mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
488 lines
30 KiB
C++
488 lines
30 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 2022-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <pybind11/cast.h>
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#include <pybind11/functional.h>
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#include <pybind11/operators.h>
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#include <pybind11/pybind11.h>
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#include <pybind11/stl.h>
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#include <torch/extension.h>
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#include <vector>
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#include "tensorrt_llm/batch_manager/peftCacheManagerConfig.h"
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#include "tensorrt_llm/common/quantization.h"
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#include "tensorrt_llm/pybind/batch_manager/algorithms.h"
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#include "tensorrt_llm/pybind/batch_manager/bindings.h"
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#include "tensorrt_llm/pybind/batch_manager/cacheTransceiver.h"
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#include "tensorrt_llm/pybind/batch_manager/kvCacheManager.h"
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#include "tensorrt_llm/pybind/batch_manager/llmRequest.h"
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#include "tensorrt_llm/pybind/executor/bindings.h"
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#include "tensorrt_llm/pybind/runtime/bindings.h"
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#include "tensorrt_llm/pybind/testing/modelSpecBinding.h"
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#include "tensorrt_llm/pybind/userbuffers/bindings.h"
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#include "tensorrt_llm/runtime/common.h"
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#include "tensorrt_llm/runtime/cudaStream.h"
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#include "tensorrt_llm/runtime/gptJsonConfig.h"
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#include "tensorrt_llm/runtime/ipcNvlsMemory.h"
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#include "tensorrt_llm/runtime/memoryCounters.h"
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#include "tensorrt_llm/runtime/samplingConfig.h"
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#include "tensorrt_llm/runtime/utils/mpiUtils.h"
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namespace py = pybind11;
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namespace tb = tensorrt_llm::batch_manager;
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namespace tpb = tensorrt_llm::pybind::batch_manager;
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namespace tc = tensorrt_llm::common;
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namespace tr = tensorrt_llm::runtime;
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namespace tle = tensorrt_llm::executor;
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using SizeType32 = tr::SizeType32;
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using TokenIdType = tr::TokenIdType;
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template <typename T>
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using OptVec = std::optional<std::vector<T>>;
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#if not defined(TRTLLM_PYBIND_MODULE)
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#error "TRTLLM_PYBIND_MODULE must be defined"
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#endif
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namespace
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{
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tr::SamplingConfig makeSamplingConfig(std::vector<tr::SamplingConfig> const& configs)
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{
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return tr::SamplingConfig(configs);
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}
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} // namespace
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PYBIND11_MODULE(TRTLLM_PYBIND_MODULE, m)
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{
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m.doc() = "TensorRT-LLM Python bindings for C++ runtime";
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m.attr("binding_type") = "pybind";
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// Create MpiComm binding first since it's used in the executor bindings
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py::classh<tensorrt_llm::mpi::MpiComm>(m, "MpiComm")
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.def_static("rank",
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[]()
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{
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auto& session = tensorrt_llm::mpi::MpiComm::session();
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return session.tensorrt_llm::mpi::MpiComm::getRank();
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})
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.def_static("size",
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[]()
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{
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auto& session = tensorrt_llm::mpi::MpiComm::session();
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return session.tensorrt_llm::mpi::MpiComm::getSize();
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})
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.def_static("local_size",
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[]()
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{
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auto& session = tensorrt_llm::mpi::MpiComm::localSession();
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return session.tensorrt_llm::mpi::MpiComm::getSize();
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})
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.def_static("local_init", []() { tensorrt_llm::mpi::MpiComm::localSession(); })
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.def_static("set_raw_mpi_session_by_fortran_handle",
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[](int64_t fortran_handle) { tensorrt_llm::mpi::MpiComm::setRawSessionByFortran(fortran_handle); })
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.def_static("split",
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[](size_t color, size_t rank)
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{
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auto& world = tensorrt_llm::mpi::MpiComm::world();
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tensorrt_llm::mpi::MpiComm::setSession(world.split(color, rank));
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});
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py::classh<tr::CudaStream>(m, "CudaStream")
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.def(py::init(
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[](py::object py_stream)
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{
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cudaStream_t stream = reinterpret_cast<cudaStream_t>(py_stream.cast<uintptr_t>());
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return tr::CudaStream{stream};
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}),
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py::arg("stream_ptr"))
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.def("get_device", &tr::CudaStream::getDevice);
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// Create submodule for executor bindings.
