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Do vectorized store/load in unary ops

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This commit is contained in:
Cheng
2025-07-07 23:34:26 +00:00
parent fb4e8b896b
commit 5962fa66bc
1 changed files with 30 additions and 5 deletions
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35
mlx/backend/cuda/unary.cu
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@@ -18,11 +18,29 @@ namespace cu {
namespace cg = cooperative_groups;
template <typename Op, typename In, typename Out, typename IdxT>
template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
__global__ void unary_v(const In* in, Out* out, IdxT size) {
IdxT index = cg::this_grid().thread_rank();
if (index < size) {
out[index] = Op{}(in[index]);
int remaining = size - index * N_READS;
if (remaining <= 0) {
return;
}
if (remaining < N_READS) {
for (int i = 0; i < remaining; ++i) {
IdxT offset = index * N_READS + i;
out[offset] = Op{}(in[offset]);
}
} else {
auto in_vec = load_vector<N_READS>(in, index);
AlignedVector<Out, N_READS> out_vec;
#pragma unroll
for (int i = 0; i < N_READS; ++i) {
out_vec.val[i] = Op{}(in_vec.val[i]);
}
store_vector<N_READS>(out, index, out_vec);
}
}
@@ -117,9 +135,16 @@ void unary_op_gpu_inplace(
using OutType = cuda_type_t<CTYPE_OUT>;
using IdxT = std::conditional_t<large(), int64_t, int32_t>;
if (contig) {
auto kernel = cu::unary_v<Op, InType, OutType, IdxT>;
// TODO: Choose optimized value based on type size.
constexpr int N_READS = 4;
auto kernel = cu::unary_v<Op, InType, OutType, IdxT, N_READS>;
auto [num_blocks, block_dims] = get_launch_args(
kernel, out.data_size(), out.shape(), out.strides(), large);
kernel,
out.data_size(),
out.shape(),
out.strides(),
large,
N_READS);
encoder.add_kernel_node(
kernel,
num_blocks,