/*
 * Copyright (c) 2022-2024, NVIDIA CORPORATION.  All rights reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#include "tensorrt_llm/runtime/gptJsonConfig.h"

#include "gptModelConfig.h"
#include "loraManager.h"
#include "tensorrt_llm/common/assert.h"
#include "tensorrt_llm/common/logger.h"
#include "tensorrt_llm/common/stringUtils.h"

#include <fstream>
#include <nlohmann/json.hpp>
#include <string_view>

using namespace tensorrt_llm::runtime;
namespace tc = tensorrt_llm::common;

namespace
{
using Json = typename nlohmann::json::basic_json;

template <typename FieldType>
FieldType parseJsonFieldOr(Json const& json, std::string_view name, FieldType defaultValue)
{
    auto value = defaultValue;
    try
    {
        value = json.at(name).template get<FieldType>();
    }
    catch (nlohmann::json::out_of_range& e)
    {
        TLLM_LOG_WARNING("Parameter %s cannot be read from json:", std::string(name).c_str());
        TLLM_LOG_WARNING(e.what());
    }
    return value;
}

template <typename FieldType>
std::optional<FieldType> parseJsonFieldOptional(Json const& json, std::string_view name)
{
    std::optional<FieldType> value = std::nullopt;
    try
    {
        value = json.at(name).template get<FieldType>();
    }
    catch (const nlohmann::json::out_of_range& e)
    {
        TLLM_LOG_WARNING(e.what());
        TLLM_LOG_WARNING("Optional value for parameter %s will not be set.", std::string(name).c_str());
    }
    catch (const nlohmann::json::type_error& e)
    {
        TLLM_LOG_WARNING(e.what());
        TLLM_LOG_WARNING("Optional value for parameter %s will not be set.", std::string(name).c_str());
    }
    return value;
}

template <typename InputType>
GptJsonConfig parseJson(InputType&& i)
{
    auto constexpr allowExceptions = true;
    auto constexpr ingoreComments = true;
    auto const json = nlohmann::json::parse(i, nullptr, allowExceptions, ingoreComments);

    auto const engineVersion = parseJsonFieldOr(json, "version", std::string("none"));

    auto const engineVersionNone = engineVersion == std::string("none");
    if (engineVersionNone)
    {
        TLLM_LOG_INFO("No engine version found in the config file, assuming engine(s) built by old builder API.");
    }
    else
    {
        TLLM_LOG_INFO("Engine version %s found in the config file, assuming engine(s) built by new builder API.",
            engineVersion.c_str());
    }

    auto const& builderConfig = engineVersionNone ? json.at("builder_config") : json.at("build_config");

    auto const name = engineVersionNone ? builderConfig.at("name").template get<std::string>()
                                        : json.at("pretrained_config").at("architecture").template get<std::string>();

    auto const tensorParallelism = engineVersionNone
        ? builderConfig.at("tensor_parallel").template get<SizeType>()
        : json.at("pretrained_config").at("mapping").at("tp_size").template get<SizeType>();
    auto const pipelineParallelism = engineVersionNone
        ? parseJsonFieldOr(builderConfig, "pipeline_parallel", 1)
        : parseJsonFieldOr(json.at("pretrained_config").at("mapping"), "pp_size", 1);

    auto const precision = engineVersionNone ? builderConfig.at("precision").template get<std::string>()
                                             : json.at("pretrained_config").at("dtype").template get<std::string>();

    auto dataType = nvinfer1::DataType::kFLOAT;
    if (!precision.compare("float32"))
        dataType = nvinfer1::DataType::kFLOAT;
    else if (!precision.compare("float16"))
        dataType = nvinfer1::DataType::kHALF;
    else if (!precision.compare("bfloat16"))
        dataType = nvinfer1::DataType::kBF16;
    else
        TLLM_CHECK_WITH_INFO(false, tc::fmtstr("Model data type '%s' not supported", precision.c_str()));

    auto modelConfig = [&engineVersionNone, &json, &builderConfig, &tensorParallelism, &dataType]()
    {
        if (engineVersionNone)
        {
            auto const vocabSize = builderConfig.at("vocab_size").template get<SizeType>();
            auto const numLayers = builderConfig.at("num_layers").template get<SizeType>();
            auto const numHeads = builderConfig.at("num_heads").template get<SizeType>() / tensorParallelism;
            auto const hiddenSize = builderConfig.at("hidden_size").template get<SizeType>() / tensorParallelism;

            auto modelConfig = GptModelConfig{vocabSize, numLayers, numHeads, hiddenSize, dataType};

            auto const sizePerHead = parseJsonFieldOr(builderConfig, "head_size", hiddenSize / numHeads);
            modelConfig.setSizePerHead(sizePerHead);