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auto mExecutor = m.def_submodule("executor", "Executor bindings");
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auto mInternal = m.def_submodule("internal", "Internal submodule of TRTLLM runtime");
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auto mInternalRuntime = mInternal.def_submodule("runtime", "Runtime internal bindings");
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auto mInternalTesting = mInternal.def_submodule("testing", "Testing internal bindings");
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auto mInternalBatchManager = mInternal.def_submodule("batch_manager", "Batch manager internal bindings");
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tensorrt_llm::pybind::executor::initBindings(mExecutor);
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tensorrt_llm::pybind::runtime::initBindingsEarly(mInternalRuntime);
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auto buildInfo = m.def_submodule("BuildInfo");
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buildInfo.attr("ENABLE_MULTI_DEVICE") = py::int_(ENABLE_MULTI_DEVICE);
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py::class_<tb::PeftCacheManagerConfig>(m, "PeftCacheManagerConfig")
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.def(py::init<SizeType32, SizeType32, SizeType32, SizeType32, SizeType32, SizeType32, SizeType32, SizeType32,
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SizeType32, std::optional<float>, std::optional<size_t>, std::optional<std::string>>(),
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py::arg("num_host_module_layer") = 0, py::arg("num_device_module_layer") = 0,
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py::arg("optimal_adapter_size") = 8, py::arg("max_adapter_size") = 64, py::arg("num_put_workers") = 1,
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py::arg("num_ensure_workers") = 1, py::arg("num_copy_streams") = 1,
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py::arg("max_pages_per_block_host") = 24, py::arg("max_pages_per_block_device") = 8,
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py::arg("device_cache_percent") = std::nullopt, py::arg("host_cache_size") = std::nullopt,
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py::arg("lora_prefetch_dir") = std::nullopt)
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.def_readwrite("num_host_module_layer", &tb::PeftCacheManagerConfig::numHostModuleLayer)
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.def_readwrite("num_device_module_layer", &tb::PeftCacheManagerConfig::numDeviceModuleLayer)
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.def_readwrite("optimal_adapter_size", &tb::PeftCacheManagerConfig::optimalAdapterSize)
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.def_readwrite("max_adapter_size", &tb::PeftCacheManagerConfig::maxAdapterSize)
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.def_readwrite("num_put_workers", &tb::PeftCacheManagerConfig::numPutWorkers)
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.def_readwrite("num_ensure_workers", &tb::PeftCacheManagerConfig::numEnsureWorkers)
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.def_readwrite("num_copy_streams", &tb::PeftCacheManagerConfig::numCopyStreams)
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.def_readwrite("max_pages_per_block_host", &tb::PeftCacheManagerConfig::maxPagesPerBlockHost)
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.def_readwrite("max_pages_per_block_device", &tb::PeftCacheManagerConfig::maxPagesPerBlockDevice)
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.def_readwrite("device_cache_percent", &tb::PeftCacheManagerConfig::deviceCachePercent)
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.def_readwrite("host_cache_size", &tb::PeftCacheManagerConfig::hostCacheSize)
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.def_readwrite("lora_prefetch_dir", &tb::PeftCacheManagerConfig::loraPrefetchDir);
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py::enum_<nvinfer1::DataType>(m, "DataType")
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.value("FLOAT", nvinfer1::DataType::kFLOAT)
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.value("HALF", nvinfer1::DataType::kHALF)
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.value("INT8", nvinfer1::DataType::kINT8)
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.value("INT32", nvinfer1::DataType::kINT32)
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.value("BOOL", nvinfer1::DataType::kBOOL)
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.value("UINT8", nvinfer1::DataType::kUINT8)
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.value("FP8", nvinfer1::DataType::kFP8)
