Commit 53ed051ce for llama.cpp

commit 53ed051ce5e8193652e449f43216ca3859454f49
Author: leejet <leejet714@gmail.com>
Date:   Thu Sep 24 15:24:57 2026 +0800

    cuda : add conv3d with implicit GEMM (#29137)

    * cuda : add conv3d with implicit GEMM

    * cuda : refine conv3d implicit GEMM and handle empty kernels

diff --git a/docs/ops.md b/docs/ops.md
index 0921392f3..c07707a7d 100644
--- a/docs/ops.md
+++ b/docs/ops.md
@@ -29,7 +29,7 @@ Legend:
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                          CONV_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              COS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
diff --git a/docs/ops/CUDA.csv b/docs/ops/CUDA.csv
index 570bdf75e..598aee957 100644
--- a/docs/ops/CUDA.csv
+++ b/docs/ops/CUDA.csv
@@ -4588,264 +4588,282 @@
 "CUDA0","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=1","support","1","yes","CUDA"
 "CUDA0","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=0","support","1","yes","CUDA"
 "CUDA0","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=1","support","1","yes","CUDA"
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+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
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+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
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+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
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+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
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 "CUDA0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=1,p0=0,d0=1","support","1","yes","CUDA"
 "CUDA0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=2,p0=0,d0=1","support","1","yes","CUDA"
 "CUDA0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=3,p0=0,d0=1","support","1","yes","CUDA"
diff --git a/ggml/src/ggml-cuda/conv3d.cu b/ggml/src/ggml-cuda/conv3d.cu
new file mode 100644
index 000000000..7e4bd57ac
--- /dev/null
+++ b/ggml/src/ggml-cuda/conv3d.cu
@@ -0,0 +1,362 @@
+#include "conv3d.cuh"
+#include "convert.cuh"
+#include "mma.cuh"
+
+struct conv3d_params {
+    int64_t IW, IH, ID;
+    int64_t OW, OH, OD;
+    int64_t KW, KH, KD;
+    int64_t ST_X, ST_Y, ST_Z;
+    int64_t PD_X, PD_Y, PD_Z;
+    int64_t DL_X, DL_Y, DL_Z;
+    int64_t IC, OC, B, TOTAL;
+};
+
+template <typename T>
+static __global__ void conv3d_kernel(const float * input, const T * weight, float * output, const conv3d_params P) {
+    const int64_t spatial = P.OW * P.OH * P.OD;
+    for (int64_t i = int64_t(blockIdx.x) * blockDim.x + threadIdx.x; i < P.TOTAL;
+         i += int64_t(gridDim.x) * blockDim.x) {
+        const int64_t x = i % P.OW, y = i / P.OW % P.OH, z = i / (P.OW * P.OH) % P.OD;
+        const int64_t co = i / spatial % P.OC, n = i / (spatial * P.OC);
+        float         sum = 0.0f;
+        for (int64_t ci = 0; ci < P.IC; ++ci) {
+            for (int64_t kz = 0; kz < P.KD; ++kz) {
+                const int64_t iz = z * P.ST_Z + kz * P.DL_Z - P.PD_Z;
+                if (iz < 0 || iz >= P.ID) {
+                    continue;
+                }
+                for (int64_t ky = 0; ky < P.KH; ++ky) {
+                    const int64_t iy = y * P.ST_Y + ky * P.DL_Y - P.PD_Y;
+                    if (iy < 0 || iy >= P.IH) {
+                        continue;
+                    }
+                    for (int64_t kx = 0; kx < P.KW; ++kx) {
+                        const int64_t ix = x * P.ST_X + kx * P.DL_X - P.PD_X;
+                        if (ix >= 0 && ix < P.IW) {
+                            const int64_t xi = (((n * P.IC + ci) * P.ID + iz) * P.IH + iy) * P.IW + ix;
+                            const int64_t wi = (((co * P.IC + ci) * P.KD + kz) * P.KH + ky) * P.KW + kx;
+                            sum += input[xi] * ggml_cuda_cast<float>(weight[wi]);
