cuda: fuse snake activation (mul, sin, sqr, mul, add)

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -39,6 +39,7 @@
 #include "ggml-cuda/rope.cuh"
 #include "ggml-cuda/roll.cuh"
 #include "ggml-cuda/scale.cuh"
+#include "ggml-cuda/snake.cuh"
 #include "ggml-cuda/softcap.cuh"
 #include "ggml-cuda/softmax.cuh"
 #include "ggml-cuda/ssm-conv.cuh"
@@ -3757,6 +3758,35 @@ static int ggml_cuda_try_fuse(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph
         return 2;
     }
 
+    // Snake activation: y = x + sin(a*x)^2 * inv_b
+    // Naive 5-op decomposition emitted by frontends: mul -> sin -> sqr -> mul -> add
+    if (ggml_can_fuse_subgraph(cgraph, i,
+            { GGML_OP_MUL, GGML_OP_SIN, GGML_OP_SQR, GGML_OP_MUL, GGML_OP_ADD },
+            { i + 4 })) {
+        const ggml_tensor * mul0 = cgraph->nodes[i];
+        const ggml_tensor * sqr  = cgraph->nodes[i + 2];
+        const ggml_tensor * mul1 = cgraph->nodes[i + 3];
+        ggml_tensor *       add  = cgraph->nodes[i + 4];
+
+        // x carries the full activation shape, a is the broadcast operand
+        const ggml_tensor * x = ggml_are_same_shape(mul0, mul0->src[0]) ? mul0->src[0] : mul0->src[1];
+        const ggml_tensor * a = (x == mul0->src[0]) ? mul0->src[1] : mul0->src[0];
+
+        // mul1 reads sqr and inv_b in either operand order
+        const ggml_tensor * inv_b = (mul1->src[0] == sqr) ? mul1->src[1] : mul1->src[0];
+
+        // closure check: the trailing add must read the same x as the leading mul
+        const ggml_tensor * x_in_add = (add->src[0] == mul1) ? add->src[1] : add->src[0];
+
+        const bool type_ok  = (x->type == GGML_TYPE_F32 || x->type == GGML_TYPE_F16 || x->type == GGML_TYPE_BF16);
+        const bool shape_ok = ggml_are_same_shape(a, inv_b) && a->ne[0] == 1 && a->ne[1] == x->ne[1];
+
+        if (type_ok && shape_ok && x_in_add == x) {
+            ggml_cuda_op_snake_fused(*cuda_ctx, x, a, inv_b, add);
+            return 4;
+        }
+    }
+
     // multi-(add or mul)
     if (node->op == GGML_OP_ADD || node->op == GGML_OP_MUL) {
         int     n_fuse = 0;

ggml/src/ggml-cuda/snake.cu

@@ -0,0 +1,71 @@
+#include "snake.cuh"
+#include "convert.cuh"
+
+// Fused Snake activation: y = x + sin^2(a * x) * inv_b
+// x: [T, C] (T contiguous), a: [1, C], inv_b: [1, C]
+// Supports F32, F16, BF16 data with F32 compute.
+
+template <typename T>
+static __global__ void snake_kernel(
+        const T     * __restrict__ x,
+        const float * __restrict__ a,
+        const float * __restrict__ inv_b,
+        T           * __restrict__ dst,
+        const int T_len,
+        const int C) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (idx >= T_len * C) return;
+
+    const int c = idx / T_len;
+
+    const float xi = ggml_cuda_cast<float>(x[idx]);
+    const float s  = sinf(a[c] * xi);
+    dst[idx] = ggml_cuda_cast<T>(xi + s * s * inv_b[c]);
+}
+
+// Internal launcher with explicit x/a/inv_b/dst tensors.
+// Shared by the public op (reads dst->src) and the fusion path (explicit args).
+static void launch_snake(ggml_backend_cuda_context & ctx,
+                         const ggml_tensor * x,
+                         const ggml_tensor * a,
+                         const ggml_tensor * inv_b,
+                         ggml_tensor *       dst) {
+    const float * a_d     = (const float *)a->data;
+    const float * inv_b_d = (const float *)inv_b->data;
+
+    const int T = (int)x->ne[0];
+    const int C = (int)x->ne[1];
+    const int total = T * C;
+
+    const int block_size = 256;
+    const int grid_size  = (total + block_size - 1) / block_size;
+
+    cudaStream_t stream = ctx.stream();
+
+    switch (x->type) {
+        case GGML_TYPE_F32: {
+            snake_kernel<<<grid_size, block_size, 0, stream>>>(
+                (const float *)x->data, a_d, inv_b_d, (float *)dst->data, T, C);
+        } break;
+        case GGML_TYPE_F16: {
+            snake_kernel<<<grid_size, block_size, 0, stream>>>(
+                (const half *)x->data, a_d, inv_b_d, (half *)dst->data, T, C);
+        } break;
+        case GGML_TYPE_BF16: {
+            snake_kernel<<<grid_size, block_size, 0, stream>>>(
+                (const nv_bfloat16 *)x->data, a_d, inv_b_d, (nv_bfloat16 *)dst->data, T, C);
+        } break;
+        default:
+            GGML_ABORT("snake: unsupported type");
+    }
+}
+
+// Fusion entry: caller supplies x/a/inv_b explicitly from the matched
+// mul -> sin -> sqr -> mul -> add pattern. The dst is the trailing add output.
+void ggml_cuda_op_snake_fused(ggml_backend_cuda_context & ctx,
+                              const ggml_tensor * x,
+                              const ggml_tensor * a,
+                              const ggml_tensor * inv_b,
+                              ggml_tensor *       dst) {
+    launch_snake(ctx, x, a, inv_b, dst);
+}

