Support enabling INT4-AWQ along with FP8 KV Cache.Support for combining repetition_penalty and presence_penalty #274.Support for batch manager to return logits from context and/or generation phases.StreamingLLM support for LLaMA (see docs/source/gpt_attention.md#streamingllm).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.Medusa decoding support (see examples/medusa/README.md).LoRA support for C++ runtime (see docs/source/lora.md).Chunked context support (see docs/source/gpt_attention.md#chunked-context).Add example for multimodal models (BLIP with OPT or T5, LlaVA).RoBERTa support, thanks to the contribution from Skywork model support.Qwen-VL support (see examples/qwenvl/README.md).Nougat support (see examples/multimodal/README.md#nougat).The support is limited to beam width = 1 and single-node single-GPU.Mamba support (see examples/mamba/README.md).Node, prefix your commands with mpirun -n 1 to run TensorRT-LLM in aĭedicated MPI environment, not the one provided by your Slurm allocation.įor example: mpirun -n 1 python3 examples/run.py. Please configure as appropriate and try again.Īs a rule of thumb, if you are running TensorRT-LLM interactively on a Slurm You must then build Open MPI using -with-pmi pointing SLURM builds PMI-1 by default, or you can manually Versions earlier than 16.05: you must use either SLURM's PMI-1 or Requires that you configure and build SLURM -with-pmix. Version 16.05 or later: you can use SLURM's PMIx support. SLURM, depending upon the SLURM version you are using: There are several options for building PMI support under The application appears to have been direct launched using "srun",īut OMPI was not built with SLURM's PMI support and therefore cannotĮxecute. INT4/INT8 weight-only) asįor a more detailed presentation of the software architecture and the keyĬoncepts used in TensorRT-LLM, we recommend you to read the following INT4 or INT8 weights (and FP16 activations a.k.a. Models to be executed using different quantization modes (seeĮxamples/gpt for concrete examples). To maximize performance and reduce memory footprint, TensorRT-LLM allows the Modified and extended to fit custom needs. TensorRT-LLM comes with several popular models pre-defined. Like GPTAttention or BertAttention, can be found in the Module bundles useful building blocks to assemble LLMs like an Attentionīlock, a MLP or the entire Transformer layer. The Python API of TensorRT-LLM is architectured to look similar to theįunctional module containing functions likeĮinsum, softmax, matmul or view. Models built with TensorRT-LLM canīe executed on a wide range of configurations going from a single GPU to It also includes aĪ production-quality system to serve LLMs. TensorRT-LLM also contains components to create Python and C++ runtimes thatĮxecute those TensorRT engines. State-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM provides users with an easy-to-use Python API to define Large
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