Add `ggml_roll` (ggml/1274)

* ggml : add ggml_roll

* use set/get_op_params & std::min

ggml/include/ggml.h

@@ -489,6 +489,7 @@ extern "C" {
         GGML_OP_UPSCALE, // nearest interpolate
         GGML_OP_PAD,
         GGML_OP_PAD_REFLECT_1D,
+        GGML_OP_ROLL,
         GGML_OP_ARANGE,
         GGML_OP_TIMESTEP_EMBEDDING,
         GGML_OP_ARGSORT,
@@ -1801,6 +1802,17 @@ extern "C" {
             int                   p0,
             int                   p1);
 
+    // Move tensor elements by an offset given for each dimension. Elements that
+    // are shifted beyond the last position are wrapped around to the beginning.
+    GGML_API struct ggml_tensor * ggml_roll(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   shift0,
+            int                   shift1,
+            int                   shift2,
+            int                   shift3);
+
+
     // Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
     // timesteps: [N,]
     // return: [N, dim]

ggml/src/ggml-cpu/ggml-cpu.c

@@ -1967,6 +1967,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_pad_reflect_1d(params, tensor);
             } break;
+        case GGML_OP_ROLL:
+            {
+                ggml_compute_forward_roll(params, tensor);
+            } break;
         case GGML_OP_ARANGE:
             {
                 ggml_compute_forward_arange(params, tensor);
@@ -2291,6 +2295,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_UPSCALE:
         case GGML_OP_PAD:
         case GGML_OP_PAD_REFLECT_1D:
+        case GGML_OP_ROLL:
         case GGML_OP_ARANGE:
         case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_ARGSORT:

ggml/src/ggml-cpu/ops.cpp

@@ -6793,6 +6793,73 @@ void ggml_compute_forward_pad_reflect_1d(
     }
 }
 
+// ggml_compute_forward_roll
+
+static int64_t ggml_wrap_index(int64_t i, int64_t ne) {
+    if (i < 0) {
+        return i + ne;
+    } else if (i >= ne) {
+        return i - ne;
+    }
+    return i;
+}
+
+static void ggml_compute_forward_roll_f32(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src_data = (const float *) src0->data;
+    float * dst_data = (float *) dst->data;
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    const int s0 = ggml_get_op_params_i32(dst, 0);
+    const int s1 = ggml_get_op_params_i32(dst, 1);
+    const int s2 = ggml_get_op_params_i32(dst, 2);
+    const int s3 = ggml_get_op_params_i32(dst, 3);
+
+    const int64_t total = ne1 * ne2 * ne3;
+    const int64_t per_thread = (total + params->nth) / params->nth;
+    const int64_t start = params->ith * per_thread;
+    const int64_t end   = std::min(start + per_thread, total);
+
+    for (int64_t i = start; i < end; ++i) {
+        const int64_t i1 = i % ne1;
+        const int64_t i2 = (i / ne1) % ne2;
+        const int64_t i3 = i / (ne2 * ne1);
+        float * dst_row = dst_data + (i3*nb3 + i2*nb2 + i1*nb1) / sizeof(float);
+
+        const int64_t i01 = ggml_wrap_index(i1 - s1, ne01);
+        const int64_t i02 = ggml_wrap_index(i2 - s2, ne02);
+        const int64_t i03 = ggml_wrap_index(i3 - s3, ne03);
+        const float * src_row = src_data + (i03*nb03 + i02*nb02 + i01*nb01) / sizeof(float);
+
+        const int64_t s = ggml_wrap_index(-s0, ne00);
+        const int64_t n = ne00 - s;
+        ggml_vec_cpy_f32(n, dst_row,     src_row + s);
+        ggml_vec_cpy_f32(s, dst_row + n, src_row);
+    }
+}
+
+void ggml_compute_forward_roll(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_roll_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("fatal error");
+            }
+    }
+}
+
 // ggml_compute_forward_arange
 
 static void ggml_compute_forward_arange_f32(

ggml/src/ggml-cpu/ops.h

@@ -72,6 +72,7 @@ void ggml_compute_forward_pool_2d_back(const struct ggml_compute_params * params
 void ggml_compute_forward_upscale(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_pad(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_pad_reflect_1d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_roll(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_arange(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_timestep_embedding(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_argsort(const struct ggml_compute_params * params, struct ggml_tensor * dst);

ggml/src/ggml.c

@@ -955,6 +955,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "UPSCALE",
     "PAD",
     "PAD_REFLECT_1D",
+    "ROLL",
     "ARANGE",
     "TIMESTEP_EMBEDDING",
     "ARGSORT",
@@ -985,7 +986,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "OPT_STEP_ADAMW",
 };
 
-static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
+static_assert(GGML_OP_COUNT == 83, "GGML_OP_COUNT != 83");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -1050,6 +1051,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "upscale(x)",
     "pad(x)",
     "pad_reflect_1d(x)",
+    "roll(x)",
     "arange(start, stop, step)",
     "timestep_embedding(timesteps, dim, max_period)",
     "argsort(x)",
@@ -1080,7 +1082,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "adamw(x)",
 };
 
-static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
+static_assert(GGML_OP_COUNT == 83, "GGML_OP_COUNT != 83");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 
@@ -4341,6 +4343,34 @@ struct ggml_tensor * ggml_pad_reflect_1d(
     return result;
 }
 
+// ggml_roll
+
+struct ggml_tensor * ggml_roll(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   shift0,
+        int                   shift1,
+        int                   shift2,
+        int                   shift3) {
+    GGML_ASSERT(a->nb[0] == ggml_type_size(a->type));
+    GGML_ASSERT(abs(shift0) < a->ne[0]);
+    GGML_ASSERT(abs(shift1) < a->ne[1]);
+    GGML_ASSERT(abs(shift2) < a->ne[2]);
+    GGML_ASSERT(abs(shift3) < a->ne[3]);
+
+    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
+
+    ggml_set_op_params_i32(result, 0, shift0);
+    ggml_set_op_params_i32(result, 1, shift1);
+    ggml_set_op_params_i32(result, 2, shift2);
+    ggml_set_op_params_i32(result, 3, shift3);
+
+    result->op     = GGML_OP_ROLL;
+    result->src[0] = a;
+
+    return result;
+}
+
 // ggml_arange
 
 struct ggml_tensor * ggml_arange(