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.value("BF16", nvinfer1::DataType::kBF16)
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.value("INT64", nvinfer1::DataType::kINT64)
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.export_values();
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py::enum_<tr::ModelConfig::ModelVariant>(m, "GptModelVariant")
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.value("GPT", tr::ModelConfig::ModelVariant::kGpt)
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.value("GLM", tr::ModelConfig::ModelVariant::kGlm)
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.value("CHATGLM", tr::ModelConfig::ModelVariant::kChatGlm)
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.value("MAMBA", tr::ModelConfig::ModelVariant::kMamba)
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.value("RECURRENTGEMMA", tr::ModelConfig::ModelVariant::kRecurrentGemma);
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py::enum_<tr::ModelConfig::KVCacheType>(m, "KVCacheType")
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.value("CONTINUOUS", tr::ModelConfig::KVCacheType::kCONTINUOUS)
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.value("PAGED", tr::ModelConfig::KVCacheType::kPAGED)
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.value("DISABLED", tr::ModelConfig::KVCacheType::kDISABLED)
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.def("from_string", &tr::ModelConfig::KVCacheTypeFromString);
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py::enum_<tr::ModelConfig::LayerType>(m, "LayerType")
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.value("ATTENTION", tr::ModelConfig::LayerType::kATTENTION)
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.value("RECURRENT", tr::ModelConfig::LayerType::kRECURRENT);
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py::enum_<tr::LoraModule::ModuleType>(m, "LoraModuleType")
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.value("INVALID", tr::LoraModule::ModuleType::kINVALID)
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.value("ATTN_QKV", tr::LoraModule::ModuleType::kATTN_QKV)
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.value("ATTN_Q", tr::LoraModule::ModuleType::kATTN_Q)
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.value("ATTN_K", tr::LoraModule::ModuleType::kATTN_K)
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.value("ATTN_V", tr::LoraModule::ModuleType::kATTN_V)
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.value("ATTN_DENSE", tr::LoraModule::ModuleType::kATTN_DENSE)
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.value("MLP_H_TO_4H", tr::LoraModule::ModuleType::kMLP_H_TO_4H)
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.value("MLP_4H_TO_H", tr::LoraModule::ModuleType::kMLP_4H_TO_H)
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.value("MLP_GATE", tr::LoraModule::ModuleType::kMLP_GATE)
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.value("CROSS_ATTN_QKV", tr::LoraModule::ModuleType::kCROSS_ATTN_QKV)
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.value("CROSS_ATTN_Q", tr::LoraModule::ModuleType::kCROSS_ATTN_Q)
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.value("CROSS_ATTN_K", tr::LoraModule::ModuleType::kCROSS_ATTN_K)
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.value("CROSS_ATTN_V", tr::LoraModule::ModuleType::kCROSS_ATTN_V)
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.value("CROSS_ATTN_DENSE", tr::LoraModule::ModuleType::kCROSS_ATTN_DENSE)
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.value("MOE_H_TO_4H", tr::LoraModule::ModuleType::kMOE_H_TO_4H)
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.value("MOE_4H_TO_H", tr::LoraModule::ModuleType::kMOE_4H_TO_H)
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.value("MOE_GATE", tr::LoraModule::ModuleType::kMOE_GATE)
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.value("MOE_ROUTER", tr::LoraModule::ModuleType::kMOE_ROUTER)
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.value("MLP_ROUTER", tr::LoraModule::ModuleType::kMLP_ROUTER)
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.value("MLP_GATE_UP", tr::LoraModule::ModuleType::kMLP_GATE_UP);
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py::class_<tr::LoraModule>(m, "LoraModule")
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.def(py::init<tr::LoraModule::ModuleType, SizeType32, SizeType32, bool, bool, SizeType32, SizeType32>(),
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py::arg("module_type"), py::arg("in_dim"), py::arg("out_dim"), py::arg("in_dim_first"),
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py::arg("out_dim_first"), py::arg("in_tp_split_dim"), py::arg("out_tp_split_dim"))
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.def_property_readonly("module_type", &tr::LoraModule::name)
