feat(ggml-cuda): CUDA implementation of CUMSUM

Branch: Mamba2SSD

Signed-off-by: Gabe Goodhart <[email protected]>

ggml/src/ggml-cuda/cumsum.cu

@@ -0,0 +1,126 @@
+#include "cumsum.cuh"
+
+// Kernel to compute cumulative sum along the innermost dimension (ne[0])
+// Each block processes one row (ne[0] elements)
+// Algorithm matches Metal implementation:
+// 1. Each warp computes prefix sum within itself
+// 2. Last thread of each warp stores result in shared memory
+// 3. All warps sync
+// 4. Each element adds the sum of all preceding warps
+
+template<typename T>
+static __global__ void cumsum_kernel(
+    const T * src, T * dst,
+    const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
+    const int64_t nb00, const int64_t nb01, const int64_t nb02, const int64_t nb03,
+    const int64_t nb0,  const int64_t nb1,  const int64_t nb2,  const int64_t nb3) {
+
+    // Shared memory to store warp sums (always use float for accumulation)
+    extern __shared__ float shmem[];
+
+    const int64_t i3 = blockIdx.z;
+    const int64_t i2 = blockIdx.y;
+    const int64_t i1 = blockIdx.x;
+
+    if (i3 >= ne03 || i2 >= ne02 || i1 >= ne01) {
+        return;
+    }
+
+    const T * src_row = (const T *) ((const char *) src + i1*nb01 + i2*nb02 + i3*nb03);
+    T       * dst_row = (T       *) ((      char *) dst + i1*nb1  + i2*nb2  + i3*nb3);
+
+    const int tid = threadIdx.x;
+    const int lane_id = tid % WARP_SIZE;
+
+    // Phase 1: Each thread processes elements at stride blockDim.x
+    // Compute warp-level prefix sums
+    for (int64_t i0 = tid; i0 < ne00; i0 += blockDim.x) {
+        // Load value and compute prefix sum within warp
+        float val = static_cast<float>(src_row[i0]);
+        val = warp_prefix_inclusive_sum(val);
+        dst_row[i0] = static_cast<T>(val);
+
+        // Last thread of warp stores its sum to shared memory at position based on data index
+        if (lane_id == WARP_SIZE - 1 || i0 == ne00 - 1) {
+            const int shmem_idx = i0 / WARP_SIZE;
+            shmem[shmem_idx] = val;
+        }
+    }
+
+    // Sync once after all warp prefix sums are computed
+    __syncthreads();
+
+    // Phase 2: Add the sum of all preceding warp groups to each element
+    for (int64_t i0 = tid; i0 < ne00; i0 += blockDim.x) {
+        const int shmem_idx = i0 / WARP_SIZE;
+        float sum = 0.0f;
+        for (int j = 0; j < shmem_idx; ++j) {
+            sum += shmem[j];
+        }
+        dst_row[i0] = static_cast<T>(static_cast<float>(dst_row[i0]) + sum);
+    }
+}
+
+template<typename T>
+static void cumsum_cuda(
+    const T * src, T * dst,
+    const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
+    const int64_t nb00, const int64_t nb01, const int64_t nb02, const int64_t nb03,
+    const int64_t nb0,  const int64_t nb1,  const int64_t nb2,  const int64_t nb3,
+    cudaStream_t stream) {
+
+    dim3 block_dims(CUDA_CUMSUM_BLOCK_SIZE, 1, 1);
+    dim3 grid_dims(ne01, ne02, ne03);
+
+    // Shared memory size: one float per warp
+    const int num_warps = (ne00 + WARP_SIZE - 1) / WARP_SIZE;
+    const size_t shmem_size = num_warps * sizeof(float);
+
+    cumsum_kernel<<<grid_dims, block_dims, shmem_size, stream>>>(
+        src, dst,
+        ne00, ne01, ne02, ne03,
+        nb00, nb01, nb02, nb03,
+        nb0, nb1, nb2, nb3
+    );
+}
+
+void ggml_cuda_op_cumsum(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == dst->type);
+    switch(src0->type) {
+        case GGML_TYPE_F32:
+            {
+                cumsum_cuda(
+                    (const float *)src0->data, (float *)dst->data,
+                    src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
+                    src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
+                    dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3],
+                    stream
+                );
+            } break;
+        case GGML_TYPE_F16:
+            {
+                cumsum_cuda(
+                    (const half *)src0->data, (half *)dst->data,
+                    src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
+                    src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
+                    dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3],
+                    stream
+                );
+            } break;
+        case GGML_TYPE_BF16:
+            {
+                cumsum_cuda(
+                    (const nv_bfloat16 *)src0->data, (nv_bfloat16 *)dst->data,
+                    src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
+                    src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
+                    dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3],
+                    stream
+                );
+            } break;
+        default:
+            GGML_ABORT("fatal error");
+    }
+}

ggml/src/ggml-cuda/cumsum.cuh

@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_CUMSUM_BLOCK_SIZE 256
+
+void ggml_cuda_op_cumsum(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

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

@@ -19,6 +19,7 @@
 #include "ggml-cuda/count-equal.cuh"
 #include "ggml-cuda/cpy.cuh"
 #include "ggml-cuda/cross-entropy-loss.cuh"
+#include "ggml-cuda/cumsum.cuh"
 #include "ggml-cuda/diagmask.cuh"
 #include "ggml-cuda/fattn.cuh"
 #include "ggml-cuda/getrows.cuh"
@@ -2512,6 +2513,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_CROSS_ENTROPY_LOSS:
             ggml_cuda_cross_entropy_loss(ctx, dst);
             break;
+        case GGML_OP_CUMSUM:
+            ggml_cuda_op_cumsum(ctx, dst);
+            break;
         case GGML_OP_RWKV_WKV6:
             ggml_cuda_op_rwkv_wkv6(ctx, dst);
             break;
@@ -3650,6 +3654,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
             return ggml_cuda_flash_attn_ext_supported(dev_ctx->device, op);
         case GGML_OP_CROSS_ENTROPY_LOSS:
         case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
+        case GGML_OP_CUMSUM:
         case GGML_OP_OPT_STEP_ADAMW:
         case GGML_OP_OPT_STEP_SGD:
             return true;