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Do vectorized store/load in binary_two ops

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This commit is contained in:
Cheng
2025-07-07 23:53:42 +00:00
parent 5962fa66bc
commit bbff91f920
1 changed files with 115 additions and 25 deletions
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138
mlx/backend/cuda/binary_two.cu
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@@ -17,47 +17,134 @@ 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
binary_ss(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
IdxT index = cg::this_grid().thread_rank();
if (index < size) {
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;
auto out = Op{}(a[0], b[0]);
out_a[0] = out[0];
out_b[0] = out[1];
out_a[offset] = out[0];
out_b[offset] = out[1];
}
} else {
AlignedVector<Out, N_READS> out_a_vec;
AlignedVector<Out, N_READS> out_b_vec;
#pragma unroll
for (int i = 0; i < N_READS; ++i) {
auto out = Op{}(a[0], b[0]);
out_a_vec.val[i] = out[0];
out_b_vec.val[i] = out[1];
}
store_vector<N_READS>(out_a, index, out_a_vec);
store_vector<N_READS>(out_b, index, out_b_vec);
}
}
template <typename Op, typename In, typename Out, typename IdxT>
template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
__global__ void
binary_sv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
IdxT index = cg::this_grid().thread_rank();
if (index < size) {
auto out = Op{}(a[0], b[index]);
out_a[index] = out[0];
out_b[index] = out[1];
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;
auto out = Op{}(a[0], b[offset]);
out_a[offset] = out[0];
out_b[offset] = out[1];
}
} else {
auto b_vec = load_vector<N_READS>(b, index);
AlignedVector<Out, N_READS> out_a_vec;
AlignedVector<Out, N_READS> out_b_vec;
#pragma unroll
for (int i = 0; i < N_READS; ++i) {
auto out = Op{}(a[0], b_vec.val[i]);
out_a_vec.val[i] = out[0];
out_b_vec.val[i] = out[1];
}
store_vector<N_READS>(out_a, index, out_a_vec);
store_vector<N_READS>(out_b, index, out_b_vec);
}
}
template <typename Op, typename In, typename Out, typename IdxT>
template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
__global__ void
binary_vs(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
IdxT index = cg::this_grid().thread_rank();
if (index < size) {
auto out = Op{}(a[index], b[0]);
out_a[index] = out[0];
out_b[index] = out[1];
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;
auto out = Op{}(a[offset], b[0]);
out_a[offset] = out[0];
out_b[offset] = out[1];
}
} else {
auto a_vec = load_vector<N_READS>(a, index);
AlignedVector<Out, N_READS> out_a_vec;
AlignedVector<Out, N_READS> out_b_vec;
#pragma unroll
for (int i = 0; i < N_READS; ++i) {
auto out = Op{}(a_vec.val[i], b[0]);
out_a_vec.val[i] = out[0];
out_b_vec.val[i] = out[1];
}
store_vector<N_READS>(out_a, index, out_a_vec);
store_vector<N_READS>(out_b, index, out_b_vec);
}
}
template <typename Op, typename In, typename Out, typename IdxT>
template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
__global__ void
binary_vv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
IdxT index = cg::this_grid().thread_rank();
if (index < size) {
auto out = Op{}(a[index], b[index]);
out_a[index] = out[0];
out_b[index] = out[1];
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;
auto out = Op{}(a[offset], b[offset]);
out_a[offset] = out[0];
out_b[offset] = out[1];
}
} else {
auto a_vec = load_vector<N_READS>(a, index);
auto b_vec = load_vector<N_READS>(b, index);
AlignedVector<Out, N_READS> out_a_vec;
AlignedVector<Out, N_READS> out_b_vec;
#pragma unroll
for (int i = 0; i < N_READS; ++i) {
auto out = Op{}(a_vec.val[i], b_vec.val[i]);
out_a_vec.val[i] = out[0];
out_b_vec.val[i] = out[1];
}
store_vector<N_READS>(out_a, index, out_a_vec);
store_vector<N_READS>(out_b, index, out_b_vec);
}
}
@@ -200,20 +287,23 @@ void binary_op_gpu_inplace(
} else {
dispatch_bool(out_a.data_size() > INT32_MAX, [&](auto large) {
using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
auto kernel = cu::binary_ss<Op, InType, OutType, IdxT>;
// TODO: Choose optimized value based on type size.
constexpr int N_READS = 4;
auto kernel = cu::binary_ss<Op, InType, OutType, IdxT, N_READS>;
if (bopt == BinaryOpType::ScalarVector) {
kernel = cu::binary_sv<Op, InType, OutType, IdxT>;
kernel = cu::binary_sv<Op, InType, OutType, IdxT, N_READS>;
} else if (bopt == BinaryOpType::VectorScalar) {
kernel = cu::binary_vs<Op, InType, OutType, IdxT>;
kernel = cu::binary_vs<Op, InType, OutType, IdxT, N_READS>;
} else if (bopt == BinaryOpType::VectorVector) {
kernel = cu::binary_vv<Op, InType, OutType, IdxT>;
kernel = cu::binary_vv<Op, InType, OutType, IdxT, N_READS>;
}
auto [num_blocks, block_dims] = get_launch_args(
kernel,
out_a.data_size(),
out_a.shape(),
out_a.strides(),
large());
large(),
N_READS);
encoder.add_kernel_node(
kernel,
num_blocks,