cuda-ab: transformer + transformer_mt clients (single-head attention + 4-layer FFN, fp32, CPU-reference checked; multi-stream and 5-launch loop variants)

1 files changed, 468 insertions, 0 deletions

diff --git a/tools/cuda-ab/transformer_mt.cu b/tools/cuda-ab/transformer_mt.cu
new file mode 100644
index 0000000..ffd23aa
--- /dev/null
+++ b/tools/cuda-ab/transformer_mt.cu
@@ -0,0 +1,468 @@
+// SPDX-License-Identifier: BSD-3-Clause
+/*
+ * transformer_mt.cu - attention forward on 3 independent streams + a
+ * 5-launch sequential loop, in one binary.
+ *
+ * Same single-head attention op as transformer.cu (X[64x256],
+ * Wq/Wk/Wv/Wo[256x256], head dim 256, fp32, softmax(Q*K^T/16), Y=(A*V)*Wo),
+ * exercised two ways:
+ *
+ *  1. Multi-stream class: three independent streams each run the full op on
+ *     their own input (X_s[i] = xval(i + s)). Every stream records an event
+ *     after its chain; stream 2 waits on streams 0 and 1 via cudaStreamWaitEvent
+ *     and the whole span is timed with cudaEventElapsedTime.
+ *  2. Repeated-launch class: the op is launched 5 times in a loop on one
+ *     stream with per-iteration inputs and per-iteration outputs, one
+ *     synchronization at the end.
+ *
+ * Both classes are verified against an internal double-precision CPU
+ * reference (tolerance 1e-3 relative, 1e-6 absolute floor).
+ *
+ * Output lines are stable and greppable:
+ *
+ *   DEVICE <name> cc=<major>.<minor> sm=<count> mem=<bytes>
+ *   TRANSFORMER_MT streams=3 bad=<mismatches> ysum=<sum>
+ *   TRANSFORMER_LOOP launches=5 bad=<mismatches> ysum=<sum>
+ *   TIMING ms=<elapsed>
+ *   STATUS OK|FAIL
+ *
+ * Build with a static CUDA runtime (--cudart=static) so the guest only needs
+ * the driver's libcuda.so, not a CUDA toolkit installation:
+ *
+ *   nvcc -O2 --cudart=static -arch=sm_75 -o transformer_mt transformer_mt.cu
+ *
+ * Then, from the guest:  /tmp/transformer_mt
+ */
+
+#include <cstdio>
+#include <cstdlib>
+#include <cstdint>
+#include <cmath>
+#include <cuda_runtime.h>
+
+#define CHECK(x)                                                         \
+	do {                                                             \
+		cudaError_t e_ = (x);                                    \
+		if (e_ != cudaSuccess) {                                 \
+			printf("CUDA_ERROR %s at line %d\n",             \
+			       cudaGetErrorString(e_), __LINE__);        \
+			printf("STATUS FAIL\n");                         \
+			return 1;                                        \
+		}                                                        \
+	} while (0)
+
+#define SEQ 64
+#define DH 256
+#define TILE 16
+#define TOL 1e-3f
+#define NST 3
+#define NLOOP 5
+
+__global__ void gemm_nn(const float *A, const float *B, float *C,
+			int M, int N, int K)
+{
+	__shared__ float As[TILE][TILE];