+                        }
+                    }
+                }
+            }
+        }
+        output[i] = sum;
+    }
+}
+
+static __global__ void conv3d_pad_f16(const float * input,
+                                      half *        output,
+                                      int           iw,
+                                      int           ih,
+                                      int           id,
+                                      int           pw,
+                                      int           ph,
+                                      int           pd,
+                                      int           px,
+                                      int           py,
+                                      int           pz,
+                                      int           total) {
+    const int i = blockIdx.x * blockDim.x + threadIdx.x;
+    if (i >= total) {
+        return;
+    }
+    const int x = i % pw - px, y = i / pw % ph - py, z = i / (pw * ph) % pd - pz;
+    const int nc = i / (pw * ph * pd);
+    output[i] =
+        __float2half((unsigned) x < (unsigned) iw && (unsigned) y < (unsigned) ih && (unsigned) z < (unsigned) id ?
+                         input[((nc * id + z) * ih + y) * iw + x] :
+                         0.0f);
+}
+
+template <int KW, int KH, int KD, bool use_mma>
+static __global__ void conv3d_implicit_gemm_f16(const half * __restrict__ input,
+                                                const half * __restrict__ weight,
+                                                float * __restrict__ output,
+                                                const conv3d_params P,
+                                                const int           split_k,
+                                                const bool          aligned_weights) {
+    using namespace ggml_cuda_mma;
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
+    constexpr int nthreads  = 4 * warp_size;
+    constexpr int BM = 64, BN = 64, BK = 64;
+    constexpr int AS = BK / 2 + 4;
+    constexpr int BS = BN / 2 + 4;
+    static_assert(AS * sizeof(half2) % sizeof(int4) == 0, "shared weight rows must be 16-byte aligned");
+    __shared__ __align__(16) half2 a_s[BM][AS];
+    __shared__ __align__(16) half2 b_s[BK][BS];
+    const int                      tid = threadIdx.y * warp_size + threadIdx.x;
+    const int                      iw = int(P.IW), ih = int(P.IH), id = int(P.ID);
+    const int                      ow = int(P.OW), oh = int(P.OH), od = int(P.OD);
+    const int                      kw = KW ? KW : int(P.KW), kh = KH ? KH : int(P.KH), kd = KD ? KD : int(P.KD);
+    const int                      ic = int(P.IC), oc = int(P.OC);
+    const int                      sx = int(P.ST_X), sy = int(P.ST_Y), sz = int(P.ST_Z);
+    const int                      dx = int(P.DL_X), dy = int(P.DL_Y), dz = int(P.DL_Z);
+    const int                      n = blockIdx.z / split_k, split = blockIdx.z % split_k;
+    const int                      m0 = blockIdx.y * BM, n0 = blockIdx.x * BN;
+    const int                      k_total   = ic * kw * kh * kd;
+    const int                      load_lane = warp_size == 32 ? threadIdx.x : threadIdx.x % (BN / 2);
+    const int load_row = threadIdx.y * (warp_size / (BN / 2)) + (warp_size == 32 ? 0 : threadIdx.x / (BN / 2));
+    const int spatial  = n0 + 2 * load_lane;
+    const int spatial0 = min(spatial, ow * oh * od - 1), spatial1 = min(spatial + 1, ow * oh * od - 1);
+    const int z0 = spatial0 / (ow * oh), y0 = spatial0 / ow % oh, x0 = spatial0 % ow;
+    const int z1 = spatial1 / (ow * oh), y1 = spatial1 / ow % oh, x1 = spatial1 % ow;
+    const int pos0                = (z0 * sz * ih + y0 * sy) * iw + x0 * sx;
+    const int pos1                = (z1 * sz * ih + y1 * sy) * iw + x1 * sx;
