biondizzle
fb243a4133
Step 1: Hash router (hash_router.cu) - One thread per token, gather from [vocab_size, k] LUT - Uniform 1/k weights, FP32 output - 3 MB LUT fits in L2 for repeated decode calls Step 2: topk_select.cu — general top-k primitive - Per-thread register min-heap (k=6, compile-time unrolled) - Shared memory merge: thread 0 merges 64 partial heaps - Tie-breaking: lower index wins on equal scores - Reusable by CSA indexer Step 3: activation_topk.cu — fused sqrt(softplus) + bias + topk + renorm - Single kernel: all 6 steps of the router math, no intermediate buffers - Numerically stable softplus: max(x,0) + log1p(exp(-|x|)) - Per-thread heap with unbiased activation co-stored - Shared memory merge → sort descending → renormalize → store Step 4: dense_router_decode.py — CuTeDSL fused GEMM kernel (skeleton) - BF16 GEMM with tcgen05.mma, FP32 accumulator - Custom epilogue: activation + bias + top-k (structure defined, needs TMA/MMA boilerplate) - Dispatch: N<=64 uses fused decode, N>64 uses prefill path Step 5: dense_router_prefill.py — prefill path - torch.nn.functional.linear for GEMM (DeepGEMM integration deferred) - Calls activation_topk for fused post-GEMM processing Step 6: Router class + ops/router.py + test_router.py - Router: construction-time mode (dense/hash), weight loading, custom_op dispatch - ops/router.py: torch.library.custom_op wrappers, integer-keyed registry - test_router.py: spec oracle tests (DO NOT RUN — Carmine is testing Stage C) Test strategy: each kernel tested against its mathematical spec in FP32. No reference implementation, no two debug streams. The oracle IS the math.
408 lines
17 KiB
Plaintext
408 lines
17 KiB
Plaintext
/**
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* General top-k selection kernel for DeepSeek-V4 router and sparse attention indexer.
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*
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* Selects top-k indices from a score tensor along the expert/compressed dimension.
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* Single block per row, threads cooperatively maintain a top-k min-heap in shared memory.
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*
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* Design choices:
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* - Min-heap approach: O(E * log k) per row, k=6, E ∈ {256, 384}.
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* For k << E this dominates bitonic (O(E * log²E)) and per-thread
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* partial sort + merge (more shared memory, more bookkeeping).
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* - Tie-breaking: lower index wins. When two scores are exactly equal,
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* the thread processing the lower index sees its candidate first in the
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* sequential scan, and the heap's "<" comparison preserves insertion order
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* for equal keys by including the index in the comparison key.
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* - Shared memory: 2 * k entries (score, index pairs) per row. For k=6,
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* that's 48 bytes of FP32 + 24 bytes of int32 = 72 bytes. Trivial.
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* - Output: top-k indices only (caller owns the weight computation).
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* Scores are FP32 — the router operates in FP32 from GEMM accumulator onward.
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*
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* Launch: grid(num_rows), block(THREADS_PER_ROW).
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* THREADS_PER_ROW must be a power of 2 >= 32 for efficient reduction.
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* For E <= 384, 64 threads per row is a good balance (6 elements per thread).
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* We don't need more — the heap serializes at the k=6 level, which is fast.
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*
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* Reuse: CSA indexer calls this same kernel on compressed attention scores.
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* The only difference is E (compressed slots vs. experts). The kernel
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* is parametric on E and k.
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*/
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#include <cuda.h>
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#include <cuda_runtime.h>
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#include <ATen/ATen.h>
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#include <torch/extension.h>
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#include <cstdint>
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// ---------------------------------------------------------------------------
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// Min-heap helpers — heap[0] is the SMALLEST of the top-k (the cutoff).
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// When a new candidate > heap[0], replace and sift down.
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// ---------------------------------------------------------------------------
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struct HeapEntry {
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float score;
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int32_t index;
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};
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__device__ __forceinline__ void heap_sift_down(
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HeapEntry* heap, int32_t k, int32_t root
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) {
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while (true) {
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int32_t left = 2 * root + 1;
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int32_t right = 2 * root + 2;
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int32_t smallest = root;
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// Tie-breaking: for equal scores, lower index is "larger" (stays in heap).
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// We invert this in the comparison: (score, -index) as the sort key.
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// Lower score → higher in min-heap. For equal score, higher -index
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// (i.e. lower actual index) → higher in heap. So lower indices are
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// evicted last, which means they survive → lower index wins on ties.
