issue/194: Support Quantization Config and Quanted Model Inference

.gitmodules

@@ -1,3 +1,6 @@
 [submodule "third_party/spdlog"]
 	path = third_party/spdlog
 	url = https://github.com/gabime/spdlog.git
+[submodule "third_party/json"]
+	path = third_party/json
+	url = https://github.com/nlohmann/json.git

csrc/config/global_config.cpp

@@ -0,0 +1,88 @@
+#include "global_config.hpp"
+
+namespace infinilm::config::global_config {
+GlobalConfig::GlobalConfig(const std::string &path) {
+    std::ifstream file(path);
+    if (file.is_open()) {
+        file >> config_json;
+        file.close();
+    } else {
+        throw std::runtime_error("Could not open config file: " + path);
+    }
+    this->quant_config = quantization::QuantConfig(config_json["quantization_config"]);
+}
+
+infinicore::nn::QuantScheme
+GlobalConfig::get_quant_scheme() const {
+    if (quant_config.get_quant_scheme() != infinicore::nn::QuantScheme::NONE) {
+        return quant_config.get_quant_scheme();
+    } else {
+        return infinicore::nn::QuantScheme::NONE;
+    }
+}
+
+std::shared_ptr<infinicore::nn::RoPE::ScalingConfig>
+GlobalConfig::get_rope_scaling() const {
+    if (!config_json.contains("rope_scaling") || config_json["rope_scaling"].is_null()) {
+        return nullptr;
+    }
+
+    const auto &rope_scaling = config_json["rope_scaling"];
+    if (!rope_scaling.is_object()) {
+        throw std::runtime_error("rope_scaling must be an object");
+    }
+
+    if (!rope_scaling.contains("type")) {
+        throw std::runtime_error("rope_scaling must contain 'type' field");
+    }
+
+    std::string type_str = rope_scaling["type"].get<std::string>();
+    if (type_str == "longrope") {
+        // Required fields for LongRopeConfig
+        if (!rope_scaling.contains("short_factor") || !rope_scaling.contains("long_factor") || !rope_scaling.contains("original_max_position_embeddings")) {
+            throw std::runtime_error(
+                "LongRopeConfig requires 'short_factor', 'long_factor', and 'original_max_position_embeddings'");
+        }
+
+        auto short_factor = rope_scaling["short_factor"].get<std::vector<float>>();
+        auto long_factor = rope_scaling["long_factor"].get<std::vector<float>>();
+        size_t original_max_position_embeddings = rope_scaling["original_max_position_embeddings"].get<size_t>();
+
+        float factor = 1.0f;
+        if (rope_scaling.contains("factor")) {
+            factor = rope_scaling["factor"].get<float>();
+        }
+
+        return std::make_shared<infinicore::nn::RoPE::LongRopeConfig>(
+            std::move(short_factor),
+            std::move(long_factor),
+            original_max_position_embeddings,
+            factor);
+    } else if (type_str == "default" || type_str == "none") {
+        // Default scaling, no scaling applied
+        return nullptr;
+    } else {
+        throw std::runtime_error("Unsupported rope_scaling type: " + type_str);
+    }
+}
+
+infinicore::DataType
+GlobalConfig::get_dtype() const {
+    try {
+        std::string dtype_str = this->get<std::string>("torch_dtype");
+        if (dtype_str == "float32") {
+            return infinicore::DataType::F32;
+        } else if (dtype_str == "float16") {
+            return infinicore::DataType::F16;
+        } else if (dtype_str == "bfloat16") {
+            return infinicore::DataType::BF16;
+        } else if (dtype_str == "int8") {
+            return infinicore::DataType::I8;
+        } else {
+            throw std::runtime_error("Unsupported dtype string: " + dtype_str);
+        }
+    } catch (const std::exception &e) {
+        throw std::runtime_error("Error getting dtype from config: " + std::string(e.what()));
+    }
+}
+} // namespace infinilm::config::global_config

