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196 lines
8.3 KiB
Plaintext
196 lines
8.3 KiB
Plaintext
#include <cudaTypedefs.h>
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#include <c10/cuda/CUDAGuard.h>
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#include <torch/all.h>
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#include <iostream>
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constexpr uint64_t THREADS_PER_EXPERT = 512;
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// threshold must match the dispatch logic in run_cutlass_moe_mm_sm90()
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constexpr int SWAP_AB_THRESHOLD = 64;
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template <bool SWAP_AB>
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__global__ void compute_problem_sizes(const int32_t* __restrict__ topk_ids,
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int32_t* problem_sizes1,
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int32_t* problem_sizes2,
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int32_t* atomic_buffer,
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const int topk_length, const int n,
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const int k) {
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int expert_id = blockIdx.x;
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int occurrences = 0;
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for (int i = threadIdx.x; i < topk_length; i += THREADS_PER_EXPERT) {
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occurrences += (topk_ids[i] == expert_id);
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}
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atomicAdd(&atomic_buffer[expert_id], occurrences);
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__syncthreads();
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if (threadIdx.x == 0) {
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int final_occurrences = atomic_buffer[expert_id];
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if constexpr (!SWAP_AB) {
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problem_sizes1[expert_id * 3] = final_occurrences;
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problem_sizes1[expert_id * 3 + 1] = 2 * n;
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problem_sizes1[expert_id * 3 + 2] = k;
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problem_sizes2[expert_id * 3] = final_occurrences;
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problem_sizes2[expert_id * 3 + 1] = k;
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problem_sizes2[expert_id * 3 + 2] = n;
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} else {
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problem_sizes1[expert_id * 3] = 2 * n;
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problem_sizes1[expert_id * 3 + 1] = final_occurrences;
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problem_sizes1[expert_id * 3 + 2] = k;
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problem_sizes2[expert_id * 3] = k;
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problem_sizes2[expert_id * 3 + 1] = final_occurrences;
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problem_sizes2[expert_id * 3 + 2] = n;
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}
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}
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}
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__global__ void compute_expert_offsets(
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const int32_t* __restrict__ problem_sizes1, int32_t* expert_offsets,
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int32_t* atomic_buffer, const int num_experts, const bool swap_ab) {
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int32_t tot_offset = 0;
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expert_offsets[0] = 0;
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for (int i = 0; i < num_experts; ++i) {
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atomic_buffer[i] = tot_offset;
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tot_offset += swap_ab ? problem_sizes1[i * 3 + 1] : problem_sizes1[i * 3];
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expert_offsets[i + 1] = tot_offset;
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}
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}
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__global__ void compute_expert_blockscale_offsets(
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const int32_t* __restrict__ problem_sizes1, int32_t* expert_offsets,
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int32_t* blockscale_offsets, int32_t* atomic_buffer, const int num_experts,
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const bool swap_ab) {
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int32_t tot_offset = 0;
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int32_t tot_offset_round = 0;
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expert_offsets[0] = 0;
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blockscale_offsets[0] = 0;
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for (int i = 0; i < num_experts; ++i) {
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int32_t cur_offset =
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swap_ab ? problem_sizes1[i * 3 + 1] : problem_sizes1[i * 3];
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atomic_buffer[i] = tot_offset;
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tot_offset += cur_offset;
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expert_offsets[i + 1] = tot_offset;
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tot_offset_round += (cur_offset + (128 - 1)) / 128 * 128;
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blockscale_offsets[i + 1] = tot_offset_round;
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}
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}
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__global__ void compute_arg_sorts(const int32_t* __restrict__ topk_ids,
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const int32_t* __restrict__ expert_offsets,
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int32_t* input_permutation,
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int32_t* output_permutation,
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int32_t* atomic_buffer, const int topk_length,
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const int topk) {
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int const blk_expert_id = blockIdx.x;
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int const num_experts = gridDim.x;
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int32_t const num_tokens = expert_offsets[num_experts];
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for (int i = threadIdx.x; i < topk_length; i += THREADS_PER_EXPERT) {
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int const expert_id = topk_ids[i];
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if (expert_id == -1 && blockIdx.x == 0) {
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// output_permutation is used to re-order the moe outputs. It is
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// used as c2 = c2[c_map], where c2 is a torch.tensor that is the
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// output of the cutlass kernels and c_map is the output_permutation.
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// c2 is initialized to zeros, therefore by setting the output_permutation
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// to num_tokens, we are guaranteed to fill the moe outputs to zero
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// for "invalid" topk_ids.