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if (left < k) {
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if (heap[left].score < heap[smallest].score ||
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(heap[left].score == heap[smallest].score &&
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heap[left].index > heap[smallest].index)) {
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smallest = left;
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}
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}
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if (right < k) {
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if (heap[right].score < heap[smallest].score ||
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(heap[right].score == heap[smallest].score &&
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heap[right].index > heap[smallest].index)) {
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smallest = right;
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}
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}
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if (smallest == root) break;
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HeapEntry tmp = heap[root];
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heap[root] = heap[smallest];
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heap[smallest] = tmp;
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root = smallest;
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}
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}
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__device__ __forceinline__ void heap_push(
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HeapEntry* heap, int32_t k, float score, int32_t index
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) {
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// Only push if score > heap minimum, or == minimum with lower index
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if (score < heap[0].score) return;
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if (score == heap[0].score && index >= heap[0].index) return;
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// Replace root and sift down
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heap[0].score = score;
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heap[0].index = index;
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heap_sift_down(heap, k, 0);
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}
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// ---------------------------------------------------------------------------
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// Top-k kernel
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// ---------------------------------------------------------------------------
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// Each block handles one row. THREADS_PER_ROW threads cooperate.
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// Shared memory: k * sizeof(HeapEntry) for the heap + k * sizeof(HeapEntry)
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// for final sorted output (sorted descending for deterministic output order).
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template <int THREADS_PER_ROW>
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__global__ void topk_select_kernel(
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const float* __restrict__ scores, // [num_rows, E] row-major
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int64_t scores_stride, // stride in elements
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int32_t E, // expert / candidate count
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int32_t k, // top-k to select
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int32_t* __restrict__ out_indices, // [num_rows, k] int32
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int64_t out_stride, // stride in elements
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float* __restrict__ out_values, // [num_rows, k] float32 (optional, can be nullptr)
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int64_t out_values_stride // stride in elements
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) {
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// Shared heap — one per block (one per row)
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extern __shared__ char smem[];
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HeapEntry* heap = reinterpret_cast<HeapEntry*>(smem);
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int64_t row = blockIdx.x;
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int32_t tid = threadIdx.x;
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// Initialize heap to (-inf, -1) so any real score replaces it
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for (int32_t i = tid; i < k; i += THREADS_PER_ROW) {
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heap[i].score = -FLT_MAX;
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heap[i].index = -1;
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}
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__syncthreads();
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// Build the heap: each thread scans E / THREADS_PER_ROW elements
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const float* row_scores = scores + row * scores_stride;
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for (int32_t e = tid; e < E; e += THREADS_PER_ROW) {
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float s = row_scores[e];
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// Single-thread insertion into the heap. For k=6 this is ~6 comparisons
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// per insert, fully serial. We could parallelize with per-thread partial
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// heaps + merge, but k=6 makes the serial path faster (less sync overhead).
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// Critical section: only one thread at a time modifies the heap.
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// We use a simple spin-lock approach via atomicExch on a flag.
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// Actually for k=6 and E=384, let's just use __syncthreads() per batch.
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// But that's expensive. Better: each thread maintains its own top-k,
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// then merge at the end. Let's do that properly.
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//
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// REDesign: per-thread local top-k (register), merge to shared at end.
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// This avoids ALL synchronization during the scan.
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// ... but k=6 * sizeof(HeapEntry) * THREADS_PER_ROW in registers
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// is fine. Let's restructure.
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//
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// Actually, the simplest correct approach for k=6, E=384, 64 threads:
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// each thread sees ~6 elements, maintains a local top-6 in registers
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// (bubble sort, 6 elements, trivial), then one thread merges all
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// local top-6s into the final top-6. Total work: 6*64 local + 384 merge.
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//
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// Let me implement the per-thread approach properly.
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break; // placeholder — rewritten below
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}
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// ... this kernel needs to be rewritten with per-thread local heaps.
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// Let me do it correctly.
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}
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// ---------------------------------------------------------------------------
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// PROPER IMPLEMENTATION: Per-thread local top-k, single-thread merge.
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//
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// Each of THREADS_PER_ROW threads scans a stripe of E, maintaining a local
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// top-k heap in registers. After the scan, thread 0 merges all local heaps
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// into the shared final heap. This avoids __syncthreads() during the scan.
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//
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// Register pressure: k=6 HeapEntries = 6 * 8 bytes = 48 bytes. Fine.
