FMHA SM100: Add TMEM+correction epilogue kernel (Priority 2)
New file: fmha_epilogue_sm100.cuh - TMEM alloc/dealloc/load/store via tcgen05 PTX - One-way correction epilogue: TMEM→regs→normalize→BF16→GMEM - D1.5 fix: O rescale in REGISTERS (TMEM→regs→multiply→TMEM) - Same pattern as MoE epilogue but with normalize instead of SwiGLU - Unblocks D2 multi-CTA and NVFP4-1.2 (register slot for FP4 pack) Test: hd=64 + hd=128, reference vs TMEM kernels
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@@ -1,172 +1,156 @@
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/**
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* Standalone CUDA test for FMHA SM100 decode kernel.
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* Launches the kernel directly via CUDA runtime, compares against CPU reference.
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* No PyTorch or pybind11 needed — just nvcc + CUDA runtime.
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* Standalone CUDA test for FMHA SM100 — Reference + TMEM kernels.
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* Tests both the Phase 1 reference and Phase 2 TMEM+epilogue kernels.
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*/
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#include "dsv4/kernels/attention/fmha_sm100.cuh"
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#include "dsv4/kernels/attention/fmha_epilogue_sm100.cuh"
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#include <stdio.h>
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#include <stdlib.h>
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#include <math.h>
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#include <float.h>
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#include <string.h>
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using namespace dsv4::kernels::attention;
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// CPU reference: simple attention
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// CPU reference
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void attention_ref_cpu(
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const float* q, const float* k, const float* v,
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float* o, float* lse,
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float* o,
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int B, int H, int sk, int HD, float scale
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) {
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for (int b = 0; b < B; b++) {
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for (int h = 0; h < H; h++) {
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const float* qh = q + (b * H + h) * HD;
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const float* kb = k + b * sk * HD;
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const float* vb = v + b * HD * sk;
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float* oh = o + (b * H + h) * HD;
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const float* qh = q + (b*H+h)*HD;
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const float* kb = k + b*sk*HD;
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const float* vb = v + b*HD*sk;
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float* oh = o + (b*H+h)*HD;
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// S = Q @ K^T * scale
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float* s = (float*)malloc(sk * sizeof(float));
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float* s = (float*)malloc(sk*sizeof(float));
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float s_max = -FLT_MAX;
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for (int c = 0; c < sk; c++) {
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float dot = 0.0f;
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for (int d = 0; d < HD; d++) dot += qh[d] * kb[c * HD + d];
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for (int d = 0; d < HD; d++) dot += qh[d] * kb[c*HD+d];
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s[c] = dot * scale;
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s_max = fmaxf(s_max, s[c]);
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}
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// Softmax
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float sum = 0.0f;
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for (int c = 0; c < sk; c++) {
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s[c] = expf(s[c] - s_max);
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sum += s[c];
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}
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for (int c = 0; c < sk; c++) { s[c] = expf(s[c] - s_max); sum += s[c]; }
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for (int c = 0; c < sk; c++) s[c] /= sum;
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// O = S @ V
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for (int d = 0; d < HD; d++) {
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oh[d] = 0.0f;
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for (int c = 0; c < sk; c++) {
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oh[d] += s[c] * vb[d * sk + c];
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}
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for (int c = 0; c < sk; c++) oh[d] += s[c] * vb[d*sk+c];
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}
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if (lse) lse[b * H + h] = logf(sum) + s_max;
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free(s);
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}
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}
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}
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// BF16 conversion helpers for CPU
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uint16_t f32_to_bf16_cpu(float f) {
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uint32_t u;
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memcpy(&u, &f, 4);
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uint16_t h = (uint16_t)(u >> 16);
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return h;
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uint16_t f32_to_bf16_cpu(float f) { uint32_t u; memcpy(&u,&f,4); return (uint16_t)(u>>16); }
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float bf16_to_f32_cpu(uint16_t h) { uint32_t u = ((uint32_t)h)<<16; float f; memcpy(&f,&u,4); return f; }
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float cosine_sim(const float* a, const float* b, int n) {
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float dot=0, na=0, nb=0;
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for(int i=0;i<n;i++) { dot+=a[i]*b[i]; na+=a[i]*a[i]; nb+=b[i]*b[i]; }
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float d = sqrtf(na)*sqrtf(nb);
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return d > 0 ? dot/d : 0;
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}
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float bf16_to_f32_cpu(uint16_t h) {
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uint32_t u = ((uint32_t)h) << 16;
