mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
431 lines
29 KiB
C++
431 lines
29 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 2022-2023 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 <memory>
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#include <pybind11/functional.h>
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#include <pybind11/operators.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/pybind/batch_manager/gptManager.h"
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#include "tensorrt_llm/pybind/batch_manager/inferenceRequest.h"
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#include "tensorrt_llm/pybind/batch_manager/llmRequest.h"
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#include "tensorrt_llm/pybind/batch_manager/namedTensor.h"
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#include "tensorrt_llm/pybind/runtime/generationInput.h"
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#include "tensorrt_llm/pybind/runtime/generationOutput.h"
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#include "tensorrt_llm/pybind/utils/pathCaster.h"
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#include "tensorrt_llm/batch_manager/BatchManager.h"
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#include "tensorrt_llm/batch_manager/batchScheduler.h"
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#include "tensorrt_llm/batch_manager/kvCacheConfig.h"
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#include "tensorrt_llm/batch_manager/trtGptModelOptionalParams.h"
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#include "tensorrt_llm/common/quantization.h"
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#include "tensorrt_llm/runtime/common.h"
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#include "tensorrt_llm/runtime/gptJsonConfig.h"
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#include "tensorrt_llm/runtime/gptSession.h"
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#include "tensorrt_llm/runtime/samplingConfig.h"
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namespace py = pybind11;
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namespace tb = tensorrt_llm::batch_manager;
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namespace tbb = tensorrt_llm::batch_manager::batch_scheduler;
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namespace tbk = tensorrt_llm::batch_manager::kv_cache_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 tpr = tensorrt_llm::pybind::runtime;
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using SizeType = tr::SizeType;
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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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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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py::class_<tpr::PromptTuningParams>(m, "PromptTuningParams")
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.def(py::init<tpr::PromptTuningParams::TensorPtr, tpr::PromptTuningParams::TensorPtr,
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tpr::PromptTuningParams::TensorPtr>(),
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py::arg("embedding_table") = py::none(), py::arg("tasks") = py::none(), py::arg("vocab_size") = py::none())
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.def_readwrite("embedding_table", &tpr::PromptTuningParams::embeddingTable)
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.def_readwrite("tasks", &tpr::PromptTuningParams::tasks)
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.def_readwrite("vocab_size", &tpr::PromptTuningParams::vocabSize)
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.def_readwrite("prompt_tuning_enabled", &tpr::PromptTuningParams::promptTuningEnabled);
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py::class_<tpr::GenerationInput>(m, "GenerationInput")
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.def(py::init<SizeType, SizeType, tpr::GenerationInput::TensorPtr, tpr::GenerationInput::TensorPtr, bool>(),
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py::arg("end_id"), py::arg("pad_id"), py::arg("ids"), py::arg("lengths"), py::arg("packed") = false)
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.def_readwrite("end_id", &tpr::GenerationInput::endId)
