refactor: migrate generation pipeline to sd::Tensor

examples/cli/main.cpp

@@ -601,7 +601,7 @@ int main(int argc, const char* argv[]) {
 
     if (gen_params.end_image_path.size() > 0) {
         vae_decode_only = false;
-        if (!load_image_and_update_size(gen_params.init_image_path, end_image)) {
+        if (!load_image_and_update_size(gen_params.end_image_path, end_image)) {
             return 1;
         }
     }

src/anima.hpp

@@ -602,20 +602,19 @@ namespace Anima {
             return Rope::embed_nd(ids, bs, axis_thetas, axes_dim);
         }
 
-        ggml_cgraph* build_graph(ggml_tensor* x,
-                                 ggml_tensor* timesteps,
-                                 ggml_tensor* context,
-                                 ggml_tensor* t5_ids     = nullptr,
-                                 ggml_tensor* t5_weights = nullptr) {
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timesteps_tensor,
+                                 const sd::Tensor<float>& context_tensor    = {},
+                                 const sd::Tensor<int32_t>& t5_ids_tensor   = {},
+                                 const sd::Tensor<float>& t5_weights_tensor = {}) {
+            ggml_tensor* x          = make_input(x_tensor);
+            ggml_tensor* timesteps  = make_input(timesteps_tensor);
+            ggml_tensor* context    = make_optional_input(context_tensor);
+            ggml_tensor* t5_ids     = make_optional_input(t5_ids_tensor);
+            ggml_tensor* t5_weights = make_optional_input(t5_weights_tensor);
             GGML_ASSERT(x->ne[3] == 1);
             ggml_cgraph* gf = new_graph_custom(ANIMA_GRAPH_SIZE);
 
-            x          = to_backend(x);
-            timesteps  = to_backend(timesteps);
-            context    = to_backend(context);
-            t5_ids     = to_backend(t5_ids);
-            t5_weights = to_backend(t5_weights);
-
             int64_t pad_h = (net.patch_size - x->ne[1] % net.patch_size) % net.patch_size;
             int64_t pad_w = (net.patch_size - x->ne[0] % net.patch_size) % net.patch_size;
             int64_t h_pad = x->ne[1] + pad_h;
@@ -667,18 +666,16 @@ namespace Anima {
             return gf;
         }
 
-        bool compute(int n_threads,
-                     ggml_tensor* x,
-                     ggml_tensor* timesteps,
-                     ggml_tensor* context,
-                     ggml_tensor* t5_ids      = nullptr,
-                     ggml_tensor* t5_weights  = nullptr,
-                     ggml_tensor** output     = nullptr,
-                     ggml_context* output_ctx = nullptr) {
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timesteps,
+                                  const sd::Tensor<float>& context    = {},
+                                  const sd::Tensor<int32_t>& t5_ids   = {},
+                                  const sd::Tensor<float>& t5_weights = {}) {
             auto get_graph = [&]() -> ggml_cgraph* {
                 return build_graph(x, timesteps, context, t5_ids, t5_weights);
             };
-            return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
         }
     };
 }  // namespace Anima

src/cache_dit.hpp

@@ -8,7 +8,9 @@
 #include <unordered_map>
 #include <vector>
 
+#include "condition_cache_utils.hpp"
 #include "ggml_extend.hpp"
+#include "tensor.hpp"
 
 struct DBCacheConfig {
     bool enabled                        = false;
@@ -771,35 +773,37 @@ struct CacheDitConditionState {
         return it != cache_diffs.end() && !it->second.diff.empty();
     }
 
-    void update_cache(const void* cond, const float* input, const float* output, size_t size) {
+    void update_cache(const void* cond, const sd::Tensor<float>& input, const sd::Tensor<float>& output) {
         CacheEntry& entry = cache_diffs[cond];
-        entry.diff.resize(size);
-        for (size_t i = 0; i < size; i++) {
-            entry.diff[i] = output[i] - input[i];
+        if (!sd::store_condition_cache_diff(&entry.diff, input, output)) {
+            entry.prev_input.clear();
+            entry.prev_output.clear();
+            entry.has_prev = false;
+            return;
         }
 
+        size_t size              = static_cast<size_t>(output.numel());
+        const float* input_data  = input.data();
+        const float* output_data = output.data();
         entry.prev_input.resize(size);
         entry.prev_output.resize(size);
         for (size_t i = 0; i < size; i++) {
-            entry.prev_input[i]  = input[i];
-            entry.prev_output[i] = output[i];
+            entry.prev_input[i]  = input_data[i];
+            entry.prev_output[i] = output_data[i];
         }
         entry.has_prev = true;
     }
 
-    void apply_cache(const void* cond, const float* input, float* output, size_t size) {
+    void apply_cache(const void* cond,
+                     const sd::Tensor<float>& input,
+                     sd::Tensor<float>* output) {
         auto it = cache_diffs.find(cond);
         if (it == cache_diffs.end() || it->second.diff.empty())
             return;
-        if (it->second.diff.size() != size)
-            return;
-
-        for (size_t i = 0; i < size; i++) {
-            output[i] = input[i] + it->second.diff[i];
-        }
+        sd::apply_condition_cache_diff(it->second.diff, input, output);
     }
 