csrc/config/global_config.hpp

@@ -0,0 +1,63 @@
+#pragma once
+
+// #include "infinicore/nn/quantization.hpp"
+#include "infinicore/nn/rope.hpp"
+#include "infinicore/ops.hpp"
+#include "quant_config.hpp"
+#include <fstream>
+#include <string>
+
+namespace infinilm::config::global_config {
+struct GlobalConfig {
+    // Global config is implemented using nlohmann/json and is primarily used for advanced configuration
+    // beyond the standard model config. It is initialized via GlobalConfig(const std::string& path)
+    // and passed through the InferEngine during inference.
+public:
+    GlobalConfig() = default;
+    GlobalConfig(const nlohmann::json &json) : config_json(json) {};
+    GlobalConfig(const std::string &path);
+
+    // Template Function to get a value by key with type safety
+    template <typename T>
+    T get(const std::string &key) const {
+        if (!config_json.contains(key)) {
+            throw std::out_of_range("Key '" + key + "' not found in config.");
+        }
+        try {
+            return config_json.at(key).get<T>();
+        } catch (const nlohmann::json::type_error &e) {
+            throw std::runtime_error("Type conversion failed for key '" + key + "': " + std::string(e.what()));
+        }
+    }
+
+    template <typename T>
+    T get_or(const std::string &key, const T &default_value) const {
+        if (!config_json.contains(key) || config_json.at(key).is_null()) {
+            return default_value;
+        }
+        try {
+            return config_json.at(key).get<T>();
+        } catch (const nlohmann::json::type_error &) {
+            // If type conversion fails, return default value
+            return default_value;
+        }
+    }
+    size_t get_kv_dim() const {
+        return get<size_t>("hidden_size") * get<size_t>("num_key_value_heads") / get<size_t>("num_attention_heads");
+    }
+    size_t get_head_dim() const {
+        if (config_json.contains("head_dim")) {
+            return get<size_t>("head_dim");
+        }
+        return get<size_t>("hidden_size") / get<size_t>("num_attention_heads");
+    }
+
+    infinicore::DataType get_dtype() const;
+    infinicore::nn::QuantScheme get_quant_scheme() const;
+    std::shared_ptr<infinicore::nn::RoPE::ScalingConfig> get_rope_scaling() const;
+
+private:
+    nlohmann::json config_json;
+    quantization::QuantConfig quant_config;
+};
+} // namespace infinilm::config::global_config

csrc/config/quant_config.cpp

@@ -0,0 +1,22 @@
+#include "quant_config.hpp"
+
+namespace infinilm::config::quantization {
+QuantConfig::QuantConfig(const nlohmann::json &json) : quantization_config(json) {
+    this->quantization_method = get_quantization_method();
+}
+
+std::shared_ptr<infinilm::quantization::BaseQuantization>
+QuantConfig::get_quantization_method() const {
+    if (quantization_config.is_null()) {
+        return nullptr;
+    }
+
+    // Determine the quantization scheme from the JSON config
+    if (quantization_config["quant_method"] == "compressed-tensors") {
+        return std::make_shared<infinilm::quantization::CompressedTensors>(quantization_config);
+    }
+    // Add other schemes as needed
+
+    return nullptr; // Default case if no matching scheme
+}
+} // namespace infinilm::config::quantization

csrc/config/quant_config.hpp

@@ -0,0 +1,28 @@
+#pragma once
+#include "../quantization/quantization.hpp"
+#include "nlohmann/json.hpp"
+
+namespace infinilm::config::quantization {
+
+class QuantConfig {
+    // QuantConfig is used to store and parse the "quantization" field from config.json.
+    // This is currently a basic version and will be extended in the future.
+public:
+    QuantConfig() = default;
+    QuantConfig(const nlohmann::json &json);
+
+    infinicore::nn::QuantScheme get_quant_scheme() const {
+        if (quantization_method != nullptr) {
+            return quantization_method->get_quant_scheme();
+        } else {
+            return infinicore::nn::QuantScheme::NONE;
+        }
+    }
+
+private:
+    nlohmann::json quantization_config;
+    std::shared_ptr<infinilm::quantization::BaseQuantization> get_quantization_method() const;
+    std::shared_ptr<infinilm::quantization::BaseQuantization> quantization_method;
+};
+
+} // namespace infinilm::config::quantization

csrc/engine/infer_engine.cpp

@@ -1,30 +1,35 @@
 #include "infer_engine.hpp"
 #include "spdlog/spdlog.h"
+#include <iostream>
 
 namespace infinilm::engine {
 
 //------------------------------------------------------
 // Constructor
 //------------------------------------------------------
 InferEngine::InferEngine(
-    const InfinilmModel::Config &config,
     const distributed::DistConfig &distributed_config,
     infinicore::Device::Type device_type,
-    const cache::CacheConfig *cache_config) // Changed parameter
-    : communication_group_(distributed_config, device_type),
-      model_config_(config) {
+    const cache::CacheConfig *cache_config,
+    const std::string &model_path) // Changed parameter
+    : communication_group_(distributed_config, device_type) {
 
     if (cache_config != nullptr) {
         cache_config_ = cache_config->unique_copy();
     }
+
+    // Load global config if model_path is provided, model_path must be valid, and config.json exists
+    this->global_config_ = std::make_shared<infinilm::config::global_config::GlobalConfig>(model_path + "/config.json");
+
     // Create one RankWorker per rank
     int world_size = communication_group_.get_world_size();
     workers_.reserve(world_size);
     for (int r = 0; r < world_size; ++r) {
         workers_.emplace_back(std::make_unique<RankWorker>(
-            model_config_,
+            // model_config_,
             communication_group_.get_rank_info(r),
-            cache_config_ != nullptr ? cache_config_.get() : nullptr));
+            cache_config_ != nullptr ? cache_config_.get() : nullptr,
+            global_config_));
     }
 }
 

csrc/engine/infer_engine.hpp

@@ -1,5 +1,6 @@
 #pragma once
 
+#include "../config/global_config.hpp"
 #include "../models/infinilm_model.hpp"
 #include "../models/llama/llama_config.hpp"
 #include "distributed/distributed.hpp"
@@ -19,10 +20,10 @@ class InferEngine {
 
