ggml : add ggml_fill()

ggml/include/ggml.h

@@ -518,6 +518,8 @@ extern "C" {
         GGML_OP_CROSS_ENTROPY_LOSS_BACK,
         GGML_OP_OPT_STEP_ADAMW,
 
+        GGML_OP_FILL,
+
         GGML_OP_COUNT,
     };
 
@@ -1818,6 +1820,12 @@ extern "C" {
             float                 stop,
             float                 step);
 
+    // fill in-place the tensor with a constant value, return view(a)
+    GGML_API struct ggml_tensor * ggml_fill(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 value);
+
     // top k elements per row
     GGML_API struct ggml_tensor * ggml_top_k(
             struct ggml_context * ctx,

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

@@ -1959,6 +1959,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_arange(params, tensor);
             } break;
+        case GGML_OP_FILL:
+            {
+                ggml_compute_forward_fill(params, tensor);
+            } break;
         case GGML_OP_TIMESTEP_EMBEDDING:
             {
                 ggml_compute_forward_timestep_embedding(params, tensor);
@@ -2242,6 +2246,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_TRANSPOSE:
         case GGML_OP_GET_ROWS_BACK:
         case GGML_OP_DIAG:
+        case GGML_OP_ARANGE:
             {
                 n_tasks = 1;
             } break;
@@ -2279,7 +2284,7 @@ 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_ARANGE:
+        case GGML_OP_FILL:
         case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_ARGSORT:
         case GGML_OP_FLASH_ATTN_EXT:

ggml/src/ggml-cpu/ops.cpp

@@ -6833,6 +6833,55 @@ void ggml_compute_forward_arange(
     }
 }
 
+// ggml_compute_forward_fill
+
+static void ggml_compute_forward_fill_f32(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+    float v;
+    memcpy(&v, dst->op_params, sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int n  = ggml_nrows(dst);
+    const int nc = dst->ne[0];
+
+    const size_t nb00 = dst->nb[0];
+
+    const size_t nb0 = dst->nb[0];
+    const size_t nb1 = dst->nb[1];
+
+    GGML_ASSERT( nb0 == sizeof(float));
+    GGML_ASSERT(nb00 == sizeof(float));
+
+    for (int j = ith; j < n; j += nth) {
+        float * dst_ptr  = (float *) ((char *)  dst->data + j*nb1);
+
+        for (int i = 0; i < nc; i++) {
+            dst_ptr[i] = v;
+        }
+    }
+}
+
+void ggml_compute_forward_fill(
+        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_fill_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("fatal error");
+            }
+    }
+}
+
 static void ggml_compute_forward_timestep_embedding_f32(
     const ggml_compute_params * params,
     ggml_tensor * dst) {

ggml/src/ggml-cpu/ops.h

@@ -73,6 +73,7 @@ void ggml_compute_forward_upscale(const struct ggml_compute_params * params, str
 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_arange(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_fill(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);
 void ggml_compute_forward_leaky_relu(const struct ggml_compute_params * params, struct ggml_tensor * dst);

ggml/src/ggml.c

@@ -982,9 +982,11 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "CROSS_ENTROPY_LOSS",
     "CROSS_ENTROPY_LOSS_BACK",
     "OPT_STEP_ADAMW",
+
+    "FILL",
 };
 
-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",
@@ -1077,9 +1079,11 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "cross_entropy_loss(x,y)",
     "cross_entropy_loss_back(x,y)",
     "adamw(x)",
+
+    "fill(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");
 
@@ -4342,6 +4346,20 @@ struct ggml_tensor * ggml_arange(
     return result;
 }
 
+struct ggml_tensor * ggml_fill(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        float                 value) {
+    struct ggml_tensor * result = ggml_view_tensor(ctx, a);
+
+    ggml_set_op_params(result, &value, sizeof(value));
+
+    result->op     = GGML_OP_FILL;
+    result->src[0] = a;
+
+    return result;
+}
+
 // ggml_timestep_embedding
 
 struct ggml_tensor * ggml_timestep_embedding(

tests/test-backend-ops.cpp

@@ -2422,6 +2422,32 @@ struct test_clamp : public test_case {
     }
 };
 
+// GGML_OP_FILL
+struct test_fill : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne;
+    float v;
+
+    std::string vars() override {
+        return VARS_TO_STR3(type, ne, v);
+    }
+
+    test_fill(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = {10, 5, 4, 3},
+            float v = 0.5f)
+        : type(type), ne(ne), v(v) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_set_name(a, "a");
+
+        ggml_tensor * out = ggml_fill(ctx, a, v);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+};
+
 // GGML_OP_DIAG_MASK_INF
 struct test_diag_mask_inf : public test_case {
     const ggml_type type;
@@ -4199,6 +4225,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_gla(GGML_TYPE_F32, 32, 64, 32, 4));
     test_cases.emplace_back(new test_gla(GGML_TYPE_F32, 32, 64, 128, 4));
 
+    test_cases.emplace_back(new test_fill(GGML_TYPE_F32));
+
     for (ggml_type type_a : all_types) {
         for (int i = 1; i < 10; ++i) {
             test_cases.emplace_back(new test_mul_mat(type_a,    GGML_TYPE_F32, 16,  i, 256, { 1,  1}, {1, 1}));