Merge remote-tracking branch 'ggml-org/master' into allozaur/svelte-webui

Author: allozaur

.github/workflows/build.yml

@@ -97,7 +97,7 @@ jobs:
           ctest -L 'main|curl' --verbose --timeout 900
 
   macOS-latest-cmake-x64:
-    runs-on: macos-latest
+    runs-on: macos-13
 
     steps:
       - name: Clone

common/json-schema-to-grammar.cpp

@@ -257,12 +257,13 @@ std::unordered_map<std::string, BuiltinRule> STRING_FORMAT_RULES = {
 };
 
 static bool is_reserved_name(const std::string & name) {
-    static std::unordered_set<std::string> RESERVED_NAMES;
-    if (RESERVED_NAMES.empty()) {
-        RESERVED_NAMES.insert("root");
-        for (const auto &p : PRIMITIVE_RULES) RESERVED_NAMES.insert(p.first);
-        for (const auto &p : STRING_FORMAT_RULES) RESERVED_NAMES.insert(p.first);
-    }
+    static const std::unordered_set<std::string> RESERVED_NAMES = [] {
+        std::unordered_set<std::string> s;
+        s.insert("root");
+        for (const auto & p : PRIMITIVE_RULES) s.insert(p.first);
+        for (const auto & p : STRING_FORMAT_RULES) s.insert(p.first);
+        return s;
+    }();
     return RESERVED_NAMES.find(name) != RESERVED_NAMES.end();
 }
 

convert_hf_to_gguf.py

@@ -6009,9 +6009,34 @@ class SeedOssModel(TextModel):
 
 
 @ModelBase.register("Olmo2ForCausalLM")
+@ModelBase.register("Olmo3ForCausalLM")
 class Olmo2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.OLMO2
 
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        rope_scaling = self.hparams.get("rope_scaling") or {}
+        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
+            self.gguf_writer.add_rope_scaling_attn_factors(rope_scaling["attention_factor"])
+            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
+
+        if "sliding_window" in self.hparams:
+            self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+
+            sliding_window_pattern = []
+            if "layer_types" in self.hparams:
+                sliding_window_pattern = [t == "sliding_attention" for t in self.hparams["layer_types"]]
+            else:
+                # Olmo2 does not use sliding window attention.
+                # Olmo3 defaults to using sliding window for all layers except every 4th.
+                for i in range(self.hparams["num_hidden_layers"]):
+                    sliding_window_pattern.append((i + 1) % 4 != 0)
+
+            self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
+
 
 @ModelBase.register("OlmoeForCausalLM")
 class OlmoeModel(TextModel):

examples/simple/simple.cpp

@@ -145,6 +145,20 @@ int main(int argc, char ** argv) {
 
     llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size());
 
+    if (llama_model_has_encoder(model)) {
+        if (llama_encode(ctx, batch)) {
+            fprintf(stderr, "%s : failed to eval\n", __func__);
+            return 1;
+        }
+
+        llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
+        if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
+            decoder_start_token_id = llama_vocab_bos(vocab);
+        }
+
+        batch = llama_batch_get_one(&decoder_start_token_id, 1);
+    }
+
     // main loop
 
     const auto t_main_start = ggml_time_us();

ggml/src/ggml-cann/common.h

@@ -526,7 +526,10 @@ struct ggml_backend_cann_context {
      */
     aclrtStream stream(int stream) {
         if (streams[stream] == nullptr) {
-            ggml_cann_set_device(device);
+            // If the device is not set here, destroying the stream later may cause a mismatch
+            // between the thread contexts where the stream was created and destroyed.
+            // However, I printed the device_id, thread_id, and stream, and they are all consistent.
+            ACL_CHECK(aclrtSetDevice(device));
             ACL_CHECK(aclrtCreateStream(&streams[stream]));
         }
         return streams[stream];

ggml/src/ggml-cann/ggml-cann.cpp

@@ -75,13 +75,12 @@
  * @param device The device ID to set.
  */
 void ggml_cann_set_device(const int32_t device) {
-    // TODO: uncomment these lines after empty context has fixed.
-    // int current_device;
-    // ACL_CHECK(aclrtGetDevice(&current_device));
+    int current_device = -1;
+    aclrtGetDevice(&current_device);
 
-    // if (device == current_device) {
-    //   return;
-    // }
+    if (device == current_device) {
+      return;
+    }
     ACL_CHECK(aclrtSetDevice(device));
 }
 
@@ -1729,6 +1728,7 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
             ggml_cann_get_rows(ctx, dst);
             break;
         case GGML_OP_SET_ROWS:
+            std::cout << "lcg GGML_OP_SET_ROWS"<< std::endl;
             ggml_cann_set_rows(ctx, dst);
             break;
         case GGML_OP_DUP:

