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(release-notes)=

Release Notes

All published functionality in the Release Notes has been fully tested and verified with known limitations documented. To share feedback about this release, access our NVIDIA Developer Forum.

TensorRT-LLM Release Next

Announcements

  • TBD

Key Features and Enhancements

  • TBD

API Changes

  • TBD

Model Updates

  • TBD

Fixed Issues

  • TBD

Infrastructure changes

  • Base Docker image for TensorRT-LLM is updated to nvcr.io/nvidia/pytorch:24.04-py3.
  • Base Docker image for TensorRT-LLM backend is updated to nvcr.io/nvidia/tritonserver:24.04-py3.
  • The dependent CUDA version is updated to 12.4.1.
  • The dependent PyTorch version is updated to 2.3.0.

TensorRT-LLM Release 0.10.0

Announcements

  • TensorRT-LLM supports TensorRT 10.0.1 and NVIDIA NGC 24.03 containers.

Key Features and Enhancements

  • The Python high level API
    • Added embedding parallel, embedding sharing, and fused MLP support.
    • Enabled the usage of the executor API.
  • Added a weight-stripping feature with a new trtllm-refit command. For more information, refer to examples/sample_weight_stripping/README.md.
  • Added a weight-streaming feature. For more information, refer to docs/source/advanced/weight-streaming.md.
  • Enhanced the multiple profiles feature; --multiple_profiles argument in trtllm-build command builds more optimization profiles now for better performance.
  • Added FP8 quantization support for Mixtral.
  • Added support for pipeline parallelism for GPT.
  • Optimized applyBiasRopeUpdateKVCache kernel by avoiding re-computation.
  • Reduced overheads between enqueue calls of TensorRT engines.
  • Added support for paged KV cache for enc-dec models. The support is limited to beam width 1.
  • Added W4A(fp)8 CUTLASS kernels for the NVIDIA Ada Lovelace architecture.
  • Added debug options (--visualize_network and --dry_run) to the trtllm-build command to visualize the TensorRT network before engine build.
  • Integrated the new NVIDIA Hopper XQA kernels for LLaMA 2 70B model.
  • Improved the performance of pipeline parallelism when enabling in-flight batching.
  • Supported quantization for Nemotron models.
  • Added LoRA support for Mixtral and Qwen.
  • Added in-flight batching support for ChatGLM models.
  • Added support to ModelRunnerCpp so that it runs with the executor API for IFB-compatible models.
  • Enhanced the custom AllReduce by adding a heuristic; fall back to use native NCCL kernel when hardware requirements are not satisfied to get the best performance.
  • Optimized the performance of checkpoint conversion process for LLaMA.
  • Benchmark
    • [BREAKING CHANGE] Moved the request rate generation arguments and logic from prepare dataset script to gptManagerBenchmark.
    • Enabled streaming and support Time To the First Token (TTFT) latency and Inter-Token Latency (ITL) metrics for gptManagerBenchmark.
    • Added the --max_attention_window option to gptManagerBenchmark.

API Changes

  • [BREAKING CHANGE] Set the default tokens_per_block argument of the trtllm-build command to 64 for better performance.
  • [BREAKING CHANGE] Migrated enc-dec models to the unified workflow.
  • [BREAKING CHANGE] Renamed GptModelConfig to ModelConfig.
  • [BREAKING CHANGE] Added speculative decoding mode to the builder API.
  • [BREAKING CHANGE] Refactor scheduling configurations
    • Unified the SchedulerPolicy with the same name in batch_scheduler and executor, and renamed it to CapacitySchedulerPolicy.
    • Expanded the existing configuration scheduling strategy from SchedulerPolicy to SchedulerConfig to enhance extensibility. The latter also introduces a chunk-based configuration called ContextChunkingPolicy.
  • [BREAKING CHANGE] The input prompt was removed from the generation output in the generate() and generate_async() APIs. For example, when given a prompt as A B, the original generation result could be <s>A B C D E where only C D E is the actual output, and now the result is C D E.
  • [BREAKING CHANGE] Switched default add_special_token in the TensorRT-LLM backend to True.
  • Deprecated GptSession and TrtGptModelV1.

