[CUDA] Added a cudaMemcpy2DAsync fast path to ggml_cuda_cpy (ggml-org#25057)

* [CUDA] Added a cudaMemcpy2DAsync fast path to ggml_cuda_cpy

Add a CUDA ggml_cpy fast path for same-type, same-shape strided copies that are just 2D pitched block copies.
When tensors are not fully contiguous but each row is contiguous, it now uses cudaMemcpy2DAsync instead of the slow element-wise scalar copy kernel.

This fixes the GDN recurrent snapshot update with -np 4, where rollback slots are separated by cache stride gaps.

* Add new tests that execute the new optimized strided copy path

* Return unsupported for strided copy in OpenVINO, as new tests are failing

Author: gaugarg-nv

ggml/src/ggml-cuda/cpy.cu

@@ -386,6 +386,46 @@ static void ggml_cpy_f32_iq4_nl_cuda(
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
 
+// check if a same-type copy reduces to a 2D strided copy (height rows of width
+// contiguous bytes), so it can use cudaMemcpy2DAsync instead of the scalar kernel
+static bool ggml_cuda_cpy_as_memcpy_2d(const ggml_tensor * src0, const ggml_tensor * src1,
+        size_t & width, size_t & height, size_t & spitch, size_t & dpitch) {
+    // require matching shape: a reshaped copy maps elements by flat order, which the
+    // prefix walk below does not handle
+    if (src0->type != src1->type || !ggml_are_same_shape(src0, src1)) {
+        return false;
+    }
+
+    // grow the contiguous prefix block shared by both tensors
+    size_t block_nb = ggml_element_size(src0);
+    int d = 0;
+    for (; d < GGML_MAX_DIMS; ++d) {
+        if (src0->nb[d] != block_nb || src1->nb[d] != block_nb) {
+            break;
+        }
+        block_nb *= src0->ne[d];
+    }
+
+    // d == 0: nothing contiguous; d == GGML_MAX_DIMS: fully contiguous (handled by memcpy)
+    if (d == 0 || d == GGML_MAX_DIMS) {
+        return false;
+    }
+
+    // dim d carries the rows; everything above it must be a single element
+    for (int i = d + 1; i < GGML_MAX_DIMS; ++i) {
+        if (src0->ne[i] != 1) {
+            return false;
+        }
+    }
+
+    width  = block_nb;
+    height = src0->ne[d];
+    spitch = src0->nb[d];
+    dpitch = src1->nb[d];
+
+    return spitch >= width && dpitch >= width;
+}
+
 void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1) {
     const int64_t ne = ggml_nelements(src0);
     GGML_ASSERT(ne == ggml_nelements(src1));
@@ -421,6 +461,8 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
     const bool can_be_transposed = nb01 == (int64_t)ggml_element_size(src0) &&
         src0->ne[3] == 1 && nb02 == ne00 * ne01 * (int64_t)ggml_element_size(src0);
 
+    size_t mc_width = 0, mc_height = 0, mc_spitch = 0, mc_dpitch = 0;
+
     if (src0->type == src1->type && contiguous_srcs) {
         GGML_ASSERT(ggml_nbytes(src0) == ggml_nbytes(src1));
 #if defined(GGML_USE_MUSA) && defined(GGML_MUSA_MUDNN_COPY)
@@ -431,6 +473,9 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
         {
             CUDA_CHECK(cudaMemcpyAsync(src1_ddc, src0_ddc, ggml_nbytes(src0), cudaMemcpyDeviceToDevice, main_stream));
         }
+    } else if (ggml_cuda_cpy_as_memcpy_2d(src0, src1, mc_width, mc_height, mc_spitch, mc_dpitch)) {
+        CUDA_CHECK(cudaMemcpy2DAsync(src1_ddc, mc_dpitch, src0_ddc, mc_spitch,
+                                     mc_width, mc_height, cudaMemcpyDeviceToDevice, main_stream));
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
         if (can_be_transposed) {
             ggml_cpy_scalar_cuda<float, float, true>

ggml/src/ggml-openvino/ggml-openvino.cpp

@@ -1053,6 +1053,10 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
             (op->ne[0] == 2 && op->ne[1] == 4 && op->ne[2] == 3 && op->ne[3] == 2)) {
             return true;
         }
+        // CPY into a strided view of a larger buffer (recurrent-state snapshots) not supported
+        if (op->view_src && ggml_nbytes(op) != ggml_nbytes(op->view_src)) {
+            return true;
+        }
         break;
     }
     case GGML_OP_MUL_MAT: {

tests/test-backend-ops.cpp

@@ -2890,12 +2890,17 @@ struct test_cpy : public test_case {
     const std::array<int64_t, 4> ne_dst;
     const std::array<int64_t, 4> permute_src;
     const std::array<int64_t, 4> permute_dst;
+    const std::array<int64_t, 4> dst_alloc; // if set, dst is a view into a larger buffer (strided)
     bool _src_use_permute;
     bool _dst_use_permute;
     bool _src_transpose;
     bool _use_dst_shape;
+    bool _use_dst_alloc;
 
     std::string vars() override {
+        if (_use_dst_alloc) {
+            return VARS_TO_STR8(type_src, type_dst, ne_src, ne_dst, permute_src, permute_dst, _src_transpose, dst_alloc);
+        }
         if (_use_dst_shape) {
             return VARS_TO_STR7(type_src, type_dst, ne_src, ne_dst, permute_src, permute_dst, _src_transpose);
         }
@@ -2943,12 +2948,15 @@ struct test_cpy : public test_case {
             std::array<int64_t, 4> ne_dst = {-1, -1, -1, -1},
             std::array<int64_t, 4> permute_src = {0, 0, 0, 0},
             std::array<int64_t, 4> permute_dst = {0, 0, 0, 0},
-            bool transpose_src = false)
+            bool transpose_src = false,
+            std::array<int64_t, 4> dst_alloc = {0, 0, 0, 0})
         : type_src(type_src), type_dst(type_dst), ne_src(ne_src), ne_dst(ne_dst), permute_src(permute_src), permute_dst(permute_dst),
+          dst_alloc(dst_alloc),
           _src_use_permute(permute_src[0] + permute_src[1] + permute_src[2] + permute_src[3] > 0),
           _dst_use_permute(permute_dst[0] + permute_dst[1] + permute_dst[2] + permute_dst[3] > 0),
           _src_transpose(transpose_src),
-          _use_dst_shape(ne_dst[0] >= 0 && ne_dst[1] >= 0 && ne_dst[2] >= 0 && ne_dst[3] >= 0){}
+          _use_dst_shape(ne_dst[0] >= 0 && ne_dst[1] >= 0 && ne_dst[2] >= 0 && ne_dst[3] >= 0),
+          _use_dst_alloc(dst_alloc[0] > 0){}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne_src.data());
@@ -2966,12 +2974,23 @@ struct test_cpy : public test_case {
         }
 
         std::array<int64_t, 4> dst_ne = _use_dst_shape ? ne_dst : std::array<int64_t, 4>{src->ne[0], src->ne[1], src->ne[2], src->ne[3]};
-        ggml_tensor * dst = ggml_new_tensor(ctx, type_dst, 4, dst_ne.data());
-        ggml_set_name(dst, "dst");
+        ggml_tensor * dst;
 
-        if (_dst_use_permute) {
-            dst = ggml_permute(ctx, dst, permute_dst[0], permute_dst[1], permute_dst[2], permute_dst[3]);
-            ggml_set_name(dst, "dst_permuted");
+        if (_use_dst_alloc) {
+            // view a sub-block of a larger buffer -> strided dst
+            ggml_tensor * dst_buf = ggml_new_tensor(ctx, type_dst, 4, dst_alloc.data());
+            ggml_set_name(dst_buf, "dst_buf");
+            dst = ggml_view_4d(ctx, dst_buf, dst_ne[0], dst_ne[1], dst_ne[2], dst_ne[3],
+                dst_buf->nb[1], dst_buf->nb[2], dst_buf->nb[3], 0);
+            ggml_set_name(dst, "dst_view");
+        } else {
+            dst = ggml_new_tensor(ctx, type_dst, 4, dst_ne.data());
+            ggml_set_name(dst, "dst");
+
+            if (_dst_use_permute) {
+                dst = ggml_permute(ctx, dst, permute_dst[0], permute_dst[1], permute_dst[2], permute_dst[3]);
+                ggml_set_name(dst, "dst_permuted");
+            }
         }
 
         ggml_tensor * out = ggml_cpy(ctx, src, dst);
@@ -8181,6 +8200,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {256, 1, 4, 1}, {-1,-1,-1,-1}, {1, 2, 0, 3}, {0, 0, 0, 0}));
     test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {2, 2097121, 1, 1}, {-1,-1,-1,-1}, {1, 0, 2, 3}));
     test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {2, 2, 524281, 1}, {-1,-1,-1,-1}, {1, 0, 2, 3}));
+    test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {128, 2, 3, 1}, {128, 2, 3, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, false, {128, 4, 3, 1})); // strided dst
+    test_cases.emplace_back(new test_cpy(GGML_TYPE_F16, GGML_TYPE_F16, {128, 2, 3, 1}, {128, 2, 3, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, false, {128, 4, 3, 1})); // strided dst
 
     // CPY - different src/dst shapes (reshaping via CPY)
     // Use permutations of {3, 5, 7, 32}. Total elements: 3*5*7*32 = 3360.