+	__shared__ float Bs[TILE][TILE];
+	int row = blockIdx.y * TILE + threadIdx.y;
+	int col = blockIdx.x * TILE + threadIdx.x;
+	float acc = 0.0f;
+
+	for (int t = 0; t < K / TILE; t++) {
+		As[threadIdx.y][threadIdx.x] = A[row * K + t * TILE + threadIdx.x];
+		Bs[threadIdx.y][threadIdx.x] =
+			B[(t * TILE + threadIdx.y) * N + col];
+		__syncthreads();
+		for (int k = 0; k < TILE; k++)
+			acc += As[threadIdx.y][k] * Bs[k][threadIdx.x];
+		__syncthreads();
+	}
+	C[row * N + col] = acc;
+}
+
+__global__ void gemm_nt(const float *A, const float *B, float *C,
+			int M, int N, int K)
+{
+	/* C[i][j] = sum_k A[i][k] * B[j][k]  (B is [N][K], row-major). */
+	__shared__ float As[TILE][TILE];
+	__shared__ float Bs[TILE][TILE];
+	int row = blockIdx.y * TILE + threadIdx.y;
+	int tcol = blockIdx.x * TILE + threadIdx.x;
+	float acc = 0.0f;
+
+	for (int t = 0; t < K / TILE; t++) {
+		As[threadIdx.y][threadIdx.x] = A[row * K + t * TILE + threadIdx.x];
+		Bs[threadIdx.y][threadIdx.x] =
+			B[(blockIdx.x * TILE + threadIdx.y) * K +
+			  t * TILE + threadIdx.x];
+		__syncthreads();
+		for (int k = 0; k < TILE; k++)
+			acc += As[threadIdx.y][k] * Bs[threadIdx.x][k];
+		__syncthreads();
+	}
+	C[row * N + tcol] = acc;
+}
+
+__global__ void softmax_rows(const float *S, float *P, int rows, int cols,
+			     float scale)
+{
+	extern __shared__ float sm[];
+	int row = blockIdx.x;
+	int t = threadIdx.x;
+	float v = S[row * cols + t] * scale;
+	int stride;
+
+	sm[t] = v;
+	__syncthreads();
+	for (stride = blockDim.x >> 1; stride > 0; stride >>= 1) {
+		if (t < stride)
+			sm[t] = fmaxf(sm[t], sm[t + stride]);
+		__syncthreads();
+	}
+	v = expf(v - sm[0]);
+	sm[t] = v;
+	__syncthreads();
+	for (stride = blockDim.x >> 1; stride > 0; stride >>= 1) {
+		if (t < stride)
+			sm[t] += sm[t + stride];
+		__syncthreads();
+	}
+	P[row * cols + t] = v / sm[0];
+}
+
+/* ------------------------------------------------------------------ host */
+
+struct attn_bufs {
+	float *q, *k, *v, *s, *a, *av, *y;
+};
+
+static void launch_chain(cudaStream_t st, const float *x, struct attn_bufs b,
+			 const float *wq, const float *wk, const float *wv,
+			 const float *wo)
+{
+	dim3 g1(DH / TILE, SEQ / TILE);
+	dim3 g2(SEQ / TILE, SEQ / TILE);
+	dim3 tb(TILE, TILE);
+
+	gemm_nn<<<g1, tb, 0, st>>>(x, wq, b.q, SEQ, DH, DH);
+	gemm_nn<<<g1, tb, 0, st>>>(x, wk, b.k, SEQ, DH, DH);
+	gemm_nn<<<g1, tb, 0, st>>>(x, wv, b.v, SEQ, DH, DH);
+	gemm_nt<<<g2, tb, 0, st>>>(b.q, b.k, b.s, SEQ, SEQ, DH);
+	softmax_rows<<<SEQ, SEQ, (size_t)SEQ * sizeof(float), st>>>(b.s, b.a,
+								    SEQ, SEQ,
+								    1.0f / 16.0f);
+	gemm_nn<<<g1, tb, 0, st>>>(b.a, b.v, b.av, SEQ, DH, SEQ);
+	gemm_nn<<<g1, tb, 0, st>>>(b.av, wo, b.y, SEQ, DH, DH);
+}
+
+static float xval(int i)
+{
+	return (float)(((i % 7) - 3)) * 0.25f;
+}
+
+static float wval(int i, int seed)
+{
