ggml: add map_ternary_f32()
This commit is contained in:
Kamil Tomsik
parent
2a5ee023ad
commit
8fcf31b4f1
2 changed files with 126 additions and 3 deletions
117
ggml.c
117
ggml.c
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@ -3465,9 +3465,10 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
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"MAP_UNARY",
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"MAP_BINARY",
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"MAP_TERNARY",
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};
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static_assert(GGML_OP_COUNT == 50, "GGML_OP_COUNT != 50");
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static_assert(GGML_OP_COUNT == 51, "GGML_OP_COUNT != 51");
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static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"none",
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@ -3527,7 +3528,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"f(x,y)",
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};
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static_assert(GGML_OP_COUNT == 50, "GGML_OP_COUNT != 50");
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static_assert(GGML_OP_COUNT == 51, "GGML_OP_COUNT != 51");
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static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
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static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
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@ -4034,6 +4035,7 @@ struct ggml_tensor * ggml_new_tensor_impl(
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/*.grad =*/ NULL,
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/*.src0 =*/ NULL,
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/*.src1 =*/ NULL,
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/*.src2 =*/ NULL,
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/*.opt =*/ { NULL },
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/*.n_tasks =*/ 0,
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/*.perf_runs =*/ 0,
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@ -6421,6 +6423,56 @@ struct ggml_tensor * ggml_map_binary_inplace_f32(
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return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
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}
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// ggml_map_ternary
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struct ggml_tensor * ggml_map_ternary_impl_f32(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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struct ggml_tensor * c,
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const ggml_ternary_op_f32_t fun,
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bool inplace) {
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GGML_ASSERT(ggml_are_same_shape(a, b));
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GGML_ASSERT(ggml_are_same_shape(b, c));
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bool is_node = false;
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if (!inplace && (a->grad || b->grad || c->grad)) {
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is_node = true;
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}
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struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
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*((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
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struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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result->op = GGML_OP_MAP_TERNARY;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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result->src0 = a;
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result->src1 = b;
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result->src2 = c;
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result->opt[0] = addr_tensor;
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return result;
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}
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struct ggml_tensor * ggml_map_ternary_f32(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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struct ggml_tensor * c,
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const ggml_ternary_op_f32_t fun) {
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return ggml_map_ternary_impl_f32(ctx, a, b, c, fun, false);
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}
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struct ggml_tensor * ggml_map_ternary_inplace_f32(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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struct ggml_tensor * c,
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const ggml_ternary_op_f32_t fun) {
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return ggml_map_ternary_impl_f32(ctx, a, b, c, fun, true);
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}
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////////////////////////////////////////////////////////////////////////////////
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void ggml_set_param(
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@ -12628,6 +12680,59 @@ static void ggml_compute_forward_map_binary(
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}
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}
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// ggml_compute_forward_map_ternary
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static void ggml_compute_forward_map_ternary_f32(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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const struct ggml_tensor * src2,
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struct ggml_tensor * dst,
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const ggml_ternary_op_f32_t fun) {
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assert(params->ith == 0);
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assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src1, src2) && ggml_are_same_shape(src0, dst));
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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return;
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}
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const int n = ggml_nrows(src0);
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const int nc = src0->ne[0];
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assert( dst->nb[0] == sizeof(float));
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assert(src0->nb[0] == sizeof(float));
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assert(src1->nb[0] == sizeof(float));
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assert(src2->nb[0] == sizeof(float));
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for (int i = 0; i < n; i++) {
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fun(nc,
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(float *) ((char *) dst->data + i*( dst->nb[1])),
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(float *) ((char *) src0->data + i*(src0->nb[1])),
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(float *) ((char *) src1->data + i*(src1->nb[1])),
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(float *) ((char *) src2->data + i*(src2->nb[1])));
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}
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}
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static void ggml_compute_forward_map_ternary(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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const struct ggml_tensor * src2,
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struct ggml_tensor * dst,
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const ggml_ternary_op_f32_t fun) {
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switch (src0->type) {
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case GGML_TYPE_F32:
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{
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ggml_compute_forward_map_ternary_f32(params, src0, src1, src2, dst, fun);
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} break;
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default:
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{
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GGML_ASSERT(false);
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} break;
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}
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}
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/////////////////////////////////
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static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
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@ -12837,6 +12942,12 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
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ggml_compute_forward_map_binary(params, tensor->src0, tensor->src1, tensor, fun);
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}
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break;
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case GGML_OP_MAP_TERNARY:
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{
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const ggml_ternary_op_f32_t fun = *((ggml_ternary_op_f32_t *)tensor->opt[0]->data);
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ggml_compute_forward_map_ternary(params, tensor->src0, tensor->src1, tensor->src2, tensor, fun);
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}
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break;
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case GGML_OP_NONE:
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{
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// nop
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@ -13517,6 +13628,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
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} break;
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case GGML_OP_MAP_UNARY:
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case GGML_OP_MAP_BINARY:
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case GGML_OP_MAP_TERNARY:
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{
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GGML_ASSERT(false); // not supported
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} break;
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@ -14062,6 +14174,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
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} break;
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case GGML_OP_MAP_UNARY:
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case GGML_OP_MAP_BINARY:
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case GGML_OP_MAP_TERNARY:
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{
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node->n_tasks = 1;
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} break;
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12
ggml.h
12
ggml.h
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@ -321,6 +321,7 @@ extern "C" {
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GGML_OP_MAP_UNARY,
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GGML_OP_MAP_BINARY,
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GGML_OP_MAP_TERNARY,
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GGML_OP_COUNT,
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};
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@ -358,6 +359,7 @@ extern "C" {
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struct ggml_tensor * grad;
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struct ggml_tensor * src0;
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struct ggml_tensor * src1;
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struct ggml_tensor * src2;
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struct ggml_tensor * opt[GGML_MAX_OPT];
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// thread scheduling
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@ -372,7 +374,7 @@ extern "C" {
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char name[32];
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char padding[16];
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char padding[8];
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};
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// computation graph
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@ -931,6 +933,7 @@ extern "C" {
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// Mapping operations
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typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *);
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typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
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typedef void (*ggml_ternary_op_f32_t)(const int, float *, const float *, const float *, const float *);
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GGML_API struct ggml_tensor * ggml_map_unary_f32(
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struct ggml_context * ctx,
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@ -943,6 +946,13 @@ extern "C" {
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struct ggml_tensor * b,
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ggml_binary_op_f32_t fun);
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GGML_API struct ggml_tensor * ggml_map_ternary_f32(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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struct ggml_tensor * c,
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const ggml_ternary_op_f32_t fun);
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//
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// automatic differentiation
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//
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