diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 87b9e778aeaa..cec158a1df3c 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -7115,18 +7115,23 @@ static void ggml_compute_backward(
             // layer concatenates the cached conv state (a leaf) with this ubatch's qkv, so the
             // second slice is the one that carries a gradient back into the model.
             const int dim = ggml_get_op_params_i32(tensor, 0);
+            // A view's first stride is the element size (ggml_view_4d takes nb[1..3] only), so the
+            // gradient is sliced from a CONTIGUOUS copy: one that arrives through a transpose (the
+            // training attention transposes V for its KQV product) has a first stride that is not,
+            // and slicing it in place read the right number of elements from the wrong places.
+            struct ggml_tensor * g = ggml_is_contiguous(grad) ? grad : ggml_cont(ctx, grad);
             size_t offset = 0;
             for (int j = 0; j < 2; ++j) {
                 struct ggml_tensor * src = tensor->src[j];
                 const size_t isrc = j == 0 ? isrc0 : isrc1;
                 const bool needs  = j == 0 ? src0_needs_grads : src1_needs_grads;
                 if (needs) {
-                    struct ggml_tensor * slice = ggml_view_4d(ctx, grad,
+                    struct ggml_tensor * slice = ggml_view_4d(ctx, g,
                             src->ne[0], src->ne[1], src->ne[2], src->ne[3],
-                            grad->nb[1], grad->nb[2], grad->nb[3], offset);
+                            g->nb[1], g->nb[2], g->nb[3], offset);
                     ggml_add_or_set(ctx, cgraph, isrc, ggml_cont(ctx, slice));
                 }
-                offset += src->ne[dim] * grad->nb[dim];
+                offset += src->ne[dim] * g->nb[dim];
             }
         } break;
         case GGML_OP_SSM_CONV: {
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 894a18fe3b20..9d3f7d5f1a9c 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -6258,6 +6258,47 @@ struct test_concat : public test_case {
     }
 };
 
+// GGML_OP_CONCAT, its gradient: the output consumed through a permute and a transpose, the way the
+// training attention consumes a walk's [cached prefix | chunk] V, so the gradient reaches CONCAT
+// non-contiguous (its first stride is not the element size). The backward used to slice that
+// gradient in place and read the right number of elements from the wrong places (fork #46).
+struct test_concat_grad_transposed : public test_case {
+    const std::array<int64_t, 4> ne_a;
+    const int64_t ne_b_d;
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return "CONCAT";
+    }
+
+    std::string vars() override {
+        return VARS_TO_STR2(ne_a, ne_b_d);
+    }
+
+    test_concat_grad_transposed(std::array<int64_t, 4> ne_a = {4, 3, 5, 1}, int64_t ne_b_d = 2)
+        : ne_a(ne_a), ne_b_d(ne_b_d) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        auto ne_b = ne_a;
+        ne_b[2] = ne_b_d;
+        ggml_tensor * a = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_a.data());
+        ggml_set_param(a);
+        ggml_set_name(a, "a");
+        ggml_tensor * b = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_b.data());
+        ggml_set_param(b);
+        ggml_set_name(b, "b");
+
+        ggml_tensor * cat = ggml_concat(ctx, a, b, 2);
+        ggml_set_name(cat, "cat");
+        // squared: the harness differentiates a SUM, whose gradient is all ones, and a scrambled
+        // tensor of ones is still ones; through SQR the gradient varies by element (2x)
+        ggml_tensor * out = ggml_sqr(ctx, ggml_cont(ctx, ggml_transpose(ctx, ggml_permute(ctx, cat, 0, 2, 1, 3))));
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+};
+
 // GGML_OP_ARGSORT
 struct test_argsort : public test_case {
     const ggml_type type;
@@ -9943,6 +9984,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
             test_cases.emplace_back(new test_concat(GGML_TYPE_I64, {11, 12, 13, 14}, 7, dim, v));
         }
     }
+    test_cases.emplace_back(new test_concat_grad_transposed());
 
     for (ggml_type type_a : { GGML_TYPE_Q4_0, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0 }) {
         for (int v : { 0, 4, 8, 12 }) {