mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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b57221b764
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
324 lines
21 KiB
C++
324 lines
21 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 2022-2024 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/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/kvCacheConfig.h"
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#include "tensorrt_llm/batch_manager/schedulerPolicy.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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tpr::PromptTuningParams::initBindings(m);
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tpr::GenerationInput::initBindings(m);
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tpr::GenerationOutput::initBindings(m);
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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<SizeType>, std::optional<float>,
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bool>(),
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py::arg("max_tokens") = py::none(), py::arg("max_attention_window") = py::none(),
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py::arg("sink_token_length") = py::none(), py::arg("free_gpu_memory_fraction") = py::none(),
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py::arg("enable_block_reuse") = false)
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.def_readwrite("max_tokens", &tbk::KvCacheConfig::maxTokens)
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.def_readwrite("max_attention_window", &tbk::KvCacheConfig::maxAttentionWindow)
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.def_readwrite("sink_token_length", &tbk::KvCacheConfig::sinkTokenLength)
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.def_readwrite("free_gpu_memory_fraction", &tbk::KvCacheConfig::freeGpuMemoryFraction)
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.def_readwrite("enable_block_reuse", &tbk::KvCacheConfig::enableBlockReuse);
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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_beam_width", &tr::GptModelConfig::getMaxBeamWidth, &tr::GptModelConfig::setMaxBeamWidth)
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.def_property("max_input_len", &tr::GptModelConfig::getMaxInputLen, &tr::GptModelConfig::setMaxInputLen)
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.def_property("max_seq_len", &tr::GptModelConfig::getMaxSequenceLen, &tr::GptModelConfig::getMaxSequenceLen)
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.def_property("max_num_tokens", &tr::GptModelConfig::getMaxNumTokens, &tr::GptModelConfig::setMaxNumTokens)
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.def_property("max_prompt_embedding_table_size", &tr::GptModelConfig::getMaxPromptEmbeddingTableSize,
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&tr::GptModelConfig::setMaxPromptEmbeddingTableSize)
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.def_property_readonly("use_prompt_tuning", &tr::GptModelConfig::usePromptTuning)
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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, std::optional<std::vector<SizeType>> const&>(),
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py::arg("tensor_parallelism") = 1, py::arg("pipeline_parallelism") = 1, py::arg("rank") = 0,
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py::arg("gpus_per_node") = tr::WorldConfig::kDefaultGpusPerNode, py::arg("device_ids") = py::none())
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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("gpus_per_group", &tr::WorldConfig::getGpusPerGroup)
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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>,
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std::optional<std::vector<SizeType>> const&>(&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(), py::arg("device_ids") = 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("frequency_penalty", &tr::SamplingConfig::frequencyPenalty)
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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, std::string, SizeType, SizeType, tr::GptModelConfig>(), py::arg("name"),
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py::arg("version"), 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("version", &tr::GptJsonConfig::getVersion)
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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(
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[](tr::GptSession::Config const& config, tr::GptModelConfig const& modelConfig,
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tr::WorldConfig const& worldConfig, py::bytearray const& bytes)
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{
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auto buf = static_cast<std::string>(bytes);
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return tr::GptSession{config, modelConfig, worldConfig, buf.data(), buf.size()};
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}),
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py::arg("config"), py::arg("model_config"), py::arg("world_config"), py::arg("engine_buffer"))
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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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tpb::NamedTensor::initBindings(m);
