multirow: add normalize flag, un-norm + LSE output, multi-tile KV merge test
This commit is contained in:
@@ -51,6 +51,7 @@ struct FmhaMultiRowParams {
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int v_head_stride, v_batch_stride;
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int o_head_stride, o_batch_stride;
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int lse_head_stride, lse_batch_stride;
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int normalize; // 1 = normalize in kernel, 0 = emit un-normalized O + LSE for multi-tile merge
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};
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template<int HD, int SK_TILE = 128>
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@@ -264,16 +265,21 @@ fmha_6warp_multirow_kernel(FmhaMultiRowParams params) {
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__syncthreads();
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// ================================================================
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// EPILOGUE: TMEM → regs → normalize → BF16 → GMEM
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// EPILOGUE: TMEM → regs → normalize (optional) → BF16 → GMEM
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//
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// CRITICAL: TMEM loads (32x32b.x8) are WARP-COLLECTIVE.
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// ALL 32 lanes must execute them. The load MUST be outside
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// the my_row_active guard. Only the GMEM store is conditional.
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//
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// When normalize=1 (single KV tile): O_norm = O_unnorm / row_sum
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// When normalize=0 (multi-tile merge): emit O_unnorm + LSE for
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// Python merge: O = Σ exp(lse_i)·O_i / Σ exp(lse_i)
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// ================================================================
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const bool do_normalize = (params.normalize != 0);
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if (my_warp_active) {
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float rm = my_row_active ? sRowMax[my_row] : 0.0f;
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float rs = my_row_active ? sRowSum[my_row] : 0.0f;
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float inv_rs = my_row_active ? (1.0f / rs) : 0.0f;
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float inv_rs = (my_row_active && do_normalize) ? (1.0f / rs) : 1.0f;
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// Read O from TMEM: N_NSUB*2 groups of 8 columns
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// ALL lanes in the warp must execute the TMEM load (warp-collective)
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@@ -1,6 +1,12 @@
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/**
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* Test multi-row FMHA kernel (6-warp, T>1 prefill).
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* Compile with -DHD_VAL=64 etc.
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*
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* Tests:
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* 1. Single KV tile, T=1..128 (normalized output)
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* 2. Single KV tile, T=1..128 (un-normalized output + LSE)
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* 3. Multi-tile KV via Python merge (s_k=256, 2 segments)
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* 4. Multi-head and batched launches
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*/
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#include <cuda_runtime.h>
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@@ -29,17 +35,16 @@ constexpr int MAX_T = 128;
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static int compute_smem() {
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size_t off = 0;
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off += 8;
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off += 128 * sizeof(float); // sRowMax
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off += 128 * sizeof(float); // sRowSum
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off += 4; // sTmemBase
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off += 128 * sizeof(float); // sRowMax
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off += 128 * sizeof(float); // sRowSum
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off = (off + 127) & ~(size_t)127;
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off += 128 * MMA_K_BF16 * sizeof(bf16_t); // sQ0
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off += 128 * MMA_K_BF16 * sizeof(bf16_t); // sK0
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off = (off + 127) & ~(size_t)127;
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off += 128 * MMA_K_BF16 * sizeof(bf16_t); // sPk
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off = (off + 127) & ~(size_t)127;
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off += 16 * MMA_K_BF16 * sizeof(bf16_t); // sV
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off += 256;
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off += 16 * MMA_K_BF16 * sizeof(bf16_t); // sV
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return (int)off;
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}
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@@ -70,8 +75,36 @@ static void reference_attention_multirow(
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}
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}
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static int test_single_T(int T, int n_h = 1, int batch = 1) {
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printf("\n=== T=%d, n_h=%d, batch=%d, HD=%d ===\n", T, n_h, batch, HD);
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// Reference that computes un-normalized O + LSE
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static void reference_attention_multirow_unnorm(
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const bf16_t* q, const bf16_t* k, const bf16_t* v,
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float* o_unnorm, float* lse_ref,
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int hd, int T, int s_k, float scale
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) {
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for (int t = 0; t < T; t++) {
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float s[512];
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for (int j = 0; j < s_k; j++) {
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float dot = 0.0f;
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for (int d = 0; d < hd; d++)
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dot += bf16_to_f32_host(q[t * hd + d]) * bf16_to_f32_host(k[j * hd + d]);
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s[j] = dot * scale;
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}
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float mx = -INFINITY;
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for (int j = 0; j < s_k; j++) mx = fmaxf(mx, s[j]);
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float sm = 0.0f;
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for (int j = 0; j < s_k; j++) { s[j] = expf(s[j] - mx); sm += s[j]; }
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// Un-normalized: don't divide by sm
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for (int d = 0; d < hd; d++) {
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float ov = 0.0f;
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for (int j = 0; j < s_k; j++) ov += s[j] * bf16_to_f32_host(v[d * s_k + j]);
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o_unnorm[t * hd + d] = ov; // un-normalized!