+    [[maybe_unused]] const int wm = threadIdx.y / 2 * 32, wn = threadIdx.y % 2 * 32;
+    using tile_ab = tile<16, 8, half2, get_input_data_layout()>;
+#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
+    // AMD accumulator fragments transpose the input fragment's row/column mapping.
+    using tile_c = tile<16, 16, float, DATA_LAYOUT_J_MAJOR>;
+#else
+    using tile_c = tile<16, 16, float>;
+#endif
+    [[maybe_unused]] tile_c    c[2][2];
+    constexpr int              RM = 4, RN = BM * BN / (nthreads * RM);
+    [[maybe_unused]] const int simt_m = tid / (BN / RN) * RM, simt_n = tid % (BN / RN) * RN;
+    [[maybe_unused]] float     c_simt[RM][RN] = {};
+    const int                  tiles          = (k_total + BK - 1) / BK;
+    const int                  begin          = int(int64_t(tiles) * split / split_k) * BK;
+    const int                  end            = int(int64_t(tiles) * (split + 1) / split_k) * BK;
+    for (int k0 = begin; k0 < end; k0 += BK) {
+        if (aligned_weights) {
+#pragma unroll
+            for (int i0 = 0; i0 < BM * BK / 8; i0 += nthreads) {
+                const int  i   = i0 + tid;
+                const int  row = i / (BK / 8), col = 8 * (i % (BK / 8));
+                const int4 v                 = m0 + row < oc && k0 + col < k_total ?
+                                                   ((const int4 *) weight)[((m0 + row) * k_total + k0 + col) / 8] :
+                                                   make_int4(0, 0, 0, 0);
+                *(int4 *) &a_s[row][col / 2] = v;
+            }
+        } else {
+#pragma unroll
+            for (int i0 = 0; i0 < BM * BK / 2; i0 += nthreads) {
+                const int i   = i0 + tid;
+                const int row = i / (BK / 2), col = 2 * (i % (BK / 2));
+                half      lo = __float2half(0.0f), hi = lo;
+                if (m0 + row < oc && k0 + col < k_total) {
+                    lo = weight[(m0 + row) * k_total + k0 + col];
+                    if (k0 + col + 1 < k_total) {
+                        hi = weight[(m0 + row) * k_total + k0 + col + 1];
+                    }
+                }
+                a_s[row][col / 2] = __halves2half2(lo, hi);
+            }
+        }
+#pragma unroll
+        for (int kb = 0; kb < BK; kb += nthreads / (BN / 2)) {
+            const int k  = kb + load_row;
+            const int ki = k0 + k;
+            const int ci = ki / (kw * kh * kd), kz = ki / (kw * kh) % kd, ky = ki / kw % kh, kx = ki % kw;
+            const int offset = ki < k_total ? ((n * ic + ci) * id + kz * dz) * ih * iw + ky * dy * iw + kx * dx : 0;
+            half      lo = __float2half(0.0f), hi = lo;
+            if (ki < k_total && spatial < ow * oh * od) {
+                lo = input[offset + pos0];
+            }
+            if (ki < k_total && spatial + 1 < ow * oh * od) {
+                hi = input[offset + pos1];
+            }
+            b_s[k][load_lane] = __halves2half2(lo, hi);
+        }
+        __syncthreads();
+        if constexpr (use_mma) {
+#pragma unroll
+            for (int k = 0; k < BK; k += 16) {
+                tile_ab a[2], b[2];
+#pragma unroll
+                for (int i = 0; i < 2; ++i) {
+                    load_ldmatrix(a[i], &a_s[wm + 16 * i][k / 2], AS);
+                    load_ldmatrix_trans(b[i], &b_s[k][(wn + 16 * i) / 2], BS);
+                }
+#pragma unroll
+                for (int i = 0; i < 2; ++i) {
+#pragma unroll
+                    for (int j = 0; j < 2; ++j) {
+                        mma(c[i][j], a[i], b[j]);
+                    }
+                }
+            }
+        } else {
+#pragma unroll 4
+            for (int k = 0; k < BK; ++k) {
+                float a[RM], b[RN];
+#pragma unroll
+                for (int i = 0; i < RM; ++i) {
+                    a[i] = __half2float(((const half *) a_s[simt_m + i])[k]);
+                }
+#pragma unroll
