remove the redundant lx

common.hpp

@@ -181,39 +181,22 @@ class GEGLU : public UnaryBlock {
 protected:
     int64_t dim_in;
     int64_t dim_out;
-    std::string prefix;
-
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, std::string prefix = "") override {
-        this->prefix              = prefix;
-        enum ggml_type wtype      = get_type(prefix + "proj.weight", tensor_storage_map, GGML_TYPE_F32);
-        enum ggml_type bias_wtype = GGML_TYPE_F32;
-        params["proj.weight"]     = ggml_new_tensor_2d(ctx, wtype, dim_in, dim_out * 2);
-        params["proj.bias"]       = ggml_new_tensor_1d(ctx, bias_wtype, dim_out * 2);
-    }
 
 public:
     GEGLU(int64_t dim_in, int64_t dim_out)
-        : dim_in(dim_in), dim_out(dim_out) {}
+        : dim_in(dim_in), dim_out(dim_out) {
+        blocks["proj"] = std::shared_ptr<GGMLBlock>(new Linear(dim_in, dim_out * 2));
+    }
 
     struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         // x: [ne3, ne2, ne1, dim_in]
         // return: [ne3, ne2, ne1, dim_out]
-        struct ggml_tensor* w = params["proj.weight"];
-        struct ggml_tensor* b = params["proj.bias"];
+        auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
 
-        if (ctx->weight_adapter) {
-            w = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, w, prefix + "proj.weight");
-            b = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, b, prefix + "proj.bias");
-        }
-
-        auto x_w    = ggml_view_2d(ctx->ggml_ctx, w, w->ne[0], w->ne[1] / 2, w->nb[1], 0);                        // [dim_out, dim_in]
-        auto x_b    = ggml_view_1d(ctx->ggml_ctx, b, b->ne[0] / 2, 0);                                            // [dim_out, dim_in]
-        auto gate_w = ggml_view_2d(ctx->ggml_ctx, w, w->ne[0], w->ne[1] / 2, w->nb[1], w->nb[1] * w->ne[1] / 2);  // [dim_out, ]
-        auto gate_b = ggml_view_1d(ctx->ggml_ctx, b, b->ne[0] / 2, b->nb[0] * b->ne[0] / 2);                      // [dim_out, ]
-
-        auto x_in = x;
-        x         = ggml_ext_linear(ctx->ggml_ctx, x_in, x_w, x_b);        // [ne3, ne2, ne1, dim_out]
-        auto gate = ggml_ext_linear(ctx->ggml_ctx, x_in, gate_w, gate_b);  // [ne3, ne2, ne1, dim_out]
+        x          = proj->forward(ctx, x);  // [ne3, ne2, ne1, dim_out*2]
+        auto x_vec = ggml_ext_chunk(ctx->ggml_ctx, x, 2, 0);
+        x          = x_vec[0];  // [ne3, ne2, ne1, dim_out]
+        auto gate  = x_vec[1];  // [ne3, ne2, ne1, dim_out]
 
         gate = ggml_gelu_inplace(ctx->ggml_ctx, gate);
 

ggml_extend.hpp

@@ -959,12 +959,15 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_linear(struct ggml_context* ctx,
         int64_t ne3 = x->ne[3];
         x           = ggml_reshape_2d(ctx, x, x->ne[0], x->ne[1] * x->ne[2] * x->ne[3]);
         x           = ggml_mul_mat(ctx, w, x);
-        x           = ggml_reshape_4d(ctx, x, x->ne[0], x->ne[1] / ne2 / ne3, ne2, ne3);
+        if (force_prec_f32) {
+            ggml_mul_mat_set_prec(x, GGML_PREC_F32);
+        }
+        x = ggml_reshape_4d(ctx, x, x->ne[0], x->ne[1] / ne2 / ne3, ne2, ne3);
     } else {
         x = ggml_mul_mat(ctx, w, x);
-    }
-    if (force_prec_f32) {
-        ggml_mul_mat_set_prec(x, GGML_PREC_F32);
+        if (force_prec_f32) {
+            ggml_mul_mat_set_prec(x, GGML_PREC_F32);
+        }
     }
     if (scale != 1.f) {
         x = ggml_scale(ctx, x, 1.f / scale);
@@ -1473,8 +1476,34 @@ __STATIC_INLINE__ size_t ggml_tensor_num(ggml_context* ctx) {
 #define MAX_GRAPH_SIZE 327680
 