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.def_readwrite("pad_id", &tpr::GenerationInput::padId)
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.def_readwrite("ids", &tpr::GenerationInput::ids)
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.def_readwrite("lengths", &tpr::GenerationInput::lengths)
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.def_readwrite("packed", &tpr::GenerationInput::packed)
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.def_readwrite("embedding_bias", &tpr::GenerationInput::embeddingBias)
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.def_readwrite("bad_words_list", &tpr::GenerationInput::badWordsList)
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.def_readwrite("stop_words_list", &tpr::GenerationInput::stopWordsList)
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.def_readwrite("max_new_tokens", &tpr::GenerationInput::maxNewTokens)
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.def_readwrite("prompt_tuning_params", &tpr::GenerationInput::promptTuningParams);
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py::class_<tpr::GenerationOutput>(m, "GenerationOutput")
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.def(py::init<tpr::GenerationOutput::TensorPtr, tpr::GenerationOutput::TensorPtr>(), py::arg("ids"),
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py::arg("lengths"))
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.def_readwrite("ids", &tpr::GenerationOutput::ids)
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.def_readwrite("lengths", &tpr::GenerationOutput::lengths)
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.def_readwrite("log_probs", &tpr::GenerationOutput::logProbs)
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.def_readwrite("context_logits", &tpr::GenerationOutput::contextLogits)
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.def_readwrite("on_token_generated", &tpr::GenerationOutput::onTokenGenerated);
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py::class_<tbk::KvCacheConfig>(m, "KvCacheConfig")
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.def(py::init<std::optional<SizeType>, std::optional<SizeType>, std::optional<float>>(),
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py::arg("max_tokens") = py::none(), py::arg("max_kv_cache_length") = py::none(),
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py::arg("free_gpu_memory_fraction") = py::none())
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.def_readwrite("max_tokens", &tbk::KvCacheConfig::maxTokens)
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.def_readwrite("max_kv_cache_length", &tbk::KvCacheConfig::maxKvCacheLength)
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.def_readwrite("free_gpu_memory_fraction", &tbk::KvCacheConfig::freeGpuMemoryFraction);
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py::class_<tr::GptSession::Config>(m, "GptSessionConfig")
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.def(py::init<SizeType, SizeType, SizeType>(), py::arg("max_batch_size"), py::arg("max_beam_width"),
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py::arg("max_sequence_length"))
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.def_readwrite("max_batch_size", &tr::GptSession::Config::maxBatchSize)
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.def_readwrite("max_beam_width", &tr::GptSession::Config::maxBeamWidth)
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.def_readwrite("max_sequence_length", &tr::GptSession::Config::maxSequenceLength)
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.def_readwrite("decoder_per_request", &tr::GptSession::Config::decoderPerRequest)
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.def_readwrite("cuda_graph_mode", &tr::GptSession::Config::cudaGraphMode)
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.def_readwrite("ctx_micro_batch_size", &tr::GptSession::Config::ctxMicroBatchSize)
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.def_readwrite("gen_micro_batch_size", &tr::GptSession::Config::genMicroBatchSize)
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.def_readwrite("kv_cache_config", &tr::GptSession::Config::kvCacheConfig);
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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::GptModelConfig::ModelVariant>(m, "GptModelVariant")
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.value("GPT", tr::GptModelConfig::ModelVariant::kGpt)
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.value("GLM", tr::GptModelConfig::ModelVariant::kGlm);
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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_kv_cache_quant", &tc::QuantMode::hasKvCacheQuant)
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.def_static("from_description", &tc::QuantMode::fromDescription, py::arg("quantize_weights") = false,
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py::arg("quantize_activations") = false, py::arg("per_token") = false, py::arg("per_channel") = false,
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py::arg("use_int4_weights") = false, py::arg("use_int8_kv_cache") = false,
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py::arg("use_fp8_kv_kache") = false, py::arg("use_fp8_qdq") = false)
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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::GptModelConfig>(m, "GptModelConfig")
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.def(py::init<SizeType, SizeType, SizeType, SizeType, nvinfer1::DataType>(), py::arg("vocab_size"),
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py::arg("num_layers"), py::arg("num_heads"), py::arg("hidden_size"), py::arg("data_type"))
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.def_property_readonly("vocab_size", &tr::GptModelConfig::getVocabSize)
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.def("vocab_size_padded", &tr::GptModelConfig::getVocabSizePadded, py::arg("world_size"))
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.def("num_layers", &tr::GptModelConfig::getNbLayers, py::arg("pipeline_parallelism") = 1)
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.def_property_readonly("num_heads", &tr::GptModelConfig::getNbHeads)
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.def_property_readonly("hidden_size", &tr::GptModelConfig::getHiddenSize)
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.def_property_readonly("size_per_head", &tr::GptModelConfig::getSizePerHead)
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.def_property_readonly("data_type", &tr::GptModelConfig::getDataType)
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.def_property("num_kv_heads", &tr::GptModelConfig::getNbKvHeads, &tr::GptModelConfig::setNbKvHeads)
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.def_property("use_gpt_attention_plugin",
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py::overload_cast<>(&tr::GptModelConfig::useGptAttentionPlugin, py::const_),
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py::overload_cast<bool>(&tr::GptModelConfig::useGptAttentionPlugin))
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.def_property("use_packed_input", py::overload_cast<>(&tr::GptModelConfig::usePackedInput, py::const_),
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py::overload_cast<bool>(&tr::GptModelConfig::usePackedInput))
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.def_property("use_paged_kv_cache", py::overload_cast<>(&tr::GptModelConfig::usePagedKvCache, py::const_),
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py::overload_cast<bool>(&tr::GptModelConfig::usePagedKvCache))
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.def_property(
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"tokens_per_block", &tr::GptModelConfig::getTokensPerBlock, &tr::GptModelConfig::setTokensPerBlock)
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.def_property("quant_mode", &tr::GptModelConfig::getQuantMode, &tr::GptModelConfig::setQuantMode)
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.def_property_readonly("supports_inflight_batching", &tr::GptModelConfig::supportsInflightBatching)
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.def_property("max_batch_size", &tr::GptModelConfig::getMaxBatchSize, &tr::GptModelConfig::setMaxBatchSize)
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.def_property("max_input_len", &tr::GptModelConfig::getMaxInputLen, &tr::GptModelConfig::setMaxInputLen)
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.def_property("max_output_len", &tr::GptModelConfig::getMaxOutputLen, &tr::GptModelConfig::setMaxOutputLen)
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.def_property("max_num_tokens", &tr::GptModelConfig::getMaxNumTokens, &tr::GptModelConfig::setMaxNumTokens)