-    bool before_condition(const void* cond, ggml_tensor* input, ggml_tensor* output, float sigma, int step_index) {
+    bool before_condition(const void* cond, const sd::Tensor<float>& input, sd::Tensor<float>* output, float sigma, int step_index) {
         if (!enabled() || step_index < 0)
             return false;
 
@@ -819,8 +823,7 @@ struct CacheDitConditionState {
 
         if (skip_current_step) {
             if (has_cache(cond)) {
-                apply_cache(cond, (float*)input->data, (float*)output->data,
-                            static_cast<size_t>(ggml_nelements(output)));
+                apply_cache(cond, input, output);
                 return true;
             }
             return false;
@@ -833,13 +836,13 @@ struct CacheDitConditionState {
         if (it == cache_diffs.end() || !it->second.has_prev)
             return false;
 
-        size_t ne = static_cast<size_t>(ggml_nelements(input));
+        size_t ne = static_cast<size_t>(input.numel());
         if (it->second.prev_input.size() != ne)
             return false;
 
-        float* input_data = (float*)input->data;
-        float diff        = CacheDitState::calculate_residual_diff(
-                   it->second.prev_input.data(), input_data, ne);
+        const float* input_data = input.data();
+        float diff              = CacheDitState::calculate_residual_diff(
+                         it->second.prev_input.data(), input_data, ne);
 
         float effective_threshold = config.residual_diff_threshold;
         if (config.Fn_compute_blocks > 0) {
@@ -859,23 +862,22 @@ struct CacheDitConditionState {
             cached_steps.push_back(current_step_index);
             continuous_cached_steps++;
             accumulated_residual_diff += diff;
-            apply_cache(cond, input_data, (float*)output->data, ne);
+            apply_cache(cond, input, output);
             return true;
         }
 
         continuous_cached_steps = 0;
         return false;
     }
 
-    void after_condition(const void* cond, ggml_tensor* input, ggml_tensor* output) {
+    void after_condition(const void* cond, const sd::Tensor<float>& input, const sd::Tensor<float>& output) {
         if (!step_is_active())
             return;
 
-        size_t ne = static_cast<size_t>(ggml_nelements(output));
-        update_cache(cond, (float*)input->data, (float*)output->data, ne);
+        update_cache(cond, input, output);
 
         if (cond == anchor_condition && taylor_config.enabled) {
-            taylor_state.update_derivatives((float*)output->data, ne, current_step_index);
+            taylor_state.update_derivatives(output.data(), static_cast<size_t>(output.numel()), current_step_index);
         }
     }
 

src/clip.hpp

@@ -957,15 +957,14 @@ struct CLIPTextModelRunner : public GGMLRunner {
         return model.forward(ctx, input_ids, embeddings, mask, max_token_idx, return_pooled, clip_skip);
     }
 
-    ggml_cgraph* build_graph(ggml_tensor* input_ids,
+    ggml_cgraph* build_graph(const sd::Tensor<int32_t>& input_ids_tensor,
                              int num_custom_embeddings    = 0,
                              void* custom_embeddings_data = nullptr,
                              size_t max_token_idx         = 0,
                              bool return_pooled           = false,
                              int clip_skip                = -1) {
-        ggml_cgraph* gf = new_graph_custom(2048);
-
-        input_ids = to_backend(input_ids);
+        ggml_cgraph* gf        = new_graph_custom(2048);
+        ggml_tensor* input_ids = make_input(input_ids_tensor);
 
         ggml_tensor* embeddings = nullptr;
 
@@ -1004,19 +1003,21 @@ struct CLIPTextModelRunner : public GGMLRunner {
         return gf;
     }
 
-    bool compute(const int n_threads,
-                 ggml_tensor* input_ids,
-                 int num_custom_embeddings,
-                 void* custom_embeddings_data,
-                 size_t max_token_idx,
-                 bool return_pooled,
-                 int clip_skip,
-                 ggml_tensor** output,
-                 ggml_context* output_ctx = nullptr) {
+    sd::Tensor<float> compute(const int n_threads,
+                              const sd::Tensor<int32_t>& input_ids,
+                              int num_custom_embeddings,
+                              void* custom_embeddings_data,
+                              size_t max_token_idx,
+                              bool return_pooled,
+                              int clip_skip) {
         auto get_graph = [&]() -> ggml_cgraph* {
             return build_graph(input_ids, num_custom_embeddings, custom_embeddings_data, max_token_idx, return_pooled, clip_skip);
         };
-        return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
+        auto result = GGMLRunner::compute<float>(get_graph, n_threads, true);
+        if (return_pooled) {
+            return take_or_empty(std::move(result));
+        }
+        return restore_trailing_singleton_dims(std::move(result), 3);
     }
 };
 

src/common_dit.hpp

@@ -4,11 +4,11 @@
 #include "ggml_extend.hpp"
 
 namespace DiT {
-    ggml_tensor* patchify(ggml_context* ctx,
-                          ggml_tensor* x,
-                          int pw,
-                          int ph,
-                          bool patch_last = true) {
+    inline ggml_tensor* patchify(ggml_context* ctx,
+                                 ggml_tensor* x,
+                                 int pw,
+                                 int ph,
+                                 bool patch_last = true) {
         // x: [N, C, H, W]
         // return: [N, h*w, C*ph*pw] if patch_last else [N, h*w, ph*pw*C]
         int64_t N = x->ne[3];
@@ -33,13 +33,13 @@ namespace DiT {
         return x;
     }
 
