20231088/vllm

History

[Misc] Add SPDX-License-Identifier headers to python source files (#12628 )

- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**

commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date:   Fri Jan 31 14:18:24 2025 -0500

    Add SPDX license headers to python source files
    
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
    also be easily used by tools to help manage license compliance.
    
The Linux Foundation runs license scans against the codebase to help
ensure
    we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
    
    More information can be found on the SPDX site:
    
    - https://spdx.dev/learn/handling-license-info/
    
    Signed-off-by: Russell Bryant <rbryant@redhat.com>

commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date:   Fri Jan 31 14:36:32 2025 -0500

    Check for SPDX headers using pre-commit
    
    Signed-off-by: Russell Bryant <rbryant@redhat.com>

---------

Signed-off-by: Russell Bryant <rbryant@redhat.com>

2025-02-02 11:58:18 -08:00

profiling.py

[Misc] Add SPDX-License-Identifier headers to python source files (#12628 )

2025-02-02 11:58:18 -08:00

README.md

[TPU] Add example for profiling TPU inference (#12531 )

2025-01-29 03:16:47 +00:00

README.md

vLLM TPU Profiling

This script is used to profile the TPU performance of vLLM for specific prefill or decode token shapes.

Note: an actual running server is a mix of both prefill of many shapes and decode of many shapes.

We assume you are on a TPU already (this was tested on TPU v6e) and have installed vLLM according to the installation guide.

In all examples below, we run several warmups before (so --enforce-eager is okay)

Profile Examples

Generate Prefill Trace

This example runs Qwen/Qwen2.5-7B-Instruct with a single request of 1024 input tokens. This is set up in attempt to profile just the prefill time and operations.

export XLA_HLO_DEBUG=1
export MODEL=Qwen/Qwen2.5-7B-Instruct
export VLLM_TPU_PROFILE_DURATION_MS=3000
export VLLM_TPU_PROFILE_DELAY_MS=0

python3 profiling.py \
    --model $MODEL \
    --input-len 1024 --output-len 1 \
    --batch-size 1 --enforce-eager \
    --max-model-len 2048 \
    --tensor-parallel-size 1 \
    --profile-result-dir profiles

Generate Decode Trace

This example runs Llama 3.1 70B with a batch of 32 requests where each has 1 input token and 128 output tokens. This is set up in attempt to profile just the 32 decodes running in parallel by having an extremely small prefill of 1 token and setting VLLM_TPU_PROFILE_DELAY_MS=1000 to skip the first second of inference (hopefully prefill).

export XLA_HLO_DEBUG=1
export MODEL=meta-llama/Llama-3.1-70B-Instruct
export VLLM_TPU_PROFILE_DURATION_MS=2000
export VLLM_TPU_PROFILE_DELAY_MS=1000

rm -rf ~/.cache/vllm/xla_cache
python3 profiling.py \
    --model $MODEL \
    --input-len 1 \
    --output-len 128 \
    --batch-size 32 \
    --enforce-eager \
    --profile-result-dir profiles \
    --max-model-len 2048 --tensor-parallel-size 8

Visualizing the profiles

Once you have collected your profiles with this script, you can visualize them using TensorBoard.

Here are most likely the dependencies you need to install:

pip install tensorflow-cpu tensorboard-plugin-profile etils importlib_resources

Then you just need to point TensorBoard to the directory where you saved the profiles and visit http://localhost:6006/ in your browser:

tensorboard --logdir profiles/ --port 6006