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.def_property("compute_context_logits",
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py::overload_cast<>(&tr::GptModelConfig::computeContextLogits, py::const_),
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py::overload_cast<bool>(&tr::GptModelConfig::computeContextLogits))
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.def_property("compute_generation_logits",
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py::overload_cast<>(&tr::GptModelConfig::computeGenerationLogits, py::const_),
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py::overload_cast<bool>(&tr::GptModelConfig::computeGenerationLogits))
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.def_property("model_variant", &tr::GptModelConfig::getModelVariant, &tr::GptModelConfig::setModelVariant)
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.def_property("use_custom_all_reduce", py::overload_cast<>(&tr::GptModelConfig::useCustomAllReduce, py::const_),
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py::overload_cast<bool>(&tr::GptModelConfig::useCustomAllReduce));
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py::class_<tr::WorldConfig>(m, "WorldConfig")
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.def(py::init<SizeType, SizeType, SizeType, SizeType>(), py::arg("tensor_parallelism") = 1,
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py::arg("pipeline_parallelism") = 1, py::arg("rank") = 0,
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py::arg("gpus_per_node") = tr::WorldConfig::kDefaultGpusPerNode)
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.def_property_readonly("size", &tr::WorldConfig::getSize)
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.def_property_readonly("tensor_parallelism", &tr::WorldConfig::getTensorParallelism)
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.def_property_readonly("pipeline_parallelism", &tr::WorldConfig::getPipelineParallelism)
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.def_property_readonly("is_tensor_parallel", &tr::WorldConfig::isTensorParallel)
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.def_property_readonly("is_pipeline_parallel", &tr::WorldConfig::isPipelineParallel)
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.def_property_readonly("rank", &tr::WorldConfig::getRank)
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.def_property_readonly("gpus_per_node", &tr::WorldConfig::getGpusPerNode)
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.def_property_readonly("device", &tr::WorldConfig::getDevice)
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.def_property_readonly("pipeline_parallel_rank", &tr::WorldConfig::getPipelineParallelRank)
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.def_property_readonly("tensor_parallel_rank", &tr::WorldConfig::getTensorParallelRank)
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.def_static("mpi",
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py::overload_cast<SizeType, std::optional<SizeType>, std::optional<SizeType>>(&tr::WorldConfig::mpi),
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py::arg("gpus_per_node") = tr::WorldConfig::kDefaultGpusPerNode, py::arg("tensor_parallelism") = py::none(),
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py::arg("pipeline_parallelism") = py::none());
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py::class_<tr::SamplingConfig>(m, "SamplingConfig")
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.def(py::init<SizeType>(), py::arg("beam_width") = 1)
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.def_readwrite("beam_width", &tr::SamplingConfig::beamWidth)
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.def_readwrite("temperature", &tr::SamplingConfig::temperature)
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.def_readwrite("min_length", &tr::SamplingConfig::minLength)
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.def_readwrite("repetition_penalty", &tr::SamplingConfig::repetitionPenalty)
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.def_readwrite("presence_penalty", &tr::SamplingConfig::presencePenalty)
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.def_readwrite("top_k", &tr::SamplingConfig::topK)
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.def_readwrite("top_p", &tr::SamplingConfig::topP)
