save more memory

Author: leejet

lora.hpp

@@ -525,19 +525,30 @@ struct LoraModel : public GGMLRunner {
             ggml_tensor* lora_mid  = nullptr;
             ggml_tensor* lora_down = nullptr;
 
+            bool is_conv2d = forward_params.op_type == WeightAdapter::ForwardParams::op_type_t::OP_CONV2D;
+
             auto iter = lora_tensors.find(lora_up_name);
             if (iter != lora_tensors.end()) {
                 lora_up = iter->second;
+                if (is_conv2d && lora_up->type != GGML_TYPE_F16) {
+                    lora_up = ggml_cast(ctx, lora_up, GGML_TYPE_F16);
+                }
             }
 
             iter = lora_tensors.find(lora_mid_name);
             if (iter != lora_tensors.end()) {
                 lora_mid = iter->second;
+                if (is_conv2d && lora_mid->type != GGML_TYPE_F16) {
+                    lora_mid = ggml_cast(ctx, lora_mid, GGML_TYPE_F16);
+                }
             }
 
             iter = lora_tensors.find(lora_down_name);
             if (iter != lora_tensors.end()) {
                 lora_down = iter->second;
+                if (is_conv2d && lora_down->type != GGML_TYPE_F16) {
+                    lora_down = ggml_cast(ctx, lora_down, GGML_TYPE_F16);
+                }
             }
 
             if (lora_up == nullptr || lora_down == nullptr) {
@@ -570,7 +581,7 @@ struct LoraModel : public GGMLRunner {
             scale_value *= multiplier;
 
             ggml_tensor* lx;
-            if (forward_params.op_type == WeightAdapter::ForwardParams::op_type_t::OP_LINEAR) {
+            if (!is_conv2d) {
                 lx = ggml_ext_linear(ctx, x, lora_down, nullptr, forward_params.linear.force_prec_f32, forward_params.linear.scale);
                 if (lora_mid) {
                     lx = ggml_ext_linear(ctx, lx, lora_mid, nullptr, forward_params.linear.force_prec_f32, forward_params.linear.scale);