Skip to content

Latest commit

 

History

History
142 lines (104 loc) · 5.5 KB

README.md

File metadata and controls

142 lines (104 loc) · 5.5 KB
Raw

Benchmark for C++ Runtime

This document explains how to benchmark the models supported by TensorRT-LLM on a single GPU, a single node with multiple GPUs or multiple nodes with multiple GPUs.

Usage

1. Build TensorRT-LLM and benchmarking source code

Please follow the installation document to build TensorRT-LLM.

Note that the benchmarking source code for C++ runtime is not built by default, you can use the argument --benchmarks in build_wheel.py to build the corresponding executable.

Windows users: Follow the Windows installation document instead, and be sure to set DLL paths as specified in Extra Steps for C++ Runtime Usage.

2. Launch C++ benchmarking (Fixed BatchSize/InputLen/OutputLen)

Prepare TensorRT-LLM engine(s)

Before you launch C++ benchmarking, please make sure that you have already built engine(s) using TensorRT-LLM API, C++ benchmarking code cannot generate engine(s) for you.

You can use the build.py script to build the engine(s). Alternatively, if you have already benchmarked Python Runtime, you can reuse the engine(s) built previously, please see that document.

Launch benchmarking

For detailed usage, you can do the following

cd cpp/build

# You can directly execute the binary for help information
./benchmarks/gptSessionBenchmark --help
./benchmarks/bertBenchmark --help

Take GPT-350M as an example for single GPU

./benchmarks/gptSessionBenchmark \
    --model gpt_350m \
    --engine_dir "../../benchmarks/gpt_350m/" \
    --batch_size "1" \
    --input_output_len "60,20"

# Expected output:
# [BENCHMARK] batch_size 1 input_length 60 output_length 20 latency(ms) 40.81

Take GPT-175B as an example for multiple GPUs

mpirun -n 8 ./benchmarks/gptSessionBenchmark \
    --model gpt_175b \
    --engine_dir "../../benchmarks/gpt_175b/" \
    --batch_size "1" \
    --input_output_len "60,20"

# Expected output:
# [BENCHMARK] batch_size 1 input_length 60 output_length 20 latency(ms) 792.14

If you want to obtain context and generation logits, you could build an enigne with --gather_context_logits and --gather_generation_logits, respectively. Enable --gather_all_token_logits will enable both of them.

If you want to get the logits, you could run gptSessionBenchmark with --print_all_logits. This will print a large number of logit values and has a certain impact on performance.

Please note that the expected outputs in that document are only for reference, specific performance numbers depend on the GPU you're using.

3. Launch Batch Manager benchmarking (Inflight/V1 batching)

Prepare dataset

Run a preprocessing script to prepare/generate dataset into a json that gptManagerBenchmark can consume later. The processed output json has input token ids, output tokens length and time delays to control request rate by gptManagerBenchmark.

This tool can be used in 2 different modes of traffic generation.

1 – Dataset

“Prompt”, “Instruction” (optional) and “Answer” specified as sentences in a Json file

The tool will tokenize the words and instruct the model to generate a specified number of output tokens for a request.

python3 prepare_dataset.py \
    --output preprocessed_dataset.json
    --request-rate 10 \
    --time-delay-dist exponential_dist \
    --tokenizer <path/to/tokenizer> \
    dataset
    --dataset <path/to/dataset> \
    --max-input-len 300

2 – Normal token length distribution

This mode allows the user to generate normal token length distributions with a mean and std deviation specified. For example, setting mean=100 and std dev=10 would generate requests where 95.4% of values are in <80,120> range following the normal probability distribution. Setting std dev=0 will generate all requests with the same mean number of tokens.

 python prepare_dataset.py \
  --output token-norm-dist.json \
  --request-rate 10 \
  --time-delay-dist constant \
  --tokenizer <path/to/tokenizer> \
   token-norm-dist \
   --num-requests 100 \
   --input-mean 100 --input-stdev 10 --output-mean 15 --output-stdev 0 --num-requests 100

For tokenizer, specifying the path to the local tokenizer that have already been downloaded, or simply the name of the tokenizer from HuggingFace like meta-llama/Llama-2-7b will both work. The tokenizer will be downloaded automatically for the latter case.

Prepare TensorRT-LLM engines

Please make sure that the engines are built with argument --use_inflight_batching and --remove_input_padding if you'd like to benchmark inflight batching, for more details, please see the document in TensorRT-LLM examples.

Launch benchmarking

For detailed usage, you can do the following

cd cpp/build

# You can directly execute the binary for help information
./benchmarks/gptManagerBenchmark --help

Take GPT-350M as an example for single GPU V1 batching

./benchmarks/gptManagerBenchmark \
    --model gpt \
    --engine_dir ../../examples/gpt/trt_engine/gpt2/fp16/1-gpu/ \
    --type V1 \
    --dataset ../../benchmarks/cpp/preprocessed_dataset.json
    --max_num_samples 500

Take GPT-350M as an example for 2-GPU inflight batching

mpirun -n 2 ./benchmarks/gptManagerBenchmark \
    --model gpt \
    --engine_dir ../../examples/gpt/trt_engine/gpt2-ib/fp16/2-gpu/ \
    --type IFB \
    --dataset ../../benchmarks/cpp/preprocessed_dataset.json
    --max_num_samples 500