+                for (int j = 0; j < RN; ++j) {
+                    b[j] = __half2float(((const half *) b_s[k])[simt_n + j]);
+                }
+#pragma unroll
+                for (int i = 0; i < RM; ++i) {
+#pragma unroll
+                    for (int j = 0; j < RN; ++j) {
+                        c_simt[i][j] += a[i] * b[j];
+                    }
+                }
+            }
+        }
+        __syncthreads();
+    }
+    if constexpr (use_mma) {
+#pragma unroll
+        for (int i = 0; i < 2; ++i) {
+#pragma unroll
+            for (int j = 0; j < 2; ++j) {
+#pragma unroll
+                for (int l = 0; l < c[i][j].ne; ++l) {
+                    const int co  = m0 + wm + 16 * i + c[i][j].get_i(l);
+                    const int pos = n0 + wn + 16 * j + c[i][j].get_j(l);
+                    if (co < oc && pos < ow * oh * od) {
+                        output[(int64_t(blockIdx.z) * oc + co) * ow * oh * od + pos] = c[i][j].x[l];
+                    }
+                }
+            }
+        }
+    } else {
+#pragma unroll
+        for (int i = 0; i < RM; ++i) {
+#pragma unroll
+            for (int j = 0; j < RN; ++j) {
+                const int co = m0 + simt_m + i, pos = n0 + simt_n + j;
+                if (co < oc && pos < ow * oh * od) {
+                    output[(int64_t(blockIdx.z) * oc + co) * ow * oh * od + pos] = c_simt[i][j];
+                }
+            }
+        }
+    }
+}
+
+static __global__ void conv3d_reduce_split_k(const float * __restrict__ partial,
+                                             float * __restrict__ output,
+                                             const int total,
+                                             const int per_batch,
+                                             const int split_k) {
+    const int i = blockIdx.x * blockDim.x + threadIdx.x;
+    if (i >= total) {
+        return;
+    }
+    const int     n   = i / per_batch;
+    // Partial slices are ordered as [batch, split, output channel, spatial position].
+    const float * src = partial + int64_t(n) * (split_k - 1) * per_batch + i;
+    float         sum = 0.0f;
+    for (int k = 0; k < split_k; ++k) {
+        sum += src[int64_t(k) * per_batch];
+    }
+    output[i] = sum;
+}
+
+template <bool use_mma>
+static void conv3d_launch_implicit_gemm(const half *          input,
+                                        const half *          weight,
+                                        float *               output,
+                                        const conv3d_params & params,
+                                        int                   split_k,
+                                        dim3                  grid,
+                                        dim3                  block,
+                                        cudaStream_t          stream) {
+    // Vector loads require both the base pointer and each weight row to be 16-byte aligned.
+    const bool aligned_weights = uintptr_t(weight) % sizeof(int4) == 0 &&
+                                 (params.IC * params.KW * params.KH * params.KD) % (sizeof(int4) / sizeof(half)) == 0;
+    if (params.KW == 3 && params.KH == 3 && params.KD == 3) {
+        conv3d_implicit_gemm_f16<3, 3, 3, use_mma>
+            <<<grid, block, 0, stream>>>(input, weight, output, params, split_k, aligned_weights);
+    } else if (params.KW == 1 && params.KH == 1 && params.KD == 3) {
+        conv3d_implicit_gemm_f16<1, 1, 3, use_mma>
+            <<<grid, block, 0, stream>>>(input, weight, output, params, split_k, aligned_weights);
+    } else if (params.KW == 1 && params.KH == 1 && params.KD == 1) {
+        conv3d_implicit_gemm_f16<1, 1, 1, use_mma>
+            <<<grid, block, 0, stream>>>(input, weight, output, params, split_k, aligned_weights);
+    } else {
+        conv3d_implicit_gemm_f16<0, 0, 0, use_mma>
