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17 changes: 15 additions & 2 deletions csrc/quantization/cutlass_w8a8/moe/moe_data.cu
Original file line number Diff line number Diff line change
Expand Up @@ -46,14 +46,26 @@ __global__ void compute_expert_offsets(
}

__global__ void compute_arg_sorts(const int* __restrict__ topk_ids,
const int32_t* __restrict__ expert_offsets,
int32_t* input_permutation,
int32_t* output_permutation,
int32_t* atomic_buffer, const int topk_length,
const int topk) {
int expert_id = blockIdx.x;
int const blk_expert_id = blockIdx.x;
int const num_experts = gridDim.x;
int32_t const num_tokens = expert_offsets[num_experts];

for (int i = threadIdx.x; i < topk_length; i += THREADS_PER_EXPERT) {
if (topk_ids[i] == expert_id) {
int const expert_id = topk_ids[i];
if (expert_id == -1 && blockIdx.x == 0) {
// output_permutation is used to re-order the moe outputs. It is
// used as c2 = c2[c_map], where c2 is a torch.tensor that is the
// output of the cutlass kernels and c_map is the output_permutation.
// c2 is initialized to zeros, therefore by setting the output_permutation
// to num_tokens, we are guaranteed to fill the moe outputs to zero
// for "invalid" topk_ids.
output_permutation[i] = num_tokens;
} else if (expert_id == blk_expert_id) {
int start = atomicAdd(&atomic_buffer[expert_id], 1);
input_permutation[start] = i / topk;
output_permutation[i] = start;
Expand Down Expand Up @@ -83,6 +95,7 @@ void get_cutlass_moe_mm_data_caller(
static_cast<int32_t*>(atomic_buffer.data_ptr()), num_experts);
compute_arg_sorts<<<num_experts, num_threads, 0, stream>>>(
static_cast<const int32_t*>(topk_ids.data_ptr()),
static_cast<const int32_t*>(expert_offsets.data_ptr()),
static_cast<int32_t*>(input_permutation.data_ptr()),
static_cast<int32_t*>(output_permutation.data_ptr()),
static_cast<int32_t*>(atomic_buffer.data_ptr()), topk_ids.numel(),
Expand Down
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