+	return (float)(((i * 13 + seed * 101 + 7) % 5) - 2) * 0.125f;
+}
+
+static void ref_gemm(const double *A, const double *B, double *C,
+		     int M, int N, int K)
+{
+	for (int i = 0; i < M; i++)
+		for (int j = 0; j < N; j++) {
+			double acc = 0.0;
+
+			for (int k = 0; k < K; k++)
+				acc += A[i * K + k] * B[k * N + j];
+			C[i * N + j] = acc;
+		}
+}
+
+static void ref_gemm_nt(const double *A, const double *B, double *C,
+			int M, int N, int K)
+{
+	for (int i = 0; i < M; i++)
+		for (int j = 0; j < N; j++) {
+			double acc = 0.0;
+
+			for (int k = 0; k < K; k++)
+				acc += A[i * K + k] * B[j * K + k];
+			C[i * N + j] = acc;
+		}
+}
+
+static void ref_softmax(const double *S, double *P, int rows, int cols,
+			double scale)
+{
+	for (int i = 0; i < rows; i++) {
+		const double *s = S + i * cols;
+		double *p = P + i * cols;
+		double m = s[0] * scale, sum = 0.0;
+
+		for (int j = 1; j < cols; j++)
+			if (s[j] * scale > m)
+				m = s[j] * scale;
+		for (int j = 0; j < cols; j++) {
+			p[j] = exp(s[j] * scale - m);
+			sum += p[j];
+		}
+		for (int j = 0; j < cols; j++)
+			p[j] /= sum;
+	}
+}
+
+static int count_bad(const float *gpu, const double *ref, int n)
+{
+	int bad = 0;
+
+	for (int i = 0; i < n; i++) {
+		double g = (double)gpu[i];
+		double r = ref[i];
+
+		if (fabs(g - r) > (double)TOL * (fabs(r) + 1e-6))
+			bad++;
+	}
+	return bad;
+}
+
+static double dsum(const float *h, int n)
+{
+	double s = 0.0;
+
+	for (int i = 0; i < n; i++)
+		s += (double)h[i];
+	return s;
+}
+
+/* Verify one attention result against the CPU reference for input offset o. */
+static int verify_one(const float *h_y, int o, double *ysum_out)
+{
+	const int seqdh = SEQ * DH, seq2 = SEQ * SEQ;
+	double *x, *q, *k, *v, *s, *a, *av, *y, *w;
+	int i, bad;
+
+	x = (double *)malloc((size_t)seqdh * sizeof(double));
+	q = (double *)malloc((size_t)seqdh * sizeof(double));
+	k = (double *)malloc((size_t)seqdh * sizeof(double));
+	v = (double *)malloc((size_t)seqdh * sizeof(double));
+	s = (double *)malloc((size_t)seq2 * sizeof(double));
+	a = (double *)malloc((size_t)seq2 * sizeof(double));
+	av = (double *)malloc((size_t)seqdh * sizeof(double));
+	y = (double *)malloc((size_t)seqdh * sizeof(double));
+	w = (double *)malloc((size_t)DH * DH * sizeof(double));
+	if (!x || !q || !k || !v || !s || !a || !av || !y || !w)
+		exit(1);
+
+	for (i = 0; i < seqdh; i++)
+		x[i] = (double)xval(i + o);
+	for (i = 0; i < DH * DH; i++)
+		w[i] = (double)wval(i, 1);
+	ref_gemm(x, w, q, SEQ, DH, DH);
+	for (i = 0; i < DH * DH; i++)
+		w[i] = (double)wval(i, 2);
+	ref_gemm(x, w, k, SEQ, DH, DH);
+	for (i = 0; i < DH * DH; i++)
+		w[i] = (double)wval(i, 3);
+	ref_gemm(x, w, v, SEQ, DH, DH);
+	ref_gemm_nt(q, k, s, SEQ, SEQ, DH);
+	ref_softmax(s, a, SEQ, SEQ, 1.0 / 16.0);
+	ref_gemm(a, v, av, SEQ, DH, SEQ);
+	for (i = 0; i < DH * DH; i++)