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}
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if (lse_ref) lse_ref[t] = logf(sm) + mx;
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}
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}
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static int test_normalized(int T, int n_h = 1, int batch = 1) {
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printf("\n=== NORMALIZED T=%d, n_h=%d, batch=%d, HD=%d ===\n", T, n_h, batch, HD);
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const float SCALE = 1.0f / sqrtf((float)HD);
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int total_heads = batch * n_h;
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@@ -104,6 +137,7 @@ static int test_single_T(int T, int n_h = 1, int batch = 1) {
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params.v_head_stride = HD * SK; params.v_batch_stride = n_h * HD * SK;
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params.o_head_stride = T * HD; params.o_batch_stride = n_h * T * HD;
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params.lse_head_stride = T; params.lse_batch_stride = n_h * T;
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params.normalize = 1;
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int smem = compute_smem();
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if (smem > 48 * 1024)
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@@ -150,27 +184,234 @@ static int test_single_T(int T, int n_h = 1, int batch = 1) {
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return failed == 0;
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}
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static int test_unnormalized(int T) {
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printf("\n=== UN-NORMALIZED T=%d, HD=%d ===\n", T, HD);
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const float SCALE = 1.0f / sqrtf((float)HD);
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bf16_t* h_q = (bf16_t*)malloc(T * HD * sizeof(bf16_t));
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bf16_t* h_k = (bf16_t*)malloc(SK * HD * sizeof(bf16_t));
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bf16_t* h_v = (bf16_t*)malloc(HD * SK * sizeof(bf16_t));
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bf16_t* h_o = (bf16_t*)calloc(T * HD, sizeof(bf16_t));
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float* h_lse = (float*)calloc(T, sizeof(float));
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srand(42 + T + 1000);
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for (int i = 0; i < T * HD; i++) h_q[i] = f32_to_bf16_host((float)(rand()%100)/100.0f - 0.5f);
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for (int i = 0; i < SK * HD; i++) h_k[i] = f32_to_bf16_host((float)(rand()%100)/100.0f - 0.5f);
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for (int i = 0; i < HD * SK; i++) h_v[i] = f32_to_bf16_host((float)(rand()%100)/100.0f - 0.5f);
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bf16_t *d_q, *d_k, *d_v, *d_o; float *d_lse;
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cudaMalloc(&d_q, T * HD * sizeof(bf16_t));
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cudaMalloc(&d_k, SK * HD * sizeof(bf16_t));
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cudaMalloc(&d_v, HD * SK * sizeof(bf16_t));
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cudaMalloc(&d_o, T * HD * sizeof(bf16_t));
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cudaMalloc(&d_lse, T * sizeof(float));
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cudaMemcpy(d_q, h_q, T * HD * sizeof(bf16_t), cudaMemcpyHostToDevice);
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cudaMemcpy(d_k, h_k, SK * HD * sizeof(bf16_t), cudaMemcpyHostToDevice);
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cudaMemcpy(d_v, h_v, HD * SK * sizeof(bf16_t), cudaMemcpyHostToDevice);
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FmhaMultiRowParams params;
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params.q = d_q; params.k = d_k; params.v = d_v; params.o = d_o; params.lse = d_lse;
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params.s_k = SK; params.T = T; params.scale = SCALE; params.head_dim = HD;
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params.q_head_stride = T * HD; params.q_batch_stride = T * HD;
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params.k_head_stride = SK * HD; params.k_batch_stride = SK * HD;
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params.v_head_stride = HD * SK; params.v_batch_stride = HD * SK;
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params.o_head_stride = T * HD; params.o_batch_stride = T * HD;
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params.lse_head_stride = T; params.lse_batch_stride = T;
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params.normalize = 0; // UN-NORMALIZED
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int smem = compute_smem();
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if (smem > 48 * 1024)
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cudaFuncSetAttribute(fmha_6warp_multirow_kernel<HD>, cudaFuncAttributeMaxDynamicSharedMemorySize, smem);
