Best Practices for Tuning the Performance of TensorRT-LLM

This document provides some best practices for tuning the performance of TensorRT-LLM.

How To Measure Performance?

TensorRT-LLM can be benchmarked using the C++ tools. We are actively developing trtllm-bench command, which is going to be the recommended way of benchmarking TensorRT-LLM.

For detailed performance data and the steps to reproduce those results, see this Document. The TensorRT-LLM backend can also be used to measure the performance of TensorRT-LLM for online serving.

Build Options to Optimize the Performance of TensorRT-LLM Models

This part summarizes how to build engines to enhance the performance of the runtime. The following options have reasonable default values but for some of them, it’s possible that tuning is needed to get the peak numbers.

Note that some of those features and how to enable them may change in the future.

max_batch_size, max_seq_len and max_num_tokens

Regarding the impacts of those three arguments to the GPU memory usage, please refer to memory.md

max_batch_size

max_batch_size defines the maximum number of requests that the engine can handle.​

It controls the maximum number of requests that can be scheduled at runtime.

Set high enough max_batch_size when building the engine so that it does not become the bottleneck of the throughput, and use runtime max_batch_size to tune it without re-building the engine if you want to get better user throughput or lower latency.

max_seq_len

max_seq_len defines the maximum sequence length of single request​

Starting from TensorRT-LLM v0.11, when --remove_input_padding and --context_fmha are enabled, max_seq_len can replace max_input_len and max_output_len, and is set to max_position_embeddings by default.

Use default max_seq_len (which is max_position_embeddings), no need to tune it unless you are very sure what max sequence lengths would be on your workloads. If the GPU memory is so limited that it cannot make sure even one request to reach max_seq_len, you’ll need to reduce it.

max_num_tokens

max_num_tokens defines the maximum number of batched input tokens after padding is removed in each batch.​

max_num_tokens is set to 8192 by default starting from v0.11, you can tune it using the runtime max_num_tokens without re-buliding the engine. It is recommended to tune --max_num_tokens for better performance.

The maximum number of tokens equals will not take effects when input padding is not removed. When input padding is removed (see Remove Input Padding), the tokens from different sequences are packed together and the maximum number of the tokens can be set to a different (lower) value, which by default to be 8192.

There are two aspects that must be considered. Firstly, some input sequences will be shorter than the maximum input length. Secondly, when in-flight sequence batching is enabled, requests in context phase will be executed with requests in generation phase. Those latter requests produce a lot fewer tokens than max_input_len (at most, beam_width tokens).

Using a more realistic value for max_num_tokens allows TensorRT-LLM to allocate more memory to store the KV cache and execute more requests together. It leads to an increased efficiency.

Increasing max_num_tokens appropriately will be beneficial to performance. When increasing --max_num_tokens to some point, GPU utilization will plateau, going beyond that saturation point may hurt both first token latency as well as total end-to-end latency.

See also chunked context.

Multiple profiles

--multiple_profiles enables multiple TensorRT optimization profiles in the built engines, it will benefits the performance especially when GEMM plugin is disabled, because more optimization profiles help TensorRT have more chances to select better kernels.

Note: This feature increases engine build time but no other adverse effects are expected.

FP8 Context Fused Multi-Head Attention

--use_fp8_context_fmha enables FP8 Context fused multi-head attention. We recommend enabling this when fp8 quantization is used to improve the context phase attention performance. Note that only NVIDIA Hopper architecture is supported.

GPT Attention Plugin and Context Fused Multi-Head Attention

The GPT attention plugin and fused multi-head attention kernel are enabled by default. For the context phase, use the --gpt_attention_plugin and --context_fmha arguments with trtllm-build to control.

The TensorRT-LLM GPT attention plugin uses efficient kernels and enables an in-place update of the KV cache. It results in reduced memory consumption as well as the removal of unneeded memory copy operations (compared with the implementation that uses the concat operator to update the KV cache).

