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ggml: add f16 out_prod support for CPU and out_prod op for Vulkan (#23997)

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This commit is contained in:
Michael Lamothe
2026-07-15 10:46:56 +02:00
committed by GitHub
parent 33a75f41c3
commit f955e394bf
6 changed files with 167 additions and 7 deletions
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ggml/src/ggml-vulkan/ggml-vulkan.cpp
+34
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@@ -961,6 +961,7 @@ struct vk_device_struct {
vk_pipeline pipeline_col2im_1d_f32;
vk_pipeline pipeline_col2im_1d_f16;
vk_pipeline pipeline_col2im_1d_bf16;
vk_pipeline pipeline_out_prod_f32;
vk_pipeline pipeline_snake_f32;
vk_pipeline pipeline_snake_f16;
vk_pipeline pipeline_snake_bf16;
@@ -5479,6 +5480,8 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
ggml_vk_create_pipeline(device, device->pipeline_col2im_1d_f16, "col2im_1d_f16", col2im_1d_f16_len, col2im_1d_f16_data, "main", 2, sizeof(vk_op_col2im_1d_push_constants), {256, 1, 1}, {}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_col2im_1d_bf16, "col2im_1d_bf16", col2im_1d_bf16_len, col2im_1d_bf16_data, "main", 2, sizeof(vk_op_col2im_1d_push_constants), {256, 1, 1}, {}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_out_prod_f32, "out_prod_f32", out_prod_f32_len, out_prod_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {256, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_snake_f32, "snake_f32", snake_f32_len, snake_f32_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_snake_f16, "snake_f16", snake_f16_len, snake_f16_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_snake_bf16, "snake_bf16", snake_bf16_len, snake_bf16_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1);
@@ -10745,6 +10748,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
return ctx->device->pipeline_add_id_f32;
}
return nullptr;
case GGML_OP_OUT_PROD:
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_out_prod_f32;
}
return nullptr;
case GGML_OP_CONCAT: {
if (src0->type != src1->type || src0->type != dst->type) {
return nullptr;
@@ -11701,6 +11709,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
case GGML_OP_DIV:
case GGML_OP_MUL:
case GGML_OP_ADD1:
case GGML_OP_OUT_PROD:
case GGML_OP_ARANGE:
case GGML_OP_FILL:
case GGML_OP_SCALE:
@@ -12014,6 +12023,24 @@ static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context& subctx, const
});
}
static void ggml_vk_out_prod(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const uint32_t src0_type_size = ggml_type_size(src0->type);
const uint32_t src1_type_size = ggml_type_size(src1->type);
const uint32_t dst_type_size = ggml_type_size(dst->type);
ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_OUT_PROD, {
(uint32_t)ggml_nelements(dst),
(uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3],
(uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
(uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3],
(uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3],
(uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size,
0,
0.0f, 0.0f, 0,
});
}
static void ggml_vk_sub(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const uint32_t src0_type_size = ggml_type_size(src0->type);
const uint32_t src1_type_size = ggml_type_size(src1->type);
@@ -14801,6 +14828,9 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
ggml_vk_add(ctx, compute_ctx, src0, src1, node);
}
break;
case GGML_OP_OUT_PROD:
ggml_vk_out_prod(ctx, compute_ctx, src0, src1, node);
break;
case GGML_OP_SUB:
ggml_vk_sub(ctx, compute_ctx, src0, src1, node);
@@ -17655,6 +17685,10 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
case GGML_OP_OPT_STEP_ADAMW:
case GGML_OP_OPT_STEP_SGD:
return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_OUT_PROD:
return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32
&& ggml_is_contiguous(op->src[1]) && op->src[1]->type == GGML_TYPE_F32
&& op->type == GGML_TYPE_F32;
case GGML_OP_LOG:
case GGML_OP_TRI:
case GGML_OP_DIAG: