demo131 - multiple issues regarding quatization, qy, etc.

* issue/204 - support graph in server scripts

* issue/208 - adapt to ali ppu

* issue/194 - add quantization modify configs accordingly

支持nv w8 1batch 1tp

增加json支持

InfiniLM 增加量化层和global config

以一种比较优雅的方式增加了quant config的支持

修改部分代码结构，删除无用代码

跟随inifnicore修改

删除所有的model_config，统一使用global_config

跟随InfiniLM最新代码修改

修改函数参数顺序

改名global config 为model config

Refactor: add new API alongside legacy interfaces with deprecation warnings

添加w4 inifnicore相关内容，以及将Quantization config划入InfiniCore

添加w4 inifnicore相关内容，以及将Quantization config划入InfiniCore

* issue/175 - qy device support

qy_page_131: add qy device

success qy inference_server.py

* Issue/170 - Add HYGON support and improve device type handling.

* Issue/193: feats for deployment
Signed-off-by: Ceng23333 <441651826@qq.com>

* skip responding eos token
Signed-off-by: Ceng23333 <441651826@qq.com>

* issue/143 use add_rmsnorm, nt flash attn, nt kv caching

* issue/204 - support graph in server scripts

* issue/208 - adapt to ali ppu

* rebase main

* issue/216 feat: support static kv cache in server

* fix llm server cache config

* demo131 - resolve mishandled conflicts

* demo131 - further adjust attn and caching logic

* demo131 - resolve merge requirements

---------
Signed-off-by: Ceng23333 <441651826@qq.com>
Co-authored-by: wooway777 <wooway777@gmail.com>
Co-authored-by: xgqdut2016 <kenan_gewei@163.com>
Co-authored-by: gongchensu <zhuyue_134@qq.com>
Co-authored-by: Ceng23333 <441651826@qq.com>
Co-authored-by: PanZezhong <panzezhong@qiyuanlab.com>
Co-authored-by: MaYuhang <2902139028@qq.com>

csrc/models/llama/llama_for_causal_lm.hpp

@@ -28,10 +28,26 @@ public:
      * @param config Model configuration
      * @param device Device to create tensors on
      */
+    /**
+     * @deprecated This function is deprecated and will be REMOVED in the next major release (v0.2.0).
+     *
+     * ⚠️ DEVELOPMENT POLICY:
+     *   - NO new development or feature additions permitted on this interface
+     *   - Only critical bug fixes (security/stability) allowed until removal
+     *   - All new code MUST migrate to the polymorphic overload below
+     *
+     * Replacement: Use the polymorphic overload of this same function name with updated signature
+     * Reason: Legacy signature lacks support for dynamic quantization modes.
+     * Removal target: v0.2.0 (Q2 2026)
+     */
     LlamaForCausalLM(const LlamaConfig &config,
                      const infinicore::Device &device,
                      engine::distributed::RankInfo rank_info = engine::distributed::RankInfo());
 
+    LlamaForCausalLM(std::shared_ptr<infinilm::config::ModelConfig> model_config,
+                     const infinicore::Device &device,
+                     engine::distributed::RankInfo rank_info = engine::distributed::RankInfo());
+
     /**
      * @brief Forward pass: compute language modeling logits
      *
@@ -45,7 +61,6 @@ public:
     const cache::CacheConfig *get_cache_config() const override;
 
     // Module information
-    const LlamaConfig &config() const { return model_->config(); }
     LlamaModel &model() { return *model_; }
     const LlamaModel &model() const { return *model_; }
 

csrc/models/llama/llama_mlp.cpp

@@ -3,7 +3,18 @@
 #include "infinicore/ops.hpp"
 
 namespace infinilm::models::llama {
-
+/**
+ * @deprecated This function is deprecated and will be REMOVED in the next major release (v0.2.0).
+ *
+ * ⚠️ DEVELOPMENT POLICY:
+ *   - NO new development or feature additions permitted on this interface
+ *   - Only critical bug fixes (security/stability) allowed until removal
+ *   - All new code MUST migrate to the polymorphic overload below
+ *
+ * Replacement: Use the polymorphic overload of this same function name with updated signature
+ * Reason: Legacy signature lacks support for dynamic quantization modes.
+ * Removal target: v0.2.0 (Q2 2026)
+ */
 LlamaMLP::LlamaMLP(const LlamaConfig &config,
                    const infinicore::Device &device,
                    engine::distributed::RankInfo rank_info)
@@ -22,6 +33,43 @@ LlamaMLP::LlamaMLP(const LlamaConfig &config,
                               dtype, device, tp_rank, tp_size, rank_info.comm);
 }
 
+LlamaMLP::LlamaMLP(std::shared_ptr<infinilm::config::ModelConfig> model_config,
+                   const infinicore::Device &device,
+                   engine::distributed::RankInfo rank_info)
+    : model_config_(model_config), hidden_size_(model_config->get<size_t>("hidden_size")),
+      intermediate_size_(model_config->get<size_t>("intermediate_size")),
+      use_bias_(model_config->get_or<bool>("mlp_bias", false)), rank_info_(rank_info) {
+
+    const auto &dtype{model_config_->get_dtype()};
+
+    int tp_rank = rank_info.tp_rank;
+    int tp_size = rank_info.tp_size;
+
+    // Initialize projection layers
+    auto quant_scheme = this->model_config_->get_quant_scheme();
+    switch (quant_scheme) {
+    case infinicore::quantization::QuantScheme::COMPRESSED_TENSOR_W8A8I8:
+        INFINILM_GATE_UP_LINEAR_W8A8_INIT(gate_up_proj, "gate_proj", "up_proj", hidden_size_, intermediate_size_, this->model_config_->get_quantization_method(), use_bias_,
+                                          dtype, device, rank_info_);
+        INFINICORE_NN_MODULE_INIT(down_proj, intermediate_size_, hidden_size_, this->model_config_->get_quantization_method(), use_bias_,
+                                  dtype, device, tp_rank, tp_size, rank_info.comm);
+        break;
+    case infinicore::quantization::QuantScheme::AWQ_W4A16:
+        INFINILM_GATE_UP_LINEAR_W4A16AWQ_INIT(gate_up_proj, "gate_proj", "up_proj", hidden_size_, intermediate_size_, this->model_config_->get_quantization_method(), use_bias_,
+                                              dtype, device, rank_info_);
+        INFINICORE_NN_MODULE_INIT(down_proj, intermediate_size_, hidden_size_, this->model_config_->get_quantization_method(), use_bias_,
+                                  dtype, device, tp_rank, tp_size, rank_info.comm);
+        break;
+
+    default:
+        INFINILM_GATE_UP_LINEAR_INIT(gate_up_proj, "gate_proj", "up_proj", hidden_size_, intermediate_size_, this->model_config_->get_quantization_method(), use_bias_,
+                                     dtype, device, rank_info_);
+        INFINICORE_NN_MODULE_INIT(down_proj, intermediate_size_, hidden_size_, this->model_config_->get_quantization_method(), use_bias_,
+                                  dtype, device, tp_rank, tp_size, rank_info.comm);
+        break;
+    }
+}
+
 infinicore::Tensor LlamaMLP::forward(const infinicore::Tensor &hidden_states) const {
     // 1. Project to gate and up
     auto hidden_states_mutable = hidden_states;

csrc/models/llama/llama_mlp.hpp

@@ -3,6 +3,7 @@
 #include "../../layers/fused_linear.hpp"
 #include "llama_config.hpp"
 
+#include "../../config/model_config.hpp"
 #include "infinicore/device.hpp"
 #include "infinicore/nn/linear.hpp"
 #include "infinicore/nn/module.hpp"
@@ -33,10 +34,26 @@ public:
      * @param device Device to create tensors on
      * @param dtype Optional data type for model parameters (defaults to F32)
      */
+    /**
+     * @deprecated This function is deprecated and will be REMOVED in the next major release (v0.2.0).
+     *
+     * ⚠️ DEVELOPMENT POLICY:
+     *   - NO new development or feature additions permitted on this interface
+     *   - Only critical bug fixes (security/stability) allowed until removal
+     *   - All new code MUST migrate to the polymorphic overload below
+     *
+     * Replacement: Use the polymorphic overload of this same function name with updated signature
+     * Reason: Legacy signature lacks support for dynamic quantization modes.
+     * Removal target: v0.2.0 (Q2 2026)
+     */
     LlamaMLP(const LlamaConfig &config,
              const infinicore::Device &device,
              engine::distributed::RankInfo rank_info = engine::distributed::RankInfo());
 
+    LlamaMLP(std::shared_ptr<infinilm::config::ModelConfig> model_config,
+             const infinicore::Device &device,
+             engine::distributed::RankInfo rank_info = engine::distributed::RankInfo());
+
     /**
      * @brief Forward pass: compute MLP output
      *
@@ -57,6 +74,8 @@ protected:
     size_t hidden_size_;
     size_t intermediate_size_;
     bool use_bias_;
+
+    std::shared_ptr<infinilm::config::ModelConfig> model_config_;
 };
 
 } // namespace infinilm::models::llama

csrc/models/llama/llama_model.cpp

@@ -6,7 +6,18 @@
 #include <iostream>
 
 namespace infinilm::models::llama {
-
+/**
+ * @deprecated This function is deprecated and will be REMOVED in the next major release (v0.2.0).
+ *
+ * ⚠️ DEVELOPMENT POLICY:
+ *   - NO new development or feature additions permitted on this interface
+ *   - Only critical bug fixes (security/stability) allowed until removal
+ *   - All new code MUST migrate to the polymorphic overload below
+ *
+ * Replacement: Use the polymorphic overload of this same function name with updated signature
+ * Reason: Legacy signature lacks support for dynamic quantization modes.
+ * Removal target: v0.2.0 (Q2 2026)
+ */
 LlamaModel::LlamaModel(const LlamaConfig &config,
                        const infinicore::Device &device,
                        engine::distributed::RankInfo rank_info)
@@ -43,6 +54,39 @@ LlamaModel::LlamaModel(const LlamaConfig &config,
     }
 }
 
+LlamaModel::LlamaModel(std::shared_ptr<infinilm::config::ModelConfig> model_config,
+                       const infinicore::Device &device,
+                       engine::distributed::RankInfo rank_info)
+    : model_config_(model_config), rank_info_(rank_info) {
+    const auto &dtype{model_config_->get_dtype()};
+    // Initialize token embeddings
+    INFINICORE_NN_MODULE_INIT(embed_tokens, model_config_->get<size_t>("vocab_size"), model_config_->get<size_t>("hidden_size"),
+                              std::nullopt, dtype, device);
+    // Initialize decoder layers with layer indices
+    // TODO: Update INFINICORE_NN_MODULE_VEC_INIT macro to support per-layer constructor arguments
+    //       (e.g., via a factory function or lambda that receives the layer index)
+    //       Currently, we can't use the macro because each layer needs a different layer_idx
+    layers_.reserve(model_config_->get<size_t>("num_hidden_layers"));
+    for (size_t i = 0; i < model_config_->get<size_t>("num_hidden_layers"); ++i) {
+        layers_.push_back(this->register_module<LlamaDecoderLayer>(
+            "layers." + std::to_string(i), model_config_, device, i, rank_info));
+    }
+    // Initialize final layer normalization
+    INFINICORE_NN_MODULE_INIT(norm, model_config_->get<size_t>("hidden_size"), model_config_->get<double>("rms_norm_eps"),
+                              dtype, device);
+    // Initialize Rotary Position Embeddings (shared across all layers)
+    // Use GPT-J-style inverse frequencies (default) and GPT_NEOX rotation pairing
+    INFINICORE_NN_MODULE_INIT(rotary_emb, model_config_->get_head_dim(), model_config_->get<size_t>("max_position_embeddings"),
+                              model_config_->get<double>("rope_theta"), infinicore::nn::RoPE::Algo::GPT_NEOX,
+                              dtype, device, model_config_->get_rope_scaling());
+
+    for (auto &layer : layers_) {
+        if (layer) {
+            layer->set_rotary_emb(rotary_emb_);
+        }
+    }
+}
+
 infinicore::Tensor LlamaModel::forward(const infinicore::Tensor &input_ids,
                                        const infinicore::Tensor &position_ids,
                                        std::optional<infinicore::Tensor> past_sequence_lengths,
@@ -79,7 +123,8 @@ void LlamaModel::reset_cache(const cache::CacheConfig *cache_config) {
         kv_cache_ = nullptr;
         return;
     }
-    if (auto kv_cache_config = dynamic_cast<const cache::StaticKVCacheConfig *>(cache_config)) {
+    if (auto kv_cache_config = dynamic_cast<const cache::StaticKVCacheConfig *>(cache_config);
+        kv_cache_config && model_config_ == nullptr) {
         kv_cache_ = std::make_shared<cache::StaticKVCache>(
             config_.head_dim,
             config_.head_dim,
@@ -90,8 +135,8 @@ void LlamaModel::reset_cache(const cache::CacheConfig *cache_config) {
             config_.dtype,
             *kv_cache_config,
             rank_info_);
-
-    } else if (auto paged_kv_cache_config = dynamic_cast<const cache::PagedKVCacheConfig *>(cache_config)) {
+    } else if (auto paged_kv_cache_config = dynamic_cast<const cache::PagedKVCacheConfig *>(cache_config);
+               paged_kv_cache_config && model_config_ == nullptr) {
         kv_cache_ = std::make_shared<cache::PagedKVCache>(
             config_.head_dim,
             config_.head_dim,
@@ -101,6 +146,27 @@ void LlamaModel::reset_cache(const cache::CacheConfig *cache_config) {
             config_.dtype,
             *paged_kv_cache_config,
             rank_info_);
+    } else if (auto kv_cache_config = dynamic_cast<const cache::StaticKVCacheConfig *>(cache_config)) {
+        kv_cache_ = std::make_shared<cache::StaticKVCache>(
+            model_config_->get_head_dim(),
+            model_config_->get_head_dim(),
+            model_config_->get<size_t>("num_key_value_heads"),
+            model_config_->get<size_t>("num_key_value_heads"),
+            model_config_->get<size_t>("num_hidden_layers"),
+            model_config_->get<size_t>("max_position_embeddings"),
+            model_config_->get_dtype(),
+            *kv_cache_config,
+            rank_info_);
+    } else if (auto paged_kv_cache_config = dynamic_cast<const cache::PagedKVCacheConfig *>(cache_config)) {
+        kv_cache_ = std::make_shared<cache::PagedKVCache>(
+            model_config_->get_head_dim(),
+            model_config_->get_head_dim(),
+            model_config_->get<size_t>("num_key_value_heads"),
+            model_config_->get<size_t>("num_key_value_heads"),
+            model_config_->get<size_t>("num_hidden_layers"),
+            model_config_->get_dtype(),
+            *paged_kv_cache_config,
+            rank_info_);
     } else {
         throw std::runtime_error("Unsupported cache type");
     }

csrc/models/llama/llama_model.hpp

 #pragma once
 
 #include "../../cache/kv_cache.hpp"
-#include "llama_config.hpp"
 #include "llama_decoder_layer.hpp"
 
 #include "infinicore/nn/embedding.hpp"
@@ -38,10 +37,26 @@ public:
      * @param device Device to create tensors on
      * @param dtype Optional data type for model parameters (defaults to F32)
      */
+    /**
+     * @deprecated This function is deprecated and will be REMOVED in the next major release (v0.2.0).
+     *
+     * ⚠️ DEVELOPMENT POLICY:
+     *   - NO new development or feature additions permitted on this interface
+     *   - Only critical bug fixes (security/stability) allowed until removal
+     *   - All new code MUST migrate to the polymorphic overload below
+     *
+     * Replacement: Use the polymorphic overload of this same function name with updated signature
+     * Reason: Legacy signature lacks support for dynamic quantization modes.
+     * Removal target: v0.2.0 (Q2 2026)
+     */
     LlamaModel(const LlamaConfig &config,
                const infinicore::Device &device,
                engine::distributed::RankInfo rank_info = engine::distributed::RankInfo());
 
+    LlamaModel(std::shared_ptr<infinilm::config::ModelConfig> model_config,
+               const infinicore::Device &device,
+               engine::distributed::RankInfo rank_info = engine::distributed::RankInfo());
+
     /**
      * @brief Forward pass: process input through the model
      *
@@ -64,8 +79,7 @@ public:
     void reset_cache(const cache::CacheConfig *cache_config);
 
     // Module information
-    const LlamaConfig &config() const { return config_; }
-    size_t num_layers() const { return config_.num_hidden_layers; }
+    size_t num_layers() const { return model_config_->get<size_t>("num_hidden_layers"); }
 
 protected:
     // Token embeddings
@@ -86,6 +100,8 @@ protected:
 
 private:
     LlamaConfig config_;
+
+    std::shared_ptr<infinilm::config::ModelConfig> model_config_;
 };
 
 } // namespace infinilm::models::llama

csrc/models/model_factory.cpp

@@ -2,11 +2,22 @@
 #include "llama/llama.hpp"
 
 namespace infinilm {
+/**
+ * @deprecated This function is deprecated and will be REMOVED in the next major release (v0.2.0).
+ *
+ * ⚠️ DEVELOPMENT POLICY:
+ *   - NO new development or feature additions permitted on this interface
+ *   - Only critical bug fixes (security/stability) allowed until removal
+ *   - All new code MUST migrate to the polymorphic overload below
+ *
+ * Replacement: Use the polymorphic overload of this same function name with updated signature
+ * Reason: Legacy signature lacks support for dynamic quantization modes.
+ * Removal target: v0.2.0 (Q2 2026)
+ */
 std::shared_ptr<InfinilmModel> InfinilmModelFactory::createModel(
     const InfinilmModel::Config &config,
     engine::distributed::RankInfo rank_info,
     const cache::CacheConfig *cache) {
-
     std::shared_ptr<InfinilmModel> model;
     if (const auto llama_config_ptr = dynamic_cast<const models::llama::LlamaConfig *>(&config)) {
         const auto &llama_config = *llama_config_ptr;
@@ -22,4 +33,24 @@ std::shared_ptr<InfinilmModel> InfinilmModelFactory::createModel(
 
     return model;
 }
+
+std::shared_ptr<InfinilmModel> InfinilmModelFactory::createModel(
+    std::shared_ptr<infinilm::config::ModelConfig> model_config,
+    engine::distributed::RankInfo rank_info,
+    const cache::CacheConfig *cache) {
+
+    std::shared_ptr<InfinilmModel> model;
+    if (true) {
+        model = std::make_shared<models::llama::LlamaForCausalLM>(
+            model_config, rank_info.device, rank_info);
+    } else {
+        throw std::invalid_argument("InfinilmModelFactory::createModel: Unsupported model config type");
+    }
+
+    if (cache) {
+        model->reset_cache(cache);
+    }
+
+    return model;
+}
 } // namespace infinilm

csrc/models/model_factory.hpp

 #pragma once
 
+#include "../config/model_config.hpp"
 #include "infinilm_model.hpp"
 
 #include "../engine/distributed/distributed.hpp"
@@ -7,9 +8,26 @@
 namespace infinilm {
 class InfinilmModelFactory {
 public:
+    /**
+     * @deprecated This function is deprecated and will be REMOVED in the next major release (v0.2.0).
+     *
+     * ⚠️ DEVELOPMENT POLICY:
+     *   - NO new development or feature additions permitted on this interface
+     *   - Only critical bug fixes (security/stability) allowed until removal
+     *   - All new code MUST migrate to the polymorphic overload below
+     *
+     * Replacement: Use the polymorphic overload of this same function name with updated signature
+     * Reason: Legacy signature lacks support for dynamic quantization modes.
+     * Removal target: v0.2.0 (Q2 2026)
+     */
     static std::shared_ptr<InfinilmModel> createModel(
         const InfinilmModel::Config &config,
         engine::distributed::RankInfo rank_info = engine::distributed::RankInfo(),
         const cache::CacheConfig *cache = nullptr);
+
+    static std::shared_ptr<InfinilmModel> createModel(
+        std::shared_ptr<infinilm::config::ModelConfig> model_config,
+        engine::distributed::RankInfo rank_info = engine::distributed::RankInfo(),
+        const cache::CacheConfig *cache = nullptr);
 };
 } // namespace infinilm

csrc/pybind11/engine/engine.hpp

@@ -63,20 +63,52 @@ inline void bind_infer_engine(py::module &m) {
             }
             return state_dict_tp_all;
         })
-        .def(
-            "forward", [](InferEngine &self, const InferEngine::Input &input) -> InferEngine::Output { return self.forward(input); }, "Run inference on all ranks with arbitrary arguments")
-        .def(
-            "reset_cache", [](InferEngine &self, std::shared_ptr<const cache::CacheConfig> cfg) {
-                self.reset_cache(cfg ? cfg.get() : nullptr);
-            },
-            py::arg("cache_config") = py::none())
+        .def("forward", [](InferEngine &self, const InferEngine::Input &input) -> InferEngine::Output { return self.forward(input); }, "Run inference on all ranks with arbitrary arguments")
+        .def("reset_cache", [](InferEngine &self, std::shared_ptr<const cache::CacheConfig> cfg) { self.reset_cache(cfg ? cfg.get() : nullptr); }, py::arg("cache_config") = py::none())
         .def("get_cache_config", [](const InferEngine &self) {
             auto cfg = self.get_cache_config();
-            return std::shared_ptr<cache::CacheConfig>(std::move(cfg->unique_copy()));
+            return std::shared_ptr<cache::CacheConfig>(std::move(cfg->unique_copy())); })
+        .def("__repr__", [](const InferEngine &self) { return "<InferEngine: " + std::string(self.get_dist_config()) + ">"; });
+
+    infer_engine
+        .def(py::init([](
+                          const std::string &model_path,
+                          const distributed::DistConfig &dist,
+                          infinicore::Device::Type dev,
+                          std::shared_ptr<const infinilm::cache::CacheConfig> cache_cfg,
+                          bool enable_graph_compiling) {
+                 return std::make_shared<InferEngine>(
+                     model_path,
+                     dist,
+                     dev,
+                     cache_cfg ? cache_cfg.get() : nullptr,
+                     enable_graph_compiling);
+             }),
+             py::arg("model_path") = "",
+             py::arg("distributed_config") = distributed::DistConfig(),
+             py::arg("device_type") = infinicore::context::getDevice().getType(),
+             py::arg("cache_config") = py::none(),
+             py::arg("enable_graph_compiling") = false)
+        .def("load_param", &InferEngine::load_param,
+             py::arg("name"), py::arg("param"),
+             "Load a parameter tensor into all workers (each worker picks its shard)")
+        .def("state_dict", [](InferEngine &self) {
+            py::list state_dict_tp_all;
+            for (const auto &state_dict_tp : self.state_dict()) {
+                py::dict result;
+                for (const auto &[name, param] : state_dict_tp) {
+                    result[py::cast(name)] = infinicore::Tensor(param);
+                }
+                state_dict_tp_all.append(result);
+            }
+            return state_dict_tp_all;
         })
-        .def("__repr__", [](const InferEngine &self) {
-            return "<InferEngine: " + std::string(self.get_dist_config()) + ">";
-        });
+        .def("forward", [](InferEngine &self, const InferEngine::Input &input) -> InferEngine::Output { return self.forward(input); }, "Run inference on all ranks with arbitrary arguments")
+        .def("reset_cache", [](InferEngine &self, std::shared_ptr<const cache::CacheConfig> cfg) { self.reset_cache(cfg ? cfg.get() : nullptr); }, py::arg("cache_config") = py::none())
+        .def("get_cache_config", [](const InferEngine &self) {
+            auto cfg = self.get_cache_config();
+            return std::shared_ptr<cache::CacheConfig>(std::move(cfg->unique_copy())); })
+        .def("__repr__", [](const InferEngine &self) { return "<InferEngine: " + std::string(self.get_dist_config()) + ">"; });
 
     py::class_<InferEngine::Input>(infer_engine, "Input")
         .def(

examples/bench.py

@@ -137,6 +137,11 @@ def get_args():
         action="store_true",
         help="Run nvidia test",
     )
+    parser.add_argument(
+        "--qy",
+        action="store_true",
+        help="Run qy test",
+    )
     parser.add_argument(
         "--metax",
         action="store_true",
@@ -278,6 +283,13 @@ class TestModel:
         # ---------------------------------------------------------------------------- #
         tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
         
+        if tokenizer.pad_token is None:
+            if tokenizer.eos_token is not None:
+                tokenizer.pad_token = tokenizer.eos_token
+                tokenizer.pad_token_id = tokenizer.eos_token_id
+            else:
+                tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+
         # ---------------------------------------------------------------------------- #
         #                        token编码
         # ---------------------------------------------------------------------------- #
@@ -290,7 +302,16 @@ class TestModel:
         ]
 
         # print(input_content, end="", flush=True)
-        input_ids_list = tokenizer.batch_encode_plus(input_content)["input_ids"]
+        # Support Transformers >= 5.0 for batch_encode_plus deprecation
+        encoding = tokenizer(
+            input_content,
+            padding=True,
+            truncation=True,
+            max_length=2048,       
+            return_tensors="pt"    
+            )
+
+        input_ids_list = encoding["input_ids"]
 
         self.model = model
         self.tokenizer = tokenizer
@@ -348,6 +369,8 @@ if __name__ == "__main__":
         device_str = "cpu"
     elif args.nvidia:
         device_str = "cuda"
+    elif args.qy:
+        device_str = "cuda"
     elif args.metax:
         device_str = "cuda"
     elif args.moore:

examples/jiuge.py

@@ -27,6 +27,11 @@ def get_args():
         action="store_true",
         help="Run nvidia test",
     )
+    parser.add_argument(
+        "--qy",
+        action="store_true",
+        help="Run qy test",
+    )
     parser.add_argument(
         "--metax",
         action="store_true",
@@ -150,7 +155,6 @@ def test(
         distributed_config=DistConfig(tp),
         enable_graph_compiling=enable_graph,
     )
-
     # ---------------------------------------------------------------------------- #
     #                        Load Weights
     # ---------------------------------------------------------------------------- #
@@ -160,7 +164,6 @@ def test(
     #                        create tokenizer
     # ---------------------------------------------------------------------------- #
     tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-
     if "llama" == model.config.model_type:
         backend = getattr(tokenizer, "backend_tokenizer", None)
         target = getattr(backend, "_tokenizer", backend)
@@ -194,9 +197,19 @@ def test(
         for prompt in prompts
     ]
 
-    input_ids_list = tokenizer.batch_encode_plus(input_contents)[
-        "input_ids"
-    ]  # List: [[1, 1128, 526, 366, 29892]]
+    # input_ids_list = tokenizer.batch_encode_plus(input_contents)[
+    #     "input_ids"
+    # ]  # List: [[1, 1128, 526, 366, 29892]]
+    
+    input_ids_list = [
+        tokenizer._encode_plus(
+            text,
+            truncation=True,
+            max_length=2048,
+            add_special_tokens=True
+        )["input_ids"]
+        for text in input_contents
+    ]
 
     # ---------------------------------------------------------------------------- #
     #                       Create KVCache
@@ -254,6 +267,8 @@ if __name__ == "__main__":
         device_str = "cpu"
     elif args.nvidia:
         device_str = "cuda"
+    elif args.qy:
+        device_str = "cuda"
     elif args.metax:
         device_str = "cuda"
     elif args.moore:
@@ -268,7 +283,7 @@ if __name__ == "__main__":
         device_str = "cuda"
     else:
         print(
-            "Usage:  python examples/jiuge.py [--cpu | --nvidia | --metax | --moore | --iluvatar | --cambricon | --ali | --hygon] --model_path=<path/to/model_dir>\n"
+            "Usage:  python examples/jiuge.py [--cpu | --nvidia | --qy | --metax | --moore | --iluvatar | --cambricon | --ali | --hygon] --model_path=<path/to/model_dir>\n"
             "such as, python examples/jiuge.py --nvidia --model_path=~/TinyLlama-1.1B-Chat-v1.0"
         )
         sys.exit(1)

python/infinilm/auto_config.py

@@ -25,5 +25,11 @@ class AutoConfig:
             config_dict["model_type"] == "qwen2" or config_dict["model_type"] == "qwen3"
         ):
             return LlamaConfig(**config_dict)
+        elif config_dict["model_type"] == "minicpm":
+            return LlamaConfig(**config_dict)
+        elif config_dict["model_type"] == "fm9g":
+            return LlamaConfig(**config_dict)
+        elif config_dict["model_type"] == "fm9g7b":
+            return LlamaConfig(**config_dict)
 
         raise ValueError(f"Unsupported model type `{config_dict['model_type']}`.")

python/infinilm/infer_engine.py

@@ -35,14 +35,21 @@ class InferEngine(_infinilm.InferEngine):
         if device is None:
             device = infinicore.device()
           
+        # super().__init__(
+        #     self.config,
+        #     distributed_config._underlying,
+        #     device._underlying.type,
+        #     cache_config,
+        #     enable_graph_compiling,
+        # )
+        
         super().__init__(
-            self.config,
+            model_path,
             distributed_config._underlying,
             device._underlying.type,
             cache_config,
             enable_graph_compiling,
         )
-
         self.use_cache = False
 
         self.enable_paged_attn = isinstance(cache_config, PagedKVCacheConfig)

python/infinilm/llm/__init__.py

@@ -18,6 +18,7 @@ from infinilm.llm.llm import (
     EngineConfig,
 )
 from infinilm.llm.scheduler import Scheduler, SchedulerOutput
+from infinilm.llm.static_scheduler import StaticScheduler, StaticSchedulerOutput
 from infinilm.llm.cache_manager import BlockManager, Block
 
 __all__ = [
@@ -38,6 +39,8 @@ __all__ = [
     # Internal (for advanced use)
     "Scheduler",
     "SchedulerOutput",
+    "StaticScheduler",
+    "StaticSchedulerOutput",
     "BlockManager",
     "Block",
 ]

python/infinilm/llm/llm.py

@@ -23,10 +23,11 @@ from infinilm.llm.request import (
 )
 from infinilm.llm.sampling_params import SamplingParams
 from infinilm.llm.scheduler import Scheduler
+from infinilm.llm.static_scheduler import StaticScheduler
 
 from infinilm.distributed import DistConfig
 from infinilm.infer_engine import InferEngine
-from infinilm.cache.cache import PagedKVCacheConfig
+from infinilm.cache.cache import PagedKVCacheConfig, StaticKVCacheConfig
 from infinilm.modeling_utils import load_model_state_dict_by_file
 from transformers import AutoTokenizer
 from tokenizers import decoders as _dec
@@ -43,10 +44,12 @@ class EngineConfig:
         device: Device type string ('cpu', 'cuda', 'mlu', etc.).
         dtype: Data type string ('float16', 'bfloat16', 'float32').
         tensor_parallel_size: Number of devices for tensor parallelism.
-        max_batch_size: Maximum batch size for inference.
+        cache_type: Cache type ('paged' or 'static').
+        max_batch_size: Maximum batch size for inference (only for paged cache).
         max_tokens: Default maximum tokens to generate.
-        num_blocks: Number of KV cache blocks.
-        block_size: Size of each KV cache block.
+        num_blocks: Number of KV cache blocks (only for paged cache).
+        block_size: Size of each KV cache block (only for paged cache).
+        max_cache_len: Maximum sequence length (only for static cache).
         temperature: Default sampling temperature.
         top_p: Default top-p sampling parameter.
         top_k: Default top-k sampling parameter.
@@ -57,10 +60,12 @@ class EngineConfig:
     device: str = "cuda"
     dtype: str = "float16"
     tensor_parallel_size: int = 1
+    cache_type: str = "paged"  # "paged" or "static"
     max_batch_size: int = 16
     max_tokens: int = 4096
     num_blocks: int = 8 * 1024
     block_size: int = 16
+    max_cache_len: int = 4096
     temperature: float = 1.0
     top_p: float = 0.8
     top_k: int = 1
@@ -76,17 +81,11 @@ class LLMEngine:
         # Initialize device and dtype
         self._init_device()
 
-        # Initialize KV cache
-        cache_config = PagedKVCacheConfig(
-            num_blocks=config.num_blocks, block_size=config.block_size
-        )
-
         # Initialize model engine
         self.model_engine = InferEngine(
             model_path=config.model_path,
             device=self.device,
             distributed_config=DistConfig(config.tensor_parallel_size),
-            cache_config=cache_config,
             enable_graph_compiling=config.enable_graph,
         )
 
@@ -101,12 +100,30 @@ class LLMEngine:
         )
         self._fix_tokenizer_decoder()
 
-        # Initialize scheduler
+        # Initialize KV cache based on cache type
+        if config.cache_type == "static":
+            cache_config = StaticKVCacheConfig(
+                max_batch_size=1, max_cache_len=config.max_cache_len
+            )
+            self.scheduler = StaticScheduler(max_cache_len=config.max_cache_len)
+            logger.info(
+                f"Using Static KV Cache with max_cache_len={config.max_cache_len}"
+            )
+        elif config.cache_type == "paged":
+            cache_config = PagedKVCacheConfig(
+                num_blocks=config.num_blocks, block_size=config.block_size
+            )
             self.scheduler = Scheduler(
                 max_batch_size=config.max_batch_size,
                 num_blocks=config.num_blocks,
                 block_size=config.block_size,
             )
+            logger.info(f"Using Paged KV Cache with num_blocks={config.num_blocks}")
+        else:
+            raise ValueError(f"Unsupported cache_type: {config.cache_type}")
+
+        self.model_engine.reset_cache(cache_config)
+        self.cache_type = config.cache_type
 
         # Get EOS token IDs from model config
         self.eos_token_ids = self.model_engine.config.eos_token_id or []
@@ -202,19 +219,21 @@ class LLMEngine:
         """Convert model input dict to infinicore tensors."""
         model_input = {}
         for key, value in model_input_dict.items():
-            if key == "input_ids":
-                model_input[key] = infinicore.from_list([value], dtype=infinicore.int64)
+            if value is None:
+                # Skip None values (block_tables/slot_mapping for static cache)
+                model_input[key] = None
             elif key in [
+                "input_ids",
                 "position_ids",
                 "past_kv_lengths",
                 "total_kv_lengths",
                 "input_offsets",
                 "slot_mapping",
+                "block_tables",
             ]:
                 model_input[key] = infinicore.from_list(value, dtype=infinicore.int64)
-            elif key == "block_tables":
-                model_input[key] = infinicore.from_list(value, dtype=infinicore.int64)
             else:
+                # temperature, top_k, top_p, etc.
                 model_input[key] = value
         return model_input
 
@@ -225,7 +244,8 @@ class LLMEngine:
         sampled_tokens: List[int],
     ):
         """Update request status after inference step."""
-        if is_prefill:
+        # Only reset req blocks for paged cache
+        if is_prefill and self.cache_type == "paged":
             self.scheduler.cache_manager.reset_req_blocks()
 
         for req, token_id in zip(requests, sampled_tokens):
@@ -293,7 +313,6 @@ class LLMEngine:
                             # Remove the stop string from the end
                             req.generated_text = req.generated_text[: -len(stop_str)]
                             break
-
             # Put output in queue if it exists (for async streaming)
             if req._output_queue is not None:
                 output = TokenOutput(
@@ -363,10 +382,12 @@ class LLM:
         device: str = "cuda",
         dtype: str = "float16",
         tensor_parallel_size: int = 1,
+        cache_type: str = "paged",
         max_batch_size: int = 16,
         max_tokens: int = 4096,
         num_blocks: int = 8 * 1024,
         block_size: int = 16,
+        max_cache_len: int = 4096,
         temperature: float = 1.0,
         top_p: float = 0.8,
         top_k: int = 1,
@@ -379,10 +400,12 @@ class LLM:
             device: Device type ('cpu', 'cuda', 'mlu', 'moore').
             dtype: Data type ('float16', 'bfloat16', 'float32').
             tensor_parallel_size: Number of devices for tensor parallelism.
-            max_batch_size: Maximum batch size for inference.
+            cache_type: Cache type ('paged' or 'static').
+            max_batch_size: Maximum batch size (only for paged cache).
             max_tokens: Default maximum tokens to generate.
-            num_blocks: Number of KV cache blocks.
-            block_size: Size of each KV cache block.
+            num_blocks: Number of KV cache blocks (only for paged cache).
+            block_size: Size of each KV cache block (only for paged cache).
+            max_cache_len: Maximum sequence length (only for static cache).
             temperature: Default sampling temperature.
             top_p: Default top-p sampling parameter.
             top_k: Default top-k sampling parameter.
@@ -393,10 +416,12 @@ class LLM:
             device=device,
             dtype=dtype,
             tensor_parallel_size=tensor_parallel_size,
+            cache_type=cache_type,
             max_batch_size=max_batch_size,
             max_tokens=max_tokens,
             num_blocks=num_blocks,
             block_size=block_size,
+            max_cache_len=max_cache_len,
             temperature=temperature,
             top_p=top_p,
             top_k=top_k,
@@ -510,10 +535,12 @@ class AsyncLLMEngine:
         device: str = "cuda",
         dtype: str = "float16",
         tensor_parallel_size: int = 1,
+        cache_type: str = "paged",
         max_batch_size: int = 16,
         max_tokens: int = 512,
         num_blocks: int = 8 * 1024,
         block_size: int = 16,
+        max_cache_len: int = 4096,
         temperature: float = 1.0,
         top_p: float = 0.8,
         top_k: int = 1,
@@ -526,10 +553,12 @@ class AsyncLLMEngine:
             device: Device type ('cpu', 'cuda', 'mlu', 'moore').
             dtype: Data type ('float16', 'bfloat16', 'float32').
             tensor_parallel_size: Number of devices for tensor parallelism.
-            max_batch_size: Maximum batch size for inference.
+            cache_type: Cache type ('paged' or 'static').
+            max_batch_size: Maximum batch size (only for paged cache).
             max_tokens: Default maximum tokens to generate.
-            num_blocks: Number of KV cache blocks.
-            block_size: Size of each KV cache block.
+            num_blocks: Number of KV cache blocks (only for paged cache).
+            block_size: Size of each KV cache block (only for paged cache).
+            max_cache_len: Maximum sequence length (only for static cache).
             temperature: Default sampling temperature.
             top_p: Default top-p sampling parameter.
             top_k: Default top-k sampling parameter.
@@ -540,10 +569,12 @@ class AsyncLLMEngine:
             device=device,
             dtype=dtype,
             tensor_parallel_size=tensor_parallel_size,
+            cache_type=cache_type,
             max_batch_size=max_batch_size,
             max_tokens=max_tokens,
             num_blocks=num_blocks,
             block_size=block_size,
+            max_cache_len=max_cache_len,
             temperature=temperature,
             top_p=top_p,
             top_k=top_k,

python/infinilm/llm/scheduler.py

@@ -103,7 +103,7 @@ class SchedulerOutput:
             block_tables.append(padded_block_table)
 
         return {
-            "input_ids": tokens,
+            "input_ids": [tokens],
             "position_ids": position_ids,
             "past_kv_lengths": cached_lens,
             "total_kv_lengths": seq_lens,
@@ -154,6 +154,10 @@ class Scheduler:
                 req = self.waiting_queue.sync_q.get_nowait()
             except queue.Empty:
                 break
+            # Skip requests that were already finished (e.g., timed out/canceled while waiting)
+            if req.is_finished():
+                self.complete_requests([req])
+                continue
 
             if not self.can_accept_request(req):
                 self.waiting_queue.sync_q.put(req)

python/infinilm/llm/static_scheduler.py

+"""
+Static Scheduler - Single-batch request scheduling for Static KV Cache.
+"""
+
+import logging
+import queue
+import janus
+from typing import List, Optional
+
+from infinilm.llm.request import RequestStatus, InferenceRequest, FinishReason
+
+logger = logging.getLogger(__name__)
+
+
+class StaticSchedulerOutput:
+    """Static scheduler output containing single request and execution phase info."""
+
+    def __init__(
+        self,
+        scheduled_requests: List[InferenceRequest],
+        is_prefill: bool = False,
+    ):
+        self.scheduled_requests = scheduled_requests
+        self.num_requests = len(scheduled_requests)
+        self.is_prefill = is_prefill
+
+    def build_model_inputs(
+        self, temperature: float = 1.0, top_p: float = 0.8, top_k: int = 1
+    ):
+        """Construct model inputs for prefill or decode phase.
+
+        Static cache model inputs:
+
+        Prefill phase:
+            - input_ids: All prompt tokens [1, prompt_length]
+            - position_ids: [0, 1, 2, ..., prompt_length-1]
+            - past_kv_lengths: [0] (no cached tokens initially)
+            - total_kv_lengths: [prompt_length]
+
+        Decode phase:
+            - input_ids: Only the last generated token [1, 1]
+            - position_ids: [current_position] (position in full sequence)
+            - past_kv_lengths: [num_cached_tokens]
+            - total_kv_lengths: [total_tokens]
+            -
+        """
+        req = self.scheduled_requests[0]
+
+        if self.is_prefill:
+            # Prefill: send all prompt tokens
+            tokens = req.get_input_tokens()
+            input_ids = [tokens]
+            position_ids = [list(range(len(tokens)))]
+            past_kv_len = 0
+            total_kv_len = len(tokens)
+            input_offsets = [0, len(tokens)]
+        else:
+            # Decode: send only the last generated token
+            last_token = req.generated_token_ids[-1]
+            current_position = req.get_total_length() - 1
+            input_ids = [[last_token]]
+            position_ids = [[current_position]]
+            past_kv_len = current_position
+            total_kv_len = req.get_total_length()
+            input_offsets = [0, 1]
+
+        return {
+            "input_ids": input_ids,
+            "position_ids": position_ids,
+            "past_kv_lengths": [past_kv_len],
+            "total_kv_lengths": [total_kv_len],
+            "input_offsets": input_offsets,
+            "block_tables": None,
+            "slot_mapping": None,
+            "temperature": temperature,
+            "top_k": top_k,
+            "top_p": top_p,
+        }
+
+
+class StaticScheduler:
+    """Request scheduler for Static KV Cache with batch_size=1.
+
+    Simplified scheduling logic:
+    - Only handles one request at a time
+    - No cache block management needed
+    - Simple waiting queue for incoming requests
+    """
+
+    def __init__(self, max_cache_len: int = 4096):
+        self.waiting_queue = janus.Queue()
+        self.running_request: Optional[InferenceRequest] = None
+        self.max_cache_len = max_cache_len
+
+    def add_request(self, request: InferenceRequest):
+        if request is not None:
+            request.status = RequestStatus.WAITING
+            self.waiting_queue.sync_q.put(request)
+
+    def schedule(self) -> Optional[StaticSchedulerOutput]:
+        """Schedule and return single request to execute."""
+        while True:
+            # Case 1: Continue running request (decode phase)
+            if self.running_request is not None:
+                req = self.running_request
+
+                if req.is_finished():
+                    self.running_request = None
+                    continue
+
+                if req.get_total_length() > self.max_cache_len:
+                    logger.warning(
+                        f"Request {req.request_id} exceeds max_cache_len={self.max_cache_len}, "
+                        "completing request."
+                    )
+                    self.running_request = None
+                    req.mark_failed(FinishReason.LENGTH)
+                    continue
+
+                return StaticSchedulerOutput(scheduled_requests=[req], is_prefill=False)
+
+            # Case 2: Get new request from waiting queue (prefill phase)
+            try:
+                req = self.waiting_queue.sync_q.get_nowait()
+            except queue.Empty:
+                return None
+
+            if req.is_finished():
+                continue
+
+            prompt_len = req.get_prompt_length()
+
+            if prompt_len > self.max_cache_len:
+                logger.error(
+                    f"Request {req.request_id} prompt length {prompt_len} "
+                    f"exceeds max_cache_len={self.max_cache_len}. Request rejected."
+                )
+
+                req.mark_failed(FinishReason.LENGTH)
+                continue
+
+            req.status = RequestStatus.RUNNING
+            self.running_request = req
+            return StaticSchedulerOutput(scheduled_requests=[req], is_prefill=True)
+
+    def complete_requests(self, requests: List[InferenceRequest]):
+        """Handle completed requests."""
+        for req in requests:
+            if req.is_finished() and req == self.running_request:
+                self.running_request = None
+                logger.debug(f"Completed request {req.request_id}")
+
+    def get_cache_stats(self) -> dict:
+        """Get cache statistics."""
+        return {
+            "max_cache_len": self.max_cache_len,
+            "running_request": (
+                self.running_request.request_id if self.running_request else None
+            ),
+            "waiting_queue_size": self.waiting_queue.sync_q.qsize(),
+        }

python/infinilm/modeling_utils.py

@@ -75,7 +75,7 @@ def load_state_dict(
             )
 
         for k in f.keys():
-            state_dict[k] = f.get_tensor(k).to(device=device, dtype=dtype)
+            state_dict[k] = f.get_tensor(k).to(device=device)
 
     return state_dict
 
@@ -155,7 +155,6 @@ def load_model_state_dict_by_file(
             model_param_infini = {}
             for key in model_param.keys():
                 model_param_infini[key] = infinicore.from_torch(model_param[key])
-
             model.load_state_dict(model_param_infini, strict=False)
             infinicore.sync_device()
 
@@ -168,7 +167,6 @@ def load_model_state_dict_by_file(
             model_param_infini[key] = infinicore.from_torch(
                 model_params[key].to(dtype=torch_dtype)
             )
-
             already_loaded_keys.append(key)
 
         model.load_state_dict(model_param_infini, strict=True)

python/infinilm/server/inference_server.py

@@ -50,6 +50,42 @@ def chunk_json(
     }
 
 
+def completion_json(
+    id_,
+    content,
+    role="assistant",
+    finish_reason="stop",
+    model: str = "unknown",
+    prompt_tokens: int = 0,
+    completion_tokens: int = 0,
+    total_tokens: int = 0,
+):
+    """Generate JSON response for non-streaming completion."""
+    return {
+        "id": id_,
+        "object": "chat.completion",
+        "created": int(time.time()),
+        "model": model,
+        "system_fingerprint": None,
+        "choices": [
+            {
+                "index": 0,
+                "message": {
+                    "role": role,
+                    "content": content,
+                },
+                "logprobs": None,
+                "finish_reason": finish_reason,
+            }
+        ],
+        "usage": {
+            "prompt_tokens": prompt_tokens,
+            "completion_tokens": completion_tokens,
+            "total_tokens": total_tokens,
+        },
+    }
+
+
 class InferenceServer:
     """HTTP server for LLM inference."""
 