Model Updates

  • Support DBRX
  • Support Qwen2
  • Support CogVLM
  • Support ByT5
  • Support LLaMA 3
  • Support Arctic (w/ FP8)
  • Support Fuyu
  • Support Persimmon
  • Support Deplot
  • Support Phi-3-Mini with long Rope
  • Support Neva
  • Support Kosmos-2
  • Support RecurrentGemma

Fixed Issues

    • Fixed some unexpected behaviors in beam search and early stopping, so that the outputs are more accurate.
  • Fixed segmentation fault with pipeline parallelism and gather_all_token_logits. (#1284)
  • Removed the unnecessary check in XQA to fix code Llama 70b Triton crashes. (#1256)
  • Fixed an unsupported ScalarType issue for BF16 LoRA. (triton-inference-server/tensorrtllm_backend#403)
  • Eliminated the load and save of prompt table in multimodal. (NVIDIA#1436)
  • Fixed an error when converting the models weights of Qwen 72B INT4-GPTQ. (#1344)
  • Fixed early stopping and failures on in-flight batching cases of Medusa. (#1449)
  • Added support for more NVLink versions for auto parallelism. (#1467)
  • Fixed the assert failure caused by default values of sampling config. (#1447)
  • Fixed a requirement specification on Windows for nvidia-cudnn-cu12. (#1446)
  • Fixed MMHA relative position calculation error in gpt_attention_plugin for enc-dec models. (#1343)

Infrastructure changes

  • Base Docker image for TensorRT-LLM is updated to nvcr.io/nvidia/pytorch:24.03-py3.
  • Base Docker image for TensorRT-LLM backend is updated to nvcr.io/nvidia/tritonserver:24.03-py3.
  • The dependent TensorRT version is updated to 10.0.1.
  • The dependent CUDA version is updated to 12.4.0.
  • The dependent PyTorch version is updated to 2.2.2.

TensorRT-LLM Release 0.9.0

Announcements

  • TensorRT-LLM requires TensorRT 9.3 and 24.02 containers.

Key Features and Enhancements

  • [BREAKING CHANGES] TopP sampling optimization with deterministic AIR TopP algorithm is enabled by default
  • [BREAKING CHANGES] Added support for embedding sharing for Gemma
  • Added support for context chunking to work with KV cache reuse
  • Enabled different rewind tokens per sequence for Medusa
  • Added BART LoRA support (limited to the Python runtime)
  • Enabled multi-LoRA for BART LoRA
  • Added support for early_stopping=False in beam search for C++ Runtime
  • Added support for logits post processor to the batch manager
  • Added support for import and convert HuggingFace Gemma checkpoints
  • Added support for loading Gemma from HuggingFace
  • Added support for auto parallelism planner for high-level API and unified builder workflow
  • Added support for running GptSession without OpenMPI
  • Added support for Medusa IFB
  • [Experimental] Added support for FP8 FMHA, note that the performance is not optimal, and we will keep optimizing it
  • Added support for more head sizes for LLaMA-like models
    • NVIDIA Ampere (SM80, SM86), NVIDIA Ada Lovelace (SM89), NVIDIA Hopper (SM90) all support head sizes [32, 40, 64, 80, 96, 104, 128, 160, 256]
  • Added support for OOTB functionality
    • T5
    • Mixtral 8x7B
  • Benchmark features
    • Added emulated static batching in gptManagerBenchmark
    • Added support for arbitrary dataset from HuggingFace for C++ benchmarks
    • Added percentile latency report to gptManagerBenchmark
  • Performance features
    • Optimized gptDecoderBatch to support batched sampling
    • Enabled FMHA for models in BART, Whisper, and NMT family
    • Removed router tensor parallelism to improve performance for MoE models
    • Improved custom all-reduce kernel
  • Infrastructure features
    • Base Docker image for TensorRT-LLM is updated to nvcr.io/nvidia/pytorch:24.02-py3
    • The dependent PyTorch version is updated to 2.2
    • Base Docker image for TensorRT-LLM backend is updated to nvcr.io/nvidia/tritonserver:24.02-py3
    • The dependent CUDA version is updated to 12.3.2 (12.3 Update 2)

