mask = offsets < N a = tl.load(A + offsets, mask=mask, other=0.0) b = tl.load(B + offsets, mask=mask, other=0.0) c = a + b tl.store(C + offsets, c, mask=mask) 7.2 优化步骤 使用共享内存:减少全局内存访问延迟。 向量化加载与存储:提升内存带宽利用率。 调整块大小:根据 GPU ...
load(y_ptr + offsets, mask=mask) output = x + y tl.store(output_ptr + offsets, output, mask=mask) def add(x: torch.Tensor, y: torch.Tensor): output = torch.empty_like(x) n_elements = output.numel() grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']), ) ...
w = tl.load(W + cols, mask=mask) b = tl.load(B + cols, mask=mask) x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32) x_hat = (x - mean) * rstd y = x_hat * w + b # Write output tl.store(Y + cols, y, mask=mask) 反向传播 层标准化算子的反向传播比前...
ifdropout_p>0.0:ifdropout_mask is not None:x=x.masked_fill(~dropout_mask,0.0)/(1.0-dropout_p)else:x=F.dropout(x,p=dropout_p)ifx1 is not None:ifdropout_mask1 is not None:x1=x1.masked_fill(~dropout_mask1,0.0)/(1.0-dropout_p)else:x1=F.dropout(x1,p=dropout_p)# 如果x1不为Non...
x = tl.load(x_ptr + offsets, mask=mask) y = tl.load(y_ptr + offsets, mask=mask) output = x + y # Write x + y back to DRAM. #将 x + y 写回 DRAM。 tl.store(output_ptr + offsets, output, mask=mask) 创建一个辅助函数从而: ...
= 1024 n = tl.arange(0, BLOCK_SIZE) # the memory address of all the elements # that we want to load can be computed as follows X = X + m * stride_xm + n * stride_xn # load input data; pad out-of-bounds elements with 0 x = tl.load(X, mask=n < N,...
load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0) # We accumulate along the K dimension. accumulator += tl.dot(a, b) # Advance the ptrs to the next K block. a_ptrs += BLOCK_SIZE_K * stride_ak b_ptrs += BLOCK_SIZE_K * stride_bk # You can fuse...
load(x_ptr + offsets, mask=mask) # randomly prune it # 随机修剪 random = tl.rand(seed, offsets) x_keep = random > p # write-back # 写回 output = tl.where(x_keep, x / (1 - p), 0.0) tl.store(output_ptr + offsets, output, mask=mask)def seeded_...
mask = offsets < n_elements x = tl.load(x_ptr + offsets, mask=mask) y = tl.load(y_ptr + offsets, mask=mask) output = x + y tl.store(output_ptr + offsets, output, mask=mask) def add(x: torch.Tensor, y: torch.Tensor): ...
(0, BLOCK_SIZE) # the memory address of all the elements # that we want to load can be computed as follows X = X + m * stride_xm + n * stride_xn # load input data; pad out-of-bounds elements with 0 x = tl.load(X, mask=n < N, other=-float('inf')) # compute ...