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float f;
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memcpy(&f, &u, 4);
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return f;
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}
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int main() {
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printf("=== FMHA SM100 Decode Kernel Test ===\n");
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const int B = 1, H = 1, HD = 64, sk = 128;
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const float scale = 1.0f / sqrtf((float)HD);
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const int smem = 128 * HD * 2 * sizeof(uint16_t) + 1024; // K + V + slack
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// Allocate host memory
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float *hq = (float*)malloc(B * H * HD * sizeof(float));
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float *hk = (float*)malloc(B * sk * HD * sizeof(float));
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float *hv = (float*)malloc(B * HD * sk * sizeof(float));
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float *ho_ref = (float*)malloc(B * H * HD * sizeof(float));
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// Init with random data
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srand(42);
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for (int i = 0; i < B * H * HD; i++) hq[i] = (float)rand() / RAND_MAX - 0.5f;
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for (int i = 0; i < B * sk * HD; i++) hk[i] = (float)rand() / RAND_MAX - 0.5f;
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for (int i = 0; i < B * HD * sk; i++) hv[i] = (float)rand() / RAND_MAX - 0.5f;
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// CPU reference
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attention_ref_cpu(hq, hk, hv, ho_ref, NULL, B, H, sk, HD, scale);
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// Convert to BF16
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uint16_t *hqb = (uint16_t*)malloc(B * H * HD * sizeof(uint16_t));
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uint16_t *hkb = (uint16_t*)malloc(B * sk * HD * sizeof(uint16_t));
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uint16_t *hvb = (uint16_t*)malloc(B * HD * sk * sizeof(uint16_t));
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uint16_t *hob = (uint16_t*)malloc(B * H * HD * sizeof(uint16_t));
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for (int i = 0; i < B * H * HD; i++) hqb[i] = f32_to_bf16_cpu(hq[i]);
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for (int i = 0; i < B * sk * HD; i++) hkb[i] = f32_to_bf16_cpu(hk[i]);
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for (int i = 0; i < B * HD * sk; i++) hvb[i] = f32_to_bf16_cpu(hv[i]);
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// Allocate GPU memory
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uint16_t *dq, *dk, *dv, *do_;
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float *d_lse;
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cudaMalloc(&dq, B * H * HD * sizeof(uint16_t));
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cudaMalloc(&dk, B * sk * HD * sizeof(uint16_t));
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cudaMalloc(&dv, B * HD * sk * sizeof(uint16_t));
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cudaMalloc(&do_, B * H * HD * sizeof(uint16_t));
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cudaMalloc(&d_lse, B * H * sizeof(float));
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// Copy to GPU
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cudaMemcpy(dq, hqb, B * H * HD * sizeof(uint16_t), cudaMemcpyHostToDevice);
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cudaMemcpy(dk, hkb, B * sk * HD * sizeof(uint16_t), cudaMemcpyHostToDevice);
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cudaMemcpy(dv, hvb, B * HD * sk * sizeof(uint16_t), cudaMemcpyHostToDevice);
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cudaMemset(do_, 0, B * H * HD * sizeof(uint16_t));
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// Launch kernel
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int test_kernel(const char* name, int HD, int sk, float scale,
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uint16_t* dq, uint16_t* dk, uint16_t* dv, uint16_t* do_gpu,
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float* d_lse, float* ho_ref, int B, int H) {
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dim3 grid(1, H, B);
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dim3 block(NTHREADS);
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int smem = (HD * sizeof(float)) + 128 + 1024; // Q + row_sums + slack
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printf("Launching fmha_decode_ref<%d> <<<(%d,%d,%d), %d>>>...\n", HD, grid.x, grid.y, grid.z, block.x);
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cudaMemset(do_gpu, 0, B*H*HD*sizeof(uint16_t));
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fmha_decode_ref<HD><<<grid, block, smem>>>(
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dq, dk, dv, do_,
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H * HD, sk * HD, H * HD,
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sk, 0, 0, scale, NULL, d_lse
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);
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if (strcmp(name, "reference") == 0) {
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fmha_decode_ref<HD><<<grid, block, smem>>>(
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dq, dk, dv, do_gpu,
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H*HD, sk*HD, H*HD,
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sk, 0, 0, scale, NULL, d_lse);
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} else {
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fmha_decode_tmem<HD><<<grid, block, smem>>>(
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dq, dk, dv, do_gpu,
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H*HD, sk*HD, H*HD,
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sk, 0, 0, scale, NULL, d_lse);
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}
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cudaError_t err = cudaDeviceSynchronize();
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if (err != cudaSuccess) {
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printf("❌ Kernel launch failed: %s\n", cudaGetErrorString(err));
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return 1;
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}
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printf("✅ Kernel launched successfully!\n");
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// Copy result back
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cudaMemcpy(hob, do_, B * H * HD * sizeof(uint16_t), cudaMemcpyDeviceToHost);
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// Compare with reference
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float cos_sim = 0.0f, norm_a = 0.0f, norm_b = 0.0f;
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for (int i = 0; i < B * H * HD; i++) {