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.def_property_readonly("in_dim", &tr::LoraModule::inDim)
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.def_property_readonly("out_dim", &tr::LoraModule::outDim)
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.def_property_readonly("in_dim_first", &tr::LoraModule::inDimFirst)
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.def_property_readonly("out_dim_first", &tr::LoraModule::outDimFirst)
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.def_property_readonly("in_tp_split_dim", &tr::LoraModule::inTpSplitDim)
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.def_property_readonly("out_tp_split_dim", &tr::LoraModule::outTpSplitDim)
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.def_static("create_lora_modules", &tr::LoraModule::createLoraModules, py::arg("lora_module_names"),
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py::arg("hidden_size"), py::arg("mlp_hidden_size"), py::arg("num_attention_heads"),
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py::arg("num_kv_attention_heads"), py::arg("attention_head_size"), py::arg("tp_size") = 1,
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py::arg("num_experts") = 0);
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py::class_<tc::QuantMode>(m, "QuantMode")
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.def_static("none", &tc::QuantMode::none)
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.def_static("int4_weights", &tc::QuantMode::int4Weights)
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.def_static("int8_weights", &tc::QuantMode::int8Weights)
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.def_static("activations", &tc::QuantMode::activations)
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.def_static("per_channel_scaling", &tc::QuantMode::perChannelScaling)
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.def_static("per_token_scaling", &tc::QuantMode::perTokenScaling)
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.def_static("per_group_scaling", &tc::QuantMode::perGroupScaling)
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.def_static("int8_kv_cache", &tc::QuantMode::int8KvCache)
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.def_static("fp8_kv_cache", &tc::QuantMode::fp8KvCache)
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.def_static("fp8_qdq", &tc::QuantMode::fp8Qdq)
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.def_property_readonly("value", &tc::QuantMode::value)
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.def("is_set", &tc::QuantMode::isSet, py::arg("mode"))
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.def_property_readonly("has_int4_weights", &tc::QuantMode::hasInt4Weights)
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.def_property_readonly("has_int8_weights", &tc::QuantMode::hasInt8Weights)
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.def_property_readonly("has_activations", &tc::QuantMode::hasActivations)
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.def_property_readonly("has_per_channel_scaling", &tc::QuantMode::hasPerChannelScaling)
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.def_property_readonly("has_per_token_scaling", &tc::QuantMode::hasPerTokenScaling)
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.def_property_readonly("has_per_group_scaling", &tc::QuantMode::hasPerGroupScaling)
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.def_property_readonly("has_static_activation_scaling", &tc::QuantMode::hasStaticActivationScaling)
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.def_property_readonly("has_int8_kv_cache", &tc::QuantMode::hasInt8KvCache)
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.def_property_readonly("has_fp8_kv_cache", &tc::QuantMode::hasFp8KvCache)
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.def_property_readonly("has_fp8_qdq", &tc::QuantMode::hasFp8Qdq)
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.def_property_readonly("has_nvfp4", &tc::QuantMode::hasNvfp4)
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.def_property_readonly("has_w4a8_mxfp4_fp8", &tc::QuantMode::hasW4a8Mxfp4Fp8)
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.def_property_readonly("has_kv_cache_quant", &tc::QuantMode::hasKvCacheQuant)
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.def_static("from_description", &tc::QuantMode::fromDescription, py::arg("quantize_weights"),
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py::arg("quantize_activations"), py::arg("per_token"), py::arg("per_channel"), py::arg("per_group"),
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py::arg("use_int4_weights"), py::arg("use_int8_kv_cache"), py::arg("use_fp8_kv_kache"),