API Changes

  • Added C++ executor API
  • Added Python bindings
  • Added advanced and multi-GPU examples for Python binding of executor C++ API
  • Added documents for C++ executor API
  • Migrated Mixtral to high-level API and unified builder workflow
  • [BREAKING CHANGES] Moved LLaMA convert checkpoint script from examples directory into the core library
  • Added support for LLM() API to accept engines built by trtllm-build command
  • [BREAKING CHANGES] Removed the model parameter from gptManagerBenchmark and gptSessionBenchmark
  • [BREAKING CHANGES] Refactored GPT with unified building workflow
  • [BREAKING CHANGES] Refactored the Qwen model to the unified build workflow
  • [BREAKING CHANGES] Removed all the LoRA related flags from convert_checkpoint.py script and the checkpoint content to trtllm-build command to generalize the feature better to more models
  • [BREAKING CHANGES] Removed the use_prompt_tuning flag, options from the convert_checkpoint.py script, and the checkpoint content to generalize the feature better to more models. Use trtllm-build --max_prompt_embedding_table_size instead.
  • [BREAKING CHANGES] Changed the trtllm-build --world_size flag to the --auto_parallel flag. The option is used for auto parallel planner only.
  • [BREAKING CHANGES] AsyncLLMEngine is removed. The tensorrt_llm.GenerationExecutor class is refactored to work with both explicitly launching with mpirun in the application level and accept an MPI communicator created by mpi4py.
  • [BREAKING CHANGES] examples/server are removed.
  • [BREAKING CHANGES] Removed LoRA related parameters from the convert checkpoint scripts.
  • [BREAKING CHANGES] Simplified Qwen convert checkpoint script.
  • [BREAKING CHANGES] Reused the QuantConfig used in trtllm-build tool to support broader quantization features.
  • Added support for TensorRT-LLM checkpoint as model input.
  • Refined SamplingConfig used in LLM.generate or LLM.generate_async APIs, with the support of beam search, a variety of penalties, and more features.
  • Added support for the StreamingLLM feature. Enable it by setting LLM(streaming_llm=...).

Model Updates

  • Added support for distil-whisper
  • Added support for HuggingFace StarCoder2
  • Added support for VILA
  • Added support for Smaug-72B-v0.1
  • Migrate BLIP-2 examples to examples/multimodal

Limitations

  • openai-triton examples are not supported on Windows.

Fixed Issues

  • Fixed a weight-only quant bug for Whisper to make sure that the encoder_input_len_range is not 0. (#992)
  • Fixed an issue that log probabilities in Python runtime are not returned. (#983)
  • Multi-GPU fixes for multimodal examples. (#1003)
  • Fixed a wrong end_id issue for Qwen. (#987)
  • Fixed a non-stopping generation issue. (#1118, #1123)
  • Fixed a wrong link in examples/mixtral/README.md. (#1181)
  • Fixed LLaMA2-7B bad results when INT8 kv cache and per-channel INT8 weight only are enabled. (#967)
  • Fixed a wrong head_size when importing a Gemma model from HuggingFace Hub. (#1148)
  • Fixed ChatGLM2-6B building failure on INT8. (#1239)
  • Fixed a wrong relative path in Baichuan documentation. (#1242)
  • Fixed a wrong SamplingConfig tensor in ModelRunnerCpp. (#1183)
  • Fixed an error when converting SmoothQuant LLaMA. (#1267)
  • Fixed an issue that examples/run.py only load one line from --input_file.
  • Fixed an issue that ModelRunnerCpp does not transfer SamplingConfig tensor fields correctly. (#1183)

TensorRT-LLM Release 0.8.0

Key Features and Enhancements

  • Chunked context support (see docs/source/gpt_attention.md#chunked-context)
  • LoRA support for C++ runtime (see docs/source/lora.md)
  • Medusa decoding support (see examples/medusa/README.md)
    • The support is limited to Python runtime for Ampere or newer GPUs with fp16 and bf16 accuracy, and the temperature parameter of sampling configuration should be 0
  • StreamingLLM support for LLaMA (see docs/source/gpt_attention.md#streamingllm)
  • Support for batch manager to return logits from context and/or generation phases
    • Include support in the Triton backend
  • Support AWQ and GPTQ for QWEN
  • Support ReduceScatter plugin
  • Support for combining repetition_penalty and presence_penalty #274
  • Support for frequency_penalty #275
  • OOTB functionality support:
    • Baichuan
    • InternLM
    • Qwen
    • BART
  • LLaMA
    • Support enabling INT4-AWQ along with FP8 KV Cache
    • Support BF16 for weight-only plugin
  • Baichuan
    • P-tuning support
    • INT4-AWQ and INT4-GPTQ support
  • Decoder iteration-level profiling improvements
  • Add masked_select and cumsum function for modeling
  • Smooth Quantization support for ChatGLM2-6B / ChatGLM3-6B / ChatGLM2-6B-32K
  • Add Weight-Only Support To Whisper #794, thanks to the contribution from @Eddie-Wang1120
  • Support FP16 fMHA on NVIDIA V100 GPU 
    Some features are not enabled for all models listed in the [examples](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples) folder.