     // Updated constructor: accept CacheConfig instead of CacheType
     InferEngine(
-        const InfinilmModel::Config &config,
         const distributed::DistConfig &distributed_config = distributed::DistConfig(),
         infinicore::Device::Type device_type = infinicore::context::getDevice().getType(),
-        const cache::CacheConfig *cache_config = nullptr);
+        const cache::CacheConfig *cache_config = nullptr,
+        const std::string &modle_path = "");
 
     // Load a parameter to all workers (each can extract its shard inside RankWorker)
     void load_param(const std::string &name, const infinicore::Tensor &param);
@@ -45,8 +46,9 @@ class InferEngine {
 protected:
     std::vector<std::unique_ptr<RankWorker>> workers_;
     distributed::CommunicationGroup communication_group_;
-    const InfinilmModel::Config &model_config_;
+    // const InfinilmModel::Config &model_config_;
     std::unique_ptr<cache::CacheConfig> cache_config_;
+    std::shared_ptr<infinilm::config::global_config::GlobalConfig> global_config_;
 };
 
 } // namespace infinilm::engine

csrc/engine/rank_worker.cpp

@@ -10,22 +10,22 @@
 
 namespace infinilm::engine {
 
-RankWorker::RankWorker(const InfinilmModel::Config &model_config,
-                       const distributed::RankInfo &rank_info,
-                       const cache::CacheConfig *cache_config)
-    : model_config_(model_config),
-      rank_info_(rank_info),
+RankWorker::RankWorker(
+    const distributed::RankInfo &rank_info,
+    const cache::CacheConfig *cache_config,
+    std::shared_ptr<infinilm::config::global_config::GlobalConfig> global_config)
+    : rank_info_(rank_info),
       job_cmd_(Command::INIT),
       has_job_(false),
       job_done_(false),
+      global_config_(global_config),
       should_exit_(false),
       init_done_(false) {
     if (cache_config != nullptr) {
         pending_cache_config_ = cache_config->unique_copy();
     }
     // start the thread
     thread_ = std::thread(&RankWorker::thread_loop, this);
-
     // Wait until the worker thread finishes initialization (model created)
     std::unique_lock<std::mutex> lk(mutex_);
     cv_.wait(lk, [&] { return init_done_; });
@@ -175,7 +175,7 @@ void RankWorker::thread_loop() {
             infinicore::context::setDevice(rank_info_.device);
 
             // Create model using factory (may be expensive)
-            model_ = InfinilmModelFactory::createModel(model_config_, rank_info_, pending_cache_config_ != nullptr ? pending_cache_config_.get() : nullptr);
+            model_ = InfinilmModelFactory::createModel(rank_info_, pending_cache_config_ != nullptr ? pending_cache_config_.get() : nullptr, global_config_);
             if (!model_) {
                 throw std::runtime_error("Failed to create model");
             }

csrc/engine/rank_worker.hpp

@@ -1,6 +1,7 @@
 #pragma once
 
 #include "../cache/cache.hpp"
+#include "../config/global_config.hpp"
 #include "../models/model_factory.hpp"
 #include "distributed/distributed.hpp"
 
@@ -54,9 +55,9 @@ class RankWorker {
         infinicore::Tensor output_ids;
     };
 
-    RankWorker(const InfinilmModel::Config &model_config,
-               const distributed::RankInfo &rank_info,
-               const cache::CacheConfig *cache_config);
+    RankWorker(const distributed::RankInfo &rank_info,
+               const cache::CacheConfig *cache_config,
+               std::shared_ptr<infinilm::config::global_config::GlobalConfig> global_config);
 
     // Submit a parameter load job and wait until the load completes on the worker thread.
     void load_param(const std::string &name,
@@ -87,10 +88,11 @@ class RankWorker {
 
 private:
     // Worker properties
-    const InfinilmModel::Config &model_config_;
+    // const InfinilmModel::Config &model_config_;
     distributed::RankInfo rank_info_;
     std::shared_ptr<InfinilmModel> model_;
     std::shared_ptr<cache::Cache> cache_;
+    std::shared_ptr<infinilm::config::global_config::GlobalConfig> global_config_;
 
     // Command for the pending job (protected by mutex_)
     Command job_cmd_;