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.def_readwrite("random_seed", &tr::SamplingConfig::randomSeed)
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.def_readwrite("top_p_decay", &tr::SamplingConfig::topPDecay)
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.def_readwrite("top_p_min", &tr::SamplingConfig::topPMin)
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.def_readwrite("top_p_reset_ids", &tr::SamplingConfig::topPResetIds)
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.def_readwrite("beam_search_diversity_rate", &tr::SamplingConfig::beamSearchDiversityRate)
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.def_readwrite("length_penalty", &tr::SamplingConfig::lengthPenalty);
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py::class_<tr::GptJsonConfig>(m, "GptJsonConfig")
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.def(py::init<std::string, std::string, SizeType, SizeType, tr::GptModelConfig>(), py::arg("name"),
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py::arg("precision"), py::arg("tensor_parallelism"), py::arg("pipeline_parallelism"),
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py::arg("model_config"))
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.def_static("parse", py::overload_cast<std::string const&>(&tr::GptJsonConfig::parse), py::arg("json"))
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.def_static(
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"parse_file", py::overload_cast<std::filesystem::path const&>(&tr::GptJsonConfig::parse), py::arg("path"))
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.def_property_readonly("model_config", &tr::GptJsonConfig::getModelConfig)
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.def_property_readonly("name", &tr::GptJsonConfig::getName)
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.def_property_readonly("precision", &tr::GptJsonConfig::getPrecision)
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.def_property_readonly("tensor_parallelism", &tr::GptJsonConfig::getTensorParallelism)
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.def_property_readonly("pipeline_parallelism", &tr::GptJsonConfig::getPipelineParallelism)
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.def_property_readonly("world_size", &tr::GptJsonConfig::getWorldSize)
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.def("engine_filename",
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py::overload_cast<const tr::WorldConfig&, const std::string&>(
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&tr::GptJsonConfig::engineFilename, py::const_),
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py::arg("world_config"), py::arg("model"))
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.def("engine_filename",
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py::overload_cast<const tr::WorldConfig&>(&tr::GptJsonConfig::engineFilename, py::const_),
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py::arg("world_config"));
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py::class_<tr::GptSession>(m, "GptSession")
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.def(py::init<tr::GptSession::Config, tr::GptModelConfig, tr::WorldConfig, std::string>(), py::arg("config"),
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py::arg("model_config"), py::arg("world_config"), py::arg("engine_file"))
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.def_property_readonly("model_config", &tr::GptSession::getModelConfig)
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.def_property_readonly("world_config", &tr::GptSession::getWorldConfig)
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.def_property_readonly("device", &tr::GptSession::getDevice)
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.def(
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"generate",
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[](tr::GptSession& self, tpr::GenerationOutput& outputs, tpr::GenerationInput const& inputs,
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tr::SamplingConfig const& samplingConfig)
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{ self.generate(*outputs.toTrtLlm(), *inputs.toTrtLlm(), samplingConfig); },
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py::arg("outputs"), py::arg("inputs"), py::arg("sampling_config"));