            // TODO:
            // Code crashes when numKvHeads <= 0. Clamping downwards to 1 prevents that, make sure this is best fix.
            auto const numKvHeads = std::max(
                parseJsonFieldOr(builderConfig, "num_kv_heads", numHeads * tensorParallelism) / tensorParallelism, 1);
            modelConfig.setNbKvHeads(numKvHeads);

            return modelConfig;
        }
        else
        {
            auto const& pretrainedConfig = json.at("pretrained_config");

            auto const vocabSize = pretrainedConfig.at("vocab_size").template get<SizeType>();
            auto const numLayers = pretrainedConfig.at("num_hidden_layers").template get<SizeType>();
            auto const numHeads
                = pretrainedConfig.at("num_attention_heads").template get<SizeType>() / tensorParallelism;
            auto const hiddenSize = pretrainedConfig.at("hidden_size").template get<SizeType>() / tensorParallelism;
            auto modelConfig = GptModelConfig{vocabSize, numLayers, numHeads, hiddenSize, dataType};

            auto const sizePerHead = parseJsonFieldOr(pretrainedConfig, "head_size", hiddenSize / numHeads);
            modelConfig.setSizePerHead(sizePerHead);

            // TODO:
            // Code crashes when numKvHeads <= 0. Clamping downwards to 1 prevents that, make sure this is best fix.
            auto const numKvHeads
                = std::max(pretrainedConfig.at("num_key_value_heads").template get<SizeType>() / tensorParallelism, 1);
            modelConfig.setNbKvHeads(numKvHeads);

            return modelConfig;
        }
    }();

    auto const maxBatchSize = parseJsonFieldOr(builderConfig, "max_batch_size", 0);
    auto const maxBeamWidth = parseJsonFieldOr(builderConfig, "max_beam_width", 0);
    auto const maxInputLen = parseJsonFieldOr(builderConfig, "max_input_len", 0);
    auto const maxSequenceLen = maxInputLen + parseJsonFieldOr(builderConfig, "max_output_len", 0);
    auto const maxDraftLen = parseJsonFieldOr(builderConfig, "max_draft_len", 0);
    auto const maxNumTokens = parseJsonFieldOptional<SizeType>(builderConfig, "max_num_tokens");
    auto const maxPromptEmbeddingTableSize
        = parseJsonFieldOr<SizeType>(builderConfig, "max_prompt_embedding_table_size", 0);
    auto const computeContextLogits = parseJsonFieldOr(builderConfig, "gather_context_logits", false);
    auto const computeGenerationLogits = parseJsonFieldOr(builderConfig, "gather_generation_logits", false);

    modelConfig.setMaxBatchSize(maxBatchSize);
    modelConfig.setMaxBeamWidth(maxBeamWidth);
    modelConfig.setMaxInputLen(maxInputLen);
    modelConfig.setMaxSequenceLen(maxSequenceLen);
    modelConfig.setMaxNumTokens(maxNumTokens);
    modelConfig.setMaxDraftLen(maxDraftLen);
    modelConfig.setMaxPromptEmbeddingTableSize(maxPromptEmbeddingTableSize);
    modelConfig.computeContextLogits(computeContextLogits);
    modelConfig.computeGenerationLogits(computeGenerationLogits);

    auto const& pluginConfig = engineVersionNone ? json.at("plugin_config") : builderConfig.at("plugin_config");

    auto const useGptAttentionPlugin = !pluginConfig.at("gpt_attention_plugin").is_null();
    auto const removeInputPadding = pluginConfig.at("remove_input_padding").template get<bool>();
    auto const pagedKvCache = pluginConfig.at("paged_kv_cache");
    auto const tokensPerBlock = pluginConfig.at("tokens_per_block");
    auto const useCustomAllReduce = pluginConfig.at("use_custom_all_reduce").template get<bool>();
    auto const useContextFMHAForGeneration = pluginConfig.at("use_context_fmha_for_generation").template get<bool>();

    modelConfig.useGptAttentionPlugin(useGptAttentionPlugin);
    modelConfig.usePackedInput(removeInputPadding);
    modelConfig.usePagedKvCache(pagedKvCache);
    modelConfig.setTokensPerBlock(tokensPerBlock);
    modelConfig.useCustomAllReduce(useCustomAllReduce);
    modelConfig.setUseContextFMHAForGeneration(useContextFMHAForGeneration);