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dim3 grid(1, 1, 1);
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fmha_6warp_multirow_kernel<HD><<<grid, 192, smem>>>(params);
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cudaError_t err = cudaDeviceSynchronize();
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if (err != cudaSuccess) {
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printf(" CUDA ERROR: %s\n", cudaGetErrorString(err));
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cudaFree(d_q); cudaFree(d_k); cudaFree(d_v); cudaFree(d_o); cudaFree(d_lse);
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free(h_q); free(h_k); free(h_v); free(h_o); free(h_lse);
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return 0;
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}
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cudaMemcpy(h_o, d_o, T * HD * sizeof(bf16_t), cudaMemcpyDeviceToHost);
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cudaMemcpy(h_lse, d_lse, T * sizeof(float), cudaMemcpyDeviceToHost);
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// Verify: O_normalized = O_unnorm / row_sum, where exp(LSE) = row_sum * exp(max)
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float o_unnorm_ref[MAX_T * 512]; float lse_ref[MAX_T];
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reference_attention_multirow_unnorm(h_q, h_k, h_v, o_unnorm_ref, lse_ref, HD, T, SK, SCALE);
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int failed = 0; float min_cos = 1.0f;
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for (int t = 0; t < T; t++) {
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// Check un-normalized O matches reference
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float cs=0,na=0,nb=0;
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for (int d=0;d<HD;d++) {
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float a=bf16_to_f32_host(h_o[t*HD+d]), b2=o_unnorm_ref[t*HD+d];
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if(fabsf(b2)>1e-4f){cs+=a*b2;na+=a*a;nb+=b2*b2;}
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}
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cs /= (sqrtf(na)*sqrtf(nb)+1e-10f);
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if(cs<min_cos) min_cos=cs;
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if(cs<0.999f) { printf(" FAIL unnorm t=%d cos=%.6f\n",t,cs); failed++; }
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// Check LSE matches reference
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float lse_err = fabsf(h_lse[t] - lse_ref[t]);
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if(lse_err > 0.01f) { printf(" FAIL lse t=%d kernel=%.6f ref=%.6f err=%.6f\n",t,h_lse[t],lse_ref[t],lse_err); failed++; }
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}
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printf(" min_cos=%.8f %s\n", min_cos, failed==0?"PASSED":"FAILED");
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cudaFree(d_q); cudaFree(d_k); cudaFree(d_v); cudaFree(d_o); cudaFree(d_lse);
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free(h_q); free(h_k); free(h_v); free(h_o); free(h_lse);
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return failed == 0;
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}
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static int test_multitile_merge(int T) {
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printf("\n=== MULTI-TILE MERGE T=%d, s_k=256, HD=%d ===\n", T, HD);
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constexpr int SK_TOTAL = 256; // 2 KV tiles
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constexpr int N_TILES = SK_TOTAL / SK; // 2
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const float SCALE = 1.0f / sqrtf((float)HD);
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bf16_t* h_q = (bf16_t*)malloc(T * HD * sizeof(bf16_t));
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bf16_t* h_k = (bf16_t*)malloc(SK_TOTAL * HD * sizeof(bf16_t));
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bf16_t* h_v = (bf16_t*)malloc(HD * SK_TOTAL * sizeof(bf16_t));
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srand(42 + T + 2000);
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for (int i = 0; i < T * HD; i++) h_q[i] = f32_to_bf16_host((float)(rand()%100)/100.0f - 0.5f);
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for (int i = 0; i < SK_TOTAL * HD; i++) h_k[i] = f32_to_bf16_host((float)(rand()%100)/100.0f - 0.5f);
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for (int i = 0; i < HD * SK_TOTAL; i++) h_v[i] = f32_to_bf16_host((float)(rand()%100)/100.0f - 0.5f);
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// Run kernel per KV tile, get un-normalized O + LSE, merge in Python
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float* h_o_merged = (float*)calloc(T * HD, sizeof(float));
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bf16_t *d_q, *d_k, *d_v, *d_o; float *d_lse;
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cudaMalloc(&d_q, T * HD * sizeof(bf16_t));