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py::enum_<tb::LlmRequestState_t>(m, "LlmRequestState")
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.value("REQUEST_STATE_UNKNOWN", tb::LlmRequestState_t::REQUEST_STATE_UNKNOWN)
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.value("REQUEST_STATE_CONTEXT_INIT", tb::LlmRequestState_t::REQUEST_STATE_CONTEXT_INIT)
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.value("REQUEST_STATE_GENERATION_IN_PROGRESS", tb::LlmRequestState_t::REQUEST_STATE_GENERATION_IN_PROGRESS)
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.value("REQUEST_STATE_GENERATION_COMPLETE", tb::LlmRequestState_t::REQUEST_STATE_GENERATION_COMPLETE);
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using LlmRequest = tpb::LlmRequest;
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py::class_<LlmRequest>(m, "LlmRequest")
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.def(py::init<LlmRequest::RequestIdType, LlmRequest::SizeType, std::vector<LlmRequest::TokenIdType>,
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tr::SamplingConfig, bool, std::optional<LlmRequest::SizeType>, std::optional<LlmRequest::SizeType>,
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std::optional<LlmRequest::TensorPtr>, std::optional<LlmRequest::TensorPtr>,
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std::optional<LlmRequest::TensorPtr>, std::optional<LlmRequest::TensorPtr>,
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std::optional<LlmRequest::SizeType>, bool, std::optional<LlmRequest::VecTokens>,
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std::optional<LlmRequest::TensorPtr>>(),
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py::arg("request_id"), py::arg("max_new_tokens"), py::arg("input_tokens"), py::arg("sampling_config"),
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py::arg("is_streaming"), py::arg("end_id") = std::nullopt, py::arg("pad_id") = std::nullopt,
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py::arg("embedding_bias") = std::nullopt, py::arg("bad_words_list") = std::nullopt,
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py::arg("stop_words_list") = std::nullopt, py::arg("prompt_embedding_table") = std::nullopt,
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py::arg("prompt_vocab_size") = std::nullopt, py::arg("return_log_probs") = false,
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py::arg("draft_tokens") = std::nullopt, py::arg("draft_logits") = std::nullopt)
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.def("get_num_tokens", &LlmRequest::getNumTokens, py::arg("beam"))
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.def_property_readonly("max_beam_num_tokens", &LlmRequest::getMaxBeamNumTokens)
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.def("get_token", &LlmRequest::getToken, py::arg("beam"), py::arg("pos"))
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.def("get_tokens", &LlmRequest::getTokens, py::arg("beam"))
|
|
.def_property_readonly("max_num_generated_tokens", &LlmRequest::getMaxNumGeneratedTokens)
|
|
.def("add_new_token", &LlmRequest::addNewToken, py::arg("token"), py::arg("beam"))
|
|
.def("add_new_tokens", &LlmRequest::addNewTokens, py::arg("beam_tokens"))
|
|
.def("set_generated_tokens", &LlmRequest::setGeneratedTokens, py::arg("generated_beam_tokens"))
|
|
.def("pause", &LlmRequest::pause, py::arg("max_input_len"))
|
|
.def_property("max_sent_token_pos", &LlmRequest::getMaxSentTokenPos, &LlmRequest::setMaxSentTokenPos)
|
|
.def_property_readonly("prompt_embedding_table", &LlmRequest::getPromptEmbeddingTable)
|
|
.def_property_readonly("prompt_vocab_size", &LlmRequest::getPromptVocabSize)
|
|
.def_property_readonly("embedding_bias", &LlmRequest::getEmbeddingBias)
|
|
.def_property_readonly("bad_words_list", &LlmRequest::getBadWordsList)
|
|
.def_property_readonly("stop_words_list", &LlmRequest::getStopWordsList)
|
|
.def_readwrite("request_id", &LlmRequest::mRequestId)
|
|
.def_readwrite("prompt_len", &LlmRequest::mPromptLen)
|
|
.def_readwrite("max_new_tokens", &LlmRequest::mMaxNewTokens)
|
|
.def_readwrite("sampling_config", &LlmRequest::mSamplingConfig)
|
|
.def_readwrite("state", &LlmRequest::mState)
|
|
.def_readwrite("is_streaming", &LlmRequest::mIsStreaming)
|
|
.def_readwrite("end_id", &LlmRequest::mEndId)
|
|
.def_readwrite("pad_id", &LlmRequest::mPadId)