    if (engineVersionNone)
    {
        auto const pagedContextFMHA = pluginConfig.at("use_paged_context_fmha").template get<bool>();
        modelConfig.setPagedContextFMHA(pagedContextFMHA);
    }

    if (engineVersionNone)
    {
        auto const mlpHiddenSize = parseJsonFieldOr(builderConfig, "mlp_hidden_size", SizeType{0}) / tensorParallelism;
        modelConfig.setMlpHiddenSize(mlpHiddenSize);

        auto useLoraPlugin = !pluginConfig.at("lora_plugin").is_null();
        if (useLoraPlugin)
        {
            auto const loraTargetModules
                = parseJsonFieldOr(builderConfig, "lora_target_modules", std::vector<std::string>{});

            modelConfig.setLoraModules(LoraModule::createLoraModules(loraTargetModules, modelConfig.getHiddenSize(),
                mlpHiddenSize, modelConfig.getNbHeads(), modelConfig.getNbKvHeads(), modelConfig.getSizePerHead(),
                tensorParallelism));

            if (modelConfig.getLoraModules().empty())
            {
                TLLM_LOG_WARNING("lora_plugin enabled, but no lora module enabled: setting useLoraPlugin to false");
                useLoraPlugin = false;
            }
        }
        modelConfig.useLoraPlugin(useLoraPlugin);
    }

    if (engineVersionNone)
    {
        auto const quantMode
            = tc::QuantMode(parseJsonFieldOr(builderConfig, "quant_mode", tc::QuantMode::none().value()));
        modelConfig.setQuantMode(quantMode);
    }
    else
    {
        auto const& quantization = json.at("pretrained_config").at("quantization");
        auto quantAlgo = parseJsonFieldOptional<std::string>(quantization, "quant_algo");
        auto kvCacheQuantAlgo = parseJsonFieldOptional<std::string>(quantization, "kv_cache_quant_algo");
        auto const quantMode = tc::QuantMode::fromQuantAlgo(quantAlgo, kvCacheQuantAlgo);
        modelConfig.setQuantMode(quantMode);
    }

    if (engineVersionNone)
    {
        if (name == std::string("chatglm_6b") || name == std::string("glm_10b"))
        {
            modelConfig.setModelVariant(GptModelConfig::ModelVariant::kGlm);
            // kGlm is only for ChatGLM-6B and GLM-10B
        }
    }
    else
    {
        if (name == "ChatGLMForCausalLM")
        {
            auto const& pretrainedConfig = json.at("pretrained_config");
            auto const chatglmVersion = pretrainedConfig.at("chatglm_version").template get<std::string>();
            if (chatglmVersion == "glm" || chatglmVersion == "chatglm")
            {
                modelConfig.setModelVariant(GptModelConfig::ModelVariant::kGlm);
                // kGlm is only for ChatGLM-6B and GLM-10B
            }
        }
    }

    return GptJsonConfig{name, engineVersion, precision, tensorParallelism, pipelineParallelism, modelConfig};
}

} // namespace

std::string GptJsonConfig::engineFilename(WorldConfig const& worldConfig, std::string const& model) const
{
    TLLM_CHECK_WITH_INFO(getTensorParallelism() == worldConfig.getTensorParallelism(), "tensor parallelism mismatch");
    TLLM_CHECK_WITH_INFO(
        getPipelineParallelism() == worldConfig.getPipelineParallelism(), "pipeline parallelism mismatch");
    auto pp = worldConfig.isPipelineParallel() ? "_pp" + std::to_string(worldConfig.getPipelineParallelism()) : "";
    if (getVersion() == std::string("none"))
    {
        return model + "_" + getPrecision() + "_tp" + std::to_string(worldConfig.getTensorParallelism()) + pp + "_rank"
            + std::to_string(worldConfig.getRank()) + ".engine";
    }
    else
    {
        return "rank" + std::to_string(worldConfig.getRank()) + ".engine";
    }
}

GptJsonConfig GptJsonConfig::parse(std::string const& json)
{
    return parseJson(json);
}

GptJsonConfig GptJsonConfig::parse(std::istream& json)
{
    return parseJson(json);
}

GptJsonConfig GptJsonConfig::parse(std::filesystem::path const& path)
{
    TLLM_CHECK_WITH_INFO(std::filesystem::exists(path), std::string("File does not exist: ") + path.string());
    std::ifstream json(path);
    return parse(json);
}