+            <<<grid, block, 0, stream>>>(input, weight, output, params, split_k, aligned_weights);
+    }
+}
+
+void ggml_cuda_op_conv3d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * kernel = dst->src[0];
+    const ggml_tensor * input  = dst->src[1];
+    GGML_ASSERT(input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(kernel->type == GGML_TYPE_F16 || kernel->type == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguous(input) && ggml_is_contiguous(kernel) && ggml_is_contiguous(dst));
+
+    const int32_t * p  = dst->op_params;
+    const int64_t   IW = input->ne[0], IH = input->ne[1], ID = input->ne[2];
+    const int64_t   OW = dst->ne[0], OH = dst->ne[1], OD = dst->ne[2];
+    const int64_t   KW = kernel->ne[0], KH = kernel->ne[1], KD = kernel->ne[2];
+    const int64_t   IC = p[9], B = p[10], OC = p[11];
+    GGML_ASSERT(IC > 0 && B > 0 && OC > 0 && input->ne[3] == IC * B && kernel->ne[3] == IC * OC);
+    GGML_ASSERT(dst->ne[3] == OC * B && p[0] > 0 && p[1] > 0 && p[2] > 0 && p[6] > 0 && p[7] > 0 && p[8] > 0);
+    const int64_t       total  = ggml_nelements(dst);
+    const conv3d_params params = { IW,   IH,   ID,   OW,   OH,   OD,   KW,   KH, KD, p[0], p[1],
+                                   p[2], p[3], p[4], p[5], p[6], p[7], p[8], IC, OC, B,    total };
+    const float *       x      = (const float *) input->data;
+    const half *        w      = (const half *) kernel->data;
+    float *             y      = (float *) dst->data;
+    cudaStream_t        stream = ctx.stream();
+    const auto &        device = ggml_cuda_info().devices[ctx.device];
+    const bool          use_mma =
+        turing_mma_available(device.cc) || amd_wmma_available(device.cc) || amd_mfma_available(device.cc);
+    const bool pointwise =
+        KW == 1 && KH == 1 && KD == 1 && p[0] == 1 && p[1] == 1 && p[2] == 1 && p[3] == 0 && p[4] == 0 && p[5] == 0;
+    const bool    use_blas = pointwise && fast_fp16_hardware_available(device.cc);
+    const int64_t limit    = INT_MAX - 256;
+    const int64_t pw = IW + 2 * int64_t(p[3]), ph = IH + 2 * int64_t(p[4]), pd = ID + 2 * int64_t(p[5]);
+    const bool    padded_fits = pw > 0 && pw <= limit && ph > 0 && ph <= limit && pd > 0 && pd <= limit &&
+                             pw * ph <= limit / pd && IC * B <= limit / (pw * ph * pd);
+    if (kernel->type == GGML_TYPE_F16 && KW > 0 && KH > 0 && KD > 0 && ggml_nelements(input) <= limit &&
+        ggml_nelements(kernel) <= limit && total <= limit && padded_fits && p[3] >= 0 && p[4] >= 0 && p[5] >= 0 &&
+        (OW - 1) * p[0] + (KW - 1) * p[6] < pw && (OH - 1) * p[1] + (KH - 1) * p[7] < ph &&
+        (OD - 1) * p[2] + (KD - 1) * p[8] < pd && (OC + 63) / 64 <= 65535 && B <= 65535) {
+        const int                  padded_total = int(pw * ph * pd * IC * B);
+        ggml_cuda_pool_alloc<half> x_half(ctx.pool(), padded_total);
+        // Match im2col's F16 input precision without materializing all patches in global memory.
+        if (p[3] == 0 && p[4] == 0 && p[5] == 0) {
+            ggml_get_to_fp16_cuda(input->type)(x, x_half.get(), padded_total, stream);
+        } else {
+            conv3d_pad_f16<<<(padded_total + 255) / 256, 256, 0, stream>>>(
+                x, x_half.get(), int(IW), int(IH), int(ID), int(pw), int(ph), int(pd), p[3], p[4], p[5], padded_total);
+        }
+        const conv3d_params padded_params = { pw,   ph, pd, OW, OH,   OD,   KW,   KH, KD, p[0], p[1],
+                                              p[2], 0,  0,  0,  p[6], p[7], p[8], IC, OC, B,    total };
+        const int           positions     = int(OW * OH * OD);
+        if (use_blas) {
+            const float    alpha = 1.0f, beta = 0.0f;
+            cublasHandle_t cublas_h = ctx.cublas_handle();
+            for (int n = 0; n < B; ++n) {
+                CUBLAS_CHECK(cublasGemmEx(cublas_h, CUBLAS_OP_N, CUBLAS_OP_N, positions, int(OC), int(IC), &alpha,