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py::arg("use_fp8_qdq"), py::arg("use_fp8_rowwise"), py::arg("use_w4a8_qserve"), py::arg("use_nvfp4"),
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py::arg("use_fp8_block_scales"), py::arg("use_w4a8_mxfp4_fp8"))
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.def_static("use_smooth_quant", &tc::QuantMode::useSmoothQuant, py::arg("per_token") = false,
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py::arg("per_channel") = false)
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.def_static("use_weight_only", &tc::QuantMode::useWeightOnly, py::arg("use_int4_weights") = false,
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py::arg("per_group") = false)
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.def_static("from_quant_algo", &tc::QuantMode::fromQuantAlgo, py::arg("quant_algo") = py::none(),
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py::arg("kv_cache_quant_algo") = py::none())
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.def(py::self + py::self)
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.def(py::self += py::self)
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.def(py::self - py::self)
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.def(py::self -= py::self)
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.def(py::self == py::self)
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.def(py::self != py::self);
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py::class_<tr::ModelConfig>(m, "ModelConfig")
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.def(py::init<SizeType32, SizeType32, SizeType32, SizeType32, SizeType32, SizeType32, nvinfer1::DataType>(),
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py::arg("vocab_size"), py::arg("num_layers"), py::arg("num_attention_layers"), py::arg("num_rnn_layers"),
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py::arg("num_heads"), py::arg("hidden_size"), py::arg("data_type"))
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.def_property_readonly("vocab_size", &tr::ModelConfig::getVocabSize)
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.def("vocab_size_padded", &tr::ModelConfig::getVocabSizePadded, py::arg("world_size"))
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.def("num_layers", &tr::ModelConfig::getNbLayers, py::arg("pipeline_parallelism") = 1,
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py::arg("pipeline_parallelism_rank") = 0)
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.def("num_attention_layers", &tr::ModelConfig::getNbAttentionLayers, py::arg("pipeline_parallelism") = 1,
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py::arg("pipeline_parallelism_rank") = 0)
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.def("num_rnn_layers", &tr::ModelConfig::getNbRnnLayers, py::arg("pipeline_parallelism") = 1,
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py::arg("pipeline_parallelism_rank") = 0)
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.def("num_kv_heads", &tr::ModelConfig::getNbKvHeads, py::arg("layer_idx"))
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.def("set_num_kv_heads", &tr::ModelConfig::setNbKvHeads, py::arg("num_kv_heads"))
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.def_property_readonly("num_heads", &tr::ModelConfig::getNbHeads)
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.def_property_readonly("hidden_size", &tr::ModelConfig::getHiddenSize)
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.def_property_readonly("size_per_head", &tr::ModelConfig::getSizePerHead)
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.def_property_readonly("data_type", &tr::ModelConfig::getDataType)
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.def_property_readonly("speculative_decoding_mode", &tr::ModelConfig::getSpeculativeDecodingMode)
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.def_property("head_size", &tr::ModelConfig::getSizePerHead, &tr::ModelConfig::setSizePerHead)
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.def_property(
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"num_kv_heads_per_layer", &tr::ModelConfig::getNumKvHeadsPerLayer, &tr::ModelConfig::setNumKvHeadsPerLayer)
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.def_property("use_gpt_attention_plugin",
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py::overload_cast<>(&tr::ModelConfig::useGptAttentionPlugin, py::const_),
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py::overload_cast<bool>(&tr::ModelConfig::useGptAttentionPlugin))
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.def_property("use_packed_input", py::overload_cast<>(&tr::ModelConfig::usePackedInput, py::const_),