+		w[i] = (double)wval(i, 4);
+	ref_gemm(av, w, y, SEQ, DH, DH);
+
+	bad = count_bad(h_y, y, seqdh);
+	*ysum_out = dsum(h_y, seqdh);
+	free(x);
+	free(q);
+	free(k);
+	free(v);
+	free(s);
+	free(a);
+	free(av);
+	free(y);
+	free(w);
+	return bad;
+}
+
+int main(void)
+{
+	cudaDeviceProp prop;
+	const int seqdh = SEQ * DH, dh2 = DH * DH;
+	const size_t b_seqdh = (size_t)seqdh * sizeof(float);
+	const size_t b_dh2 = (size_t)dh2 * sizeof(float);
+	const size_t b_seq2 = (size_t)SEQ * SEQ * sizeof(float);
+	cudaStream_t st[NST], st_loop;
+	cudaEvent_t ev[NST], t0, t1;
+	struct attn_bufs ab[NST], lb;
+	float *d_x[NST], *h_y[NST];
+	float *d_xl[NLOOP], *d_yl[NLOOP], *h_yl[NLOOP];
+	float *d_wq, *d_wk, *d_wv, *d_wo, *wl;
+	float ms = 0.0f;
+	int bad_mt = 0, bad_loop = 0, s, i;
+
+	setvbuf(stdout, NULL, _IONBF, 0);
+
+	CHECK(cudaGetDeviceProperties(&prop, 0));
+	printf("DEVICE %s cc=%d.%d sm=%d mem=%llu\n", prop.name,
+	       prop.major, prop.minor, prop.multiProcessorCount,
+	       (unsigned long long)prop.totalGlobalMem);
+
+	CHECK(cudaMalloc(&d_wq, b_dh2));
+	CHECK(cudaMalloc(&d_wk, b_dh2));
+	CHECK(cudaMalloc(&d_wv, b_dh2));
+	CHECK(cudaMalloc(&d_wo, b_dh2));
+	wl = (float *)malloc(b_dh2);
+	if (!wl)
+		return 1;
+	for (i = 0; i < dh2; i++)
+		wl[i] = wval(i, 1);
+	CHECK(cudaMemcpy(d_wq, wl, b_dh2, cudaMemcpyHostToDevice));
+	for (i = 0; i < dh2; i++)
+		wl[i] = wval(i, 2);
+	CHECK(cudaMemcpy(d_wk, wl, b_dh2, cudaMemcpyHostToDevice));
+	for (i = 0; i < dh2; i++)
+		wl[i] = wval(i, 3);
+	CHECK(cudaMemcpy(d_wv, wl, b_dh2, cudaMemcpyHostToDevice));
+	for (i = 0; i < dh2; i++)
+		wl[i] = wval(i, 4);
+	CHECK(cudaMemcpy(d_wo, wl, b_dh2, cudaMemcpyHostToDevice));
+
+	/* ------------- class 1: 3 independent streams ------------- */
+	for (s = 0; s < NST; s++) {
+		float *hx = (float *)malloc(b_seqdh);
+
+		if (!hx)
+			return 1;
+		for (i = 0; i < seqdh; i++)
+			hx[i] = xval(i + s);
+		h_y[s] = (float *)malloc(b_seqdh);
+		if (!h_y[s])
+			return 1;
+
+		CHECK(cudaMalloc(&d_x[s], b_seqdh));
+		CHECK(cudaMalloc(&ab[s].q, b_seqdh));
+		CHECK(cudaMalloc(&ab[s].k, b_seqdh));
+		CHECK(cudaMalloc(&ab[s].v, b_seqdh));
+		CHECK(cudaMalloc(&ab[s].s, b_seq2));
+		CHECK(cudaMalloc(&ab[s].a, b_seq2));
+		CHECK(cudaMalloc(&ab[s].av, b_seqdh));
+		CHECK(cudaMalloc(&ab[s].y, b_seqdh));
+		CHECK(cudaStreamCreate(&st[s]));
+		CHECK(cudaEventCreate(&ev[s]));
+		CHECK(cudaMemcpyAsync(d_x[s], hx, b_seqdh,
+				      cudaMemcpyHostToDevice, st[s]));
+		free(hx);
+	}
+	CHECK(cudaEventCreate(&t0));
+	CHECK(cudaEventCreate(&t1));
+	CHECK(cudaEventRecord(t0));
+
+	for (s = 0; s < NST; s++) {
+		launch_chain(st[s], d_x[s], ab[s], d_wq, d_wk, d_wv, d_wo);
+		CHECK(cudaGetLastError());
+		CHECK(cudaEventRecord(ev[s], st[s]));
+		CHECK(cudaMemcpyAsync(h_y[s], ab[s].y, b_seqdh,
+				      cudaMemcpyDeviceToHost, st[s]));