 struct WeightAdapter {
-    virtual ggml_tensor* patch_weight(ggml_context* ggml_ctx, ggml_tensor* weight, const std::string& weight_name) = 0;
-    virtual size_t get_extra_graph_size()                                                                          = 0;
+    struct ForwardParams {
+        enum class op_type_t {
+            OP_LINEAR,
+            OP_CONV2D,
+        } op_type;
+        struct {
+            bool force_prec_f32 = false;
+            float scale         = 1.f;
+        } linear;
+        struct {
+            int s0      = 1;
+            int s1      = 1;
+            int p0      = 0;
+            int p1      = 0;
+            int d0      = 1;
+            int d1      = 1;
+            bool direct = false;
+            float scale = 1.f;
+        } conv2d;
+    };
+    virtual ggml_tensor* patch_weight(ggml_context* ctx, ggml_tensor* weight, const std::string& weight_name) = 0;
+    virtual ggml_tensor* forward_with_lora(ggml_context* ctx,
+                                           ggml_tensor* x,
+                                           ggml_tensor* w,
+                                           ggml_tensor* b,
+                                           const std::string& prefix,
+                                           ForwardParams forward_params)                                      = 0;
+    virtual size_t get_extra_graph_size()                                                                     = 0;
 };
 
 struct GGMLRunnerContext {
@@ -2070,14 +2099,15 @@ public:
     struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         struct ggml_tensor* w = params["weight"];
         struct ggml_tensor* b = nullptr;
-        if (ctx->weight_adapter) {
-            w = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, w, prefix + "weight");
-        }
         if (bias) {
             b = params["bias"];
-            if (ctx->weight_adapter) {
-                b = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, b, prefix + "bias");
-            }
+        }
+        if (ctx->weight_adapter) {
+            WeightAdapter::ForwardParams forward_params;
+            forward_params.op_type               = WeightAdapter::ForwardParams::op_type_t::OP_LINEAR;
+            forward_params.linear.force_prec_f32 = force_prec_f32;
+            forward_params.linear.scale          = scale;
+            return ctx->weight_adapter->forward_with_lora(ctx->ggml_ctx, x, w, b, prefix, forward_params);
         }
         return ggml_ext_linear(ctx->ggml_ctx, x, w, b, force_prec_f32, scale);
     }
@@ -2177,17 +2207,21 @@ public:
     struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         struct ggml_tensor* w = params["weight"];
         struct ggml_tensor* b = nullptr;
-        if (ctx->weight_adapter) {
-            w = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, w, prefix + "weight");
-            if (w->type != GGML_TYPE_F16) {
-                w = ggml_cast(ctx->ggml_ctx, w, GGML_TYPE_F16);
-            }
-        }
         if (bias) {
             b = params["bias"];
-            if (ctx->weight_adapter) {
-                b = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, b, prefix + "bias");
-            }
+        }
+        if (ctx->weight_adapter) {
+            WeightAdapter::ForwardParams forward_params;
+            forward_params.op_type       = WeightAdapter::ForwardParams::op_type_t::OP_CONV2D;
+            forward_params.conv2d.s0     = stride.second;
+            forward_params.conv2d.s1     = stride.first;
+            forward_params.conv2d.p0     = padding.second;
+            forward_params.conv2d.p1     = padding.first;
+            forward_params.conv2d.d0     = dilation.second;
+            forward_params.conv2d.d1     = dilation.first;
+            forward_params.conv2d.direct = ctx->conv2d_direct_enabled;
+            forward_params.conv2d.scale  = scale;
+            return ctx->weight_adapter->forward_with_lora(ctx->ggml_ctx, x, w, b, prefix, forward_params);
         }
         return ggml_ext_conv_2d(ctx->ggml_ctx,
                                 x,

lora.hpp

@@ -9,7 +9,7 @@
 struct LoraModel : public GGMLRunner {
     std::string lora_id;
     float multiplier = 1.0f;
-    std::map<std::string, struct ggml_tensor*> lora_tensors;
+    std::unordered_map<std::string, struct ggml_tensor*> lora_tensors;
     std::map<ggml_tensor*, ggml_tensor*> original_tensor_to_final_tensor;
     std::set<std::string> applied_lora_tensors;
     std::string file_path;
@@ -71,6 +71,10 @@ struct LoraModel : public GGMLRunner {
 
         model_loader.load_tensors(on_new_tensor_cb, n_threads);
 
