COD的做法主要是把Cell Index(跟Tile Index差不多一个意思)标量化,循环之中只处理与标量化的Cell Index相等的Lane //获取当前片元的Tileuintv_cellIdx=GetCellIdx();//获取当前Lane的在Warp中的Indexuintv_laneID=WaveGetLaneIndex();//先标记所有Lane为aliveulong execMask=0xffffffff;//例如:v_laneID=3 ...
(1.0 / 255.0))).cuda() tenFlow = torch.FloatTensor(numpy.ascontiguousarray(run.read_flo('./images/flow.flo').transpose(2, 0, 1)[None, :, :, :])).cuda() tenMetric = torch.nn.functional.l1_loss(input=tenOne, target=run.backwarp(tenIn=tenTwo, tenFlow=tenFlow), reduction='none...
ggml-cuda.cu Comment on lines +6443 to +6449 const int warp_id = threadIdx.y; const int lane_id = threadIdx.x; const int num_warps = blockDim.y; // number of warps const int iq3 = blockIdx.z; const int iq2 = blockIdx.y; const int iq1 = blockIdx.x * Q; ...
首先是利用torch.autograd.Function实现 Flash Attention 的自定义算子 class_attention(torch.autograd.Function):@staticmethoddefforward(ctx,q,k,v,causal,sm_scale):# 这里 q k v 的 shape 是 [B, H, S, D],因此 Lq 为 head_dimLq,Lk,Lv=q.shapep[-1],k.shape[-1],v.shape[-1]assertLq==Lk...
, 1]], device='cuda:0') ipdb> token_type_ids # None ipdb> 核心也就一个地方,就是基于attention mask来构造position_ids,位置信息 MossForCausalLM forward (inference only) 目前所在的类:<class 'transformers_modules.fnlp.moss-moon-003-sft.7119d446173035561f40977fb9cb999995bb7517.modeling_moss....
对于深度和位姿网络的warp操作,我们使用spatial transformer网络从源图像中采样合成图像。为了在3D空间中获得高斯基元后渲染新颖的视图,基于splat的光栅化渲染也是完全可微的。这两个操作以及其他可微分部分使端到端的联合训练成为可能。我们将来自深度网络的图像特征融合到高斯网络中。此共享特征将尺度感知位置与其他高斯...
float maxval = -FLT_MAX; float sumval = 0.0f; const float4* x_vec = reinterpret_cast<const float4*>(x); for (int i = warp.thread_rank(); i < pos_by_4; i += warp.size()) { float4 v = x_vec[i]; float old_maxval = maxval; for(int k = 0; k < 4; ++k) { ...
[None, :, :, :])).cuda() tenMetric = torch.nn.functional.l1_loss(input=tenOne, target=run.backwarp(tenIn=tenTwo, tenFlow=tenFlow), reduction='none').mean([1], True) for intTime, fltTime in enumerate(numpy.linspace(0.0, 1.0, 11).tolist()): tenSummation = softsplat.softsplat...
So, theoretical memory write request per warp is 65536*1024/32/4 =524,288 32 threads per warp, 4 FP32 per STG.E.128 It matches the memory write request in ncu reports. However, for memory read, the indices load is compiled to the folllowing triton line. x1 = (xindex // 4096) tm...
对于深度和位姿网络的warp操作,我们使用spatial transformer网络从源图像中采样合成图像。为了在3D空间中获得高斯基元后渲染新颖的视图,基于splat的光栅化渲染也是完全可微的。这两个操作以及其他可微分部分使端到端的联合训练成为可能。我们将来自深度网络的图像特征融合到高斯网络中。此共享特征将尺度感知位置与其他高斯...