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py::overload_cast<bool>(&tr::ModelConfig::usePackedInput))
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.def_property("kv_cache_type", py::overload_cast<>(&tr::ModelConfig::getKVCacheType, py::const_),
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py::overload_cast<tr::ModelConfig::KVCacheType>(&tr::ModelConfig::setKVCacheType))
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.def_property("tokens_per_block", &tr::ModelConfig::getTokensPerBlock, &tr::ModelConfig::setTokensPerBlock)
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.def_property("quant_mode", &tr::ModelConfig::getQuantMode, &tr::ModelConfig::setQuantMode)
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.def_property_readonly("supports_inflight_batching", &tr::ModelConfig::supportsInflightBatching)
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.def_property("max_batch_size", &tr::ModelConfig::getMaxBatchSize, &tr::ModelConfig::setMaxBatchSize)
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.def_property("max_beam_width", &tr::ModelConfig::getMaxBeamWidth, &tr::ModelConfig::setMaxBeamWidth)
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.def_property("max_input_len", &tr::ModelConfig::getMaxInputLen, &tr::ModelConfig::setMaxInputLen)
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.def_property("max_seq_len", &tr::ModelConfig::getMaxSequenceLen, &tr::ModelConfig::setMaxSequenceLen)
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.def_property("max_num_tokens", &tr::ModelConfig::getMaxNumTokens, &tr::ModelConfig::setMaxNumTokens)
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.def_property("max_prompt_embedding_table_size", &tr::ModelConfig::getMaxPromptEmbeddingTableSize,
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&tr::ModelConfig::setMaxPromptEmbeddingTableSize)
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.def_property_readonly("use_prompt_tuning", &tr::ModelConfig::usePromptTuning)
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.def_property_readonly("use_mrope", &tr::ModelConfig::useMrope)
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.def_property("use_lora_plugin", py::overload_cast<>(&tr::ModelConfig::useLoraPlugin, py::const_),
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py::overload_cast<bool>(&tr::ModelConfig::useLoraPlugin))
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.def_property("layer_types", &tr::ModelConfig::getLayerTypes, &tr::ModelConfig::setLayerTypes)
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.def_property("compute_context_logits", py::overload_cast<>(&tr::ModelConfig::computeContextLogits, py::const_),
|
|
py::overload_cast<bool>(&tr::ModelConfig::computeContextLogits))
|
|
.def_property("compute_generation_logits",
|
|
py::overload_cast<>(&tr::ModelConfig::computeGenerationLogits, py::const_),
|
|
py::overload_cast<bool>(&tr::ModelConfig::computeGenerationLogits))
|
|
.def_property("model_variant", &tr::ModelConfig::getModelVariant, &tr::ModelConfig::setModelVariant)
|
|
.def_property(
|
|
"use_cross_attention", &tr::ModelConfig::useCrossAttention, &tr::ModelConfig::setUseCrossAttention)
|
|
.def_property("lora_modules", &tr::ModelConfig::getLoraModules, &tr::ModelConfig::setLoraModules)
|
|
.def_property("max_lora_rank", &tr::ModelConfig::getMaxLoraRank, &tr::ModelConfig::setMaxLoraRank)
|
|
.def_property("mlp_hidden_size", &tr::ModelConfig::getMlpHiddenSize, &tr::ModelConfig::setMlpHiddenSize)
|
|
.def_property("size_per_head", &tr::ModelConfig::getSizePerHead, &tr::ModelConfig::setSizePerHead);
|
|
|
|
py::class_<tr::WorldConfig>(m, "WorldConfig")
|
|
.def(py::init<SizeType32, SizeType32, SizeType32, SizeType32, SizeType32,
|
|
std::optional<std::vector<SizeType32>> const&, bool>(),
|
|
py::arg("tensor_parallelism") = 1, py::arg("pipeline_parallelism") = 1, py::arg("context_parallelism") = 1,
|
|
py::arg("rank") = 0, py::arg("gpus_per_node") = tr::WorldConfig::kDefaultGpusPerNode,
|
|
py::arg("device_ids") = py::none(), py::arg("enable_attention_dp") = false)
|
|
.def_property_readonly("size", &tr::WorldConfig::getSize)
|
|
.def_property_readonly("tensor_parallelism", &tr::WorldConfig::getTensorParallelism)
|
|
.def_property_readonly("pipeline_parallelism", &tr::WorldConfig::getPipelineParallelism)
|
|
.def_property_readonly("context_parallelism", &tr::WorldConfig::getContextParallelism)
|
|
.def_property_readonly("is_tensor_parallel", &tr::WorldConfig::isTensorParallel)
|
|
.def_property_readonly("is_pipeline_parallel", &tr::WorldConfig::isPipelineParallel)
|
|
.def_property_readonly("is_context_parallel", &tr::WorldConfig::isContextParallel)
|
|
.def_property_readonly("rank", &tr::WorldConfig::getRank)
|
|
.def_property_readonly("local_rank", &tr::WorldConfig::getLocalRank)
|
|