Model Updates

  • Phi-1.5/2.0
  • Mamba support (see examples/mamba/README.md)
    • The support is limited to beam width = 1 and single-node single-GPU
  • Nougat support (see examples/multimodal/README.md#nougat)
  • Qwen-VL support (see examples/qwenvl/README.md)
  • RoBERTa support, thanks to the contribution from @erenup
  • Skywork model support
  • Add example for multimodal models (BLIP with OPT or T5, LlaVA)

Refer to the {ref}support-matrix-software section for a list of supported models.

  • API
    • Add a set of High-level APIs for end-to-end generation tasks (see examples/high-level-api/README.md)
    • [BREAKING CHANGES] Migrate models to the new build workflow, including LLaMA, Mistral, Mixtral, InternLM, ChatGLM, Falcon, GPT-J, GPT-NeoX, Medusa, MPT, Baichuan and Phi (see docs/source/new_workflow.md)
    • [BREAKING CHANGES] Deprecate LayerNorm and RMSNorm plugins and removed corresponding build parameters
    • [BREAKING CHANGES] Remove optional parameter maxNumSequences for GPT manager
  • Fixed Issues
    • Fix the first token being abnormal issue when --gather_all_token_logits is enabled #639
    • Fix LLaMA with LoRA enabled build failure #673
    • Fix InternLM SmoothQuant build failure #705
    • Fix Bloom int8_kv_cache functionality #741
    • Fix crash in gptManagerBenchmark #649
    • Fix Blip2 build error #695
    • Add pickle support for InferenceRequest #701
    • Fix Mixtral-8x7b build failure with custom_all_reduce #825
    • Fix INT8 GEMM shape #935
    • Minor bug fixes
  • Performance
    • [BREAKING CHANGES] Increase default freeGpuMemoryFraction parameter from 0.85 to 0.9 for higher throughput
    • [BREAKING CHANGES] Disable enable_trt_overlap argument for GPT manager by default
    • Performance optimization of beam search kernel
    • Add bfloat16 and paged kv cache support for optimized generation MQA/GQA kernels
    • Custom AllReduce plugins performance optimization
    • Top-P sampling performance optimization
    • LoRA performance optimization
    • Custom allreduce performance optimization by introducing a ping-pong buffer to avoid an extra synchronization cost
    • Integrate XQA kernels for GPT-J (beamWidth=4)
  • Documentation
    • Batch manager arguments documentation updates
    • Add documentation for best practices for tuning the performance of TensorRT-LLM (See docs/source/perf_best_practices.md)
    • Add documentation for Falcon AWQ support (See examples/falcon/README.md)
    • Update to the docs/source/new_workflow.md documentation
    • Update AWQ INT4 weight only quantization documentation for GPT-J
    • Add blog: Speed up inference with SOTA quantization techniques in TRT-LLM
    • Refine TensorRT-LLM backend README structure #133
    • Typo fix #739

TensorRT-LLM Release 0.7.1

Key Features and Enhancements

  • Speculative decoding (preview)

  • Added a Python binding for GptManager

  • Added a Python class ModelRunnerCpp that wraps C++ gptSession

  • System prompt caching

  • Enabled split-k for weight-only cutlass kernels

  • FP8 KV cache support for XQA kernel

  • New Python builder API and trtllm-build command (already applied to blip2 and OPT)

  • Support StoppingCriteria and LogitsProcessor in Python generate API

  • FHMA support for chunked attention and paged KV cache

  • Performance enhancements include:

    • MMHA optimization for MQA and GQA
    • LoRA optimization: cutlass grouped GEMM
    • Optimize Hopper warp specialized kernels
    • Optimize AllReduce for parallel attention on Falcon and GPT-J
    • Enable split-k for weight-only cutlass kernel when SM>=75

  • Added {ref}workflow documentation

Model Updates

  • BART and mBART support in encoder-decoder models
  • FairSeq Neural Machine Translation (NMT) family
  • Mixtral-8x7B model
  • Support weight loading for HuggingFace Mixtral model
  • OpenAI Whisper
  • Mixture of Experts support
  • MPT - Int4 AWQ / SmoothQuant support
  • Baichuan FP8 quantization support

Fixed Issues

  • Fixed tokenizer usage in quantize.py #288
  • Fixed LLaMa with LoRA error
  • Fixed LLaMA GPTQ failure
  • Fixed Python binding for InferenceRequest issue
  • Fixed CodeLlama SQ accuracy issue

Known Issues

  • The hang reported in issue #149 has not been reproduced by the TensorRT-LLM team. If it is caused by a bug in TensorRT-LLM, that bug may be present in that release.