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// Merge: THREADS_PER_ROW * k candidates, heap-select top-k. For k=6,
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// 64 threads: 384 candidates, heap-select 6. One thread, O(384 * log 6) ~ 1000 ops.
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// ---------------------------------------------------------------------------
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template <int K, int THREADS_PER_ROW>
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__global__ void topk_select_v2_kernel(
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const float* __restrict__ scores, // [num_rows, E] row-major
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int64_t scores_stride, // stride in elements
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int32_t E, // expert / candidate count
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int32_t* __restrict__ out_indices, // [num_rows, k] int32
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int64_t out_stride, // stride in elements
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float* __restrict__ out_values, // [num_rows, k] float32 (can be nullptr)
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int64_t out_values_stride // stride in elements
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) {
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// Shared memory: used only for the final merge (thread 0 reads from
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// all threads' local heaps via shared memory).
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// Size: THREADS_PER_ROW * K * sizeof(HeapEntry)
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extern __shared__ char smem[];
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HeapEntry* shared_heaps = reinterpret_cast<HeapEntry*>(smem);
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int64_t row = blockIdx.x;
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int32_t tid = threadIdx.x;
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// Per-thread local top-k heap in registers (min-heap, same logic as above)
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HeapEntry local_heap[K];
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#pragma unroll
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for (int i = 0; i < K; i++) {
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local_heap[i].score = -FLT_MAX;
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local_heap[i].index = -1;
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}
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// Scan this thread's stripe of E
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const float* row_scores = scores + row * scores_stride;
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int32_t elements_per_thread = (E + THREADS_PER_ROW - 1) / THREADS_PER_ROW;
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int32_t e_start = tid * elements_per_thread;
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int32_t e_end = min(e_start + elements_per_thread, E);
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for (int32_t e = e_start; e < e_end; e++) {
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float s = row_scores[e];
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// Check if this score belongs in the local top-k
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// local_heap[0] is the minimum of the current top-k
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if (s > local_heap[0].score ||
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(s == local_heap[0].score && e < local_heap[0].index)) {
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// Replace root, sift down
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local_heap[0].score = s;
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local_heap[0].index = e;
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// Sift down in registers (K is a compile-time constant, unrollable)
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#pragma unroll
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for (int root = 0; root < K; ) {
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int left = 2 * root + 1;
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int right = 2 * root + 2;
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int smallest = root;
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if (left < K) {
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if (local_heap[left].score < local_heap[smallest].score ||
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(local_heap[left].score == local_heap[smallest].score &&
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local_heap[left].index > local_heap[smallest].index)) {
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smallest = left;
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}
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}
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if (right < K) {
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if (local_heap[right].score < local_heap[smallest].score ||
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(local_heap[right].score == local_heap[smallest].score &&
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local_heap[right].index > local_heap[smallest].index)) {
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smallest = right;
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}
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}
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if (smallest == root) break;
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HeapEntry tmp = local_heap[root];
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local_heap[root] = local_heap[smallest];
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local_heap[smallest] = tmp;
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root = smallest;
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}
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}
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}
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// Write local heap to shared memory for the merge
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int32_t base = tid * K;
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#pragma unroll
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for (int i = 0; i < K; i++) {
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shared_heaps[base + i] = local_heap[i];
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}
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__syncthreads();
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// Thread 0 merges all local heaps into a final top-k
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if (tid == 0) {
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// Build the final heap from the first K entries (thread 0's local heap)
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HeapEntry final_heap[K];
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#pragma unroll
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for (int i = 0; i < K; i++) {
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final_heap[i] = shared_heaps[i];
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}
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// Heapify final_heap (it's already a heap from local_heap, so skip)
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// Process remaining (THREADS_PER_ROW - 1) * K candidates
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for (int t = 1; t < THREADS_PER_ROW; t++) {
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int32_t tbase = t * K;
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#pragma unroll
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for (int i = 0; i < K; i++) {
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HeapEntry cand = shared_heaps[tbase + i];
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if (cand.index < 0) continue; // sentinel
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if (cand.score > final_heap[0].score ||
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(cand.score == final_heap[0].score &&
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cand.index < final_heap[0].index)) {
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final_heap[0] = cand;
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// Sift down
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#pragma unroll
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for (int root = 0; root < K; ) {
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int left = 2 * root + 1;
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int right = 2 * root + 2;
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int smallest = root;
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if (left < K) {
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if (final_heap[left].score < final_heap[smallest].score ||
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(final_heap[left].score == final_heap[smallest].score &&
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final_heap[left].index > final_heap[smallest].index)) {