+        if (tensors_to_create.empty()) {
+            return true;
+        }
+
         for (const auto& pair : tensors_to_create) {
             const auto& name         = pair.first;
             const auto& ts           = pair.second;
@@ -97,7 +101,7 @@ struct LoraModel : public GGMLRunner {
         tensor_preprocessed = true;
         // I really hate these hardcoded processes.
         if (model_tensors.find("cond_stage_model.1.transformer.text_model.encoder.layers.0.self_attn.in_proj.weight") != model_tensors.end()) {
-            std::map<std::string, ggml_tensor*> new_lora_tensors;
+            std::unordered_map<std::string, ggml_tensor*> new_lora_tensors;
             for (auto& [old_name, tensor] : lora_tensors) {
                 std::string new_name = old_name;
 
@@ -125,7 +129,7 @@ struct LoraModel : public GGMLRunner {
         }
     }
 
-    ggml_tensor* get_lora_diff(const std::string& model_tensor_name, ggml_context* ctx) {
+    ggml_tensor* get_lora_weight_diff(const std::string& model_tensor_name, ggml_context* ctx) {
         ggml_tensor* updown = nullptr;
         int index           = 0;
         while (true) {
@@ -201,21 +205,50 @@ struct LoraModel : public GGMLRunner {
 
             index++;
         }
-
-        // diff
-        if (updown == nullptr) {
-            std::string lora_diff_name = "lora." + model_tensor_name + ".diff";
-
-            if (lora_tensors.find(lora_diff_name) != lora_tensors.end()) {
-                updown = ggml_ext_cast_f32(ctx, lora_tensors[lora_diff_name]);
-                applied_lora_tensors.insert(lora_diff_name);
-            }
-        }
-
         return updown;
     }
 
-    ggml_tensor* get_loha_diff(const std::string& model_tensor_name, ggml_context* ctx) {
+    ggml_tensor* get_raw_weight_diff(const std::string& model_tensor_name, ggml_context* ctx) {
+        ggml_tensor* updown = nullptr;
+        int index           = 0;
+        while (true) {
+            std::string key;
+            if (index == 0) {
+                key = model_tensor_name;
+            } else {
+                key = model_tensor_name + "." + std::to_string(index);
+            }
+
+            std::string diff_name = "lora." + key + ".diff";
+
+            ggml_tensor* curr_updown = nullptr;
+
+            auto iter = lora_tensors.find(diff_name);
+            if (iter != lora_tensors.end()) {
+                curr_updown = ggml_ext_cast_f32(ctx, iter->second);
+            } else {
+                break;
+            }
+
+            applied_lora_tensors.insert(diff_name);
+
+            float scale_value = 1.0f;
+            scale_value *= multiplier;
+
+            curr_updown = ggml_scale_inplace(ctx, curr_updown, scale_value);
+
+            if (updown == nullptr) {
+                updown = curr_updown;
+            } else {
+                updown = ggml_concat(ctx, updown, curr_updown, ggml_n_dims(updown) - 1);
+            }
+
+            index++;
+        }
+        return updown;
+    }
+
+    ggml_tensor* get_loha_weight_diff(const std::string& model_tensor_name, ggml_context* ctx) {
         ggml_tensor* updown = nullptr;
         int index           = 0;
         while (true) {
@@ -318,7 +351,7 @@ struct LoraModel : public GGMLRunner {
         return updown;
     }
 
-    ggml_tensor* get_lokr_diff(const std::string& model_tensor_name, ggml_context* ctx) {
+    ggml_tensor* get_lokr_weight_diff(const std::string& model_tensor_name, ggml_context* ctx) {
         ggml_tensor* updown = nullptr;
         int index           = 0;
         while (true) {
@@ -435,16 +468,23 @@ struct LoraModel : public GGMLRunner {
         return updown;
     }
 
