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* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com> Co-authored-by: zhang-ge-hao <842720660@qq.com>
51 lines
2.7 KiB
Markdown
51 lines
2.7 KiB
Markdown
# Benchmark for Python Runtime
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This document explains how to benchmark the models supported by TensorRT-LLM on a single GPU, a single node with
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multiple GPUs or multiple nodes with multiple GPUs.
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## Overview
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The benchmark implementation and entrypoint can be found in [`benchmarks/python/benchmark.py`](./benchmark.py). There are some other scripts in the directory:
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* [`benchmarks/python/allowed_configs.py`](./allowed_configs.py) to define configuration for each supported model.
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* [`benchmarks/python/build.py`](./build.py) to build supported models for benchmarking.
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* [`benchmarks/python/base_benchmark.py`](./base_benchmark.py) to implement the base class for benchmark.
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* [`benchmarks/python/gpt_benchmark.py`](./gpt_benchmark.py) to implement benchmark scripts for GPT and GPT-like(LLaMA/OPT/GPT-J/SmoothQuant-GPT) models.
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* [`benchmarks/python/bert_benchmark.py`](./bert_benchmark.py) to implement benchmark scripts for BERT models.
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* [`benchmarks/python/enc_dec_benchmark.py`](./enc_dec_benchmark.py) to implement benchmark scripts for Encoder-Decoder models.
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## Usage
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Please use `help` option for detailed usages.
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```
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python benchmark.py -h
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```
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### 1. Single GPU benchmark
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Take GPT-350M as an example:
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```
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python benchmark.py \
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-m gpt_350m \
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--mode plugin \
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--batch_size "1;8;64" \
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--input_output_len "60,20;128,20"
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```
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Expected outputs:
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```
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[BENCHMARK] model_name gpt_350m world_size 1 num_heads 16 num_kv_heads 16 num_layers 24 hidden_size 1024 vocab_size 51200 precision float16 batch_size 1 input_length 60 output_length 20 gpu_peak_mem(gb) 4.2 build_time(s) 25.67 tokens_per_sec 483.54 percentile95(ms) 41.537 percentile99(ms) 42.102 latency(ms) 41.362 compute_cap sm80
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[BENCHMARK] model_name gpt_350m world_size 1 num_heads 16 num_kv_heads 16 num_layers 24 hidden_size 1024 vocab_size 51200 precision float16 batch_size 8 input_length 60 output_length 20 gpu_peak_mem(gb) 4.28 build_time(s) 25.67 tokens_per_sec 3477.28 percentile95(ms) 46.129 percentile99(ms) 46.276 latency(ms) 46.013 compute_cap sm80
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[BENCHMARK] model_name gpt_350m world_size 1 num_heads 16 num_kv_heads 16 num_layers 24 hidden_size 1024 vocab_size 51200 precision float16 batch_size 64 input_length 60 output_length 20 gpu_peak_mem(gb) 4.8 build_time(s) 25.67 tokens_per_sec 19698.07 percentile95(ms) 65.739 percentile99(ms) 65.906 latency(ms) 64.981 compute_cap sm80
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...
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```
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*Please note that the expected outputs is only for reference, specific performance numbers depend on the GPU you're using.*
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### 2. Multi-GPU benchmark
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Take GPT-175B as an example:
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```
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mpirun -n 8 python benchmark.py \
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-m gpt_175b \
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--mode plugin \
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--batch_size "1;8;64" \
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--input_output_len "60,20;128,20"
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```
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