqueeze(0)
        k = k.unsqueeze(0)
        v = v.unsqueeze(0)
        q = q.transpose(1, 2)
        k = k.transpose(1, 2)
        v = v.transpose(1, 2)
        attn_out = spas_sage_attn.core.spas_sage2_attn_meansim_cuda(q, k, v, tensor_layout)
        _, H, N, D = attn_out.shape
        attn_out = attn_out.permute(2, 1, 3, 0).contiguous().view(N, H * D)
        return attn_out


if __name__ == "__main__":
    import matplotlib.pyplot as plt

    # 1. 构造输入
    q = torch.randn(32760, 12, 128, dtype=torch.bfloat16).cuda()
    k = torch.randn(32760, 12, 128, dtype=torch.bfloat16).cuda()
    v = torch.randn(32760, 12, 128, dtype=torch.bfloat16).cuda()

    # 2. 直接用PyTorch计算注意力
    q_ = q.float()
    k_ = k.float()
    v_ = v.float()
    attn_weights = torch.matmul(q_, k_.transpose(-2, -1)) / (128**0.5)
    attn_weights = torch.softmax(attn_weights, dim=-1)
    output_pt = torch.matmul(attn_weights, v_)

    # 3. 用spas_sage2_attn_meansim_cuda计算注意力
    q = q.unsqueeze(0)  # shape: (1, 32760, 12, 128)
    k = k.unsqueeze(0)
    v = v.unsqueeze(0)
    q = q.transpose(1, 2)  # shape: (1, 12, 32760, 128)
    k = k.transpose(1, 2)
    v = v.transpose(1, 2)
    output_cuda = spas_sage_attn.core.spas_sage2_attn_meansim_cuda(q, k, v, tensor_layout="HND")
    output_cuda = output_cuda.float()

    # 4. 取左上角[3000, 3000]，只取第一个head
    output_pt_crop = output_pt[0, :3000, :3000].cpu().detach().numpy()
    output_cuda_crop = output_cuda[0, 0, :3000, :3000].cpu().detach().numpy()

    # 5. 保存图片
    save_dir = os.path.expanduser("~/Log/10-22/")
    os.makedirs(save_dir, exist_ok=True)

    plt.imshow(output_pt_crop, aspect="auto")
    plt.title("PyTorch Attention (left-top 3000x3000)")
    plt.savefig(os.path.join(save_dir, "attn.png"))
    plt.close()

    plt.imshow(output_cuda_crop, aspect="auto")
    plt.title("spas_sage2_attn_meansim_cuda (left-top 3000x3000)")
    plt.savefig(os.path.join(save_dir, "spas_attn.png"))
    plt.close()