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cudaMalloc(&d_k, SK * HD * sizeof(bf16_t)); // single tile
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cudaMalloc(&d_v, HD * SK * sizeof(bf16_t)); // single tile
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cudaMalloc(&d_o, T * HD * sizeof(bf16_t));
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cudaMalloc(&d_lse, T * sizeof(float));
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cudaMemcpy(d_q, h_q, T * HD * sizeof(bf16_t), cudaMemcpyHostToDevice);
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int smem = compute_smem();
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if (smem > 48 * 1024)
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cudaFuncSetAttribute(fmha_6warp_multirow_kernel<HD>, cudaFuncAttributeMaxDynamicSharedMemorySize, smem);
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float* lse_per_tile = (float*)malloc(N_TILES * T * sizeof(float));
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float* o_per_tile = (float*)malloc(N_TILES * T * HD * sizeof(float));
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for (int tile = 0; tile < N_TILES; tile++) {
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// K/V for this tile
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cudaMemcpy(d_k, h_k + tile * SK * HD, SK * HD * sizeof(bf16_t), cudaMemcpyHostToDevice);
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cudaMemcpy(d_v, h_v + tile * HD * SK, HD * SK * sizeof(bf16_t), cudaMemcpyHostToDevice);
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FmhaMultiRowParams params;
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params.q = d_q; params.k = d_k; params.v = d_v; params.o = d_o; params.lse = d_lse;
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params.s_k = SK; params.T = T; params.scale = SCALE; params.head_dim = HD;
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params.q_head_stride = T * HD; params.q_batch_stride = T * HD;
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params.k_head_stride = SK * HD; params.k_batch_stride = SK * HD;
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params.v_head_stride = HD * SK; params.v_batch_stride = HD * SK;
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params.o_head_stride = T * HD; params.o_batch_stride = T * HD;
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params.lse_head_stride = T; params.lse_batch_stride = T;
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params.normalize = 0; // UN-NORMALIZED for merge
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dim3 grid(1, 1, 1);
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fmha_6warp_multirow_kernel<HD><<<grid, 192, smem>>>(params);
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cudaError_t err = cudaDeviceSynchronize();
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if (err != cudaSuccess) {
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printf(" CUDA ERROR tile %d: %s\n", tile, cudaGetErrorString(err));
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cudaFree(d_q); cudaFree(d_k); cudaFree(d_v); cudaFree(d_o); cudaFree(d_lse);
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free(h_q); free(h_k); free(h_v); free(h_o_merged);
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free(lse_per_tile); free(o_per_tile);
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return 0;
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}
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bf16_t* h_o_tile = (bf16_t*)malloc(T * HD * sizeof(bf16_t));
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cudaMemcpy(h_o_tile, d_o, T * HD * sizeof(bf16_t), cudaMemcpyDeviceToHost);
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cudaMemcpy(lse_per_tile + tile * T, d_lse, T * sizeof(float), cudaMemcpyDeviceToHost);
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for (int i = 0; i < T * HD; i++)
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o_per_tile[tile * T * HD + i] = bf16_to_f32_host(h_o_tile[i]);
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free(h_o_tile);
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}
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// Python KV merge: O = Σ exp(lse_i)·O_i / Σ exp(lse_i)
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for (int t = 0; t < T; t++) {
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float lse_max = -INFINITY;
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for (int tile = 0; tile < N_TILES; tile++)
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lse_max = fmaxf(lse_max, lse_per_tile[tile * T + t]);
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float sum_w = 0.0f;
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for (int tile = 0; tile < N_TILES; tile++)
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sum_w += expf(lse_per_tile[tile * T + t] - lse_max);
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for (int d = 0; d < HD; d++) {
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float ov = 0.0f;
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for (int tile = 0; tile < N_TILES; tile++)
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ov += expf(lse_per_tile[tile * T + t] - lse_max) * o_per_tile[tile * T * HD + t * HD + d];