|
|
.def_readwrite("batch_slot", &LlmRequest::mBatchSlot)
|
|
.def_property_readonly("return_log_probs", &LlmRequest::returnLogProbs)
|
|
.def_property_readonly("log_probs", py::overload_cast<>(&LlmRequest::getLogProbs, py::const_))
|
|
.def("get_log_probs", py::overload_cast<SizeType>(&LlmRequest::getLogProbs, py::const_))
|
|
.def("set_log_probs", &LlmRequest::setLogProbs, py::arg("log_probs"), py::arg("beam"))
|
|
.def_property_readonly("cum_log_probs", &LlmRequest::getCumLogProbs)
|
|
.def("set_cum_log_prob", &LlmRequest::setCumLogProb, py::arg("cum_log_prob"), py::arg("beam"))
|
|
.def_property_readonly("orig_prompt_len", &LlmRequest::getOrigPromptLen)
|
|
.def("has_draft_tokens", &LlmRequest::hasDraftTokens)
|
|
.def_property(
|
|
"draft_tokens", [](LlmRequest& self) { return *self.getDraftTokens(); },
|
|
[](LlmRequest& self, LlmRequest::VecTokens& draftTokens)
|
|
{ self.setDraftTokens(std::make_shared<LlmRequest::VecTokens>(std::move(draftTokens))); })
|
|
.def_property(
|
|
"draft_logits", [](LlmRequest& self) { return self.getDraftLogits(); },
|
|
[](LlmRequest& self, LlmRequest::TensorPtr& logits)
|
|
{ self.setDraftLogits(std::make_optional<LlmRequest::TensorPtr>(logits)); });
|
|
|
|
py::class_<tpb::NamedTensor>(m, "NamedTensor")
|
|
.def(py::init<tpb::NamedTensor::TensorPtr, std::string>(), py::arg("tensor"), py::arg("name"))
|
|
.def_readwrite("tensor", &tpb::NamedTensor::tensor)
|
|
.def_readonly("name", &tpb::NamedTensor::name);
|
|
|
|
auto tensorNames = m.def_submodule("tensor_names");
|
|
// Input tensor names
|
|
tensorNames.attr("INPUT_IDS") = py::str(tb::inference_request::kInputIdsTensorName);
|
|
tensorNames.attr("DRAFT_INPUT_IDS") = py::str(tb::inference_request::kDraftInputIdsTensorName);
|
|
tensorNames.attr("DRAFT_LOGITS") = py::str(tb::inference_request::kDraftLogitsTensorName);
|
|
tensorNames.attr("MAX_NEW_TOKENS") = py::str(tb::inference_request::kMaxNewTokensTensorName);
|
|
tensorNames.attr("BEAM_WIDTH") = py::str(tb::inference_request::kBeamWidthTensorName);
|
|
tensorNames.attr("END_ID") = py::str(tb::inference_request::kEndIdTensorName);
|
|
tensorNames.attr("PAD_ID") = py::str(tb::inference_request::kPadIdTensorName);
|
|
tensorNames.attr("BAD_WORDS_LIST") = py::str(tb::inference_request::kBadWordsListTensorName);
|
|
tensorNames.attr("STOP_WORDS_LIST") = py::str(tb::inference_request::kStopWordsListTensorName);
|
|
tensorNames.attr("EMBEDDING_BIAS") = py::str(tb::inference_request::kEmbeddingBiasTensorName);
|
|
tensorNames.attr("TEMPERATURE") = py::str(tb::inference_request::kTemperatureTensorName);
|
|
tensorNames.attr("RUNTIME_TOP_K") = py::str(tb::inference_request::kRuntimeTopKTensorName);
|
|
tensorNames.attr("RUNTIME_TOP_P") = py::str(tb::inference_request::kRuntimeTopPTensorName);
|
|
tensorNames.attr("LENGTH_PENALTY") = py::str(tb::inference_request::kLengthPenaltyTensorName);
|
|
tensorNames.attr("REPETITION_PENALTY") = py::str(tb::inference_request::kRepetitionPenaltyTensorName);
|
|
tensorNames.attr("MIN_LENGTH") = py::str(tb::inference_request::kMinLengthTensorName);
|
|
tensorNames.attr("PRESENCE_PENALTY") = py::str(tb::inference_request::kPresencePenaltyTensorName);
|
|
tensorNames.attr("RANDOM_SEED") = py::str(tb::inference_request::kRandomSeedTensorName);
|
|
tensorNames.attr("RETURN_LOG_PROBS") = py::str(tb::inference_request::kReturnLogProbsTensorName);
|
|
tensorNames.attr("PROMPT_EMBEDDING_TABLE") = py::str(tb::inference_request::kPromptEmbeddingTableName);
|
|
tensorNames.attr("PROMPT_VOCAB_SIZE") = py::str(tb::inference_request::kPromptVocabSizeName);
|
|
|
|
// Output tensor names
|
|
tensorNames.attr("OUTPUT_IDS") = py::str(tb::inference_request::kOutputIdsTensorName);
|
|
tensorNames.attr("SEQUENCE_LENGTH") = py::str(tb::inference_request::kSequenceLengthTensorName);
|
|
tensorNames.attr("OUTPUT_LOG_PROBS") = py::str(tb::inference_request::kLogProbsTensorName);
|
|
tensorNames.attr("CUM_LOG_PROBS") = py::str(tb::inference_request::kCumLogProbsTensorName);
|
|
|
|
using InferenceRequest = tpb::InferenceRequest;
|
|
py::class_<InferenceRequest>(m, "InferenceRequest")
|
|
.def(py::init<uint64_t>())
|
|
.def(py::init<uint64_t, InferenceRequest::TensorMap const&>(), "deprecated: use direct tensor access instead")
|
|
.def_property("input_ids", &InferenceRequest::getInputIdsUnchecked, &InferenceRequest::setInputIds)
|
|
.def_property(
|
|