.def_property_readonly("node_rank", &tr::WorldConfig::getNodeRank)
|
|
.def_property_readonly("gpus_per_node", &tr::WorldConfig::getGpusPerNode)
|
|
.def_property_readonly("gpus_per_group", &tr::WorldConfig::getGpusPerGroup)
|
|
.def_property_readonly("device", &tr::WorldConfig::getDevice)
|
|
.def_property_readonly("pipeline_parallel_rank", &tr::WorldConfig::getPipelineParallelRank)
|
|
.def_property_readonly("tensor_parallel_rank", &tr::WorldConfig::getTensorParallelRank)
|
|
.def_property_readonly("context_parallel_rank", &tr::WorldConfig::getContextParallelRank)
|
|
.def_property_readonly("enable_attention_dp", &tr::WorldConfig::enableAttentionDP)
|
|
.def_static("mpi",
|
|
py::overload_cast<SizeType32, std::optional<SizeType32>, std::optional<SizeType32>,
|
|
std::optional<SizeType32>, std::optional<std::vector<SizeType32>> const&, bool>(&tr::WorldConfig::mpi),
|
|
py::arg("gpus_per_node") = tr::WorldConfig::kDefaultGpusPerNode, py::arg("tensor_parallelism") = py::none(),
|
|
py::arg("pipeline_parallelism") = py::none(), py::arg("context_parallelism") = py::none(),
|
|
py::arg("device_ids") = py::none(), py::arg("enable_attention_dp") = false);
|
|
|
|
auto SamplingConfigGetState = [](tr::SamplingConfig const& config) -> py::tuple
|
|
{
|
|
return py::make_tuple(config.beamWidth, config.temperature, config.minLength, config.repetitionPenalty,
|
|
config.presencePenalty, config.frequencyPenalty, config.topK, config.topP, config.randomSeed,
|
|
config.topPDecay, config.topPMin, config.topPResetIds, config.beamSearchDiversityRate, config.lengthPenalty,
|
|
config.earlyStopping, config.noRepeatNgramSize, config.numReturnSequences, config.minP,
|
|
config.beamWidthArray);
|
|
};
|
|
auto SamplingConfigSetState = [](py::tuple t) -> tr::SamplingConfig
|
|
{
|
|
if (t.size() != 19)
|
|
{
|
|
throw std::runtime_error("Invalid SamplingConfig state!");
|
|
}
|
|
|
|
tr::SamplingConfig config;
|
|
config.beamWidth = t[0].cast<SizeType32>();
|
|
config.temperature = t[1].cast<OptVec<float>>();
|
|
config.minLength = t[2].cast<OptVec<SizeType32>>();
|
|
config.repetitionPenalty = t[3].cast<OptVec<float>>();
|
|
config.presencePenalty = t[4].cast<OptVec<float>>();
|
|
config.frequencyPenalty = t[5].cast<OptVec<float>>();
|
|
config.topK = t[6].cast<OptVec<SizeType32>>();
|
|
config.topP = t[7].cast<OptVec<float>>();
|
|
config.randomSeed = t[8].cast<OptVec<uint64_t>>();
|
|
config.topPDecay = t[9].cast<OptVec<float>>();
|
|
config.topPMin = t[10].cast<OptVec<float>>();
|
|
config.topPResetIds = t[11].cast<OptVec<TokenIdType>>();
|
|
config.beamSearchDiversityRate = t[12].cast<OptVec<float>>();
|
|
config.lengthPenalty = t[13].cast<OptVec<float>>();
|
|
config.earlyStopping = t[14].cast<OptVec<SizeType32>>();
|
|
config.noRepeatNgramSize = t[15].cast<OptVec<SizeType32>>();
|
|
config.numReturnSequences = t[16].cast<SizeType32>();
|
|
config.minP = t[17].cast<OptVec<float>>();
|
|
config.beamWidthArray = t[18].cast<OptVec<std::vector<SizeType32>>>();
|
|
|
|
return config;
|
|
};
|
|
|
|
py::classh<tr::SamplingConfig>(m, "SamplingConfig")
|
|
.def(py::init<SizeType32>(), py::arg("beam_width") = 1)
|
|
.def(py::init<tle::SamplingConfig, std::optional<tle::ExternalDraftTokensConfig>>(),
|
|
py::arg("executor_sample_config"), py::arg("external_draft_tokens_config") = std::nullopt)
|
|
.def_readwrite("beam_width", &tr::SamplingConfig::beamWidth)
|
|
.def_readwrite("temperature", &tr::SamplingConfig::temperature)
|
|
.def_readwrite("min_length", &tr::SamplingConfig::minLength)
|
|
.def_readwrite("repetition_penalty", &tr::SamplingConfig::repetitionPenalty)
|
|
.def_readwrite("presence_penalty", &tr::SamplingConfig::presencePenalty)
|
|
.def_readwrite("frequency_penalty", &tr::SamplingConfig::frequencyPenalty)
|
|
.def_readwrite("top_k", &tr::SamplingConfig::topK)
|
|
.def_readwrite("top_p", &tr::SamplingConfig::topP)
|
|
.def_readwrite("random_seed", &tr::SamplingConfig::randomSeed)
|
|
.def_readwrite("top_p_decay", &tr::SamplingConfig::topPDecay)
|
|
.def_readwrite("top_p_min", &tr::SamplingConfig::topPMin)
|
|
.def_readwrite("top_p_reset_ids", &tr::SamplingConfig::topPResetIds)
|
|
.def_readwrite("beam_search_diversity_rate", &tr::SamplingConfig::beamSearchDiversityRate)
|
|
.def_readwrite("length_penalty", &tr::SamplingConfig::lengthPenalty)
|
|
.def_readwrite("early_stopping", &tr::SamplingConfig::earlyStopping)
|
|
.def_readwrite("no_repeat_ngram_size", &tr::SamplingConfig::noRepeatNgramSize)
|
|
.def_readwrite("num_return_sequences", &tr::SamplingConfig::numReturnSequences)
|
|
.def_readwrite("min_p", &tr::SamplingConfig::minP)
|
|
.def_readwrite("beam_width_array", &tr::SamplingConfig::beamWidthArray)
|
|
.def_readwrite("normalize_log_probs", &tr::SamplingConfig::normalizeLogProbs)
|
|
.def(py::pickle(SamplingConfigGetState, SamplingConfigSetState))
|
|
.def("__eq__", &tr::SamplingConfig::operator==);
