ggml : add unary and binary map operations (#874)

* GGML map ops proof of concept.

* Various cleanups.

Add handling for task setting.

Add handling for ggml_compute_backward.

Rename functions to ggml_map_unary_f32 and ggml_map_binary_f32

Fix compiler warnings related to casting function pointers and `void *`

Reorder functions and definitions based on the GGML op number.

Use typedefs for map op function pointer types.

* Fix position of map ops cases in ggml_compute_forward

ggml.c

@@ -2712,9 +2712,12 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
 
     "FLASH_ATTN",
     "FLASH_FF",
+
+    "MAP_UNARY",
+    "MAP_BINARY",
 };
 
-static_assert(GGML_OP_COUNT == 36, "GGML_OP_COUNT != 36");
+static_assert(GGML_OP_COUNT == 38, "GGML_OP_COUNT != 38");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -2757,9 +2760,12 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
 
     "flash_attn(x)",
     "flash_ff(x)",
+
+    "f(x)",
+    "f(x,y)",
 };
 
-static_assert(GGML_OP_COUNT == 36, "GGML_OP_COUNT != 36");
+static_assert(GGML_OP_COUNT == 38, "GGML_OP_COUNT != 38");
 
 static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
 static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
@@ -4907,6 +4913,90 @@ struct ggml_tensor * ggml_flash_ff(
     return result;
 }
 
+// ggml_map_unary
+
+struct ggml_tensor * ggml_map_unary_impl_f32(
+        struct ggml_context        * ctx,
+        struct ggml_tensor         * a,
+        const  ggml_unary_op_f32_t fun,
+        bool   inplace) {
+    bool is_node = false;
+
+    if (!inplace && a->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
+    *((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
+    struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op = GGML_OP_MAP_UNARY;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src0 = a;
+    result->opt[0] = addr_tensor;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_map_unary_f32(
+        struct ggml_context        * ctx,
+        struct ggml_tensor         * a,
+        const  ggml_unary_op_f32_t fun) {
+    return ggml_map_unary_impl_f32(ctx, a, fun, false);
+}
+
+struct ggml_tensor * ggml_map_unary_inplace_f32(
+        struct ggml_context        * ctx,
+        struct ggml_tensor         * a,
+        const  ggml_unary_op_f32_t fun) {
+    return ggml_map_unary_impl_f32(ctx, a, fun, true);
+}
+
+// ggml_map_binary
+
+struct ggml_tensor * ggml_map_binary_impl_f32(
+        struct ggml_context         * ctx,
+        struct ggml_tensor          * a,
+        struct ggml_tensor          * b,
+        const  ggml_binary_op_f32_t fun,
+        bool   inplace) {
+    GGML_ASSERT(ggml_are_same_shape(a, b));
+
+    bool is_node = false;
+
+    if (!inplace && (a->grad || b->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
+    *((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
+    struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op = GGML_OP_MAP_BINARY;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src0 = a;
+    result->src1 = b;
+    result->opt[0] = addr_tensor;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_map_binary_f32(
+        struct ggml_context         * ctx,
+        struct ggml_tensor          * a,
+        struct ggml_tensor          * b,
+        const  ggml_binary_op_f32_t fun) {
+    return ggml_map_binary_impl_f32(ctx, a, b, fun, false);
+}
+
+struct ggml_tensor * ggml_map_binary_inplace_f32(
+        struct ggml_context         * ctx,
+        struct ggml_tensor          * a,
+        struct ggml_tensor          * b,
+        const  ggml_binary_op_f32_t fun) {
+    return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
+}
+
 ////////////////////////////////////////////////////////////////////////////////
 
 void ggml_set_param(
@@ -8875,6 +8965,111 @@ static void ggml_compute_forward_flash_ff(
     }
 }
 
+// ggml_compute_forward_map_unary
+
+static void ggml_compute_forward_map_unary_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst,
+        const ggml_unary_op_f32_t fun) {
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert( dst->nb[0] == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        fun(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+
+static void ggml_compute_forward_map_unary(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst,
+        const ggml_unary_op_f32_t fun) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_map_unary_f32(params, src0, dst, fun);
+            } break;
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_I8:
+        case GGML_TYPE_I16:
+        case GGML_TYPE_I32:
+        case GGML_TYPE_F16:
+        case GGML_TYPE_COUNT:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_map_binary
+
+static void ggml_compute_forward_map_binary_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst,
+        const ggml_binary_op_f32_t fun) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert( dst->nb[0] == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+    assert(src1->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        fun(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])),
+                (float *) ((char *) src1->data + i*(src1->nb[1])));
+    }
+}
+
+
+static void ggml_compute_forward_map_binary(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst,
+        const ggml_binary_op_f32_t fun) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_map_binary_f32(params, src0, src1, dst, fun);
+            } break;
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_I8:
+        case GGML_TYPE_I16:
+        case GGML_TYPE_I32:
+        case GGML_TYPE_F16:
+        case GGML_TYPE_COUNT:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
 /////////////////////////////////
 
 static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
@@ -9024,6 +9219,18 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_flash_ff(params, tensor->src0, tensor->src1, tensor->opt[0], tensor->opt[1], tensor->opt[2], tensor);
             } break;
+        case GGML_OP_MAP_UNARY:
+            {
+                const ggml_unary_op_f32_t fun = *((ggml_unary_op_f32_t *)tensor->opt[0]->data);
+                ggml_compute_forward_map_unary(params, tensor->src0, tensor, fun);
+            }
+            break;
+        case GGML_OP_MAP_BINARY:
+            {
+                const ggml_binary_op_f32_t fun = *((ggml_binary_op_f32_t *)tensor->opt[0]->data);
+                ggml_compute_forward_map_binary(params, tensor->src0, tensor->src1, tensor, fun);
+            }
+            break;
         case GGML_OP_NONE:
             {
                 // nop
@@ -9283,6 +9490,11 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             {
                 GGML_ASSERT(false); // not supported
             } break;
+        case GGML_OP_MAP_UNARY:
+        case GGML_OP_MAP_BINARY:
+            {
+                GGML_ASSERT(false); // not supported
+            } break;
         case GGML_OP_NONE:
             {
                 // nop
@@ -9775,6 +9987,11 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
 
                         work_size = MAX(work_size, cur);
                     } break;
+                case GGML_OP_MAP_UNARY:
+                case GGML_OP_MAP_BINARY:
+                    {
+                        node->n_tasks = 1;
+                    } break;
                 case GGML_OP_NONE:
                     {
                         node->n_tasks = 1;

ggml.h

@@ -253,6 +253,9 @@ enum ggml_op {
     GGML_OP_FLASH_ATTN,
     GGML_OP_FLASH_FF,
 
+    GGML_OP_MAP_UNARY,
+    GGML_OP_MAP_BINARY,
+
     GGML_OP_COUNT,
 };
 
@@ -652,6 +655,21 @@ struct ggml_tensor * ggml_flash_ff(
         struct ggml_tensor  * c0,
         struct ggml_tensor  * c1);
 
+// Mapping operations
+typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *);
+typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
+
+struct ggml_tensor * ggml_map_unary_f32(
+        struct ggml_context        * ctx,
+        struct ggml_tensor         * a,
+        const  ggml_unary_op_f32_t fun);
+
+struct ggml_tensor * ggml_map_binary_f32(
+        struct ggml_context         * ctx,
+        struct ggml_tensor          * a,
+        struct ggml_tensor          * b,
+        const  ggml_binary_op_f32_t fun);
+
 //
 // automatic differentiation
 //