+	}
+	/* event-ordered join: stream 2 waits on streams 0 and 1 */
+	CHECK(cudaStreamWaitEvent(st[2], ev[0], 0));
+	CHECK(cudaStreamWaitEvent(st[2], ev[1], 0));
+	CHECK(cudaEventRecord(t1, st[2]));
+	for (s = 0; s < NST; s++)
+		CHECK(cudaStreamSynchronize(st[s]));
+	CHECK(cudaEventElapsedTime(&ms, t0, t1));
+
+	for (s = 0; s < NST; s++) {
+		double ys;
+
+		bad_mt += verify_one(h_y[s], s, &ys);
+	}
+	printf("TRANSFORMER_MT streams=%d bad=%d ysum=%.6f\n", NST, bad_mt,
+	       dsum(h_y[0], seqdh));
+
+	/* ----------------- class 2: 5-launch loop ----------------- */
+	CHECK(cudaStreamCreate(&st_loop));
+	CHECK(cudaMalloc(&lb.q, b_seqdh));
+	CHECK(cudaMalloc(&lb.k, b_seqdh));
+	CHECK(cudaMalloc(&lb.v, b_seqdh));
+	CHECK(cudaMalloc(&lb.s, b_seq2));
+	CHECK(cudaMalloc(&lb.a, b_seq2));
+	CHECK(cudaMalloc(&lb.av, b_seqdh));
+	CHECK(cudaMalloc(&lb.y, b_seqdh)); /* unused; op writes per-iter y */
+	for (i = 0; i < NLOOP; i++) {
+		float *hx = (float *)malloc(b_seqdh);
+
+		if (!hx)
+			return 1;
+		for (s = 0; s < seqdh; s++)
+			hx[s] = xval(s + 11 * i);
+		CHECK(cudaMalloc(&d_xl[i], b_seqdh));
+		CHECK(cudaMalloc(&d_yl[i], b_seqdh));
+		CHECK(cudaMemcpy(d_xl[i], hx, b_seqdh,
+				 cudaMemcpyHostToDevice));
+		h_yl[i] = (float *)malloc(b_seqdh);
+		if (!h_yl[i])
+			return 1;
+		free(hx);
+	}
+
+	for (i = 0; i < NLOOP; i++) {
+		struct attn_bufs b = lb;
+
+		b.y = d_yl[i];
+		launch_chain(st_loop, d_xl[i], b, d_wq, d_wk, d_wv, d_wo);
+		CHECK(cudaGetLastError());
+	}
+	CHECK(cudaStreamSynchronize(st_loop));
+	for (i = 0; i < NLOOP; i++) {
+		double ys;
+
+		CHECK(cudaMemcpy(h_yl[i], d_yl[i], b_seqdh,
+				 cudaMemcpyDeviceToHost));
+		bad_loop += verify_one(h_yl[i], 11 * i, &ys);
+	}
+	printf("TRANSFORMER_LOOP launches=%d bad=%d ysum=%.6f\n", NLOOP,
+	       bad_loop, dsum(h_yl[NLOOP - 1], seqdh));
+	printf("TIMING ms=%.3f\n", ms);
+
+	if (bad_mt != 0 || bad_loop != 0) {
+		printf("STATUS FAIL\n");
+		return 1;
+	}
+
+	printf("STATUS OK\n");
+	for (s = 0; s < NST; s++) {
+		CHECK(cudaEventDestroy(ev[s]));
+		CHECK(cudaStreamDestroy(st[s]));
+		CHECK(cudaFree(d_x[s]));
+		CHECK(cudaFree(ab[s].q));
+		CHECK(cudaFree(ab[s].k));
+		CHECK(cudaFree(ab[s].v));
+		CHECK(cudaFree(ab[s].s));
+		CHECK(cudaFree(ab[s].a));
+		CHECK(cudaFree(ab[s].av));
+		CHECK(cudaFree(ab[s].y));
+		free(h_y[s]);
+	}
+	for (i = 0; i < NLOOP; i++) {
+		CHECK(cudaFree(d_xl[i]));
+		CHECK(cudaFree(d_yl[i]));
+		free(h_yl[i]);
+	}
+	CHECK(cudaEventDestroy(t0));
+	CHECK(cudaEventDestroy(t1));
+	CHECK(cudaStreamDestroy(st_loop));
+	CHECK(cudaFree(lb.q));
+	CHECK(cudaFree(lb.k));
+	CHECK(cudaFree(lb.v));
+	CHECK(cudaFree(lb.s));
+	CHECK(cudaFree(lb.a));
+	CHECK(cudaFree(lb.av));
+	CHECK(cudaFree(lb.y));
+	CHECK(cudaFree(d_wq));
+	CHECK(cudaFree(d_wk));
+	CHECK(cudaFree(d_wv));
+	CHECK(cudaFree(d_wo));
+	free(wl);
+	return 0;
+}