+                                          x_half.get() + int64_t(n) * IC * positions, CUDA_R_16F, positions, w,
+                                          CUDA_R_16F, int(IC), &beta, y + int64_t(n) * OC * positions, CUDA_R_32F,
+                                          positions, CUBLAS_COMPUTE_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP));
+            }
+            return;
+        }
+        const int64_t blocks  = ((positions + 63) / 64) * ((OC + 63) / 64) * B;
+        const int     target  = 8 * device.nsm;
+        const int     split_k = int(std::min({ int64_t(32), int64_t(65535) / B, (IC * KW * KH * KD + 63) / 64,
+                                               std::max(int64_t(1), (target + blocks - 1) / blocks) }));
+        ggml_cuda_pool_alloc<float> partial(ctx.pool());
+        float *                     result = split_k == 1 ? y : partial.alloc(total * split_k);
+        const dim3                  block(device.warp_size, 4);
+        const dim3 grid(unsigned((positions + 63) / 64), unsigned((OC + 63) / 64), unsigned(B * split_k));
+        if (use_mma) {
+            conv3d_launch_implicit_gemm<true>(x_half.get(), w, result, padded_params, split_k, grid, block, stream);
+        } else {
+            conv3d_launch_implicit_gemm<false>(x_half.get(), w, result, padded_params, split_k, grid, block, stream);
+        }
+        if (split_k > 1) {
+            conv3d_reduce_split_k<<<unsigned((total + 255) / 256), 256, 0, stream>>>(result, y, int(total),
+                                                                                     int(OC * positions), split_k);
+        }
+        return;
+    }
+    const int blocks = int(std::min(int64_t(65535), (total + 255) / 256));
+    if (kernel->type == GGML_TYPE_F16) {
+        conv3d_kernel<<<blocks, 256, 0, stream>>>(x, w, y, params);
+    } else {
+        conv3d_kernel<<<blocks, 256, 0, stream>>>(x, (const float *) kernel->data, y, params);
+    }
+}
diff --git a/ggml/src/ggml-cuda/conv3d.cuh b/ggml/src/ggml-cuda/conv3d.cuh
new file mode 100644
index 000000000..b321dcc3d
--- /dev/null
+++ b/ggml/src/ggml-cuda/conv3d.cuh
@@ -0,0 +1,8 @@
+#ifndef GGML_CUDA_CONV3D_CUH
+#define GGML_CUDA_CONV3D_CUH
+
+#include "common.cuh"
+
+void ggml_cuda_op_conv3d(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+#endif
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 27b83503d..9dd34c866 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -17,6 +17,7 @@
 #include "ggml-cuda/conv2d.cuh"
 #include "ggml-cuda/conv2d-dw.cuh"
 #include "ggml-cuda/conv2d-transpose.cuh"
+#include "ggml-cuda/conv3d.cuh"
 #include "ggml-cuda/convert.cuh"
 #include "ggml-cuda/count-equal.cuh"
 #include "ggml-cuda/cpy.cuh"
@@ -2322,6 +2323,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_CONV_2D:
             ggml_cuda_op_conv2d(ctx, dst);
             break;
+        case GGML_OP_CONV_3D:
+            ggml_cuda_op_conv3d(ctx, dst);
+            break;
         case GGML_OP_CONV_2D_DW:
             ggml_cuda_op_conv2d_dw(ctx, dst);
             break;
@@ -5512,6 +5516,10 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_IM2COL_3D:
         case GGML_OP_CONV_2D:
             return (ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]));
+        case GGML_OP_CONV_3D:
+            return (op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32) &&
+                   op->src[1]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32 &&
+                   ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]) && ggml_is_contiguous(op);
         case GGML_OP_CONV_2D_DW:
             return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_CONV_TRANSPOSE_2D:
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 8402fcfe0..4c67805d4 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -52,6 +52,10 @@
 #endif