-    ggml_tensor* get_diff(const std::string& model_tensor_name, ggml_context* ctx, ggml_tensor* model_tensor) {
+    ggml_tensor* get_weight_diff(const std::string& model_tensor_name, ggml_context* ctx, ggml_tensor* model_tensor, bool with_lora = true) {
         // lora
-        ggml_tensor* diff = get_lora_diff(model_tensor_name, ctx);
+        ggml_tensor* diff = nullptr;
+        if (with_lora) {
+            diff = get_lora_weight_diff(model_tensor_name, ctx);
+        }
+        // diff
+        if (diff == nullptr) {
+            diff = get_raw_weight_diff(model_tensor_name, ctx);
+        }
         // loha
         if (diff == nullptr) {
-            diff = get_loha_diff(model_tensor_name, ctx);
+            diff = get_loha_weight_diff(model_tensor_name, ctx);
         }
         // lokr
         if (diff == nullptr) {
-            diff = get_lokr_diff(model_tensor_name, ctx);
+            diff = get_lokr_weight_diff(model_tensor_name, ctx);
         }
         if (diff != nullptr) {
             if (ggml_nelements(diff) < ggml_nelements(model_tensor)) {
@@ -461,6 +501,146 @@ struct LoraModel : public GGMLRunner {
         return diff;
     }
 
+    ggml_tensor* get_out_diff(ggml_context* ctx,
+                              ggml_tensor* x,
+                              WeightAdapter::ForwardParams forward_params,
+                              const std::string& model_tensor_name) {
+        ggml_tensor* out_diff = nullptr;
+        int index             = 0;
+        while (true) {
+            std::string key;
+            if (index == 0) {
+                key = model_tensor_name;
+            } else {
+                key = model_tensor_name + "." + std::to_string(index);
+            }
+
+            std::string lora_down_name = "lora." + key + ".lora_down";
+            std::string lora_up_name   = "lora." + key + ".lora_up";
+            std::string lora_mid_name  = "lora." + key + ".lora_mid";
+            std::string scale_name     = "lora." + key + ".scale";
+            std::string alpha_name     = "lora." + key + ".alpha";
+
+            ggml_tensor* lora_up   = nullptr;
+            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) {
+                break;
+            }
+
+            applied_lora_tensors.insert(lora_up_name);
+            applied_lora_tensors.insert(lora_down_name);
+
+            if (lora_mid) {
+                applied_lora_tensors.insert(lora_mid_name);
+            }
+
+            float scale_value = 1.0f;
+
+            int64_t rank = lora_down->ne[ggml_n_dims(lora_down) - 1];
+            iter         = lora_tensors.find(scale_name);
+            if (iter != lora_tensors.end()) {
+                scale_value = ggml_ext_backend_tensor_get_f32(iter->second);
+                applied_lora_tensors.insert(scale_name);
+            } else {
+                iter = lora_tensors.find(alpha_name);
+                if (iter != lora_tensors.end()) {
+                    float alpha = ggml_ext_backend_tensor_get_f32(iter->second);
+                    scale_value = alpha / rank;
+                    // LOG_DEBUG("rank %s %ld %.2f %.2f", alpha_name.c_str(), rank, alpha, scale_value);
+                    applied_lora_tensors.insert(alpha_name);
+                }
+            }
+            scale_value *= multiplier;
+
+            ggml_tensor* lx;
+            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);
+                }
+                lx = ggml_ext_linear(ctx, lx, lora_up, nullptr, forward_params.linear.force_prec_f32, forward_params.linear.scale);
+            } else {  // OP_CONV2D
+                lx = ggml_ext_conv_2d(ctx,
+                                      x,
+                                      lora_down,
+                                      nullptr,
+                                      forward_params.conv2d.s0,
+                                      forward_params.conv2d.s1,
+                                      forward_params.conv2d.p0,
+                                      forward_params.conv2d.p1,
+                                      forward_params.conv2d.d0,
+                                      forward_params.conv2d.d1,
+                                      forward_params.conv2d.direct,
+                                      forward_params.conv2d.scale);
+                if (lora_mid) {
+                    lx = ggml_ext_conv_2d(ctx,
+                                          lx,
+                                          lora_mid,
+                                          nullptr,
+                                          1,
+                                          1,
+                                          0,
+                                          0,
+                                          1,
+                                          1,
+                                          forward_params.conv2d.direct,
+                                          forward_params.conv2d.scale);
+                }
+                lx = ggml_ext_conv_2d(ctx,
+                                      lx,
+                                      lora_up,
+                                      nullptr,
+                                      1,
+                                      1,
+                                      0,
+                                      0,
+                                      1,
+                                      1,
+                                      forward_params.conv2d.direct,
+                                      forward_params.conv2d.scale);
+            }
+
+            auto curr_out_diff = ggml_scale_inplace(ctx, lx, scale_value);
+
+            if (out_diff == nullptr) {
+                out_diff = curr_out_diff;
+            } else {
+                out_diff = ggml_concat(ctx, out_diff, curr_out_diff, ggml_n_dims(out_diff) - 1);
+            }
+
+            index++;
+        }
+        return out_diff;
+    }
+
     struct ggml_cgraph* build_lora_graph(const std::map<std::string, ggml_tensor*>& model_tensors, SDVersion version) {
         size_t lora_graph_size = LORA_GRAPH_BASE_SIZE + lora_tensors.size() * 10;
         struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, lora_graph_size, false);
@@ -475,7 +655,7 @@ struct LoraModel : public GGMLRunner {
             ggml_tensor* model_tensor     = it.second;
 