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output_permutation[i] = num_tokens;
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} else if (expert_id == blk_expert_id) {
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int start = atomicAdd(&atomic_buffer[expert_id], 1);
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input_permutation[start] = i / topk;
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output_permutation[i] = start;
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}
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}
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}
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void get_cutlass_moe_mm_data_caller(
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const torch::Tensor& topk_ids, torch::Tensor& expert_offsets,
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torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
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torch::Tensor& input_permutation, torch::Tensor& output_permutation,
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const int64_t num_experts, const int64_t n, const int64_t k,
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const std::optional<torch::Tensor>& blockscale_offsets) {
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auto stream = at::cuda::getCurrentCUDAStream(topk_ids.device().index());
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auto options_int32 =
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torch::TensorOptions().dtype(torch::kInt32).device(topk_ids.device());
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torch::Tensor atomic_buffer = torch::zeros(num_experts, options_int32);
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int num_threads = min(THREADS_PER_EXPERT, topk_ids.numel());
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// Swap-AB should be disabled for FP4 path
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bool may_swap_ab = (!blockscale_offsets.has_value()) &&
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(topk_ids.numel() <= SWAP_AB_THRESHOLD);
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if (may_swap_ab) {
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compute_problem_sizes<true><<<num_experts, num_threads, 0, stream>>>(
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static_cast<const int32_t*>(topk_ids.data_ptr()),
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static_cast<int32_t*>(problem_sizes1.data_ptr()),
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static_cast<int32_t*>(problem_sizes2.data_ptr()),
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static_cast<int32_t*>(atomic_buffer.data_ptr()), topk_ids.numel(), n,
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k);
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} else {
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compute_problem_sizes<false><<<num_experts, num_threads, 0, stream>>>(
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static_cast<const int32_t*>(topk_ids.data_ptr()),
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static_cast<int32_t*>(problem_sizes1.data_ptr()),
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static_cast<int32_t*>(problem_sizes2.data_ptr()),
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static_cast<int32_t*>(atomic_buffer.data_ptr()), topk_ids.numel(), n,
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k);
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}
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if (blockscale_offsets.has_value()) {
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// fp4 path
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compute_expert_blockscale_offsets<<<1, 1, 0, stream>>>(
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static_cast<const int32_t*>(problem_sizes1.data_ptr()),
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static_cast<int32_t*>(expert_offsets.data_ptr()),
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static_cast<int32_t*>(blockscale_offsets.value().data_ptr()),
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static_cast<int32_t*>(atomic_buffer.data_ptr()), num_experts,
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may_swap_ab);
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} else {
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compute_expert_offsets<<<1, 1, 0, stream>>>(
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static_cast<const int32_t*>(problem_sizes1.data_ptr()),
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static_cast<int32_t*>(expert_offsets.data_ptr()),
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static_cast<int32_t*>(atomic_buffer.data_ptr()), num_experts,
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may_swap_ab);
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}
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compute_arg_sorts<<<num_experts, num_threads, 0, stream>>>(
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static_cast<const int32_t*>(topk_ids.data_ptr()),
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static_cast<const int32_t*>(expert_offsets.data_ptr()),
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static_cast<int32_t*>(input_permutation.data_ptr()),
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static_cast<int32_t*>(output_permutation.data_ptr()),
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static_cast<int32_t*>(atomic_buffer.data_ptr()), topk_ids.numel(),
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topk_ids.size(1));
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}
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__global__ void compute_pplx_data(int32_t* expert_offsets,
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int32_t* problem_sizes1,
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int32_t* problem_sizes2,
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const int32_t* __restrict__ expert_num_tokens,
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const int padded_m, const int n,
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const int k) {
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int expert_idx = threadIdx.x;
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expert_offsets[expert_idx] = expert_idx * padded_m;
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problem_sizes1[expert_idx * 3] = expert_num_tokens[expert_idx];
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problem_sizes1[expert_idx * 3 + 1] = 2 * n;
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problem_sizes1[expert_idx * 3 + 2] = k;
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problem_sizes2[expert_idx * 3] = expert_num_tokens[expert_idx];
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problem_sizes2[expert_idx * 3 + 1] = k;
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problem_sizes2[expert_idx * 3 + 2] = n;
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}
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void get_cutlass_pplx_moe_mm_data_caller(torch::Tensor& expert_offsets,
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torch::Tensor& problem_sizes1,
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torch::Tensor& problem_sizes2,
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const torch::Tensor& expert_num_tokens,
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const int64_t num_local_experts,
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const int64_t padded_m,
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const int64_t n, const int64_t k) {
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auto stream = at::cuda::getCurrentCUDAStream(expert_offsets.device().index());
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compute_pplx_data<<<1, num_local_experts, 0, stream>>>(
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static_cast<int32_t*>(expert_offsets.data_ptr()),
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static_cast<int32_t*>(problem_sizes1.data_ptr()),
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static_cast<int32_t*>(problem_sizes2.data_ptr()),
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static_cast<const int32_t*>(expert_num_tokens.data_ptr()), padded_m, n,
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k);
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} |