@@ -59,10 +95,12 @@ class InferenceServer:
         device: str = "cuda",
         dtype: str = "float16",
         tensor_parallel_size: int = 1,
+        cache_type: str = "paged",
         max_tokens: int = 4096,
         max_batch_size: int = 16,
         num_blocks: int = 8 * 1024,
         block_size: int = 16,
+        max_cache_len: int = 4096,
         temperature: float = 1.0,
         top_p: float = 0.8,
         top_k: int = 1,
@@ -77,10 +115,12 @@ class InferenceServer:
             device: Device type ('cpu', 'cuda', 'mlu', 'moore').
             dtype: Data type ('float16', 'bfloat16', 'float32').
             tensor_parallel_size: Number of devices for tensor parallelism.
+            cache_type: Cache type ('paged' or 'static').
             max_tokens: Default maximum tokens to generate.
-            max_batch_size: Maximum batch size for inference.
-            num_blocks: Number of KV cache blocks.
-            block_size: Size of each KV cache block.
+            max_batch_size: Maximum batch size for inference (only for paged cache).
+            num_blocks: Number of KV cache blocks (only for paged cache).
+            block_size: Size of each KV cache block (only for paged cache).
+            max_cache_len: Maximum sequence length (only for static cache).
             temperature: Default sampling temperature.
             top_p: Default top-p sampling parameter.
             top_k: Default top-k sampling parameter.
@@ -94,10 +134,12 @@ class InferenceServer:
         self.device = device
         self.dtype = dtype
         self.tensor_parallel_size = tensor_parallel_size
+        self.cache_type = cache_type
         self.max_tokens = max_tokens
         self.max_batch_size = max_batch_size
         self.num_blocks = num_blocks
         self.block_size = block_size
+        self.max_cache_len = max_cache_len
         self.temperature = temperature
         self.top_p = top_p
         self.top_k = top_k
@@ -124,10 +166,12 @@ class InferenceServer:
                 device=self.device,
                 dtype=self.dtype,
                 tensor_parallel_size=self.tensor_parallel_size,
+                cache_type=self.cache_type,
                 max_batch_size=self.max_batch_size,
                 max_tokens=self.max_tokens,
                 num_blocks=self.num_blocks,
                 block_size=self.block_size,
+                max_cache_len=self.max_cache_len,
                 temperature=self.temperature,
                 top_p=self.top_p,
                 top_k=self.top_k,
@@ -396,12 +440,15 @@ class InferenceServer:
             output_text = output_text.strip()
             finish_reason = self._convert_finish_reason(req.finish_reason)
 
-            response = chunk_json(
+            response = completion_json(
                 request_id,
                 content=output_text,
                 role="assistant",
                 finish_reason=finish_reason or "stop",
                 model=self.model_id,
+                prompt_tokens=req.get_prompt_length(),
+                completion_tokens=req.get_num_generated_tokens(),
+                total_tokens=req.get_total_length(),
             )
             return response
 
@@ -450,6 +497,13 @@ def parse_args():
         "--model_path", type=str, required=True, help="Path to model directory"
     )
     parser.add_argument("--tp", type=int, default=1, help="Tensor parallelism degree")
+    parser.add_argument(
+        "--cache_type",
+        type=str,
+        default="paged",
+        choices=["paged", "static"],
+        help="Cache type: paged or static",
+    )
     parser.add_argument(
         "--max_tokens",
         type=int,
@@ -457,13 +511,28 @@ def parse_args():
         help="Maximum number of tokens to generate",
     )
     parser.add_argument(
-        "--max_batch_size", type=int, default=8, help="Maximum batch size"
+        "--max_batch_size",
+        type=int,
+        default=8,
+        help="Maximum batch size (paged cache only)",
     )
     parser.add_argument(
-        "--num_blocks", type=int, default=8 * 1024, help="Number of blocks for KV cache"
+        "--num_blocks",
+        type=int,
+        default=8 * 1024,
+        help="Number of blocks for KV cache (paged cache only)",
+    )
+    parser.add_argument(
+        "--block_size",
+        type=int,
+        default=16,
+        help="Block size for KV cache (paged cache only)",
     )
     parser.add_argument(
-        "--block_size", type=int, default=16, help="Block size for KV cache"
+        "--max_cache_len",
+        type=int,
+        default=4096,
+        help="Maximum sequence length (static cache only)",
     )
     parser.add_argument(
         "--dtype",
@@ -483,6 +552,7 @@ def parse_args():
     parser.add_argument("--port", type=int, default=8000, help="Server port")
     parser.add_argument("--cpu", action="store_true", help="Use CPU")
     parser.add_argument("--nvidia", action="store_true", help="Use NVIDIA GPU")
+    parser.add_argument("--qy", action="store_true", help="Use QY GPU")
     parser.add_argument("--metax", action="store_true", help="Use MetaX device")
     parser.add_argument("--moore", action="store_true", help="Use Moore device")
     parser.add_argument("--iluvatar", action="store_true", help="Use Iluvatar device")
@@ -513,6 +583,8 @@ def main():
         device = "cpu"
     elif args.nvidia:
         device = "cuda"
+    elif args.qy:
+        device = "cuda"
     elif args.metax:
         device = "cuda"
     elif args.moore:
@@ -525,7 +597,7 @@ def main():
         device = "cuda"
     else:
         print(
-            "Usage: python infinilm.server.inference_server [--cpu | --nvidia | --metax | --moore | --iluvatar | --cambricon | --ali] "
+            "Usage: python infinilm.server.inference_server [--cpu | --nvidia | --qy | --metax | --moore | --iluvatar | --cambricon | --ali] "
             "--model_path=<path/to/model_dir> --max_tokens=MAX_TOKENS --max_batch_size=MAX_BATCH_SIZE"
             "\n"
             "Example: python infinilm.server.inference_server --nvidia --model_path=/data/shared/models/9G7B_MHA/ "
@@ -540,10 +612,12 @@ def main():
         device=device,
         dtype=args.dtype,
         tensor_parallel_size=args.tp,
+        cache_type=args.cache_type,
         max_tokens=args.max_tokens,
         max_batch_size=args.max_batch_size,
         num_blocks=args.num_blocks,
         block_size=args.block_size,
+        max_cache_len=args.max_cache_len,
         temperature=args.temperature,
         top_p=args.top_p,
         top_k=args.top_k,

src/dataloader/weights_loader.cpp

@@ -81,7 +81,6 @@ std::shared_ptr<Tensor> Loader::get(const std::string &name, int rank) {
 __C void
 loadModelWeight(struct ModelWeights *weights_, const char *name, void *data) {
     std::string name_str(name);
-    // std::cout << "Loading weight: " << name_str << std::endl;
     auto weights = reinterpret_cast<infinicore::weights::Loader *>(weights_);
     weights->load(name_str, data);
 }

test/bench/test_benchmark.py

@@ -9,7 +9,6 @@ from infinilm.modeling_utils import load_model_state_dict_by_file
 from infinilm.distributed import DistConfig
 from infinilm.cache import StaticKVCacheConfig, PagedKVCacheConfig
 from infinilm.infer_engine import GenerationConfig, InferEngine
-from infinilm.cache import StaticKVCacheConfig
 from datasets import load_dataset, Dataset
 from abc import ABC, abstractmethod