ggml/src/ggml-cuda/snake.cuh

@@ -0,0 +1,8 @@
+#include "common.cuh"
+
+// Fusion entry point. Caller supplies x/a/inv_b explicitly.
+void ggml_cuda_op_snake_fused(ggml_backend_cuda_context & ctx,
+                              const ggml_tensor * x,
+                              const ggml_tensor * a,
+                              const ggml_tensor * inv_b,
+                              ggml_tensor *       dst);

tests/test-backend-ops.cpp

@@ -3556,6 +3556,74 @@ struct test_relu_sqr : public test_case {
     }
 };
 
+// SNAKE activation fusion: y = x + sin(a*x)^2 * inv_b
+// CUDA backend matches the naive 5-op chain (mul, sin, sqr, mul, add)
+// and dispatches a single fused kernel. CPU runs the chain naively, so
+// agreement between backends proves the fused kernel preserves the math.
+struct test_snake_fuse : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 2> ne;   // [T, C]
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return "SNAKE_FUSE";
+    }
+
+    bool run_whole_graph() override { return true; }
+
+    double max_nmse_err() override {
+        // BF16 epsilon ~ 7.8e-3, F16 epsilon ~ 9.7e-4: relax tolerance to match
+        // the natural roundoff drift between the naive CPU chain and the fused
+        // CUDA kernel. F32 keeps the default tight bound.
+        switch (type) {
+            case GGML_TYPE_BF16: return 5e-3;
+            case GGML_TYPE_F16:  return 5e-5;
+            default:             return 1e-7;
+        }
+    }
+
+    std::string vars() override {
+        return VARS_TO_STR2(type, ne);
+    }
+
+    test_snake_fuse(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 2> ne = {256, 192})
+        : type(type), ne(ne) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * x = ggml_new_tensor_2d(ctx, type, ne[0], ne[1]);
+        ggml_set_name(x, "x");
+
+        ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, ne[1]);
+        ggml_set_name(a, "a");
+
+        ggml_tensor * inv_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, ne[1]);
+        ggml_set_name(inv_b, "inv_b");
+
+        // exact 5-op chain that BigVGAN / Vocos frontends emit
+        ggml_tensor * ax     = ggml_mul(ctx, x, a);
+        ggml_tensor * sin_ax = ggml_sin(ctx, ax);
+        ggml_tensor * sin_sq = ggml_sqr(ctx, sin_ax);
+        ggml_tensor * scaled = ggml_mul(ctx, sin_sq, inv_b);
+        ggml_tensor * out    = ggml_add(ctx, x, scaled);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        // x in [-pi, pi] to exercise sin periodicity, params in default [-1, 1]
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+            const std::string name = ggml_get_name(t);
+            if (name == "x") {
+                init_tensor_uniform(t, -3.14159f, 3.14159f);
+            } else {
+                init_tensor_uniform(t);
+            }
+        }
+    }
+};
+
 // GGML_OP_SSM_CONV
 struct test_ssm_conv : public test_case {
     const ggml_type type;
@@ -7412,6 +7480,15 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         test_cases.emplace_back(new test_relu_sqr(type, { 5, 7, 11, 13 }));
     }
 
+    // SNAKE activation fusion: x + sin(a*x)^2 * inv_b
+    for (ggml_type type : { GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16 }) {
+        test_cases.emplace_back(new test_snake_fuse(type, {   5,   7}));   // primes sub-block
+        test_cases.emplace_back(new test_snake_fuse(type, {  33,  32}));   // boundary
+        test_cases.emplace_back(new test_snake_fuse(type, {1025,  13}));   // large prime, grid-stride
+        test_cases.emplace_back(new test_snake_fuse(type, { 128,  16}));   // power-of-two
+        test_cases.emplace_back(new test_snake_fuse(type, { 256, 192}));   // BigVGAN-ish
+    }
+
     // glu ops
     for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) {
         for (int v : {0, 1}) {
@@ -8914,6 +8991,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     test_cases.emplace_back(new test_pad_reflect_1d(GGML_TYPE_F32, {3000, 384, 1, 1}));
     test_cases.emplace_back(new test_pad_reflect_1d(GGML_TYPE_F32, {3000, 384, 4, 1}));
 
+    // SNAKE activation fusion at BigVGAN scale (T=7680 = 24 kHz x 320 ms, C=192)
+    test_cases.emplace_back(new test_snake_fuse(GGML_TYPE_F32,  {7680, 192}));
+    test_cases.emplace_back(new test_snake_fuse(GGML_TYPE_F16,  {7680, 192}));
+    test_cases.emplace_back(new test_snake_fuse(GGML_TYPE_BF16, {7680, 192}));
+
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 16416, 1, 128, {8,  1}, {4, 1}, {0, 2, 1, 3}));
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 1, 16416, {8,  1}, {4, 1}, {0, 1, 2, 3}, 2*16416));