 static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) {
+    if (ggml_is_empty(tensor)) {
+        return;
+    }
+
     size_t nels = ggml_nelements(tensor);
     std::vector<float> data(nels);
     {
@@ -6383,6 +6387,7 @@ struct test_conv_3d : public test_case {
     const int d0, d1, d2;
     // Types
     const ggml_type type_kernel;
+    const int kernel_offset;

     std::string op_desc(ggml_tensor * t) override {
         GGML_UNUSED(t);
@@ -6391,7 +6396,7 @@ struct test_conv_3d : public test_case {

     std::string vars() override {
         return VARS_TO_STR11(N, IC, ID, IH, IW, OC, KD, KH, KW, s0, s1) + "," +
-               VARS_TO_STR8(s2, p0, p1, p2, d0, d1, d2, type_kernel);
+               VARS_TO_STR9(s2, p0, p1, p2, d0, d1, d2, type_kernel, kernel_offset);
     }

     double max_nmse_err() override {
@@ -6407,7 +6412,7 @@ struct test_conv_3d : public test_case {
         const int64_t OH = calc_conv_output_size(IH, KH, s1, p1, d1);
         const int64_t OW = calc_conv_output_size(IW, KW, s0, p0, d0);

-        return (uint64_t)N * OC * OD * OH * OW * (2 * IC * KD * KH * KW - 1);
+        return (uint64_t)N * OC * OD * OH * OW * std::max<int64_t>(0, 2 * IC * KD * KH * KW - 1);
     }

     test_conv_3d(
@@ -6416,13 +6421,13 @@ struct test_conv_3d : public test_case {
         int s0, int s1, int s2,
         int p0, int p1, int p2,
         int d0, int d1, int d2,
-        ggml_type type_kernel
+        ggml_type type_kernel, int kernel_offset = 0
     ) : N(N), IC(IC), ID(ID), IH(IH), IW(IW),
         OC(OC), KD(KD), KH(KH), KW(KW),
         s0(s0), s1(s1), s2(s2),
         p0(p0), p1(p1), p2(p2),
         d0(d0), d1(d1), d2(d2),
-        type_kernel(type_kernel) {}
+        type_kernel(type_kernel), kernel_offset(kernel_offset) {}

     ggml_tensor * build_graph(ggml_context * ctx) override {
         // GGML input tensor is packed as [W, H, D, C*N]
@@ -6432,7 +6437,16 @@ struct test_conv_3d : public test_case {

         // GGML kernel tensor is packed as [KW, KH, KD, IC*OC]
         const int64_t ne_kernel[] = {KW, KH, KD, IC * OC};
-        ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel);
+        ggml_tensor * kernel;
+        if (kernel_offset == 0) {
+            kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel);
+        } else {
+            ggml_tensor * storage = ggml_new_tensor_1d(ctx, type_kernel, KW * KH * KD * IC * OC + kernel_offset);
+            const size_t element_size = ggml_type_size(type_kernel);
+            kernel = ggml_view_4d(ctx, storage, KW, KH, KD, IC * OC,
+                                 KW * element_size, KW * KH * element_size, KW * KH * KD * element_size,
+                                 kernel_offset * element_size);
+        }
         ggml_set_name(kernel, "kernel");

         ggml_tensor * out = ggml_conv_3d_direct(ctx, kernel, input, s0, s1, s2, p0, p1, p2, d0, d1, d2, (int)IC, (int)N, (int)OC);
@@ -9446,6 +9460,15 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         }
         // Case with kernel size 1
         test_cases.emplace_back(new test_conv_3d(1, 4, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, kernel_type));
+        test_cases.emplace_back(new test_conv_3d(2, 8, 5, 11, 9, 65, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, kernel_type));
+        test_cases.emplace_back(new test_conv_3d(2, 5, 7, 9, 13, 17, 2, 3, 4, 2, 1, 3, 3, 2, 2, 2, 1, 2, kernel_type));
+        test_cases.emplace_back(new test_conv_3d(3, 16, 3, 7, 9, 33, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, kernel_type));
+        test_cases.emplace_back(new test_conv_3d(2, 8, 7, 5, 9, 33, 3, 1, 1, 1, 1, 2, 0, 0, 2, 1, 1, 2, kernel_type));
+        test_cases.emplace_back(new test_conv_3d(2, 3, 1, 2, 1, 7, 1, 1, 1, 1, 1, 1, 3, 4, 2, 1, 1, 1, kernel_type));
+        test_cases.emplace_back(new test_conv_3d(2, 8, 5, 7, 9, 17, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, kernel_type, 1));
+        test_cases.emplace_back(new test_conv_3d(1, 1, 2, 2, 2, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, kernel_type));
+        test_cases.emplace_back(new test_conv_3d(1, 1, 2, 2, 2, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, kernel_type));
+        test_cases.emplace_back(new test_conv_3d(1, 1, 2, 2, 2, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, kernel_type));
     }

     for(uint32_t Cout : {1, 9}){