|
|
|
|
m.def("make_sampling_config", &makeSamplingConfig, py::arg("configs"));
|
|
|
|
py::class_<tr::GptJsonConfig>(m, "GptJsonConfig")
|
|
.def(py::init<std::string, std::string, std::string, SizeType32, SizeType32, SizeType32, SizeType32,
|
|
tr::ModelConfig, std::optional<tr::RuntimeDefaults>>(),
|
|
py::arg("name"), py::arg("version"), py::arg("precision"), py::arg("tensor_parallelism"),
|
|
py::arg("pipeline_parallelism"), py::arg("context_parallelism"), py::arg("gpus_per_node"),
|
|
py::arg("model_config"), py::arg("runtime_defaults") = py::none())
|
|
.def_static("parse", py::overload_cast<std::string const&>(&tr::GptJsonConfig::parse), py::arg("json"))
|
|
.def_static(
|
|
"parse_file", py::overload_cast<std::filesystem::path const&>(&tr::GptJsonConfig::parse), py::arg("path"))
|
|
.def_property_readonly("model_config", &tr::GptJsonConfig::getModelConfig)
|
|
.def_property_readonly("name", &tr::GptJsonConfig::getName)
|
|
.def_property_readonly("version", &tr::GptJsonConfig::getVersion)
|
|
.def_property_readonly("precision", &tr::GptJsonConfig::getPrecision)
|
|
.def_property_readonly("tensor_parallelism", &tr::GptJsonConfig::getTensorParallelism)
|
|
.def_property_readonly("pipeline_parallelism", &tr::GptJsonConfig::getPipelineParallelism)
|
|
.def_property_readonly("context_parallelism", &tr::GptJsonConfig::getContextParallelism)
|
|
.def_property_readonly("gpus_per_node", &tr::GptJsonConfig::getGpusPerNode)
|
|
.def_property_readonly("world_size", &tr::GptJsonConfig::getWorldSize)
|
|
.def_property_readonly("runtime_defaults", &tr::GptJsonConfig::getRuntimeDefaults)
|
|
.def("engine_filename",
|
|
py::overload_cast<tr::WorldConfig const&, std::string const&>(
|
|
&tr::GptJsonConfig::engineFilename, py::const_),
|
|
py::arg("world_config"), py::arg("model"))
|
|
.def("engine_filename",
|
|
py::overload_cast<tr::WorldConfig const&>(&tr::GptJsonConfig::engineFilename, py::const_),
|
|
py::arg("world_config"));
|
|
|
|
py::enum_<tb::LlmRequestState>(m, "LlmRequestState")
|
|
.value("UNKNOWN", tb::LlmRequestState::kUNKNOWN)
|
|
.value("ENCODER_INIT", tb::LlmRequestState::kENCODER_INIT)
|
|
.value("CONTEXT_INIT", tb::LlmRequestState::kCONTEXT_INIT)
|
|
.value("GENERATION_IN_PROGRESS", tb::LlmRequestState::kGENERATION_IN_PROGRESS)
|
|
.value("GENERATION_TO_COMPLETE", tb::LlmRequestState::kGENERATION_TO_COMPLETE)
|
|
.value("GENERATION_COMPLETE", tb::LlmRequestState::kGENERATION_COMPLETE)
|
|
.value("DISAGG_GENERATION_INIT", tb::LlmRequestState::kDISAGG_GENERATION_INIT)
|
|
.value("DISAGG_CONTEXT_TRANS_IN_PROGRESS", tb::LlmRequestState::kDISAGG_CONTEXT_TRANS_IN_PROGRESS)
|
|
.value("DISAGG_CONTEXT_COMPLETE", tb::LlmRequestState::kDISAGG_CONTEXT_COMPLETE)
|
|
.value("DISAGG_GENERATION_TRANS_IN_PROGRESS", tb::LlmRequestState::kDISAGG_GENERATION_TRANS_IN_PROGRESS)
|
|
.value("DISAGG_GENERATION_TRANS_COMPLETE", tb::LlmRequestState::kDISAGG_GENERATION_TRANS_COMPLETE)
|
|
.value("DISAGG_CONTEXT_INIT_AND_TRANS", tb::LlmRequestState::kDISAGG_CONTEXT_INIT_AND_TRANS);
|
|
|
|
py::class_<tr::MemoryCounters>(m, "MemoryCounters")
|
|
.def_static("instance", &tr::MemoryCounters::getInstance, py::return_value_policy::reference)
|
|
.def_property_readonly("gpu", &tr::MemoryCounters::getGpu)
|
|
.def_property_readonly("cpu", &tr::MemoryCounters::getCpu)
|
|
.def_property_readonly("pinned", &tr::MemoryCounters::getPinned)
|
|
.def_property_readonly("uvm", &tr::MemoryCounters::getUVM);
|
|
|
|
tensorrt_llm::pybind::runtime::initBindings(mInternalRuntime);
|
|
tensorrt_llm::pybind::testing::initBindings(mInternalTesting);
|
|
tpb::initBindings(mInternalBatchManager);
|
|
tb::kv_cache_manager::KVCacheManagerBindings::initBindings(mInternalBatchManager);
|
|
tb::BasePeftCacheManagerBindings::initBindings(mInternalBatchManager);
|
|
tb::CacheTransceiverBindings::initBindings(mInternalBatchManager);
|
|
|
|
auto mInternalAlgorithms = mInternal.def_submodule("algorithms", "Algorithms internal bindings");
|
|
tpb::algorithms::initBindings(mInternalAlgorithms);
|
|
|
|
auto mUserbuffers = mInternal.def_submodule("userbuffers", "User buffers internal bindings");
|
|
tensorrt_llm::kernels::userbuffers::UserBufferBindings::initBindings(mUserbuffers);
|
|
|
|
// NVLS allocators
|
|
py::class_<tr::IpcNvlsHandle>(m, "IpcNvlsHandle")
|
|
.def(py::init<>())
|
|
.def_readwrite("uc_ptr", &tr::IpcNvlsHandle::uc_ptr)
|
|
.def_readwrite("mc_ptr", &tr::IpcNvlsHandle::mc_ptr)
|
|
.def_readwrite("size", &tr::IpcNvlsHandle::size)
|
|
.def("get_ipc_ptrs",
|
|
[](tr::IpcNvlsHandle& self) { return reinterpret_cast<uintptr_t>(self.ipc_uc_ptrs.data()); });
|
|
|
|
m.def("ipc_nvls_allocate", &tr::ipcNvlsAllocate, py::return_value_policy::reference);
|
|
m.def("ipc_nvls_free", &tr::ipcNvlsFree);
|
|
m.def("ipc_nvls_supported", &tr::ipcNvlsSupported);
|
|
}
|