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -4423,8 +4423,8 @@ static void ggml_vk_print_gpu_info(size_t idx) {
 
 static bool ggml_vk_instance_validation_ext_available();
 static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
-
 static bool ggml_vk_instance_debug_utils_ext_available(const std::vector<vk::ExtensionProperties> & instance_extensions);
+static bool ggml_vk_device_is_supported(const vk::PhysicalDevice & vkdev);
 
 static void ggml_vk_instance_init() {
     if (vk_instance_initialized) {
@@ -4540,7 +4540,7 @@ static void ggml_vk_instance_init() {
             new_driver.pNext = &new_id;
             devices[i].getProperties2(&new_props);
 
-            if (new_props.properties.deviceType == vk::PhysicalDeviceType::eDiscreteGpu || new_props.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu) {
+            if ((new_props.properties.deviceType == vk::PhysicalDeviceType::eDiscreteGpu || new_props.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu) && ggml_vk_device_is_supported(devices[i])) {
                 // Check if there are two physical devices corresponding to the same GPU
                 auto old_device = std::find_if(
                     vk_instance.device_indices.begin(),
@@ -12738,6 +12738,20 @@ static bool ggml_vk_instance_debug_utils_ext_available(
     UNUSED(instance_extensions);
 }
 
+static bool ggml_vk_device_is_supported(const vk::PhysicalDevice & vkdev) {
+    VkPhysicalDeviceFeatures2 device_features2;
+    device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
+
+    VkPhysicalDeviceVulkan11Features vk11_features;
+    vk11_features.pNext = nullptr;
+    vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
+    device_features2.pNext = &vk11_features;
+
+    vkGetPhysicalDeviceFeatures2(vkdev, &device_features2);
+
+    return vk11_features.storageBuffer16BitAccess;
+}
+
 static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props, vk_device_architecture arch) {
     switch (props.vendorID) {
     case VK_VENDOR_ID_INTEL:

src/llama-model.cpp

@@ -1350,6 +1350,14 @@ void llama_model::load_hparams(llama_model_loader & ml) {
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
+                const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+                if (found_swa && hparams.n_swa > 0) {
+                    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+                    hparams.set_swa_pattern(4);
+                } else {
+                    hparams.swa_type = LLAMA_SWA_TYPE_NONE;
+                }
+
                 switch (hparams.n_layer) {
                     case 16: type = LLM_TYPE_1B; break;
                     case 32: type = LLM_TYPE_7B; break;
@@ -12233,6 +12241,7 @@ struct llm_build_olmo : public llm_graph_context {
     }
 };
 
+template <bool iswa>
 struct llm_build_olmo2 : public llm_graph_context {
     llm_build_olmo2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
         const int64_t n_embd_head = hparams.n_embd_head_v;
@@ -12248,7 +12257,14 @@ struct llm_build_olmo2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv();
+        using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;
+        inp_attn_type * inp_attn = nullptr;
+
+        if constexpr (iswa) {
+            inp_attn = build_attn_inp_kv_iswa();
+        } else {
+            inp_attn = build_attn_inp_kv();
+        }
 
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -12281,17 +12297,36 @@ struct llm_build_olmo2 : public llm_graph_context {
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
                 Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 
-                Qcur = ggml_rope_ext(
+                const bool is_swa = hparams.is_swa(il);
+
+                if (is_swa) {
+                    // For sliding window layers, Olmo3 use regular rope with no yarn rope scaling.
+                    // This is achieved here by setting freq_scale and attn_factor to 1.
+                    // We also set ext_factor to 0 to avoid a few unnecessary computations.
+                    Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, 1.0,
+                        0.0, 1.0, beta_fast, beta_slow
+                        );
+
+                    Kcur = ggml_rope_ext(
+                        ctx0, Kcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, 1.0,
+                        0.0, 1.0, beta_fast, beta_slow
+                        );
+                } else {
+                    Qcur = ggml_rope_ext(
                         ctx0, Qcur, inp_pos, nullptr,
                         n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow
                         );
 
-                Kcur = ggml_rope_ext(
+                    Kcur = ggml_rope_ext(
                         ctx0, Kcur, inp_pos, nullptr,
                         n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow
                         );
+                }
 
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
@@ -19131,7 +19166,11 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             } break;
         case LLM_ARCH_OLMO2:
             {
-                llm = std::make_unique<llm_build_olmo2>(*this, params);
+                if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
+                    llm = std::make_unique<llm_build_olmo2<true>>(*this, params);
+                } else {
+                    llm = std::make_unique<llm_build_olmo2<false>>(*this, params);
+                }
             } break;
         case LLM_ARCH_OLMOE:
             {

src/llama-quant.cpp

@@ -725,7 +725,9 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         // attention layers have a non-zero number of kv heads
         int32_t n_attn_layer = model.hparams.n_layer - std::count(n_head_kv_iter, n_head_kv_iter + model.hparams.n_layer, 0);
         if (llama_model_has_encoder(&model)) {
-            n_attn_layer *= 3;
+            // now n_attn_layer is the number of attention layers in the encoder
+            // for each decoder block, there are 2 attention layers
+            n_attn_layer += 2 * model.hparams.dec_n_layer;
         }
         GGML_ASSERT((qs.n_attention_wv == n_attn_layer - pruned_attention_w) && "n_attention_wv is unexpected");
     }