Enabling the fused multi-head attention, during the context phase, will trigger a kernel that performs the MHA/MQA/GQA block using a single kernel, for more details, see this Document.

Remove Input Padding

The remove input padding feature is enabled by default, the --remove_input_padding argument in trtllm-build is used to control it.

When input padding is removed, the different tokens are packed together. It reduces both the amount of computations and memory consumption. For more details, see this Document.

Paged KV Cache

Paged KV cache is enabled by default, the --paged_kv_cache argument in trtllm-build is used to control it.

The paged KV cache helps manage memory for the KV cache more efficiently (see this Document). It usually leads to an increase in the batch size and an improved efficiency.

Reduce Norm Fusion

There is an experimental feature called “Reduce Norm Fusion” available to extend the custom AllReduce functionality. It can be enabled by using the --reduce_fusion enable argument with trtllm-build when the custom AllReduce is already enabled.

This feature aims to fuse the ResidualAdd and LayerNorm kernels after AllReduce into a single kernel, resulting in improved end-to-end performance.

Please note that currently, this feature is only supported for the llama model. It is recommended to enable this feature when the batch size is small and the generation phase time is the dominant factor.

Embedding Parallelism, Embedding Sharing, and Look-Up Plugin

The embedding parallelism feature enables the sharding of the embedding table across multiple GPUs, so that the memory usage could be reduced and the throughput improved. The embedding sharing feature enables the sharing of the embedding table between look_up and lm_head layers to reduced memory usage.

It is recommended to enable embedding parallelism to improve throughput with --use_parallel_embedding and --embedding_sharding_dim in convert_checkpoint.py.

Embedding sharing is by default enabled if following conditions are met:

1. look_up and lm_head layers have identical weights.

2. --gemm_plugin is not used when building the engine.

3. For tensor parallelism cases, -embedding_sharding_dim 0 must be set. In other words, we must enable embedding parallelism along the vocab dimension,

See those Examples for details.

Horizontal Fusion in Gated-MLP

Horizontal fusion in Gated-MLP combines two Matmul operations into a single one followed by a separate SwiGLU kernel. It can effectively reduce latency. This feature is enabled by default.

GEMM Plugin

The GEMM plugin utilizes NVIDIA cuBLASLt to perform GEMM operations. On FP16 and BF16, it’s recommended to be enabled for better performance and smaller GPU memory usage. On FP8, it’s recommended to be disabled.

FP8 GEMM Plugin for Small Batch Size Performance Optimization

FP8 gemm plugin is an experimental feature aimed to improve performance in small-batch-size cases(e.g. BS<=4) and can be enabled by --gemm_plugin fp8 when building FP8 models. Although inputs with larger batch size can be correctly inferenced, the performance may decrease as batch size grows. Therefore, this feature is only recommended for latency reduction in small-batch-size scenarios currently.

GEMM + SwiGLU Fusion in Gated-MLP

The GEMM + SwiGLU fusion in Gated-MLP combines two Matmul operations and one SwiGLU operation into a single kernel. Currently this is only supported for FP8 precision on Hopper. While this fusion improves performance, it can slightly reduce accuracy in FP8 PTQ because one quantization scaling factor is discarded.

We recommend enabling this feature for large models running on Hopper with FP8 precision. Use the following trtllm-build arguments to enable it:

• For large models: --use_fused_mlp=enable --gemm_swiglu_plugin=fp8

• For small batch sizes: --use_fused_mlp=enable --low_latency_gemm_swiglu_plugin=fp8 to improve latency.

We do not recommend enabling this feature for very small workloads or if the accuracy loss is unacceptable.

BERT Attention Plugin and Context Fused Multi-Head Attention

BERT attention plugin and context fused multi-head attention are both recommended for the BERT model. They are enabled by default using the --bert_attention_plugin and --context_fmha arguments with trtllm-build.