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float gpu_val = bf16_to_f32_cpu(hob[i]);
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float ref_val = ho_ref[i];
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cos_sim += gpu_val * ref_val;
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norm_a += gpu_val * gpu_val;
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norm_b += ref_val * ref_val;
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}
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float denom = sqrtf(norm_a) * sqrtf(norm_b);
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if (denom > 0) cos_sim /= denom;
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printf("\nhd=%d, s_k=%d: cos %.6f %s\n", HD, sk, cos_sim, cos_sim > 0.999f ? "✅ PASS" : "❌ FAIL");
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if (cos_sim < 0.999f) {
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printf("First 8 values (GPU vs Ref):\n");
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for (int i = 0; i < 8; i++) {
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printf(" [%d] GPU=%f Ref=%f\n", i, bf16_to_f32_cpu(hob[i]), ho_ref[i]);
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}
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printf(" ❌ %s: kernel failed: %s\n", name, cudaGetErrorString(err));
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return 0;
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}
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// Cleanup
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cudaFree(dq); cudaFree(dk); cudaFree(dv); cudaFree(do_); cudaFree(d_lse);
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free(hq); free(hk); free(hv); free(ho_ref);
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free(hqb); free(hkb); free(hvb); free(hob);
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// Copy result and compare
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uint16_t* hob = (uint16_t*)malloc(B*H*HD*sizeof(uint16_t));
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cudaMemcpy(hob, do_gpu, B*H*HD*sizeof(uint16_t), cudaMemcpyDeviceToHost);
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return cos_sim > 0.999f ? 0 : 1;
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float* ho_gpu = (float*)malloc(B*H*HD*sizeof(float));
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for (int i = 0; i < B*H*HD; i++) ho_gpu[i] = bf16_to_f32_cpu(hob[i]);
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float cos = cosine_sim(ho_gpu, ho_ref, B*H*HD);
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int pass = cos > 0.999f;
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printf(" %s hd=%d s_k=%d: cos %.6f %s\n", name, HD, sk, cos, pass ? "✅" : "❌");
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if (!pass) {
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printf(" GPU[:4] = %.6f %.6f %.6f %.6f\n", ho_gpu[0], ho_gpu[1], ho_gpu[2], ho_gpu[3]);
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printf(" Ref[:4] = %.6f %.6f %.6f %.6f\n", ho_ref[0], ho_ref[1], ho_ref[2], ho_ref[3]);
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}
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free(hob); free(ho_gpu);
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return pass;
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}
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int main() {
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printf("=== FMHA SM100 Decode Kernel Test Suite ===\n\n");
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int all_pass = 1;
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int head_dims[] = {64, 128};
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int s_ks[] = {128};
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for (int t = 0; t < 2; t++) {
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int HD = head_dims[t];
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int sk = s_ks[0];
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float scale = 1.0f / sqrtf((float)HD);
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int B = 1, H = 1;
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printf("--- hd=%d, s_k=%d ---\n", HD, sk);
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// Alloc
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float *hq=(float*)malloc(B*H*HD*4), *hk=(float*)malloc(B*sk*HD*4);
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float *hv=(float*)malloc(B*HD*sk*4), *ho_ref=(float*)malloc(B*H*HD*4);
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srand(42);
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for(int i=0;i<B*H*HD;i++) hq[i]=(float)rand()/RAND_MAX-0.5f;
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for(int i=0;i<B*sk*HD;i++) hk[i]=(float)rand()/RAND_MAX-0.5f;
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for(int i=0;i<B*HD*sk;i++) hv[i]=(float)rand()/RAND_MAX-0.5f;
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attention_ref_cpu(hq,hk,hv,ho_ref,B,H,sk,HD,scale);
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uint16_t *hqb=(uint16_t*)malloc(B*H*HD*2), *hkb=(uint16_t*)malloc(B*sk*HD*2);
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uint16_t *hvb=(uint16_t*)malloc(B*HD*sk*2);
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for(int i=0;i<B*H*HD;i++) hqb[i]=f32_to_bf16_cpu(hq[i]);
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for(int i=0;i<B*sk*HD;i++) hkb[i]=f32_to_bf16_cpu(hk[i]);
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for(int i=0;i<B*HD*sk;i++) hvb[i]=f32_to_bf16_cpu(hv[i]);
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uint16_t *dq,*dk,*dv,*do_;
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float *d_lse;
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cudaMalloc(&dq,B*H*HD*2); cudaMalloc(&dk,B*sk*HD*2);
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cudaMalloc(&dv,B*HD*sk*2); cudaMalloc(&do_,B*H*HD*2);
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cudaMalloc(&d_lse,B*H*4);
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cudaMemcpy(dq,hqb,B*H*HD*2,cudaMemcpyHostToDevice);
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cudaMemcpy(dk,hkb,B*sk*HD*2,cudaMemcpyHostToDevice);
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cudaMemcpy(dv,hvb,B*HD*sk*2,cudaMemcpyHostToDevice);
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all_pass &= test_kernel("reference", HD, sk, scale, dq,dk,dv,do_,d_lse,ho_ref,B,H);
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all_pass &= test_kernel("tmem_epilogue", HD, sk, scale, dq,dk,dv,do_,d_lse,ho_ref,B,H);
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cudaFree(dq);cudaFree(dk);cudaFree(dv);cudaFree(do_);cudaFree(d_lse);
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free(hq);free(hk);free(hv);free(ho_ref);free(hqb);free(hkb);free(hvb);
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printf("\n");
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}
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printf("%s\n", all_pass ? "✅ ALL TESTS PASSED!" : "❌ SOME TESTS FAILED");
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return all_pass ? 0 : 1;
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}
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