"draft_input_ids", &InferenceRequest::getDraftInputIdsUnchecked, &InferenceRequest::setDraftInputIds)
|
|
.def_property("draft_logits", &InferenceRequest::getDraftLogitsUnchecked, &InferenceRequest::setDraftLogits)
|
|
.def_property("max_new_tokens", &InferenceRequest::getMaxNewTokensUnchecked, &InferenceRequest::setMaxNewTokens)
|
|
.def_property("beam_width", &InferenceRequest::getBeamWidthUnchecked, &InferenceRequest::setBeamWidth)
|
|
.def_property("end_id", &InferenceRequest::getEndIdUnchecked, &InferenceRequest::setEndId)
|
|
.def_property("pad_id", &InferenceRequest::getPadIdUnchecked, &InferenceRequest::setPadId)
|
|
.def_property("bad_words_list", &InferenceRequest::getBadWordsListUnchecked, &InferenceRequest::setBadWordsList)
|
|
.def_property(
|
|
"stop_words_list", &InferenceRequest::getStopWordsListUnchecked, &InferenceRequest::setStopWordsList)
|
|
.def_property(
|
|
"embedding_bias", &InferenceRequest::getEmbeddingBiasUnchecked, &InferenceRequest::setEmbeddingBias)
|
|
.def_property("temperature", &InferenceRequest::getTemperatureUnchecked, &InferenceRequest::setTemperature)
|
|
.def_property("runtime_top_k", &InferenceRequest::getRuntimeTopKUnchecked, &InferenceRequest::setRuntimeTopK)
|
|
.def_property("runtime_top_p", &InferenceRequest::getRuntimeTopPUnchecked, &InferenceRequest::setRuntimeTopP)
|
|
.def_property(
|
|
"length_penalty", &InferenceRequest::getLengthPenaltyUnchecked, &InferenceRequest::setLengthPenalty)
|
|
.def_property("repetition_penalty", &InferenceRequest::getRepetitionPenaltyUnchecked,
|
|
&InferenceRequest::setRepetitionPenalty)
|
|
.def_property("min_length", &InferenceRequest::getMinLengthUnchecked, &InferenceRequest::setMinLength)
|
|
.def_property(
|
|
"presence_penalty", &InferenceRequest::getPresencePenaltyUnchecked, &InferenceRequest::setPresencePenalty)
|
|
.def_property("random_seed", &InferenceRequest::getRandomSeedUnchecked, &InferenceRequest::setRandomSeed)
|
|
.def_property(
|
|
"return_log_probs", &InferenceRequest::getReturnLogProbsUnchecked, &InferenceRequest::setReturnLogProbs)
|
|
.def_property("prompt_embedding_table", &InferenceRequest::getPromptEmbeddingTableUnchecked,
|
|
&InferenceRequest::setPromptEmbeddingTable)
|
|
.def_property(
|
|
"prompt_vocab_size", &InferenceRequest::getPromptVocabSizeUnchecked, &InferenceRequest::setPromptVocabSize)
|
|
.def_property("is_streaming", &InferenceRequest::isStreaming, &InferenceRequest::setIsStreaming)
|
|
.def_property_readonly("request_id", &InferenceRequest::getRequestId);
|
|
|
|
py::enum_<tb::TrtGptModelType>(m, "TrtGptModelType")
|
|
.value("V1", tb::TrtGptModelType::V1)
|
|
.value("InflightBatching", tb::TrtGptModelType::InflightBatching)
|
|
.value("InflightFusedBatching", tb::TrtGptModelType::InflightFusedBatching);
|
|
|
|
py::enum_<tbb::SchedulerPolicy>(m, "SchedulerPolicy")
|
|
.value("MAX_UTILIZATION", tbb::SchedulerPolicy::MAX_UTILIZATION)
|
|
.value("GUARANTEED_NO_EVICT", tbb::SchedulerPolicy::GUARANTEED_NO_EVICT);
|
|
|
|
py::class_<tb::TrtGptModelOptionalParams>(m, "TrtGptModelOptionalParams")
|
|
.def(py::init<tbk::KvCacheConfig, std::optional<SizeType>, bool>(),
|
|
py::arg("kv_cache_config") = tbk::KvCacheConfig{}, py::arg("max_num_sequences") = py::none(),
|
|
py::arg("enable_trt_overlap") = true)
|
|
.def_readwrite("kv_cache_config", &tb::TrtGptModelOptionalParams::kvCacheConfig)
|
|
.def_readwrite("max_num_sequences", &tb::TrtGptModelOptionalParams::maxNumSequences)
|
|
.def_readwrite("enable_trt_overlap", &tb::TrtGptModelOptionalParams::enableTrtOverlap);
|
|
|
|
py::class_<tpb::GptManager>(m, "GptManager")
|
|
.def(py::init<std::filesystem::path const&, tb::TrtGptModelType, int32_t, tb::batch_scheduler::SchedulerPolicy,
|
|
tpb::GetInferenceRequestsCallback, tpb::SendResponseCallback, tb::PollStopSignalCallback,
|
|
tb::ReturnBatchManagerStatsCallback, const tb::TrtGptModelOptionalParams&, std::optional<uint64_t>>(),
|
|
py::arg("trt_engine_path"), py::arg("model_type"), py::arg("max_beam_width"), py::arg("scheduler_policy"),
|
|
py::arg("get_inference_requests_cb"), py::arg("send_response_cb"), py::arg("poll_stop_signal_cb") = nullptr,
|
|
py::arg("return_batch_manager_stats_cb") = nullptr,
|
|
py::arg("optional_params") = tb::TrtGptModelOptionalParams(), py::arg("terminate_req_id") = std::nullopt)
|
|
.def("shutdown", &tpb::GptManager::exit)
|
|
.def("__enter__", &tpb::GptManager::enter)
|
|
.def("__exit__", &tpb::GptManager::exit);
|
|
}
|