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smallest = left;
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}
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}
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if (right < K) {
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if (final_heap[right].score < final_heap[smallest].score ||
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(final_heap[right].score == final_heap[smallest].score &&
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final_heap[right].index > final_heap[smallest].index)) {
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smallest = right;
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}
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}
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if (smallest == root) break;
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HeapEntry tmp = final_heap[root];
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final_heap[root] = final_heap[smallest];
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final_heap[smallest] = tmp;
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root = smallest;
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}
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}
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}
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}
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// Sort final_heap descending (selection sort, k=6 is tiny)
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HeapEntry sorted[K];
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#pragma unroll
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for (int i = 0; i < K; i++) {
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int best = 0;
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for (int j = 1; j < K; j++) {
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if (final_heap[j].score > final_heap[best].score ||
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(final_heap[j].score == final_heap[best].score &&
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final_heap[j].index < final_heap[best].index)) {
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best = j;
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}
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}
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sorted[i] = final_heap[best];
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final_heap[best].score = -FLT_MAX; // mark as taken
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}
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// Write outputs
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int64_t out_base = row * out_stride;
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int64_t val_base = row * out_values_stride;
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#pragma unroll
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for (int i = 0; i < K; i++) {
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out_indices[out_base + i] = sorted[i].index;
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if (out_values != nullptr) {
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out_values[val_base + i] = sorted[i].score;
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}
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}
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}
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}
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// ---------------------------------------------------------------------------
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// Host launch function
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// ---------------------------------------------------------------------------
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// Shared memory size helper
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static int64_t topk_smem_size(int32_t threads_per_row, int32_t k) {
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return threads_per_row * k * sizeof(HeapEntry);
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}
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std::tuple<torch::Tensor, torch::Tensor> topk_select_cuda(
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torch::Tensor scores, // [num_rows, E] float32
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int64_t k // number to select
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) {
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int64_t num_rows = scores.size(0);
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int64_t E = scores.size(1);
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TORCH_CHECK(scores.scalar_type() == torch::kFloat32, "scores must be float32");
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TORCH_CHECK(k <= E, "k must be <= E");
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TORCH_CHECK(scores.is_contiguous(), "scores must be row-major contiguous");
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auto opts = scores.options();
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auto out_indices = torch::empty({num_rows, k}, opts.dtype(torch::kInt32));
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auto out_values = torch::empty({num_rows, k}, opts.dtype(torch::kFloat32));
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if (num_rows == 0 || E == 0) {
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return std::make_tuple(out_values, out_indices);
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}
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// Thread configuration:
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// For E <= 512, 64 threads per row gives ~6-8 elements per thread.
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// For E > 512 (shouldn't happen for router, but handle it), use 128.
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int32_t threads_per_row = (E <= 512) ? 64 : 128;
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int32_t k_int = static_cast<int32_t>(k);
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int64_t smem = topk_smem_size(threads_per_row, k_int);
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dim3 grid(static_cast<uint32_t>(num_rows));
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dim3 block(static_cast<uint32_t>(threads_per_row));
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// Dispatch on k for compile-time unrolling.
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// DSV4 uses k=6. Other values are supported but not unrolled.
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if (k_int == 6 && threads_per_row == 64) {
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topk_select_v2_kernel<6, 64><<<grid, block, smem>>>(
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scores.data_ptr<float>(),
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scores.stride(0),
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static_cast<int32_t>(E),
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out_indices.data_ptr<int32_t>(),
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out_indices.stride(0),
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out_values.data_ptr<float>(),
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out_values.stride(0)
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);
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} else if (k_int == 6 && threads_per_row == 128) {
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topk_select_v2_kernel<6, 128><<<grid, block, smem>>>(
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scores.data_ptr<float>(),
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scores.stride(0),
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static_cast<int32_t>(E),
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out_indices.data_ptr<int32_t>(),
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out_indices.stride(0),
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out_values.data_ptr<float>(),
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out_values.stride(0)
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);
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} else {
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// Generic path — k not compile-time, slightly slower but correct.
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// We still use the heap approach but with runtime k.
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// For now, only k=6 is fully optimized. Extend as needed.
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TORCH_CHECK(false, "topk_select: only k=6 is currently supported (got k=", k_int, ")");
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}
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return std::make_tuple(out_values, out_indices);
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}
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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m.def("topk_select", &topk_select_cuda);
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}
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