Runtime Options to Optimize the Performance of TensorRT-LLM Models

This part summarizes the runtime configuration knobs that can be tweaked to enhance the performance of already built engines. Note that currently the configurations can be modified using the Executor API as well as the TensorRT-LLM backend.

Capacity Scheduler Policy

There currently are three batch scheduler policies: GUARANTEED_NO_EVICT (default), MAX_UTILIZATION and STATIC_BATCH.

The scheduling policy can be set to MAX_UTILIZATION to pack as many requests as possible at each iteration of the forward loop, when in-flight sequence batching is enabled. It maximizes the utilization of the GPUs by aggressively scheduling requests at the risk of having to pause requests if the KV cache size limit is reached.

For a more conservative approach with respect to the KV cache limitations in terms of memory allocation, CapacitySchedulerPolicy should be set to GUARANTEED_NO_EVICT to guarantee that a started request is never paused.

If the goal is to maximizes the throughput, users should try MAX_UTILIZATION. However, they need to keep in mind that it may have a negative impact on latency if requests have to be paused.

STATIC_BATCH is a legacy mode and is not recommended for production usage.

Context Chunking Policy

Context chunking will increase the chance of batch processing between the context and the generation phase, thereby balancing the calculation amount of each iteration and increasing throughput.

There currently are two context chunking policies: FIRST_COME_FIRST_SERVED (default) and EQUAL_PROGRESS.

FIRST_COME_FIRST_SERVED should achieve overall better performance, while EQUAL_PROGRESS can be helpful in theory to make sure time to first token (TTFT) for most requests are relatively similar.

Batching Type

The batching type can be set to INFLIGHT (default) and STATIC. It is recommended to use INFLIGHT to increase throughput and reduce latency.

Max Tokens in Paged KV Cache and KV Cache Free GPU Memory Fraction

The max_tokens_in_paged_kv_cache and kv_cache_free_gpu_mem_fraction parameters can be used to control the maximum number of tokens handled by the KV cache manager. Setting them properly helps better control the amount of available memory for the KV cache manager during inference. Keeping in mind that increasing the amount of memory available to the KV cache manager tends to translate to a higher achievable throughput.

The max_tokens_in_paged_kv_cache flag directly sets the maximum number of tokens in the KV cache manager. When left unset, that value will be computed based on the kv_cache_free_gpu_mem_fraction setting.

The kv_cache_free_gpu_mem_fraction is a floating-point number between 0.0 and 1.0 that indicates the maximum fraction of GPU memory (after loading the model) that will be used for the KV cache. The default value is 0.90 and means that 90% of the free GPU memory will be used to save tokens in the KV cache. Based on that value, TensorRT-LLM can determine the maximum number of tokens in the KV cache manager.

When both parameters are set, the maximum number of tokens in the KV cache manager will be set to the smaller value between max_tokens_in_paged_kv_cache and the value computed from the amount of memory available for the KV cache.

Unless users clearly know the maximum number of tokens in the KV cache needed by the model, it is recommended to leave max_tokens_in_paged_kv_cache unset. For kv_cache_free_gpu_mem_fraction, if no other programs are executed on the same GPU, it is recommended to test with a as high value as 0.95 to target a high throughput. Note that the kv_cache_free_gpu_mem_fraction parameter cannot be set to 1.0 because some amount of memory has to be reserved for inputs and outputs.

Maximum Attention Window Size

The max_attention_window_size flag sets the maximum number of tokens that are attended to in order to generate one token when using techniques like sliding window attention. See this Document for more details. It defaults to the maximum sequence length (max_seq_len when building the engine), which means that the feature is disabled by default.

When set to a smaller value than max_seq_len (during engine build), only the KV cache of the last max_attention_window_size tokens will be stored. If the input sequence length at runtime exceeds the max_attention_window_size value, the accuracy may start dropping, but the runtime performance will be better (due to the reduction in terms of computations and GPU memory allocation). Users can modify that value to increase runtime performance at the expense of reduced accuracy.