-    ggml_tensor* unpatchify(ggml_context* ctx,
-                            ggml_tensor* x,
-                            int64_t h,
-                            int64_t w,
-                            int ph,
-                            int pw,
-                            bool patch_last = true) {
+    inline ggml_tensor* unpatchify(ggml_context* ctx,
+                                   ggml_tensor* x,
+                                   int64_t h,
+                                   int64_t w,
+                                   int ph,
+                                   int pw,
+                                   bool patch_last = true) {
         // x: [N, h*w, C*ph*pw] if patch_last else [N, h*w, ph*pw*C]
         // return: [N, C, H, W]
         int64_t N = x->ne[2];
@@ -64,10 +64,10 @@ namespace DiT {
         return x;
     }
 
-    ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
-                                   ggml_tensor* x,
-                                   int ph,
-                                   int pw) {
+    inline ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
+                                          ggml_tensor* x,
+                                          int ph,
+                                          int pw) {
         int64_t W = x->ne[0];
         int64_t H = x->ne[1];
 
@@ -77,23 +77,23 @@ namespace DiT {
         return x;
     }
 
-    ggml_tensor* pad_and_patchify(GGMLRunnerContext* ctx,
-                                  ggml_tensor* x,
-                                  int ph,
-                                  int pw,
-                                  bool patch_last = true) {
+    inline ggml_tensor* pad_and_patchify(GGMLRunnerContext* ctx,
+                                         ggml_tensor* x,
+                                         int ph,
+                                         int pw,
+                                         bool patch_last = true) {
         x = pad_to_patch_size(ctx, x, ph, pw);
         x = patchify(ctx->ggml_ctx, x, ph, pw, patch_last);
         return x;
     }
 
-    ggml_tensor* unpatchify_and_crop(ggml_context* ctx,
-                                     ggml_tensor* x,
-                                     int64_t H,
-                                     int64_t W,
-                                     int ph,
-                                     int pw,
-                                     bool patch_last = true) {
+    inline ggml_tensor* unpatchify_and_crop(ggml_context* ctx,
+                                            ggml_tensor* x,
+                                            int64_t H,
+                                            int64_t W,
+                                            int ph,
+                                            int pw,
+                                            bool patch_last = true) {
         int pad_h = (ph - H % ph) % ph;
         int pad_w = (pw - W % pw) % pw;
         int64_t h = ((H + pad_h) / ph);
@@ -105,4 +105,4 @@ namespace DiT {
     }
 }  // namespace DiT
 
-#endif  // __COMMON_DIT_HPP__
+#endif  // __COMMON_DIT_HPP__

src/condition_cache_utils.hpp

@@ -0,0 +1,64 @@
+#ifndef __CONDITION_CACHE_UTILS_HPP__
+#define __CONDITION_CACHE_UTILS_HPP__
+
+#include <vector>
+
+#include "tensor.hpp"
+
+namespace sd {
+
+    inline bool store_condition_cache_diff(std::vector<float>* diff,
+                                           const sd::Tensor<float>& input,
+                                           const sd::Tensor<float>& output) {
+        if (diff == nullptr || input.empty() || output.empty()) {
+            return false;
+        }
+
+        size_t input_size  = static_cast<size_t>(input.numel());
+        size_t output_size = static_cast<size_t>(output.numel());
+        if (input_size == 0 || input_size != output_size) {
+            diff->clear();
+            return false;
+        }
+
+        const float* input_data  = input.data();
+        const float* output_data = output.data();
+        if (input_data == nullptr || output_data == nullptr) {
+            diff->clear();
+            return false;
+        }
+
+        diff->resize(output_size);
+        for (size_t i = 0; i < output_size; ++i) {
+            (*diff)[i] = output_data[i] - input_data[i];
+        }
+        return true;
+    }
+
+    inline bool apply_condition_cache_diff(const std::vector<float>& diff,
+                                           const sd::Tensor<float>& input,
+                                           sd::Tensor<float>* output) {
+        if (output == nullptr || input.empty() || diff.empty()) {
+            return false;
+        }
+
+        size_t input_size = static_cast<size_t>(input.numel());
+        if (input_size == 0 || diff.size() != input_size) {
+            return false;
+        }
+
+        *output            = input;
+        float* output_data = output->data();
+        if (output_data == nullptr) {
+            return false;
+        }
+
+        for (size_t i = 0; i < input_size; ++i) {
+            output_data[i] += diff[i];
+        }
+        return true;
+    }
+
+}  // namespace sd
+
+#endif  // __CONDITION_CACHE_UTILS_HPP__