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h_o_merged[t * HD + d] = ov / sum_w;
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}
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}
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// Compare with full reference
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float o_ref[MAX_T * 512];
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reference_attention_multirow(h_q, h_k, h_v, o_ref, nullptr, HD, T, SK_TOTAL, SCALE);
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int failed = 0; float min_cos = 1.0f;
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for (int t = 0; t < T; t++) {
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float cs=0,na=0,nb=0;
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for (int d=0;d<HD;d++) {
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float a=h_o_merged[t*HD+d], b2=o_ref[t*HD+d];
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if(fabsf(b2)>1e-4f){cs+=a*b2;na+=a*a;nb+=b2*b2;}
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}
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cs /= (sqrtf(na)*sqrtf(nb)+1e-10f);
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if(cs<min_cos) min_cos=cs;
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if(cs<0.999f) { printf(" FAIL merge t=%d cos=%.6f\n",t,cs); failed++; }
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}
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printf(" min_cos=%.8f %s\n", min_cos, failed==0?"PASSED":"FAILED");
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cudaFree(d_q); cudaFree(d_k); cudaFree(d_v); cudaFree(d_o); cudaFree(d_lse);
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free(h_q); free(h_k); free(h_v); free(h_o_merged);
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free(lse_per_tile); free(o_per_tile);
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return failed == 0;
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}
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int main() {
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printf("Multi-row FMHA test (HD=%d)\n", HD);
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int ok = 1;
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// Single-head, single-batch: T=1..128
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ok &= test_single_T(1);
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ok &= test_single_T(2);
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ok &= test_single_T(4);
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ok &= test_single_T(8);
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ok &= test_single_T(16);
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ok &= test_single_T(32);
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ok &= test_single_T(64);
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ok &= test_single_T(128);
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// Multi-head prefill
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ok &= test_single_T(4, 4, 1); // 4 heads, T=4
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ok &= test_single_T(16, 4, 1); // 4 heads, T=16
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ok &= test_single_T(32, 4, 1); // 4 heads, T=32
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ok &= test_single_T(64, 4, 1); // 4 heads, T=64
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// Batched
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ok &= test_single_T(1, 2, 2); // 2 heads, 2 batch, T=1
|
||||
ok &= test_single_T(16, 2, 2); // 2 heads, 2 batch, T=16
|
||||
|
||||
// 1. Normalized output (single KV tile)
|
||||
printf("\n--- Normalized output tests ---\n");
|
||||
ok &= test_normalized(1);
|
||||
ok &= test_normalized(2);
|
||||
ok &= test_normalized(4);
|
||||
ok &= test_normalized(8);
|
||||
ok &= test_normalized(16);
|
||||
ok &= test_normalized(32);
|
||||
ok &= test_normalized(64);
|
||||
ok &= test_normalized(128);
|
||||
|
||||
// 2. Un-normalized output + LSE
|
||||
printf("\n--- Un-normalized output + LSE tests ---\n");
|
||||
ok &= test_unnormalized(1);
|
||||
ok &= test_unnormalized(4);
|
||||
ok &= test_unnormalized(16);
|
||||
ok &= test_unnormalized(32);
|
||||
ok &= test_unnormalized(64);
|
||||
ok &= test_unnormalized(128);
|
||||
|
||||
// 3. Multi-tile KV merge
|
||||
printf("\n--- Multi-tile KV merge tests ---\n");
|
||||
ok &= test_multitile_merge(1);
|
||||
ok &= test_multitile_merge(4);
|
||||
ok &= test_multitile_merge(16);
|
||||
ok &= test_multitile_merge(32);
|
||||
ok &= test_multitile_merge(64);
|
||||
ok &= test_multitile_merge(128);
|
||||
|
||||
// 4. Multi-head and batched
|
||||
printf("\n--- Multi-head and batched tests ---\n");
|
||||
ok &= test_normalized(4, 4, 1); // 4 heads, T=4
|
||||
ok &= test_normalized(16, 4, 1); // 4 heads, T=16
|
||||
ok &= test_normalized(64, 4, 1); // 4 heads, T=64
|
||||
ok &= test_normalized(1, 2, 2); // 2 heads, 2 batch, T=1
|
||||
ok &= test_normalized(16, 2, 2); // 2 heads, 2 batch, T=16
|
||||
|
||||
printf("\n%s\n", ok ? "ALL PASSED" : "SOME FAILED");
|
||||
return ok ? 0 : 1;
|
||||
|
||||
Reference in New Issue
Block a user