             // lora
-            ggml_tensor* diff = get_diff(model_tensor_name, compute_ctx, model_tensor);
+            ggml_tensor* diff = get_weight_diff(model_tensor_name, compute_ctx, model_tensor);
             if (diff == nullptr) {
                 continue;
             }
@@ -555,10 +735,9 @@ public:
         : lora_models(lora_models) {
     }
 
-    // TODO: cache result for multi run?
-    ggml_tensor* patch_weight(ggml_context* ctx, ggml_tensor* weight, const std::string& weight_name) override {
+    ggml_tensor* patch_weight(ggml_context* ctx, ggml_tensor* weight, const std::string& weight_name, bool with_lora) {
         for (auto& lora_model : lora_models) {
-            ggml_tensor* diff = lora_model->get_diff(weight_name, ctx, weight);
+            ggml_tensor* diff = lora_model->get_weight_diff(weight_name, ctx, weight, with_lora);
             if (diff == nullptr) {
                 continue;
             }
@@ -571,6 +750,47 @@ public:
         return weight;
     }
 
+    ggml_tensor* patch_weight(ggml_context* ctx, ggml_tensor* weight, const std::string& weight_name) override {
+        return patch_weight(ctx, weight, weight_name, true);
+    }
+
+    ggml_tensor* forward_with_lora(ggml_context* ctx,
+                                   ggml_tensor* x,
+                                   ggml_tensor* w,
+                                   ggml_tensor* b,
+                                   const std::string& prefix,
+                                   WeightAdapter::ForwardParams forward_params) override {
+        w = patch_weight(ctx, w, prefix + "weight", false);
+        if (b) {
+            b = patch_weight(ctx, b, prefix + "bias", false);
+        }
+        ggml_tensor* out;
+        if (forward_params.op_type == ForwardParams::op_type_t::OP_LINEAR) {
+            out = ggml_ext_linear(ctx, x, w, b, forward_params.linear.force_prec_f32, forward_params.linear.scale);
+        } else {  // OP_CONV2D
+            out = ggml_ext_conv_2d(ctx,
+                                   x,
+                                   w,
+                                   b,
+                                   forward_params.conv2d.s0,
+                                   forward_params.conv2d.s1,
+                                   forward_params.conv2d.p0,
+                                   forward_params.conv2d.p1,
+                                   forward_params.conv2d.d0,
+                                   forward_params.conv2d.d1,
+                                   forward_params.conv2d.direct,
+                                   forward_params.conv2d.scale);
+        }
+        for (auto& lora_model : lora_models) {
+            ggml_tensor* out_diff = lora_model->get_out_diff(ctx, x, forward_params, prefix + "weight");
+            if (out_diff == nullptr) {
+                continue;
+            }
+            out = ggml_add_inplace(ctx, out, out_diff);
+        }
+        return out;
+    }
+
     size_t get_extra_graph_size() override {
         size_t lora_tensor_num = 0;
         for (auto& lora_model : lora_models) {