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tpb::LlmRequest::initBindings(m);
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auto tensorNames = m.def_submodule("tensor_names");
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// Input tensor names
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tensorNames.attr("INPUT_IDS") = py::str(tb::inference_request::kInputIdsTensorName);
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tensorNames.attr("DRAFT_INPUT_IDS") = py::str(tb::inference_request::kDraftInputIdsTensorName);
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tensorNames.attr("DRAFT_LOGITS") = py::str(tb::inference_request::kDraftLogitsTensorName);
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tensorNames.attr("MAX_NEW_TOKENS") = py::str(tb::inference_request::kMaxNewTokensTensorName);
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tensorNames.attr("BEAM_WIDTH") = py::str(tb::inference_request::kBeamWidthTensorName);
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tensorNames.attr("END_ID") = py::str(tb::inference_request::kEndIdTensorName);
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tensorNames.attr("PAD_ID") = py::str(tb::inference_request::kPadIdTensorName);
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tensorNames.attr("BAD_WORDS_LIST") = py::str(tb::inference_request::kBadWordsListTensorName);
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tensorNames.attr("STOP_WORDS_LIST") = py::str(tb::inference_request::kStopWordsListTensorName);
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tensorNames.attr("EMBEDDING_BIAS") = py::str(tb::inference_request::kEmbeddingBiasTensorName);
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tensorNames.attr("TEMPERATURE") = py::str(tb::inference_request::kTemperatureTensorName);
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tensorNames.attr("RUNTIME_TOP_K") = py::str(tb::inference_request::kRuntimeTopKTensorName);
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tensorNames.attr("RUNTIME_TOP_P") = py::str(tb::inference_request::kRuntimeTopPTensorName);
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tensorNames.attr("LENGTH_PENALTY") = py::str(tb::inference_request::kLengthPenaltyTensorName);
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tensorNames.attr("REPETITION_PENALTY") = py::str(tb::inference_request::kRepetitionPenaltyTensorName);
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tensorNames.attr("MIN_LENGTH") = py::str(tb::inference_request::kMinLengthTensorName);
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tensorNames.attr("PRESENCE_PENALTY") = py::str(tb::inference_request::kPresencePenaltyTensorName);
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tensorNames.attr("FREQUENCY_PENALTY") = py::str(tb::inference_request::kFrequencyPenaltyTensorName);
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tensorNames.attr("RANDOM_SEED") = py::str(tb::inference_request::kRandomSeedTensorName);
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tensorNames.attr("RETURN_LOG_PROBS") = py::str(tb::inference_request::kReturnLogProbsTensorName);
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tensorNames.attr("RETURN_CONTEXT_LOGITS") = py::str(tb::inference_request::kReturnContextLogitsTensorName);
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tensorNames.attr("RETURN_GENERATION_LOGITS") = py::str(tb::inference_request::kReturnGenerationLogitsTensorName);
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tensorNames.attr("PROMPT_EMBEDDING_TABLE") = py::str(tb::inference_request::kPromptEmbeddingTableName);
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tensorNames.attr("PROMPT_VOCAB_SIZE") = py::str(tb::inference_request::kPromptVocabSizeName);
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|
|
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// Output tensor names
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tensorNames.attr("OUTPUT_IDS") = py::str(tb::inference_request::kOutputIdsTensorName);
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tensorNames.attr("SEQUENCE_LENGTH") = py::str(tb::inference_request::kSequenceLengthTensorName);
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tensorNames.attr("OUTPUT_LOG_PROBS") = py::str(tb::inference_request::kLogProbsTensorName);
|
|
tensorNames.attr("CUM_LOG_PROBS") = py::str(tb::inference_request::kCumLogProbsTensorName);
|
|
|
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tpb::InferenceRequest::initBindings(m);
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|
|
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py::enum_<tb::TrtGptModelType>(m, "TrtGptModelType")
|
|
.value("V1", tb::TrtGptModelType::V1)
|
|
.value("InflightBatching", tb::TrtGptModelType::InflightBatching)
|
|
.value("InflightFusedBatching", tb::TrtGptModelType::InflightFusedBatching);
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|
|
|
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, bool>(),
|
|
py::arg_v("kv_cache_config", tbk::KvCacheConfig{}, "KvCacheConfig()"),
|
|
py::arg("enable_trt_overlap") = false)
|
|
.def_readwrite("kv_cache_config", &tb::TrtGptModelOptionalParams::kvCacheConfig)
|
|
.def_readwrite("enable_trt_overlap", &tb::TrtGptModelOptionalParams::enableTrtOverlap)
|
|
.def_readwrite("device_ids", &tb::TrtGptModelOptionalParams::deviceIds)
|
|
.def_readwrite("normalize_log_probs", &tb::TrtGptModelOptionalParams::normalizeLogProbs);
|
|
|
|
tpb::GptManager::initBindings(m);
|
|
}
|