147 lines
5.3 KiB
C++
147 lines
5.3 KiB
C++
#include <fstream>
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#include <fmt/ranges.h>
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#include <spdlog/spdlog.h>
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#include <nvml.h>
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#include "backend.h"
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#include "hardware.h"
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void huggingface::tgi::backends::InitializeBackend() {
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SPDLOG_INFO("Initializing Backend...");
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nvmlInit_v2();
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initTrtLlmPlugins();
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const auto numGpus = huggingface::hardware::cuda::GetNumDevices();
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if (numGpus.has_value()) {
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SPDLOG_INFO("Detected {:d} Nvidia GPU(s)", numGpus.value());
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} else {
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SPDLOG_WARN("Failed to detected Nvidia GPU(s) on the system");
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}
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}
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[[nodiscard]]
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tle::ExecutorConfig huggingface::tgi::backends::GetExecutorConfig(const json &config, const std::string &workerPath) {
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tle::ExecutorConfig execConfig(1);
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// Retrieve the compute capabilities to enable some options at runtime
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const auto computeCapabilities = huggingface::hardware::cuda::GetCudaComputeCapabilities();
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// Single engine (TP = PP = 1) -> using leader mode (no MPI involved)
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if (config["/pretrained_config/mapping/world_size"_json_pointer].get<uint8_t>() == 1) {
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SPDLOG_INFO("Detected single engine deployment, using leader mode");
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execConfig.setParallelConfig(tle::ParallelConfig(
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tle::CommunicationType::kMPI,
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tle::CommunicationMode::kLEADER,
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std::nullopt,
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std::nullopt,
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std::nullopt
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));
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} else { // Multiple engines -> using orchestrator mode (MPI involved)
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SPDLOG_INFO("Detected sharded engine deployment, using orchestrator mode");
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execConfig.setParallelConfig(tle::ParallelConfig(
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tle::CommunicationType::kMPI,
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tle::CommunicationMode::kORCHESTRATOR,
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std::nullopt,
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std::nullopt,
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tle::OrchestratorConfig(true, workerPath, nullptr, true)
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));
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}
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// Define some configuration variables
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execConfig.setKvCacheConfig(tle::KvCacheConfig(true));
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execConfig.setEnableChunkedContext(computeCapabilities.isPostAmpere());
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return execConfig;
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}
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tle::SamplingConfig huggingface::tgi::backends::GetSamplingConfig(
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uint32_t topK,
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float_t topP,
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float_t temperature,
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float_t repetition_penalty,
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float_t frequency_penalty,
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uint64_t seed) {
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return tle::SamplingConfig(
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1, // TGI only use a single beam
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topK,
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topP,
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std::nullopt,
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std::nullopt,
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std::nullopt,
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seed,
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temperature,
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temperature,
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std::nullopt,
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repetition_penalty,
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std::nullopt,
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frequency_penalty
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);
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}
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huggingface::tgi::backends::TensorRtLlmBackend::TensorRtLlmBackend(
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const std::filesystem::path &enginesFolder,
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const std::filesystem::path &executorWorker
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) :
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config(json::parse(std::ifstream(enginesFolder / "config.json"))),
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executor(
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enginesFolder,
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tensorrt_llm::executor::ModelType::kDECODER_ONLY,
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GetExecutorConfig(config, executorWorker.string()
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)) {
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SPDLOG_INFO(FMT_STRING("Engine (version={})"), config["/version"_json_pointer].get_ref<const std::string &>());
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}
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bool huggingface::tgi::backends::TensorRtLlmBackend::IsReady() const {
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return executor.canEnqueueRequests();
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}
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[[nodiscard("Returned number of requests needs to be consumed")]]
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size_t huggingface::tgi::backends::TensorRtLlmBackend::NumResponsesReady() const {
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return executor.getNumResponsesReady();
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}
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[[nodiscard("Returned request id needs to be provided back to gather generated tokens")]]
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tle::IdType huggingface::tgi::backends::TensorRtLlmBackend::Submit(
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const std::vector<tle::TokenIdType> &tokens,
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const int32_t topK,
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const float_t topP,
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const float_t temperature,
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const float_t repetition_penalty,
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const float_t frequency_penalty,
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const uint64_t seed
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) {
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#ifdef NDEBUG
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SPDLOG_DEBUG(
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FMT_STRING("Submitting inference over {:d} tokens to the executor ({:d} already in-flight)"),
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tokens.size(),
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executor.getLatestIterationStats().back().numActiveRequests
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);
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#else
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SPDLOG_DEBUG(
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FMT_STRING("Submitting inference [{}] to the executor ({:d} already in-flight)"),
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fmt::join(tokens, ", "),
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executor.getLatestIterationStats().front().numActiveRequests
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);
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#endif
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const auto maxNumTokens = config["/build_config/max_num_tokens"_json_pointer].get<size_t>();
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const auto maxNewTokens = static_cast<int32_t>(std::max(1ul, maxNumTokens - tokens.size()));
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const auto sampling = GetSamplingConfig(topK, topP, temperature, repetition_penalty, frequency_penalty, seed);
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const auto output = tle::OutputConfig(true, false, false, true, false);
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return executor.enqueueRequest(
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tle::Request{tokens, maxNewTokens, true, sampling, output});
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}
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[[nodiscard("Generated tokens result must be used")]]
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std::vector<tle::Response> huggingface::tgi::backends::TensorRtLlmBackend::Poll(const tle::IdType requestId) {
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SPDLOG_DEBUG(FMT_STRING("Polling status for request {:d}"), requestId);
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return executor.awaitResponses(requestId);
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}
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void huggingface::tgi::backends::TensorRtLlmBackend::Shutdown() {
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SPDLOG_INFO("Shutting down executor");
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executor.shutdown();
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}
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