导语:这是一篇关于Pytorch中各类乘法操作的总结和使用说明。 torch.dot():Computes the dot product (inner product) of two tensors. 计算两个1-D 张量的点乘(内乘)。 torch.dot(torch.tensor([2, 3]), torch.tensor([2, 1])) out: tensor(7) torch.mm()
// This thread belongs to 'm_row_group_id_A'-th group of threads.// This group iterates over M-rows of the Asub_pipe tile.int m_row_group_id_A = threadIdx.x / NUM_H2_ELEMENTS_IN_K_DIM;for (int r_a_tile...
wmma::load_matrix_sync(b_frag_inner_pipe[next_inner_pipe_idx], &Bsub_pipe[compute_pipe_idx][b_col_start_in_tile_next][0], WMMA_K + SKEW_HALF); } wmma::mma_sync(acc_frag[n_tile], a_frag, b_frag_inner_pipe[current_inner_pipe_idx], acc_frag[n_tile]); current_inner_pipe_i...
If we consider the mathematical interpretation of tensor contraction, it involves summing the products of corresponding elements along specified dimensions. This operation is similar to the dot product between two vectors. PyTorch's tensordot operation generalizes this concept to tensors of any shape, ...
In [16]: torch.dot? Docstring: dot(tensor1, tensor2) -> float Computes the dot product (inner product) of two tensors. .. note:: This function does not :ref:`broadcast <broadcasting-semantics>`. Example:: >>> torch.dot(torch.Tensor([2, 3...
思路:采用双缓冲cp.async管线,使全局内存加载与Tensor-Core计算重叠。 第4轮:3.46毫秒,达到参考性能的41.0% 思路:给定pytorch代码,使用隐式矩阵乘法(implicit matmul)的CUDA Kernel替换操作。给定的GEMM内核可能会有帮助。 作者评论:因为优化涉及到使用GEMM,所以在这一轮开始时,使用了一个之前生成的现有优秀GEMM内核...
value (Tensor): Value tensor; shape :math:`(N, ..., S, Ev)`. attn_mask (optional Tensor): Attention mask; shape :math:`(N, ..., L, S)`. Two types of masks are supported. A boolean mask where a value of True indicates that the element *should* take part in attention. ...
input (Tensor) – the input tensor. dim (int or tuple of python:ints) – the dimension or dimensions to reduce. keepdim (bool) – whether the output tensor has dim retained or not. 例程 累加全部元素 >>> a = torch.randn(1, 3) ...
本想练练手合成点数据,没想到却一不小心干翻了PyTorch专家内核!斯坦福华人团队用纯CUDA-C写出的AI生成内核,瞬间惊艳圈内并登上Hacker News热榜。团队甚至表示:本来不想发这个结果的。 就在刚刚,斯坦福HAI华人大神团队又出惊人神作了。 他们用纯CUDA-C语言编写的快速AI
Tensors and Dynamic neural networks in Python with strong GPU acceleration - [cuDNN][SDPA] Remove `TORCH_CUDNN_SDPA_ENABLED=1`, enable cuDNN SDPA …· pytorch/pytorch@f845a7a